feat(ceus): 任务书 02 第一阶段——底层和分割之前的算法；修复 C14
任务书 01 遗留修复（C14）： - library 新增 check_key_image(path)：关键图能逐像素保真重编码时返回 None，否则返回原因； _write_clean_png 的判断移过去，自身保留断言性质报错（走到这里说明 build 漏了检查）。 - build 写库前对每例调用，返回原因的整例跳过，记 C14「关键图能原样重编码」，涉及值写原因。 - 原来三条「关键图格式不对整次构建停下」的测试改为整例跳过，另加非 PNG、library 漏检报错两条。 - 结构.md 第八部分、guides/装配两个库.md 相应几行改掉。 第一阶段（src/cadx/ceus/）： - 照搬旧 geometry、video_decoder、research_config、split_screen、green_prompt、prompt_frames、 mapping，去掉产品层，注释改中文。 - 配置类去掉全部默认值，strict、extra=forbid、frozen；新建 config/ceus.yaml（paths、ceus 两段）， 数值照抄旧 yaml，逐项注明出处。删去 source_view/target_view、effective_region、mapping 锚点； 旧代码写死的分屏抽帧数 25 和搜索区间 (0.35, 0.65) 改成配置，值不变。 - 视频解码改为按路径用 cv2 逐帧读。 - 坐标换算改为两个视图矩形左上角之差，不再用按某种宽度写死的锚点。 - 造影画面在哪一半：两半各自选提示帧，恰好一半有才定，否则抛 ContrastSideError（CeusCaseError 子类，带步号和原因，供第三阶段记 failed.csv）。 - 旧测试照搬（改 import 与配置读取方式），因上述改动不再成立的删去；新增两处修改的手算测试。 - import-linter 加「ceus 不引用其他 cadx 模块」。依赖加 numpy、opencv-python-headless。 Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
This commit is contained in:
+5
-3
@@ -4,7 +4,7 @@
|
||||
|
||||
## 当前目标
|
||||
|
||||
第 2 步「装配两个库（不含 CEUS）」已按任务书 01 实现，等监督者验收。之后是第 3 步 CEUS 定量或会诊口径讨论（`结构.md` 第七部分）。
|
||||
第 3 步 CEUS 定量，按任务书 02 分四个阶段做；第一阶段（底层和分割之前的算法）与 C14 修复已提交，下一个是第二阶段（MedSAM2 与后半段算法）。
|
||||
|
||||
## 已完成
|
||||
|
||||
@@ -17,6 +17,8 @@
|
||||
|
||||
- 2026-10-16 任务书 01 装配两个库：delivery、library、answers、runs（快照）、build 五个模块，`scripts/build.py`、`scripts/scan_library.py`、`config/build.yaml`，91 条测试，`make check` 通过（6 条导入契约）。两轮独立审阅的会悄悄漏答案的意见已修（C11–C13 加严、泄露扫描 NFKC、关键图只收能保真重编码的 PNG、YAML 拒绝重复键）。真实 40 例：40 例中 34 例进库，跳过 6 例全为 C10（跨表不一致，与 `待定问题/04` 一致），复核清单 2 条，泄露扫描命中 0；构建号与提交号见提交说明和本次汇报。使用说明 `guides/装配两个库.md`。
|
||||
|
||||
- 2026-10-16 任务书 02 第一阶段：`src/cadx/ceus/` 迁入配置（去掉全部默认值）、视频解码（按路径逐帧读）、分屏、绿框、提示帧、两半坐标换算；新写判断造影画面在哪一半（两半各选提示帧，恰好一半有才行，否则 `ContrastSideError`）；坐标换算改为两个视图左上角之差，不再用锚点。`config/ceus.yaml` 只有 `paths`、`ceus` 两段。同一提交修了任务书 01 的关键图问题：不能保真重编码的关键图整例跳过，记 C14。`make check` 通过（348 条测试、7 条导入契约）。
|
||||
|
||||
## 未完成
|
||||
|
||||
- 2026-10-16 任务书 01 由执行者完成（提交 bceb01e），监督者验收通过：真实 40 例进库 34、跳过 6（C10 跨表不一致），独立脚本核对资料库内容与跳过名单一致，泄露扫描 0 命中。
|
||||
@@ -37,8 +39,8 @@
|
||||
|
||||
## 下一步
|
||||
|
||||
- 监督者验收任务书 01。
|
||||
- 待负责人定：关键图格式不合要求时整次停下还是整例跳过；化名种子在 git 里，拿到种子可反推进库病例的病例号先后（专家拿不到种子）；含「造影提示：」的那份超声描述所在病例被 C10 跳过，故不在复核清单里。
|
||||
- 任务书 02 第二阶段：MedSAM2 拷代码和权重、segment 模块、其余 11 个 ceus 模块迁移。
|
||||
- 待负责人定：化名种子在 git 里，拿到种子可反推进库病例的病例号先后（专家拿不到种子）；含「造影提示：」的那份超声描述所在病例被 C10 跳过，故不在复核清单里。
|
||||
- 向临床方要编码映射表（性别、诊断、症状、既往史、化验档位）。
|
||||
- 把 `待定问题/04`（6 例跨表不一致）、`待定问题/06`（1 份超声描述中段的「造影提示：」）转给医生；06 定之前的构建不做正式会诊。
|
||||
- 等临床方交付 40 例的医生标注工作簿（含造影剂到达时间和注射时间）。
|
||||
|
||||
@@ -0,0 +1,155 @@
|
||||
# CEUS 定量的配置，给 scripts/ceus_quant.py 用。
|
||||
#
|
||||
# 分三段：paths（位置）、segment（分割设置，任务书 02 第二阶段加）、ceus（算法参数）。
|
||||
# ceus 段的字段定义和校验在 src/cadx/ceus/_config.py；paths 段和最外层由 scripts/ceus_quant.py 定义。
|
||||
# 每个字段必填，可以没有的值写 null。
|
||||
#
|
||||
# ceus 段的数值逐个照抄旧项目 config/ceus_roi_default.yaml，没有改动（待定问题/02：参数何时冻结）。
|
||||
# 注释里的出处：
|
||||
# - 「出自旧项目」：旧 yaml 注释原样引用的来源。其中 findings 指旧项目
|
||||
# research-wiki/findings/2026-06-16-roi-tic-config-parameter-reference.md（伙伴参考组件
|
||||
# references/ROI/ 那套参数的原始口径），§2A 指「删键即静默退化、yaml 值与代码默认不同」的那批；
|
||||
# design/00NN 指旧项目 research-wiki/design/；《临床口径契约》在 Linear，抄本可能过期。
|
||||
# - 「旧项目工程侧定的，无出处」：旧 yaml 没给来源的值。
|
||||
|
||||
paths:
|
||||
delivery_dir: data/交付包_20260908_40例 # 交付包位置，相对仓库根目录
|
||||
runs_root: runs/ceus # 每次运行一个子目录，只追加不删
|
||||
work_root: work # 可删掉重算的中间文件（分割缓存）
|
||||
|
||||
ceus:
|
||||
config_version: "4" # 参数口径版本号，由人改。出自旧项目：Lognormal-v2 参数口径（design/0044）
|
||||
|
||||
views: # 出自旧项目：findings §3.1 视图切分
|
||||
layout: horizontal_split # 屏幕左右各一半。出自旧项目 findings §3.1
|
||||
split_sample_frames: 25 # 找分屏线只用均匀抽的 25 帧。旧 debug 脚本写死的数，旧项目工程侧定的，无出处
|
||||
split_search_band: [0.35, 0.65] # 只在宽度的这一段里找分屏线。旧 detect_split_x 的参数默认值，旧项目工程侧定的，无出处
|
||||
|
||||
prompt_source: # 出自旧项目：findings §3.2 绿框检测
|
||||
type: color_polygon # 出自旧项目 findings §3.2
|
||||
hsv_lower: [35, 60, 60] # 出自旧项目 findings §3.2
|
||||
hsv_upper: [90, 255, 255] # 出自旧项目 findings §3.2
|
||||
ignore_regions: [[0.0, 0.0, 1.0, 0.0834]] # 抹掉顶部约 8.34% 的文字带。出自旧项目 findings §3.2
|
||||
min_area: 10000 # 外接框面积 w×h（findings §2D）。出自旧项目 findings §3.2
|
||||
|
||||
pan_source: # 紫圈检测（待定问题/08）。出自旧项目 design/0030 决策四；旧 yaml 注明全部是占位、待真实素材标定
|
||||
type: color_polygon # 旧项目工程侧定的，无出处
|
||||
hsv_lower: [125, 60, 60] # 占位，待标定。出自旧项目 design/0030 决策四
|
||||
hsv_upper: [165, 255, 255] # 占位，待标定。出自旧项目 design/0030 决策四
|
||||
ignore_regions: [[0.0, 0.0, 1.0, 0.0834]] # 占位，同绿框那条顶部文字带。出自旧项目 design/0030 决策四
|
||||
min_area: 2000 # 占位，待标定。出自旧项目 design/0030 决策四
|
||||
min_detected_frame_fraction: 0.9 # 占位，待标定。出自旧项目 design/0030 决策四
|
||||
|
||||
keyframes: # 提示帧选择（不是第 8 步的关键帧）。出自旧项目：findings §3.3
|
||||
change_metric: mask_iou # 出自旧项目 findings §3.3
|
||||
iou_threshold: 0.92 # 出自旧项目 findings §3.3
|
||||
min_gap_frames: 1 # 出自旧项目 findings §3.3
|
||||
|
||||
smoothing: # 出自旧项目：findings §3.6 轮廓分辨率与时间平滑
|
||||
contour_points: 64 # 出自旧项目 findings §3.6
|
||||
temporal_window: 15 # 须奇数。出自旧项目 findings §3.6
|
||||
|
||||
roi_shape: # 最终轮廓与绿框的融合（待定问题/01）。出自旧项目：findings §3.7
|
||||
source: prompt_prior # findings §2A：非代码默认 medsam2。出自旧项目 findings §3.7
|
||||
keyframe_shape_strength: 1.0 # 出自旧项目 findings §3.7
|
||||
propagation_shape_strength: 0.75 # 出自旧项目 findings §3.7
|
||||
min_area_ratio_to_prompt: 0.70 # 须 < max。出自旧项目 findings §3.7
|
||||
max_area_ratio_to_prompt: 1.25 # 出自旧项目 findings §3.7
|
||||
|
||||
interior_dark_region: # 暗区识别。出自旧项目：findings §3.8
|
||||
enabled: true # findings §2A：非代码默认 false。出自旧项目 findings §3.8
|
||||
analysis_view: source # findings §2A：非代码默认 target。出自旧项目 findings §3.8
|
||||
exclude_prompt_color: true # findings §2A：非代码默认 false。出自旧项目 findings §3.8
|
||||
threshold_method: auto_ensemble # findings §2A：非代码默认 otsu。出自旧项目 findings §3.8
|
||||
candidate_methods: [otsu, adaptive, percentile, kmeans, clahe_otsu] # 出自旧项目 findings §3.8
|
||||
threshold_offset: 0 # 出自旧项目 findings §3.8
|
||||
min_area: 300 # findings §2A：非代码默认 64。出自旧项目 findings §3.8
|
||||
min_area_ratio: 0.015 # findings §2A：非代码默认 0.0。出自旧项目 findings §3.8
|
||||
preserve_holes: true # 出自旧项目 findings §3.8
|
||||
fill_holes: false # 出自旧项目 findings §3.8
|
||||
morphology_kernel: 5 # 出自旧项目 findings §3.8
|
||||
fragment_penalty_weight: 0.08 # 出自旧项目 findings §3.8
|
||||
temporal_consistency_weight: 0.35 # 出自旧项目 findings §3.8
|
||||
contour_points: 64 # 出自旧项目 findings §3.8
|
||||
# 以下是各方法的子参数：参考 yaml 没列，取参考代码的默认值。出自旧项目 findings §2C、§3.8
|
||||
percentile: 45.0 # 出自旧项目 findings §2C
|
||||
threshold: 96 # absolute 法的固定灰度阈值。出自旧项目 findings §2C
|
||||
adaptive_block_size: 31 # 须奇数 ≥3。出自旧项目 findings §2C
|
||||
adaptive_c: 5.0 # 出自旧项目 findings §2C
|
||||
kmeans_max_iter: 30 # 出自旧项目 findings §2C
|
||||
kmeans_attempts: 3 # 出自旧项目 findings §2C
|
||||
clahe_clip_limit: 2.0 # 出自旧项目 findings §2C
|
||||
clahe_tile_grid_size: 8 # 出自旧项目 findings §2C
|
||||
|
||||
constraints: # 出自旧项目：findings §3.9 提示帧位置约束
|
||||
keyframe_position:
|
||||
enabled: true # findings §2A：非代码默认 false。出自旧项目 findings §3.9
|
||||
mode: center_interpolation # 出自旧项目 findings §3.9
|
||||
strength: 1.0 # 截到 [0, 1]。出自旧项目 findings §3.9
|
||||
|
||||
tic_analysis: # 出自旧项目：findings §3.11 TIC 曲线（参考 yaml 整块不存在，取参考代码默认值）
|
||||
brightness_metric: grayscale_mean # 出自旧项目 findings §3.11
|
||||
exclude_prompt_color: true # 出自旧项目 findings §3.11
|
||||
prompt_exclude_dilate: 3 # 出自旧项目 findings §3.11
|
||||
baseline: # 增强前基线。出自旧项目 design/0030 决策二
|
||||
preferred_duration_s: 2.0 # 出自旧项目：Linear《临床口径契约》
|
||||
minimum_duration_s: 1.0 # 出自旧项目：Linear《临床口径契约》
|
||||
stability_sd_multiplier: 3.0 # 临床口径里没有这个判据，旧项目工程侧占位，无出处
|
||||
reference_region: # 病灶外背景圆。出自旧项目 findings §3.11
|
||||
enabled: true # 出自旧项目 findings §3.11
|
||||
mode: auto_near_roi # 出自旧项目 findings §3.11
|
||||
radius_px: 24 # 出自旧项目 findings §3.11
|
||||
min_distance_px: 12 # 出自旧项目 findings §3.11
|
||||
nonblack_threshold: 12 # 出自旧项目 findings §3.11
|
||||
min_valid_fraction: 0.65 # 出自旧项目 findings §3.11
|
||||
center_xy: null # manual 模式必填。出自旧项目 findings §3.11
|
||||
fitting: # Lognormal 拟合。出自旧项目 design/0044：工程验收占位，代表性开发集标定后一次性锁定
|
||||
algorithm_version: lognormal-v2 # 出自旧项目 design/0044
|
||||
detect_duplicate_frames: true # 出自旧项目 design/0044
|
||||
candidate_window_s: 1.5 # 二阶 Savitzky-Golay 只做候选检测，窗口按秒。出自旧项目 design/0044
|
||||
candidate_long_window_s: 5.0 # 宏观团注候选，抑制逐帧掩膜与压缩波动。出自旧项目 design/0044
|
||||
candidate_polyorder: 2 # 出自旧项目 design/0044
|
||||
max_candidate_gap_s: 0.5 # 出自旧项目 design/0044
|
||||
duplicate_mean_absdiff_threshold: 0.25 # 8 位灰度的平均绝对差。出自旧项目 design/0044
|
||||
minimum_valid_samples: 20 # 出自旧项目 design/0044
|
||||
minimum_peak_distance_s: 1.0 # 出自旧项目 design/0044
|
||||
minimum_relative_peak_height: 0.10 # 出自旧项目 design/0044
|
||||
multi_peak_minimum_separation_s: 5.0 # 出自旧项目 design/0044
|
||||
multi_peak_minimum_relative_height: 0.50 # 出自旧项目 design/0044
|
||||
multi_peak_maximum_valley_fraction: 0.25 # 出自旧项目 design/0044
|
||||
sigma_min: 0.08 # 出自旧项目 design/0044
|
||||
sigma_max: 2.5 # 出自旧项目 design/0044
|
||||
robust_loss: soft_l1 # 出自旧项目 design/0044
|
||||
max_nfev: 4000 # 出自旧项目 design/0044
|
||||
minimum_r2: 0.35 # 出自旧项目 design/0044
|
||||
maximum_nrmse: 0.45 # 出自旧项目 design/0044
|
||||
maximum_residual_noise_ratio: 2.0 # 残差不高于基线噪声同量级时允许低 R²。出自旧项目 design/0044
|
||||
boundary_margin_s: 0.25 # 出自旧项目 design/0044
|
||||
estimated_baseline_percentile: 15.0 # 无稳定开头时的有界基线先验。出自旧项目 design/0044
|
||||
estimated_baseline_lookback_s: 10.0 # 允许 t0 位于视频起点前，但截图不外推。出自旧项目 design/0044
|
||||
maximum_perf_area_step_fraction: 0.15 # 相邻帧去暗区面积比例最大变化。出自旧项目 design/0044
|
||||
parameter_boundary_fraction: 0.001 # 形状参数贴边判据占允许区间的比例。出自旧项目 design/0044
|
||||
anchor_frames: # 7 张关键帧定位。出自旧项目 design/0040 决策二
|
||||
# 下面这批数旧项目注明镜像自 Linear《临床口径契约》（2026-08-25 版，同步日期 2026-09-01），
|
||||
# 动任何一个之前先去那边核对。各容差窗口的出处在第二阶段迁移关键帧模块时逐个再核对。
|
||||
arrival: # 到达判据，门槛取两个候选的高者。出自旧项目：《临床口径契约》5.2
|
||||
baseline_sd_multiplier: 3.0 # 候选一：基线抖动 ×3.0。出自旧项目：《临床口径契约》5.2
|
||||
relative_peak_threshold: 0.10 # 候选二：峰高 ×0.10。出自旧项目：《临床口径契约》5.2
|
||||
persistence_s: 0.5 # 越线后连续保持 0.5 秒。出自旧项目：《临床口径契约》（08-23 确认）
|
||||
# 主峰突出度门槛：基线抖动 ×3。与到达判据那个 3.0 是两个独立的数，改一个不连带改另一个。
|
||||
# 出自旧项目：《临床口径契约》第六节
|
||||
peak_prominence_baseline_sd_multiplier: 3.0
|
||||
baseline_frame_backoff_s: 0.3 # B0 锚点 = 到达时刻再往前 0.3 秒。出自旧项目：《临床口径契约》5.11
|
||||
d1_window_after_s: 0.5 # D1 窗 [锚点, 锚点+0.5]。出自旧项目：《临床口径契约》第 12 题
|
||||
d2_window_s: 0.3 # D2 窗 ±0.3 秒。出自旧项目：《临床口径契约》第 12 题
|
||||
d3_window_s: 0.5 # D3 窗 ±0.5 秒。出自旧项目：《临床口径契约》第 12 题
|
||||
# D4 窗 ±0.5 秒：契约空着、第 12 题委托工程侧。旧项目工程侧定，理由是 D4 与 D2/D3 同属曲线
|
||||
# 形状锚点，洗出段比洗入段平缓、导数极值定位误差更大，取 D2/D3 中较宽的一档。无外部出处
|
||||
d4_window_s: 0.5
|
||||
b0_window_s: 0.5 # B0 窗 ±0.5 秒：契约空着，旧项目工程侧取与 D4 同档，无外部出处
|
||||
late_phase:
|
||||
preferred_time_s: 60.0 # F2 锚点：推注后 60 秒。出自旧项目：《临床口径契约》
|
||||
tolerance_s: 2.0 # F2 容差 ±2 秒。出自旧项目：《临床口径契约》第 12 题
|
||||
# F1 锚点：推注后 20 秒。出自旧项目：《临床口径契约》（08-16 口头定）。
|
||||
# F1 没有容差键：旧项目工程侧暂定「无上限，取最近的一帧」，无出处
|
||||
f1_anchor_s: 20.0
|
||||
@@ -4,6 +4,8 @@ version = "0.0.0"
|
||||
description = "胰腺囊性肿瘤多模态会诊诊断的研究代码"
|
||||
requires-python = ">=3.12"
|
||||
dependencies = [
|
||||
"numpy>=2.5.4",
|
||||
"opencv-python-headless>=5.0.0.93",
|
||||
"openpyxl>=3.1.5",
|
||||
"pillow>=12.3.0",
|
||||
"pydantic>=2.14.1",
|
||||
@@ -89,6 +91,16 @@ forbidden_modules = [
|
||||
"cadx.consult", "cadx.evaluate", "cadx.verdicts",
|
||||
]
|
||||
|
||||
[[tool.importlinter.contracts]]
|
||||
# 结构.md 第三部分依赖表：ceus 是纯计算，不引用其他 cadx 模块（含 segment，分割结果由脚本传进来）。
|
||||
name = "ceus 不引用其他 cadx 模块"
|
||||
type = "forbidden"
|
||||
source_modules = ["cadx.ceus"]
|
||||
forbidden_modules = [
|
||||
"cadx.delivery", "cadx.library", "cadx.answers", "cadx.runs", "cadx.build",
|
||||
"cadx.segment", "cadx.llm", "cadx.ledger", "cadx.consult", "cadx.evaluate", "cadx.verdicts",
|
||||
]
|
||||
|
||||
[[tool.importlinter.contracts]]
|
||||
# 结构.md 第三部分依赖表：build 只依赖 delivery、library、answers、runs（ceus 落地时加入）。
|
||||
name = "build 只引用 delivery、library、answers、runs"
|
||||
|
||||
@@ -138,7 +138,7 @@ runs/ceus/<运行号>/ ────► ② 装配两个库 config/bu
|
||||
### 几条边界
|
||||
|
||||
- **专家能看见什么，只在 build 一处决定。** build 按白名单把每路允许的内容写进 `library/`，此外任何模块都不挑选给专家的材料。consult 只拿到某一次构建的路径，经 library 按路读出；它不依赖 delivery、answers、build，读原始资料和答案的代码对它来说根本不存在。报告的「印象 / 结论」段（医生写的诊断倾向）由 delivery 认出、由 build 剔除，`library/` 里只有「所见」描述。
|
||||
- **交付资料的检查不过，整例跳过。** delivery 逐例检查（编号、文件、取值、跨表是否一致；检查项 C01–C13 的速查表见 `../guides/装配两个库.md`，规则以 `src/cadx/delivery/_cases.py`、`_text.py` 为准），build 把没过的病例整例跳过：资料库和答案库的病理诊断里都没有它，答案库的跳过清单写明它没过哪一项、涉及哪个文件哪一列。一例遇到第一项不过就不再往下查，清单上每例只记一项。整张表读不了时整次构建停下。规则见 `AGENTS.md`「错误怎么处理」。
|
||||
- **交付资料的检查不过，整例跳过。** delivery 逐例检查（编号、文件、取值、跨表是否一致；检查项 C01–C14 的速查表见 `../guides/装配两个库.md`，规则以 `src/cadx/delivery/_cases.py`、`_text.py` 为准），build 把没过的病例整例跳过：资料库和答案库的病理诊断里都没有它，答案库的跳过清单写明它没过哪一项、涉及哪个文件哪一列。一例遇到第一项不过就不再往下查，清单上每例只记一项。整张表读不了时整次构建停下。规则见 `AGENTS.md`「错误怎么处理」。
|
||||
- **三路互相隔开。** 每个专家的提示词只用自己那一路的对象拼。consult 里拼某一路提示词的函数只接收那一路的对象，不接收整个病例。这是研究设计本身：模拟各科医生各看各的检查结果，再在会诊时交换意见。会诊时一路能看到另一路的什么，由 consult 的会诊流程决定，在会诊的任务书里定。
|
||||
- **我们自己调模型只有一个入口：consult 的「调一次模型」。** 它按固定顺序做五步：把完整请求存进账本的大块存储、写「调用开始」、经 llm 调用、把完整回复存进大块存储、写「调用结束」。llm 只管调用，不写账本，所以任何一次模型调用都绕不开登记。中断后重启时，从账本找出开始了没结束的调用，记为「状态未知」，不重做已有结果的调用。做重复运行（同一病例、同一专家调多次，测一致性）时，每一次都恰好登记一次。
|
||||
- **CEUS 算法不知道分割模型是谁，也不管中间文件放哪。** ceus 接收视频路径、参数和分割结果，交出曲线、拟合、关键帧和指标；中间文件写在哪、按什么复用，由 `scripts/ceus_quant.py` 决定（目前只有它一个使用者，出现第二个使用者再收进模块）。segment 只管「给提示、出逐帧掩码」，不认识分屏和绿框。
|
||||
@@ -263,11 +263,12 @@ X 是超声报告工作簿的「推注开始时间s」，含义是视频第 0
|
||||
|
||||
文件格式：数组用 npz（读取时 `allow_pickle=False`，pickle 读取时会执行任意代码），每例的标量和摘要用 json，跨病例的表用 csv，图用 png。缺失值写 null，不写 NaN 或 0：0 是合法的测量值，NaN 在 json 里不合法。
|
||||
|
||||
导入边界用 import-linter 检查，写进 `make check`。契约在 `pyproject.toml`，检查范围是 `src/cadx`，**不含 `scripts/` 和 `tests/`**。下面只列契约实际强制的（共 6 条），没有的保护不写：
|
||||
导入边界用 import-linter 检查，写进 `make check`。契约在 `pyproject.toml`，检查范围是 `src/cadx`，**不含 `scripts/` 和 `tests/`**。下面只列契约实际强制的（共 7 条），没有的保护不写：
|
||||
|
||||
- `cadx` 不引用 `torch`、`sam2`。
|
||||
- delivery、library、answers、runs 互不引用。
|
||||
- delivery、library、answers、runs 不引用其他 `cadx` 模块（build 和规划中的各模块）。
|
||||
- ceus 不引用其他 `cadx` 模块（含 segment：分割结果由 `scripts/ceus_quant.py` 传进来）。
|
||||
- build 不引用 delivery、library、answers、runs 以外的 `cadx` 模块（ceus 落地时放行）。
|
||||
- 只有 delivery 引用 `openpyxl`（交付包的 Excel 只有它碰）。
|
||||
- 只有 library 引用 `PIL`（写资料库的图片时去掉元数据）。
|
||||
@@ -337,7 +338,8 @@ Python 环境用 uv；安装与检查命令见 `../guides/开发环境.md`。
|
||||
|
||||
### library（`src/cadx/library/`）
|
||||
|
||||
- `write_library(build_dir, build_id, git_commit, git_clean, config_sha256, codes_decoded, uncompared, ceus_source, cases)`：把一次构建的各例三路内容写进一个已存在的空目录，关键图去掉附加数据重新编码，最后写 `manifest.json`。
|
||||
- `write_library(build_dir, build_id, git_commit, git_clean, config_sha256, codes_decoded, uncompared, ceus_source, cases)`：把一次构建的各例三路内容写进一个已存在的空目录，关键图去掉附加数据重新编码，最后写 `manifest.json`。关键图不能保真重编码时报错停下（说明 build 漏了 C14）。
|
||||
- `check_key_image(path)`：关键图能逐像素保真重编码时返回 None，不能时（不是 PNG、位深或颜色类型不行、带 tRNS 透明色）返回原因。
|
||||
- `LibraryCase`：要写进资料库的一例：化名、临床一路的字段、超声描述、关键图源文件、MR 描述。
|
||||
- `list_aliases(build_dir)`：一次构建的全部化名（按化名排序）。
|
||||
- `read_clinical(build_dir, alias)`、`read_ultrasound(build_dir, alias)`、`read_mr(build_dir, alias)`：按路读出一例，读时核对 SHA-256，拒绝 `.partial` 目录。
|
||||
@@ -363,9 +365,19 @@ Python 环境用 uv；安装与检查命令见 `../guides/开发环境.md`。
|
||||
- `make_build_id(now, git)`：构建号 `YYYYMMDD-HHMMSS-<git 短哈希>`，工作区不干净时末尾加 `-dirty`。
|
||||
- `write_snapshot(snapshot_dir, build_id, git, started_at, finished_at, uv_lock, configs)`：新建快照目录，复制配置原文并写 `snapshot.json`（构建号、git 状态、Python 版本、`uv.lock` 哈希、起止时间、各配置的哈希）。
|
||||
|
||||
### ceus（`src/cadx/ceus/`，目前只有第 1、2 步和坐标换算）
|
||||
|
||||
- `CeusConfig` 及各段的配置类：`config/ceus.yaml` 里 `ceus` 段的字段定义（pydantic，字段必填、不许多写、读入后不可改）；`paths` 段由 `scripts/ceus_quant.py` 定义。
|
||||
- `VideoFile(path)`：按路径用 cv2 读视频：`probe()` 给 fps、帧数、宽高；`iter_frames()` 每次从第 0 帧逐帧读；`frame_times_s()` 给逐帧显示时间戳。
|
||||
- `split_views(frames, views_cfg)`：第 1 步，在均匀抽的帧上找分屏线，交出分屏线和左右两半的 `ViewRect`；找不到可信的分屏线抛 `SplitDetectionError`。
|
||||
- `locate_contrast_side(frames, left_view, right_view, prompt_cfg, keyframes_cfg)`：第 2 步，两半各自找绿框、选提示帧，恰好一半有就定为造影画面，交出 `ContrastSide`（哪一半、造影视图、另一半视图、提示帧）；两半都有或都没有抛 `ContrastSideError`。
|
||||
- `view_offset`、`map_source_mask_to_target`、`target_polygons_to_source`：两半之间的坐标换算，平移量是两个视图左上角之差。
|
||||
- `CeusCaseError`：只落在这一例上的失败，带失败的步号和原因；`SplitDetectionError`、`ContrastSideError` 是它的子类。脚本据此把这一例记进 `failed.csv`、接着跑下一例。
|
||||
- 其余导出（`ViewRect` 与几何函数、`extract_color_prompt`、`select_prompt_frames` 等）是各步的组成部分，供第二阶段的模块和测试用。
|
||||
|
||||
### build（`src/cadx/build/`）
|
||||
|
||||
- `run_build(config_path, repo_root, now)`：完整做一次构建：读配置和映射表、读交付包、取化名、先写进 `.partial` 目录再改名，交出 `BuildSummary`。
|
||||
- `run_build(config_path, repo_root, now)`：完整做一次构建：读配置和映射表、读交付包、对每例做 C14（`check_key_image` 返回原因的整例跳过，记进跳过清单）、取化名、先写进 `.partial` 目录再改名，交出 `BuildSummary`。
|
||||
- `BuildSummary`：供终端打印的计数：构建号、两个库的路径、病例总数、进库数、跳过数、认不出编号的行数、按检查编号分的跳过数、复核清单条数、跳过清单说明句。
|
||||
- `BuildConfig`：`build.yaml` 的字段定义（pydantic，字段必填、不许多写、读入后不可改）。
|
||||
- `load_build_config(path, repo_root)`：读并校验 `build.yaml`，相对路径按仓库根目录解析。
|
||||
|
||||
@@ -135,7 +135,7 @@ CSV 用带 BOM 的 UTF-8（文件开头有一个不可见的标记），医生
|
||||
两个库先写进 `<根目录>/<构建号>.partial/`，全部写完、清单算完后再改名成 `<构建号>/`：先改答案库，最后改资料库。所以：
|
||||
|
||||
- **资料库的 `<构建号>/` 出现，才算这次构建完成。** 会诊只读资料库，读取函数也拒绝名字以 `.partial` 结尾的目录，没写完的构建不会被用上。
|
||||
- 看到 `.partial` 目录，说明那次构建中途出错。目前已知的一种是某张关键图的格式（位深、颜色类型、透明色）不能逐像素保真重编码：这本是只落在一例上的问题，但现在的代码让整次构建停下，而不是跳过这一例。它不会被自动删除，留着给人查原因；查完由人决定删不删。
|
||||
- 看到 `.partial` 目录，说明那次构建中途出错。它不会被自动删除，留着给人查原因；查完由人决定删不删。
|
||||
- 只有答案库的 `<构建号>/`、没有同号的资料库，同样是没完成的构建。
|
||||
- 配置、映射表或整张表的检查（T1–T5）不过时，构建在建目录之前就停下，不留任何目录。
|
||||
- 目标目录已经存在时报错，不覆盖。构建号带到秒，正常不会重名。
|
||||
@@ -147,7 +147,7 @@ CSV 用带 BOM 的 UTF-8（文件开头有一个不可见的标记），医生
|
||||
交付资料里的问题分两种（`AGENTS.md`「错误怎么处理」）：
|
||||
|
||||
- **整张表的问题**（T1–T5）：一例也读不出来，整次构建停下，报错信息以检查编号开头，写明哪个文件哪一项不符。
|
||||
- **只落在某一个病例上的问题**（C01–C13）：跳过这一整例，接着处理下一例，在 `skipped.csv` 里记一行。这一例不进资料库，也不进答案库的 `aliases.csv`、`diagnoses.csv`。
|
||||
- **只落在某一个病例上的问题**（C01–C14）：跳过这一整例，接着处理下一例，在 `skipped.csv` 里记一行。这一例不进资料库，也不进答案库的 `aliases.csv`、`diagnoses.csv`。
|
||||
|
||||
先对三张表的每一行和每个病例目录做 C01（编号认不认得出）；认得出的，再逐例按 C02 到 C13 的顺序检查，**遇到第一项没过就停下**，清单上每例只记这一项（C01 记的是认不出的那一行，不算某一例，见下文）。后面的检查没做过，不代表没问题：医生改好这一项后，重新构建时它可能卡在后面某一项。这句说明写在答案库 `manifest.json` 的 `skipped_note` 里，终端也会打印；CSV 文件开头不能加注释行，所以没写在 `skipped.csv` 里。为什么整例跳过而不是停下整次构建：负责人 2026-10-16「都是遇到整个病例跳过，让医生核对去吧」。评价时要报告跳过了几例、各因为什么。
|
||||
|
||||
@@ -156,7 +156,7 @@ CSV 用带 BOM 的 UTF-8（文件开头有一个不可见的标记），医生
|
||||
| 列 | 含义 |
|
||||
| --- | --- |
|
||||
| 病例 | 三位病例号。C01（编号认不出）的行不知道是哪个病人，写成「表的路径 第 N 行」（N 是 Excel 行号，第 1 行是表头）；病例目录名不在 1–999 之间时写成「病例目录 目录名」 |
|
||||
| 检查编号 | C01–C13 |
|
||||
| 检查编号 | C01–C14 |
|
||||
| 检查说明 | 这项检查的一句话说明 |
|
||||
| 文件 | 涉及的文件，相对交付包根目录，多个用「; 」隔开 |
|
||||
| 列 | 涉及的列，多个用「; 」隔开；不涉及具体列（如目录里的文件不对）时为空 |
|
||||
@@ -166,7 +166,7 @@ C01 的行不计入病例数，也不计入终端的「跳过病例」，单独
|
||||
|
||||
### 检查编号速查
|
||||
|
||||
规则细节以代码为准：T1–T4 在 `src/cadx/delivery/_tables.py`，T5 在 `src/cadx/build/_config.py`，C01–C10 在 `src/cadx/delivery/_cases.py`，C11–C13 的词表和正则在 `src/cadx/delivery/_text.py`，表名、工作表名、表头在 `src/cadx/delivery/_shape.py`。每条规则为什么这样定见任务书 `../任务书/01-装配两个库.md` 第二部分。
|
||||
规则细节以代码为准：T1–T4 在 `src/cadx/delivery/_tables.py`，T5 在 `src/cadx/build/_config.py`，C01–C10 在 `src/cadx/delivery/_cases.py`，C11–C13 的词表和正则在 `src/cadx/delivery/_text.py`，C14 由 build 调 library 的 `check_key_image`（`src/cadx/library/_write.py`）在写库前做，表名、工作表名、表头在 `src/cadx/delivery/_shape.py`。每条规则为什么这样定见任务书 `../任务书/01-装配两个库.md` 第二部分。
|
||||
|
||||
| 编号 | 查什么 | 不过时 |
|
||||
| --- | --- | --- |
|
||||
@@ -188,6 +188,7 @@ C01 的行不计入病例数，也不计入终端的「跳过病例」，单独
|
||||
| C11 | 报告文字里没有结论段标题：段首（全文开头或换行之后）出现「印象」「结论」「提示」等关键词加冒号 | 跳过这一例 |
|
||||
| C12 | 报告文字里没有亚型名（缩写、中文名及其常见变体） | 跳过这一例 |
|
||||
| C13 | 报告文字里没有人口学信息：没有「数字岁」，没有「男」「女」两个字 | 跳过这一例 |
|
||||
| C14 | 关键图能原样重编码：是 PNG，位深和颜色类型能逐像素保真（8 位灰度、RGB、灰度加透明通道、RGBA，或 1 位灰度），不带 tRNS 透明色；否则重编码会悄悄降位或丢透明。涉及值写不过的原因 | 跳过这一例 |
|
||||
|
||||
几点说明：
|
||||
|
||||
|
||||
@@ -4,6 +4,7 @@
|
||||
"""
|
||||
|
||||
import hashlib
|
||||
import json
|
||||
import random
|
||||
import string
|
||||
from collections import Counter
|
||||
@@ -20,10 +21,11 @@ from cadx.delivery import (
|
||||
SKIPPED_NOTE,
|
||||
CaseRecord,
|
||||
CodeMaps,
|
||||
SkipRecord,
|
||||
read_delivery,
|
||||
review_sentences,
|
||||
)
|
||||
from cadx.library import LibraryCase, write_library
|
||||
from cadx.library import LibraryCase, check_key_image, write_library
|
||||
from cadx.runs import make_build_id, read_git_state, write_snapshot
|
||||
|
||||
ALIAS_ALPHABET = string.ascii_lowercase + string.digits
|
||||
@@ -31,6 +33,9 @@ ALIAS_LENGTH = 4
|
||||
# 化名里出现亚型缩写会被泄露扫描（不分大小写）误报，抽到就重抽。
|
||||
_ALIAS_BANNED = ("ipmn", "spn", "mcn", "scn")
|
||||
|
||||
C14 = "C14"
|
||||
C14_DESCRIPTION = "关键图能原样重编码"
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class BuildSummary:
|
||||
@@ -75,6 +80,30 @@ def _clinical_fields(
|
||||
}
|
||||
|
||||
|
||||
def _check_key_images(
|
||||
cases: Sequence[CaseRecord], delivery_dir: Path
|
||||
) -> tuple[list[CaseRecord], list[SkipRecord]]:
|
||||
"""C14：关键图不能逐像素保真重编码的病例整例跳过，原因写进涉及值。"""
|
||||
kept: list[CaseRecord] = []
|
||||
skipped: list[SkipRecord] = []
|
||||
for case in cases:
|
||||
reason = check_key_image(case.key_image)
|
||||
if reason is None:
|
||||
kept.append(case)
|
||||
continue
|
||||
skipped.append(
|
||||
SkipRecord(
|
||||
case=case.case_no,
|
||||
check=C14,
|
||||
description=C14_DESCRIPTION,
|
||||
files=(case.key_image.relative_to(delivery_dir).as_posix(),),
|
||||
columns=(),
|
||||
value_json=json.dumps(reason, ensure_ascii=False),
|
||||
)
|
||||
)
|
||||
return kept, skipped
|
||||
|
||||
|
||||
def _sha256(path: Path) -> str:
|
||||
return hashlib.sha256(path.read_bytes()).hexdigest()
|
||||
|
||||
@@ -91,6 +120,8 @@ def run_build(config_path: Path, repo_root: Path, now: datetime) -> BuildSummary
|
||||
config = load_build_config(config_path, repo_root)
|
||||
code_maps = load_code_maps(config.code_maps) if config.code_maps is not None else None
|
||||
delivery = read_delivery(config.delivery_dir, code_maps)
|
||||
passed, key_image_skips = _check_key_images(delivery.cases, config.delivery_dir)
|
||||
skipped = [*delivery.skipped, *key_image_skips]
|
||||
|
||||
git = read_git_state(repo_root)
|
||||
build_id = make_build_id(now, git)
|
||||
@@ -104,8 +135,8 @@ def run_build(config_path: Path, repo_root: Path, now: datetime) -> BuildSummary
|
||||
for partial in (library_partial, answers_partial):
|
||||
partial.mkdir(parents=True, exist_ok=False)
|
||||
|
||||
aliases = make_aliases([case.case_no for case in delivery.cases], config.alias_seed)
|
||||
by_alias = sorted(delivery.cases, key=lambda case: aliases[case.case_no])
|
||||
aliases = make_aliases([case.case_no for case in passed], config.alias_seed)
|
||||
by_alias = sorted(passed, key=lambda case: aliases[case.case_no])
|
||||
|
||||
configs = {"build.yaml": config_path}
|
||||
if config.code_maps is not None:
|
||||
@@ -152,7 +183,7 @@ def run_build(config_path: Path, repo_root: Path, now: datetime) -> BuildSummary
|
||||
skipped_note=SKIPPED_NOTE,
|
||||
aliases=[
|
||||
AliasRow(alias=aliases[c.case_no], case_no=c.case_no, video_id=c.video_id)
|
||||
for c in delivery.cases
|
||||
for c in passed
|
||||
],
|
||||
diagnoses=[
|
||||
DiagnosisRow(
|
||||
@@ -163,7 +194,7 @@ def run_build(config_path: Path, repo_root: Path, now: datetime) -> BuildSummary
|
||||
mr_code=c.diagnosis.mr,
|
||||
name=None if code_maps is None else code_maps["Diagnosis"][c.diagnosis.clinical],
|
||||
)
|
||||
for c in delivery.cases
|
||||
for c in passed
|
||||
],
|
||||
skipped=[
|
||||
SkippedRow(
|
||||
@@ -174,7 +205,7 @@ def run_build(config_path: Path, repo_root: Path, now: datetime) -> BuildSummary
|
||||
columns=s.columns,
|
||||
value_json=s.value_json,
|
||||
)
|
||||
for s in delivery.skipped
|
||||
for s in skipped
|
||||
],
|
||||
reviews=reviews,
|
||||
)
|
||||
@@ -183,14 +214,14 @@ def run_build(config_path: Path, repo_root: Path, now: datetime) -> BuildSummary
|
||||
_rename(answers_partial, answers_dir)
|
||||
_rename(library_partial, library_dir)
|
||||
|
||||
by_check = Counter(s.check for s in delivery.skipped)
|
||||
by_check = Counter(s.check for s in skipped)
|
||||
return BuildSummary(
|
||||
build_id=build_id,
|
||||
library_dir=library_dir,
|
||||
answers_dir=answers_dir,
|
||||
total_cases=delivery.total_cases,
|
||||
passed_cases=len(delivery.cases),
|
||||
skipped_cases=len(delivery.skipped) - delivery.unrecognized_rows,
|
||||
passed_cases=len(passed),
|
||||
skipped_cases=len(skipped) - delivery.unrecognized_rows,
|
||||
unrecognized_rows=delivery.unrecognized_rows,
|
||||
skipped_by_check=dict(sorted(by_check.items())),
|
||||
review_rows=len(reviews),
|
||||
|
||||
@@ -0,0 +1,113 @@
|
||||
"""ceus：CEUS 定量的全部算法和参数，纯计算，不引用其他 cadx 模块。
|
||||
|
||||
目前有第 1、2 步（视频解码、分屏、找绿框、选提示帧、判断造影画面在哪一半）和两半之间的坐标换算；
|
||||
第 3 步以后随任务书 02 第二阶段加入。
|
||||
某一例不满足算法前提时抛 `CeusCaseError`（带失败的步号和原因）。
|
||||
"""
|
||||
|
||||
from cadx.ceus._config import (
|
||||
AnchorArrivalConfig,
|
||||
AnchorFramesConfig,
|
||||
AnchorLatePhaseConfig,
|
||||
CeusConfig,
|
||||
ConstraintsConfig,
|
||||
InteriorDarkRegionConfig,
|
||||
KeyframePositionConstraint,
|
||||
KeyframesConfig,
|
||||
PanSourceConfig,
|
||||
PromptSourceConfig,
|
||||
ReferenceRegionConfig,
|
||||
RoiShapeConfig,
|
||||
SmoothingConfig,
|
||||
TicAnalysisConfig,
|
||||
TicBaselineConfig,
|
||||
TicFittingConfig,
|
||||
ViewsConfig,
|
||||
)
|
||||
from cadx.ceus._contrast_side import ContrastSide, ContrastSideError, locate_contrast_side
|
||||
from cadx.ceus._errors import CeusCaseError
|
||||
from cadx.ceus._geometry import (
|
||||
ViewRect,
|
||||
crop_frame,
|
||||
local_rect_within,
|
||||
mask_iou,
|
||||
odd_window,
|
||||
optional_positive_int,
|
||||
polygon_area,
|
||||
polygon_center,
|
||||
polygon_to_mask,
|
||||
polygons_to_mask,
|
||||
rect_from_values,
|
||||
resample_closed_contour,
|
||||
triangular_smooth,
|
||||
)
|
||||
from cadx.ceus._green_prompt import (
|
||||
ColorPrompt,
|
||||
PromptObservation,
|
||||
collect_prompt_observations,
|
||||
extract_color_prompt,
|
||||
prompt_color_mask,
|
||||
)
|
||||
from cadx.ceus._mapping import map_source_mask_to_target, target_polygons_to_source, view_offset
|
||||
from cadx.ceus._prompt_frames import PromptFrame, select_prompt_frames
|
||||
from cadx.ceus._split_screen import (
|
||||
SplitDetectionError,
|
||||
detect_split_x,
|
||||
sample_frames_for_split,
|
||||
split_views,
|
||||
view_rect,
|
||||
)
|
||||
from cadx.ceus._video import VideoFile
|
||||
|
||||
__all__ = [
|
||||
"AnchorArrivalConfig",
|
||||
"AnchorFramesConfig",
|
||||
"AnchorLatePhaseConfig",
|
||||
"CeusCaseError",
|
||||
"CeusConfig",
|
||||
"ColorPrompt",
|
||||
"ConstraintsConfig",
|
||||
"ContrastSide",
|
||||
"ContrastSideError",
|
||||
"InteriorDarkRegionConfig",
|
||||
"KeyframePositionConstraint",
|
||||
"KeyframesConfig",
|
||||
"PanSourceConfig",
|
||||
"PromptFrame",
|
||||
"PromptObservation",
|
||||
"PromptSourceConfig",
|
||||
"ReferenceRegionConfig",
|
||||
"RoiShapeConfig",
|
||||
"SmoothingConfig",
|
||||
"SplitDetectionError",
|
||||
"TicAnalysisConfig",
|
||||
"TicBaselineConfig",
|
||||
"TicFittingConfig",
|
||||
"VideoFile",
|
||||
"ViewRect",
|
||||
"ViewsConfig",
|
||||
"collect_prompt_observations",
|
||||
"crop_frame",
|
||||
"detect_split_x",
|
||||
"extract_color_prompt",
|
||||
"local_rect_within",
|
||||
"locate_contrast_side",
|
||||
"map_source_mask_to_target",
|
||||
"mask_iou",
|
||||
"odd_window",
|
||||
"optional_positive_int",
|
||||
"polygon_area",
|
||||
"polygon_center",
|
||||
"polygon_to_mask",
|
||||
"polygons_to_mask",
|
||||
"prompt_color_mask",
|
||||
"rect_from_values",
|
||||
"resample_closed_contour",
|
||||
"sample_frames_for_split",
|
||||
"select_prompt_frames",
|
||||
"split_views",
|
||||
"target_polygons_to_source",
|
||||
"triangular_smooth",
|
||||
"view_offset",
|
||||
"view_rect",
|
||||
]
|
||||
@@ -0,0 +1,533 @@
|
||||
"""CEUS 定量的参数：`config/ceus.yaml` 里 `ceus` 段的字段定义和校验。
|
||||
|
||||
照搬旧项目 `research_config.py` 的配置类，去掉全部默认值：每个字段必填，可以没有的值显式写
|
||||
null；多写或拼错的键报错；读入后不能修改（`AGENTS.md`「不设默认值」）。各参数的取值与出处写在
|
||||
`config/ceus.yaml` 的注释里，这里只写字段的含义和校验规则。
|
||||
|
||||
旧配置里以下字段删掉了，不留着不用：
|
||||
- `views.source_view` / `views.target_view`：造影画面在哪一半改由两半各自找绿框决定（`_views.py`）；
|
||||
- `views.effective_region`：裁画面黑边只有旧 `service.py` 用，40 例结果走的 debug 路径不裁；
|
||||
- `mapping`（含锚点）：两半之间的坐标换算改成两个视图矩形左上角之差（`_mapping.py`），
|
||||
不再需要按某一种视频宽度写死的锚点。
|
||||
|
||||
`paths` 段和整份文件最外层的模型由 `scripts/ceus_quant.py` 定义，不在这里。
|
||||
"""
|
||||
|
||||
from typing import Literal, Self
|
||||
|
||||
from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator
|
||||
|
||||
_STRICT = ConfigDict(strict=True, extra="forbid", frozen=True)
|
||||
|
||||
|
||||
def _require_positive_odd(value: int, field_name: str) -> int:
|
||||
"""正奇数校验：时间平滑窗口、自适应阈值块大小都要求奇数（窗口要有中心）。"""
|
||||
if value < 1:
|
||||
raise ValueError(f"{field_name} 必须为正整数，收到 {value}")
|
||||
if value % 2 == 0:
|
||||
raise ValueError(f"{field_name} 必须为奇数，收到 {value}")
|
||||
return value
|
||||
|
||||
|
||||
class ViewsConfig(BaseModel):
|
||||
"""分屏：超声机录造影时屏幕左右各一半，目前只支持左右分屏。
|
||||
|
||||
找分屏线只用均匀抽的 `split_sample_frames` 帧，在宽度的 `split_search_band` 比例区间里找。
|
||||
这两项在旧代码里是写死的数（旧 debug 脚本的 25、旧 `detect_split_x` 参数默认的 (0.35, 0.65)），
|
||||
这里改成配置，值不变。
|
||||
"""
|
||||
|
||||
model_config = _STRICT
|
||||
|
||||
layout: Literal["horizontal_split"]
|
||||
split_sample_frames: int
|
||||
split_search_band: list[float]
|
||||
|
||||
@field_validator("split_sample_frames")
|
||||
@classmethod
|
||||
def _sample_frames_positive(cls, value: int) -> int:
|
||||
if value < 1:
|
||||
raise ValueError(f"views.split_sample_frames 必须 ≥1，收到 {value}")
|
||||
return value
|
||||
|
||||
@field_validator("split_search_band")
|
||||
@classmethod
|
||||
def _band_pair(cls, value: list[float]) -> list[float]:
|
||||
if len(value) != 2:
|
||||
raise ValueError(f"views.split_search_band 必须是 [lo, hi] 两个数，收到 {value}")
|
||||
return value
|
||||
|
||||
|
||||
class PromptSourceConfig(BaseModel):
|
||||
"""绿框的 HSV 颜色检测。`min_area` 是候选外接框面积 w×h，不是轮廓面积。"""
|
||||
|
||||
model_config = _STRICT
|
||||
|
||||
type: Literal["color_polygon"]
|
||||
hsv_lower: list[int]
|
||||
hsv_upper: list[int]
|
||||
ignore_regions: list[list[float]]
|
||||
min_area: int
|
||||
|
||||
|
||||
class PanSourceConfig(BaseModel):
|
||||
"""紫圈的 HSV 颜色检测：医生用紫色轮廓圈的一块正常胰腺，作对照。
|
||||
|
||||
和绿框是同一类检测器，只是换一组颜色范围。`min_detected_frame_fraction` 是这一组独有的：
|
||||
紫圈每一帧都在，整段视频里检出的帧占比偏低，说明颜色范围没标定对，对照区整段不可用。
|
||||
"""
|
||||
|
||||
model_config = _STRICT
|
||||
|
||||
type: Literal["color_polygon"]
|
||||
hsv_lower: list[int]
|
||||
hsv_upper: list[int]
|
||||
ignore_regions: list[list[float]]
|
||||
min_area: int
|
||||
min_detected_frame_fraction: float
|
||||
|
||||
@field_validator("hsv_lower", "hsv_upper")
|
||||
@classmethod
|
||||
def _hsv_triple(cls, value: list[int]) -> list[int]:
|
||||
"""HSV 阈值须是三个分量，否则要到 `cv2.inRange` 里才报错，离配置很远。"""
|
||||
if len(value) != 3:
|
||||
raise ValueError(f"pan_source HSV 阈值必须是 3 个分量，收到 {value}")
|
||||
return value
|
||||
|
||||
@field_validator("min_area")
|
||||
@classmethod
|
||||
def _min_area_positive(cls, value: int) -> int:
|
||||
"""min_area 须 ≥1。"""
|
||||
if value < 1:
|
||||
raise ValueError(f"pan_source.min_area 必须 ≥1，收到 {value}")
|
||||
return value
|
||||
|
||||
@field_validator("min_detected_frame_fraction")
|
||||
@classmethod
|
||||
def _fraction_within_bounds(cls, value: float) -> float:
|
||||
"""占比须在 (0, 1]：取 0 这道门形同虚设，越界则永远判不出有效。"""
|
||||
if not 0.0 < value <= 1.0:
|
||||
raise ValueError(f"pan_source.min_detected_frame_fraction 必须 ∈ (0, 1]，收到 {value}")
|
||||
return value
|
||||
|
||||
@model_validator(mode="after")
|
||||
def _hsv_bounds_ordered(self) -> Self:
|
||||
"""逐分量上界 ≥ 下界。写反了 `cv2.inRange` 不报错，只会恒返回空掩膜。"""
|
||||
pairs = zip(self.hsv_lower, self.hsv_upper, strict=True)
|
||||
for index, (low, high) in enumerate(pairs):
|
||||
if high < low:
|
||||
raise ValueError(
|
||||
"pan_source.hsv_upper 每一分量都必须 ≥ hsv_lower，"
|
||||
f"第 {index} 个分量收到 lower={low}, upper={high}"
|
||||
)
|
||||
return self
|
||||
|
||||
|
||||
class KeyframesConfig(BaseModel):
|
||||
"""提示帧的选择：绿框形状相对上一个提示帧的 IoU 跌破阈值就记一帧新的。
|
||||
|
||||
键名沿用旧配置的 `keyframes`；它选的是给分割模型的提示帧，不是第 8 步那 7 张关键帧。
|
||||
"""
|
||||
|
||||
model_config = _STRICT
|
||||
|
||||
change_metric: Literal["mask_iou"]
|
||||
iou_threshold: float
|
||||
min_gap_frames: int
|
||||
|
||||
|
||||
class SmoothingConfig(BaseModel):
|
||||
"""病灶轮廓的重采样点数和逐顶点时间平滑窗口（须正奇数）。"""
|
||||
|
||||
model_config = _STRICT
|
||||
|
||||
contour_points: int
|
||||
temporal_window: int
|
||||
|
||||
@field_validator("temporal_window")
|
||||
@classmethod
|
||||
def _temporal_window_odd(cls, value: int) -> int:
|
||||
"""三角平滑窗口须正奇数，偶数窗口没有中心。"""
|
||||
return _require_positive_odd(value, "smoothing.temporal_window")
|
||||
|
||||
@field_validator("contour_points")
|
||||
@classmethod
|
||||
def _contour_points_positive(cls, value: int) -> int:
|
||||
"""少于 3 个点围不成多边形。"""
|
||||
if value < 3:
|
||||
raise ValueError(f"smoothing.contour_points 必须 ≥3，收到 {value}")
|
||||
return value
|
||||
|
||||
|
||||
class RoiShapeConfig(BaseModel):
|
||||
"""最终病灶轮廓与绿框的融合（`待定问题/01`）。两个强度截到 [0, 1]，面积比须有序。"""
|
||||
|
||||
model_config = _STRICT
|
||||
|
||||
source: str
|
||||
keyframe_shape_strength: float
|
||||
propagation_shape_strength: float
|
||||
min_area_ratio_to_prompt: float
|
||||
max_area_ratio_to_prompt: float
|
||||
|
||||
@field_validator("keyframe_shape_strength", "propagation_shape_strength")
|
||||
@classmethod
|
||||
def _strength_in_unit_interval(cls, value: float) -> float:
|
||||
"""混合权重截到 [0, 1]。"""
|
||||
return min(1.0, max(0.0, value))
|
||||
|
||||
@model_validator(mode="after")
|
||||
def _area_ratio_ordered(self) -> Self:
|
||||
"""面积比下限须小于上限。"""
|
||||
if self.min_area_ratio_to_prompt >= self.max_area_ratio_to_prompt:
|
||||
raise ValueError(
|
||||
"roi_shape.min_area_ratio_to_prompt 必须 < max_area_ratio_to_prompt，"
|
||||
f"收到 min={self.min_area_ratio_to_prompt}, "
|
||||
f"max={self.max_area_ratio_to_prompt}"
|
||||
)
|
||||
return self
|
||||
|
||||
|
||||
class InteriorDarkRegionConfig(BaseModel):
|
||||
"""暗区（病灶里不进造影剂、一直发黑的囊液部分）的识别：方法选择、评分和各方法的子参数。"""
|
||||
|
||||
model_config = _STRICT
|
||||
|
||||
enabled: bool
|
||||
analysis_view: Literal["source", "target"]
|
||||
exclude_prompt_color: bool
|
||||
threshold_method: Literal[
|
||||
"auto_ensemble",
|
||||
"otsu",
|
||||
"percentile",
|
||||
"absolute",
|
||||
"adaptive",
|
||||
"kmeans",
|
||||
"clahe_otsu",
|
||||
]
|
||||
candidate_methods: list[
|
||||
Literal["otsu", "adaptive", "percentile", "kmeans", "clahe_otsu", "absolute"]
|
||||
]
|
||||
threshold_offset: int
|
||||
min_area: int
|
||||
min_area_ratio: float
|
||||
preserve_holes: bool
|
||||
fill_holes: bool
|
||||
morphology_kernel: int
|
||||
fragment_penalty_weight: float
|
||||
temporal_consistency_weight: float
|
||||
contour_points: int
|
||||
percentile: float
|
||||
threshold: int # absolute 法的固定灰度阈值
|
||||
adaptive_block_size: int
|
||||
adaptive_c: float
|
||||
kmeans_max_iter: int
|
||||
kmeans_attempts: int
|
||||
clahe_clip_limit: float
|
||||
clahe_tile_grid_size: int
|
||||
|
||||
@field_validator("adaptive_block_size")
|
||||
@classmethod
|
||||
def _adaptive_block_size_odd(cls, value: int) -> int:
|
||||
"""自适应阈值的块大小须正奇数且 ≥3（OpenCV 的要求）。"""
|
||||
validated = _require_positive_odd(value, "interior_dark_region.adaptive_block_size")
|
||||
if validated < 3:
|
||||
raise ValueError(f"interior_dark_region.adaptive_block_size 必须 ≥3，收到 {validated}")
|
||||
return validated
|
||||
|
||||
@field_validator("percentile")
|
||||
@classmethod
|
||||
def _percentile_within_bounds(cls, value: float) -> float:
|
||||
"""百分位须在 [0, 100]。"""
|
||||
if not 0.0 <= value <= 100.0:
|
||||
raise ValueError(f"interior_dark_region.percentile 必须 ∈ [0, 100]，收到 {value}")
|
||||
return value
|
||||
|
||||
|
||||
class KeyframePositionConstraint(BaseModel):
|
||||
"""提示帧上病灶轮廓的位置约束：把轮廓中心平移向绿框中心。strength 截到 [0, 1]。"""
|
||||
|
||||
model_config = _STRICT
|
||||
|
||||
enabled: bool
|
||||
mode: Literal["center_interpolation"]
|
||||
strength: float
|
||||
|
||||
@field_validator("strength")
|
||||
@classmethod
|
||||
def _strength_in_unit_interval(cls, value: float) -> float:
|
||||
"""截到 [0, 1]，过冲会把轮廓推出画面。"""
|
||||
return min(1.0, max(0.0, value))
|
||||
|
||||
|
||||
class ConstraintsConfig(BaseModel):
|
||||
"""约束段。"""
|
||||
|
||||
model_config = _STRICT
|
||||
|
||||
keyframe_position: KeyframePositionConstraint
|
||||
|
||||
|
||||
class ReferenceRegionConfig(BaseModel):
|
||||
"""病灶外背景圆的选取。manual 模式必须给中心坐标，auto_near_roi 时为 null。"""
|
||||
|
||||
model_config = _STRICT
|
||||
|
||||
enabled: bool
|
||||
mode: Literal["auto_near_roi", "manual"]
|
||||
radius_px: int
|
||||
min_distance_px: int
|
||||
nonblack_threshold: int
|
||||
min_valid_fraction: float
|
||||
center_xy: list[float] | None
|
||||
|
||||
@model_validator(mode="after")
|
||||
def _manual_mode_requires_center(self) -> Self:
|
||||
"""manual 模式没有中心就取不到背景圆，配置错误直接报错。"""
|
||||
if self.mode == "manual" and self.center_xy is None:
|
||||
raise ValueError("reference_region.mode=manual 时 center_xy 必填")
|
||||
return self
|
||||
|
||||
|
||||
class TicBaselineConfig(BaseModel):
|
||||
"""增强前基线区间：视频开始到造影剂到达之前那段稳定区间，在上面算基线均值和标准差。"""
|
||||
|
||||
model_config = _STRICT
|
||||
|
||||
preferred_duration_s: float
|
||||
minimum_duration_s: float
|
||||
stability_sd_multiplier: float
|
||||
|
||||
@model_validator(mode="after")
|
||||
def _durations_ordered_and_positive(self) -> Self:
|
||||
"""两个时长为正且最短 ≤ 首选；写反了「首选取不到退到最短」这条退路就比首选还长。"""
|
||||
if self.minimum_duration_s <= 0.0 or self.preferred_duration_s <= 0.0:
|
||||
raise ValueError(
|
||||
"tic_analysis.baseline 的两个时长必须为正，收到 "
|
||||
f"preferred={self.preferred_duration_s}, "
|
||||
f"minimum={self.minimum_duration_s}"
|
||||
)
|
||||
if self.minimum_duration_s > self.preferred_duration_s:
|
||||
raise ValueError(
|
||||
"tic_analysis.baseline.minimum_duration_s 必须 <= "
|
||||
f"preferred_duration_s，收到 minimum={self.minimum_duration_s}, "
|
||||
f"preferred={self.preferred_duration_s}"
|
||||
)
|
||||
return self
|
||||
|
||||
|
||||
class TicFittingConfig(BaseModel):
|
||||
"""TIC 的约束 Lognormal 拟合：候选窗、重复帧门槛、参数边界和质量门。"""
|
||||
|
||||
model_config = _STRICT
|
||||
|
||||
algorithm_version: Literal["lognormal-v2"]
|
||||
detect_duplicate_frames: bool
|
||||
candidate_window_s: float
|
||||
candidate_long_window_s: float
|
||||
candidate_polyorder: Literal[2]
|
||||
max_candidate_gap_s: float
|
||||
duplicate_mean_absdiff_threshold: float
|
||||
minimum_valid_samples: int
|
||||
minimum_peak_distance_s: float
|
||||
minimum_relative_peak_height: float
|
||||
multi_peak_minimum_separation_s: float
|
||||
multi_peak_minimum_relative_height: float
|
||||
multi_peak_maximum_valley_fraction: float
|
||||
sigma_min: float
|
||||
sigma_max: float
|
||||
robust_loss: Literal["soft_l1"]
|
||||
max_nfev: int
|
||||
minimum_r2: float
|
||||
maximum_nrmse: float
|
||||
maximum_residual_noise_ratio: float
|
||||
boundary_margin_s: float
|
||||
estimated_baseline_percentile: float
|
||||
estimated_baseline_lookback_s: float
|
||||
maximum_perf_area_step_fraction: float
|
||||
parameter_boundary_fraction: float
|
||||
|
||||
@field_validator(
|
||||
"candidate_window_s",
|
||||
"candidate_long_window_s",
|
||||
"max_candidate_gap_s",
|
||||
"duplicate_mean_absdiff_threshold",
|
||||
"minimum_peak_distance_s",
|
||||
"multi_peak_minimum_separation_s",
|
||||
"sigma_min",
|
||||
"sigma_max",
|
||||
"max_nfev",
|
||||
"boundary_margin_s",
|
||||
"estimated_baseline_lookback_s",
|
||||
)
|
||||
@classmethod
|
||||
def _positive(cls, value: float) -> float:
|
||||
if value <= 0:
|
||||
raise ValueError(f"tic_analysis.fitting 的正数参数必须 >0，收到 {value}")
|
||||
return value
|
||||
|
||||
@field_validator("minimum_valid_samples")
|
||||
@classmethod
|
||||
def _enough_samples(cls, value: int) -> int:
|
||||
if value < 8:
|
||||
raise ValueError("tic_analysis.fitting.minimum_valid_samples 必须 >=8")
|
||||
return value
|
||||
|
||||
@field_validator(
|
||||
"minimum_relative_peak_height",
|
||||
"multi_peak_minimum_relative_height",
|
||||
"multi_peak_maximum_valley_fraction",
|
||||
"maximum_nrmse",
|
||||
"maximum_perf_area_step_fraction",
|
||||
)
|
||||
@classmethod
|
||||
def _unit_fraction(cls, value: float) -> float:
|
||||
if not 0.0 < value <= 1.0:
|
||||
raise ValueError(f"tic_analysis.fitting 的比例参数必须在 (0,1]，收到 {value}")
|
||||
return value
|
||||
|
||||
@field_validator("parameter_boundary_fraction")
|
||||
@classmethod
|
||||
def _boundary_fraction(cls, value: float) -> float:
|
||||
if not 0.0 < value < 0.5:
|
||||
raise ValueError("tic_analysis.fitting.parameter_boundary_fraction 必须在 (0,0.5) 内")
|
||||
return value
|
||||
|
||||
@field_validator("maximum_residual_noise_ratio")
|
||||
@classmethod
|
||||
def _positive_noise_ratio(cls, value: float) -> float:
|
||||
if value <= 0.0:
|
||||
raise ValueError("maximum_residual_noise_ratio 必须 >0")
|
||||
return value
|
||||
|
||||
@field_validator("minimum_r2")
|
||||
@classmethod
|
||||
def _r2_range(cls, value: float) -> float:
|
||||
if not -1.0 <= value <= 1.0:
|
||||
raise ValueError(f"minimum_r2 必须在 [-1,1]，收到 {value}")
|
||||
return value
|
||||
|
||||
@field_validator("estimated_baseline_percentile")
|
||||
@classmethod
|
||||
def _baseline_percentile(cls, value: float) -> float:
|
||||
if not 0.0 < value < 50.0:
|
||||
raise ValueError("estimated_baseline_percentile 必须在 (0,50) 内")
|
||||
return value
|
||||
|
||||
@model_validator(mode="after")
|
||||
def _sigma_bounds_ordered(self) -> Self:
|
||||
if self.sigma_min >= self.sigma_max:
|
||||
raise ValueError("tic_analysis.fitting.sigma_min 必须 < sigma_max")
|
||||
return self
|
||||
|
||||
|
||||
class AnchorArrivalConfig(BaseModel):
|
||||
"""造影剂到达判据：门槛取「基线标准差 × 倍数」与「峰高 × 比例」的高者，越线后须持续一段时间。"""
|
||||
|
||||
model_config = _STRICT
|
||||
|
||||
baseline_sd_multiplier: float
|
||||
relative_peak_threshold: float
|
||||
persistence_s: float
|
||||
|
||||
@field_validator("baseline_sd_multiplier", "relative_peak_threshold", "persistence_s")
|
||||
@classmethod
|
||||
def _positive(cls, value: float) -> float:
|
||||
"""三个判据数都须为正；0 或负值会让门槛恒真或恒假而不报错。"""
|
||||
if value <= 0.0:
|
||||
raise ValueError(f"anchor_frames.arrival 判据数必须为正，收到 {value}")
|
||||
return value
|
||||
|
||||
|
||||
class AnchorLatePhaseConfig(BaseModel):
|
||||
"""F2（推注后固定时点的晚期帧）的锚点秒数与容差。"""
|
||||
|
||||
model_config = _STRICT
|
||||
|
||||
preferred_time_s: float
|
||||
tolerance_s: float
|
||||
|
||||
@field_validator("preferred_time_s", "tolerance_s")
|
||||
@classmethod
|
||||
def _positive(cls, value: float) -> float:
|
||||
"""锚点秒数与容差都须为正。"""
|
||||
if value <= 0.0:
|
||||
raise ValueError(f"anchor_frames.late_phase 的数必须为正，收到 {value}")
|
||||
return value
|
||||
|
||||
|
||||
class AnchorFramesConfig(BaseModel):
|
||||
"""7 张关键帧的定位参数。
|
||||
|
||||
`peak_prominence_baseline_sd_multiplier` 与 `arrival.baseline_sd_multiplier` 是两个独立的数
|
||||
（即便取值相同），改一个不连带改另一个，所以是两个键。
|
||||
"""
|
||||
|
||||
model_config = _STRICT
|
||||
|
||||
arrival: AnchorArrivalConfig
|
||||
peak_prominence_baseline_sd_multiplier: float
|
||||
baseline_frame_backoff_s: float
|
||||
d1_window_after_s: float
|
||||
d2_window_s: float
|
||||
d3_window_s: float
|
||||
d4_window_s: float
|
||||
b0_window_s: float
|
||||
late_phase: AnchorLatePhaseConfig
|
||||
f1_anchor_s: float
|
||||
|
||||
@field_validator(
|
||||
"peak_prominence_baseline_sd_multiplier",
|
||||
"baseline_frame_backoff_s",
|
||||
"d1_window_after_s",
|
||||
"d2_window_s",
|
||||
"d3_window_s",
|
||||
"d4_window_s",
|
||||
"b0_window_s",
|
||||
"f1_anchor_s",
|
||||
)
|
||||
@classmethod
|
||||
def _positive(cls, value: float) -> float:
|
||||
"""倍数、退让、容差、锚点秒数都须为正；0 会让窗口退化成一个点。"""
|
||||
if value <= 0.0:
|
||||
raise ValueError(f"anchor_frames 的倍数与容差必须为正，收到 {value}")
|
||||
return value
|
||||
|
||||
|
||||
class TicAnalysisConfig(BaseModel):
|
||||
"""TIC 四条曲线（病灶整体、病灶去暗区、紫圈、背景圆）的采样、基线、拟合与关键帧。
|
||||
|
||||
旧配置里「在曲线最前面插一个时间 0、值 0 的合成点」的三个键已删：那个点不是量出来的，
|
||||
会在它与首个真实帧之间造出一段最陡的假上升。基线改由 `baseline` 从真实帧里识别。
|
||||
"""
|
||||
|
||||
model_config = _STRICT
|
||||
|
||||
brightness_metric: Literal["grayscale_mean"]
|
||||
exclude_prompt_color: bool
|
||||
prompt_exclude_dilate: int
|
||||
baseline: TicBaselineConfig
|
||||
reference_region: ReferenceRegionConfig
|
||||
fitting: TicFittingConfig
|
||||
anchor_frames: AnchorFramesConfig
|
||||
|
||||
|
||||
class CeusConfig(BaseModel):
|
||||
"""`config/ceus.yaml` 的 `ceus` 段：CEUS 定量的全部算法参数。
|
||||
|
||||
`config_version` 标记这一批产物用的是哪一套参数口径，由人改，不从内容哈希派生；
|
||||
不许是空串。
|
||||
"""
|
||||
|
||||
model_config = _STRICT
|
||||
|
||||
config_version: str = Field(min_length=1)
|
||||
views: ViewsConfig
|
||||
prompt_source: PromptSourceConfig
|
||||
pan_source: PanSourceConfig
|
||||
keyframes: KeyframesConfig
|
||||
smoothing: SmoothingConfig
|
||||
roi_shape: RoiShapeConfig
|
||||
interior_dark_region: InteriorDarkRegionConfig
|
||||
constraints: ConstraintsConfig
|
||||
tic_analysis: TicAnalysisConfig
|
||||
@@ -0,0 +1,76 @@
|
||||
"""第 2 步：判断造影画面在哪一半，并在那一半上选出提示帧。
|
||||
|
||||
哪一半是造影画面，各例不一定相同。绿框只画在造影那一半，所以两半各自按同样的办法
|
||||
（`collect_prompt_observations` + `select_prompt_frames`，参数相同）选提示帧：恰好一半选出
|
||||
至少一帧，这一半就是造影画面；两半都选出或都选不出，这一例失败（`ContrastSideError`）。
|
||||
旧代码按配置写死造影在左半，造影在右半的病例就找不到绿框。
|
||||
"""
|
||||
|
||||
from collections.abc import Sequence
|
||||
from dataclasses import dataclass
|
||||
from typing import Literal
|
||||
|
||||
from cadx.ceus._config import KeyframesConfig, PromptSourceConfig
|
||||
from cadx.ceus._errors import CeusCaseError
|
||||
from cadx.ceus._geometry import Frame, ViewRect, crop_frame
|
||||
from cadx.ceus._green_prompt import collect_prompt_observations
|
||||
from cadx.ceus._prompt_frames import PromptFrame, select_prompt_frames
|
||||
|
||||
PROMPT_STEP = 2
|
||||
|
||||
type Side = Literal["left", "right"]
|
||||
|
||||
|
||||
class ContrastSideError(CeusCaseError):
|
||||
"""两半都有或都没有绿框，定不出造影画面在哪一半。"""
|
||||
|
||||
def __init__(self, reason: str) -> None:
|
||||
super().__init__(PROMPT_STEP, reason)
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class ContrastSide:
|
||||
"""造影画面在哪一半、两个视图矩形（整帧像素坐标），以及在造影那一半上选出的提示帧。
|
||||
|
||||
`contrast_view` 是造影那一半（绿框在这里，TIC 在这里采亮度）；`target_view` 是另一半
|
||||
（分割在这里做）。提示帧的掩膜是 `contrast_view` 的局部坐标。
|
||||
"""
|
||||
|
||||
side: Side
|
||||
contrast_view: ViewRect
|
||||
target_view: ViewRect
|
||||
prompt_frames: list[PromptFrame]
|
||||
|
||||
|
||||
def _prompt_frames_in(
|
||||
frames: Sequence[Frame],
|
||||
view: ViewRect,
|
||||
prompt_cfg: PromptSourceConfig,
|
||||
keyframes_cfg: KeyframesConfig,
|
||||
) -> list[PromptFrame]:
|
||||
observations = collect_prompt_observations(
|
||||
(crop_frame(frame, view) for frame in frames), prompt_cfg
|
||||
)
|
||||
return select_prompt_frames(observations, keyframes_cfg)
|
||||
|
||||
|
||||
def locate_contrast_side(
|
||||
frames: Sequence[Frame],
|
||||
left_view: ViewRect,
|
||||
right_view: ViewRect,
|
||||
prompt_cfg: PromptSourceConfig,
|
||||
keyframes_cfg: KeyframesConfig,
|
||||
) -> ContrastSide:
|
||||
"""两半各自选提示帧，恰好一半有就定为造影画面；否则抛 `ContrastSideError`。"""
|
||||
left = _prompt_frames_in(frames, left_view, prompt_cfg, keyframes_cfg)
|
||||
right = _prompt_frames_in(frames, right_view, prompt_cfg, keyframes_cfg)
|
||||
if left and not right:
|
||||
return ContrastSide("left", left_view, right_view, left)
|
||||
if right and not left:
|
||||
return ContrastSide("right", right_view, left_view, right)
|
||||
if left and right:
|
||||
raise ContrastSideError(
|
||||
f"左右两半都找到绿框（左半 {len(left)} 个提示帧，右半 {len(right)} 个），"
|
||||
"定不出造影画面在哪一半"
|
||||
)
|
||||
raise ContrastSideError("左右两半都没有找到绿框，选不出提示帧")
|
||||
@@ -0,0 +1,15 @@
|
||||
"""只落在一段视频上的失败。
|
||||
|
||||
CEUS 定量第 1–6 步里，某一例的视频不满足算法前提（找不到分屏线、两半都有或都没有绿框等）时
|
||||
抛 `CeusCaseError`：这一例记进 `failed.csv`，接着跑下一例（`AGENTS.md`「错误怎么处理」）。
|
||||
其余异常是程序或配置的问题，整次运行停下。
|
||||
"""
|
||||
|
||||
|
||||
class CeusCaseError(Exception):
|
||||
"""一例失败：失败在第几步（结构.md 第四部分的 1–9 步）、原因。"""
|
||||
|
||||
def __init__(self, step: int, reason: str) -> None:
|
||||
super().__init__(f"第 {step} 步：{reason}")
|
||||
self.step = step
|
||||
self.reason = reason
|
||||
@@ -0,0 +1,202 @@
|
||||
"""几何、重采样与平滑的纯函数，供分屏、绿框、提示帧、多边形、TIC 等模块共用。
|
||||
|
||||
- `ViewRect`：整数像素坐标的不可变矩形；
|
||||
- 矩形：`rect_from_values`、`crop_frame`、`local_rect_within`；
|
||||
- 掩膜：`mask_iou`、`polygon_to_mask`、`polygons_to_mask`；
|
||||
- 多边形：`polygon_area`、`polygon_center`、`resample_closed_contour`；
|
||||
- 时间平滑：`triangular_smooth`；配置解析：`odd_window`、`optional_positive_int`。
|
||||
|
||||
算法等价于伙伴参考组件 `references/ROI/roi_tracking_pipeline.py`（经旧项目重写）。
|
||||
"""
|
||||
|
||||
from collections.abc import Sequence
|
||||
from dataclasses import dataclass
|
||||
from typing import Any
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
import numpy.typing as npt
|
||||
|
||||
type Mask = npt.NDArray[np.bool_]
|
||||
type Points = npt.NDArray[np.float32]
|
||||
type Frame = npt.NDArray[np.uint8]
|
||||
type AnyArray = npt.NDArray[Any]
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class ViewRect:
|
||||
"""整数像素坐标的矩形，左闭右开 `[x1, x2) × [y1, y2)`。
|
||||
|
||||
构造时强制 `0 <= x1 < x2` 且 `0 <= y1 < y2`，非法矩形直接报错，不让退化或负坐标往下游走。
|
||||
"""
|
||||
|
||||
x1: int
|
||||
y1: int
|
||||
x2: int
|
||||
y2: int
|
||||
|
||||
def __post_init__(self) -> None:
|
||||
if not (0 <= self.x1 < self.x2 and 0 <= self.y1 < self.y2):
|
||||
raise ValueError(
|
||||
f"非法 ViewRect：要求 0<=x1<x2 且 0<=y1<y2，得到 "
|
||||
f"({self.x1}, {self.y1}, {self.x2}, {self.y2})。"
|
||||
)
|
||||
|
||||
@property
|
||||
def width(self) -> int:
|
||||
"""宽度 `x2 - x1`。"""
|
||||
return self.x2 - self.x1
|
||||
|
||||
@property
|
||||
def height(self) -> int:
|
||||
"""高度 `y2 - y1`。"""
|
||||
return self.y2 - self.y1
|
||||
|
||||
|
||||
def mask_iou(mask_a: AnyArray, mask_b: AnyArray) -> float:
|
||||
"""两个二值掩膜的交并比；并集为空时返回 0.0。"""
|
||||
intersection = np.logical_and(mask_a, mask_b).sum()
|
||||
union = np.logical_or(mask_a, mask_b).sum()
|
||||
return float(intersection / union) if union else 0.0
|
||||
|
||||
|
||||
def crop_frame[T: np.generic](frame: npt.NDArray[T], rect: ViewRect) -> npt.NDArray[T]:
|
||||
"""按矩形切出子图 `frame[y1:y2, x1:x2]`（视图切片，不复制）。"""
|
||||
return frame[rect.y1 : rect.y2, rect.x1 : rect.x2]
|
||||
|
||||
|
||||
def local_rect_within(parent: ViewRect, child: ViewRect) -> ViewRect:
|
||||
"""把 `child` 换算成相对 `parent` 左上角的坐标；越界时 `ViewRect` 构造报错。"""
|
||||
return ViewRect(
|
||||
child.x1 - parent.x1,
|
||||
child.y1 - parent.y1,
|
||||
child.x2 - parent.x1,
|
||||
child.y2 - parent.y1,
|
||||
)
|
||||
|
||||
|
||||
def rect_from_values(values: Sequence[float], width: int, height: int) -> ViewRect:
|
||||
"""把 `[x1, y1, x2, y2]` 解析成像素矩形。
|
||||
|
||||
四个值都在 [0, 1] 时按归一化坐标乘宽高，否则按像素取整。结果裁到画面内，并保证
|
||||
`x2 >= x1 + 1`、`y2 >= y1 + 1`，不出退化矩形。
|
||||
"""
|
||||
if len(values) != 4:
|
||||
raise ValueError("手动裁剪坐标必须是 [x1, y1, x2, y2] 四元组。")
|
||||
if all(0.0 <= float(value) <= 1.0 for value in values):
|
||||
scales = (width, height, width, height)
|
||||
x1, y1, x2, y2 = (
|
||||
round(float(value) * scale) for value, scale in zip(values, scales, strict=True)
|
||||
)
|
||||
else:
|
||||
x1, y1, x2, y2 = (round(float(value)) for value in values)
|
||||
x1 = int(np.clip(x1, 0, width - 1))
|
||||
y1 = int(np.clip(y1, 0, height - 1))
|
||||
x2 = int(np.clip(x2, x1 + 1, width))
|
||||
y2 = int(np.clip(y2, y1 + 1, height))
|
||||
return ViewRect(x1, y1, x2, y2)
|
||||
|
||||
|
||||
def polygon_to_mask(polygon: AnyArray, height: int, width: int) -> Mask:
|
||||
"""把多边形（`(N, 2)`，x、y 像素坐标）栅格化为 `(height, width)` 的布尔掩膜。"""
|
||||
canvas = np.zeros((height, width), dtype=np.uint8)
|
||||
points = np.round(polygon).astype(np.int32).reshape(-1, 1, 2)
|
||||
cv2.fillPoly(canvas, [points], (255,))
|
||||
return canvas > 0
|
||||
|
||||
|
||||
def polygons_to_mask(polygons: Sequence[AnyArray], height: int, width: int) -> Mask:
|
||||
"""把「外轮廓 + 洞」的多边形列表一次栅格化成带孔掩膜。
|
||||
|
||||
整个列表放进同一次 `cv2.fillPoly`：一次调用里的多条轮廓按奇偶规则填充，被包住的内轮廓
|
||||
成为孔。逐个多边形各自栅格化再取并集会把洞当成外轮廓填回去，暗区面积就虚大了。
|
||||
顶点数少于 3 的项跳过；全部退化时返回全 False。
|
||||
"""
|
||||
canvas = np.zeros((height, width), dtype=np.uint8)
|
||||
contours = [
|
||||
np.round(polygon).astype(np.int32).reshape(-1, 1, 2)
|
||||
for polygon in polygons
|
||||
if len(polygon) >= 3
|
||||
]
|
||||
if contours:
|
||||
cv2.fillPoly(canvas, contours, (255,))
|
||||
return canvas > 0
|
||||
|
||||
|
||||
def polygon_area(polygon: AnyArray) -> float:
|
||||
"""鞋带公式算多边形面积（像素²）。直接在浮点顶点上算、不取整，避免量化误差。"""
|
||||
points = np.asarray(polygon, dtype=np.float64)
|
||||
x = points[:, 0]
|
||||
y = points[:, 1]
|
||||
cross = x * np.roll(y, -1) - np.roll(x, -1) * y
|
||||
return abs(0.5 * float(cross.sum()))
|
||||
|
||||
|
||||
def polygon_center(polygon: AnyArray) -> Points:
|
||||
"""多边形质心（一阶矩）；面积退化到约 0 时取顶点均值，避免除零。"""
|
||||
contour = np.round(polygon).astype(np.float32).reshape(-1, 1, 2)
|
||||
moments = cv2.moments(contour)
|
||||
if abs(moments["m00"]) > 1e-6:
|
||||
return np.array(
|
||||
[moments["m10"] / moments["m00"], moments["m01"] / moments["m00"]],
|
||||
dtype=np.float32,
|
||||
)
|
||||
return np.asarray(polygon.mean(axis=0), dtype=np.float32)
|
||||
|
||||
|
||||
def resample_closed_contour(contour: AnyArray | None, num_points: int) -> Points | None:
|
||||
"""沿弧长把闭合轮廓等距重采样到 `num_points` 个点。
|
||||
|
||||
轮廓为 None、顶点少于 3 个或周长不超过 1 像素时返回 None。
|
||||
"""
|
||||
if contour is None or len(contour) < 3:
|
||||
return None
|
||||
points = np.asarray(contour, dtype=np.float32)
|
||||
closed = np.vstack([points, points[0]])
|
||||
deltas = np.diff(closed, axis=0)
|
||||
segment_lengths = np.sqrt((deltas * deltas).sum(axis=1))
|
||||
perimeter = float(segment_lengths.sum())
|
||||
if perimeter <= 1.0:
|
||||
return None
|
||||
cumulative = np.concatenate([[0.0], np.cumsum(segment_lengths)])
|
||||
samples = np.linspace(0.0, perimeter, num_points, endpoint=False)
|
||||
resampled = np.zeros((num_points, 2), dtype=np.float32)
|
||||
resampled[:, 0] = np.interp(samples, cumulative, closed[:, 0])
|
||||
resampled[:, 1] = np.interp(samples, cumulative, closed[:, 1])
|
||||
return resampled
|
||||
|
||||
|
||||
def triangular_smooth(values: AnyArray, window: int) -> AnyArray:
|
||||
"""沿第 0 轴（时间）做三角加权滑动平均。
|
||||
|
||||
权重 `1, 2, …, r+1, …, 2, 1` 归一化，边界复制端值。窗口 ≤1 时原样返回副本；偶数窗口没有
|
||||
中心，报错。
|
||||
"""
|
||||
if window <= 1:
|
||||
return values.copy()
|
||||
if window % 2 == 0:
|
||||
raise ValueError("时间平滑窗口必须为奇数。")
|
||||
radius = window // 2
|
||||
ramp = np.arange(1, radius + 2, dtype=np.float32)
|
||||
weights = np.concatenate([ramp, ramp[-2::-1]])
|
||||
weights /= weights.sum()
|
||||
flat = values.reshape(values.shape[0], -1)
|
||||
padded = np.pad(flat, ((radius, radius), (0, 0)), mode="edge")
|
||||
smoothed = np.zeros_like(flat)
|
||||
for index in range(flat.shape[0]):
|
||||
smoothed[index] = np.sum(padded[index : index + window] * weights[:, None], axis=0)
|
||||
return smoothed.reshape(values.shape)
|
||||
|
||||
|
||||
def odd_window(value: int) -> int:
|
||||
"""把整数变成不小于 1 的奇数：偶数加 1，小于 1 的取 1。"""
|
||||
floored = max(int(value), 1)
|
||||
return floored if floored % 2 == 1 else floored + 1
|
||||
|
||||
|
||||
def optional_positive_int(value: Any) -> int | None:
|
||||
"""可选的正整数配置：None、"auto" 或不大于 0 的值都返回 None，其余按整数解析。"""
|
||||
if value is None or value == "auto":
|
||||
return None
|
||||
parsed = int(value)
|
||||
return parsed if parsed > 0 else None
|
||||
@@ -0,0 +1,186 @@
|
||||
"""第 2 步的绿框检测：在已切出的一半画面上找医生画的彩色轮廓。
|
||||
|
||||
- `prompt_color_mask`：TIC 采样时要从亮度统计里剔除医生画上去的线，这里给出那组颜色的掩膜，
|
||||
受 `exclude_prompt_color` 开关和 `prompt_exclude_dilate` 膨胀核控制；
|
||||
- `extract_color_prompt`：HSV 范围内的像素 → 抹掉顶部文字带 → 形态学闭运算加膨胀把断开的描边
|
||||
连起来 → 外轮廓 → 按外接框面积 w×h 过滤、取最大者 → 实心填充成掩膜；没有候选返回 None；
|
||||
- `collect_prompt_observations`：逐帧调上一个函数，没检出的帧用 None 占位，帧号与下标对齐。
|
||||
|
||||
颜色来自参数：喂绿框那组配置检绿框，喂紫圈那组配置（`PanSourceConfig`）检紫圈。帧是已经切出的
|
||||
一半画面，返回的外接框和掩膜都是这一半的局部坐标。
|
||||
"""
|
||||
|
||||
from collections.abc import Iterable, Sequence
|
||||
from dataclasses import dataclass
|
||||
from typing import Protocol
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
||||
from cadx.ceus._config import PromptSourceConfig, TicAnalysisConfig
|
||||
from cadx.ceus._geometry import AnyArray, Frame, Mask, rect_from_values
|
||||
|
||||
# 形态学结构元：5×5，闭运算与膨胀各一次。
|
||||
_MORPH_KERNEL = np.ones((5, 5), dtype=np.uint8)
|
||||
|
||||
|
||||
class _ColorHsvConfigLike(Protocol):
|
||||
"""一组 HSV 颜色范围（`PromptSourceConfig`、`PanSourceConfig` 都符合）。"""
|
||||
|
||||
@property
|
||||
def hsv_lower(self) -> list[int]: ...
|
||||
@property
|
||||
def hsv_upper(self) -> list[int]: ...
|
||||
|
||||
|
||||
class _ColorContourConfigLike(_ColorHsvConfigLike, Protocol):
|
||||
"""一整套彩色轮廓检测配置：HSV 范围、忽略区、面积下限。"""
|
||||
|
||||
@property
|
||||
def ignore_regions(self) -> list[list[float]]: ...
|
||||
@property
|
||||
def min_area(self) -> int: ...
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class ColorPrompt:
|
||||
"""一帧的检测结果（这一半画面的局部坐标）。
|
||||
|
||||
`mask` 是最大候选轮廓实心填充的布尔掩膜；`contour` 是 `cv2.findContours` 原样的 `(N, 1, 2)`
|
||||
int32 轮廓点；`bbox_xyxy` 是含端点的外接框（`x2 = x + w - 1`）；`bbox_area` 是外接框面积 w×h，
|
||||
用于 `min_area` 过滤和排序；`contour_area` 只在外接框面积相同时作次级排序；`candidate_count`
|
||||
是通过 `min_area` 的候选数。
|
||||
"""
|
||||
|
||||
mask: Mask
|
||||
contour: AnyArray
|
||||
bbox_xyxy: tuple[int, int, int, int]
|
||||
bbox_area: int
|
||||
contour_area: float
|
||||
candidate_count: int
|
||||
|
||||
@property
|
||||
def width(self) -> int:
|
||||
"""含端点的外接框宽度 `x2 - x1 + 1`。"""
|
||||
return self.bbox_xyxy[2] - self.bbox_xyxy[0] + 1
|
||||
|
||||
@property
|
||||
def height(self) -> int:
|
||||
"""含端点的外接框高度 `y2 - y1 + 1`。"""
|
||||
return self.bbox_xyxy[3] - self.bbox_xyxy[1] + 1
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class PromptObservation:
|
||||
"""一帧的观测：帧号和检测结果（没检出时为 None）。"""
|
||||
|
||||
frame_index: int
|
||||
prompt: ColorPrompt | None
|
||||
|
||||
@property
|
||||
def detected(self) -> bool:
|
||||
"""这一帧是否检出。"""
|
||||
return self.prompt is not None
|
||||
|
||||
|
||||
def prompt_color_mask(
|
||||
source_bgr: Frame,
|
||||
color_cfg: _ColorHsvConfigLike,
|
||||
tic_cfg: TicAnalysisConfig,
|
||||
) -> Mask:
|
||||
"""TIC 采样时要剔除的那组颜色的像素掩膜。
|
||||
|
||||
`exclude_prompt_color` 关闭时返回全 False（不剔除任何像素）；开启时取 HSV 范围内的像素，
|
||||
`prompt_exclude_dilate` 大于 1 时再按这个核大小膨胀一次（核为 1 时膨胀是恒等操作）。
|
||||
"""
|
||||
if not tic_cfg.exclude_prompt_color:
|
||||
return np.zeros(source_bgr.shape[:2], dtype=bool)
|
||||
lower = np.asarray(color_cfg.hsv_lower, dtype=np.uint8)
|
||||
upper = np.asarray(color_cfg.hsv_upper, dtype=np.uint8)
|
||||
hsv = cv2.cvtColor(source_bgr, cv2.COLOR_BGR2HSV)
|
||||
mask: Mask = np.asarray(cv2.inRange(hsv, lower, upper)) > 0
|
||||
kernel_size = tic_cfg.prompt_exclude_dilate
|
||||
if kernel_size > 1:
|
||||
kernel = np.ones((kernel_size, kernel_size), dtype=np.uint8)
|
||||
mask = np.asarray(cv2.dilate(mask.astype(np.uint8) * 255, kernel, iterations=1)) > 0
|
||||
return mask
|
||||
|
||||
|
||||
def _apply_ignore_regions(mask: AnyArray, regions: Sequence[Sequence[float]]) -> None:
|
||||
"""把忽略区（归一化或像素坐标的矩形）在掩膜上原地置 0，用来抹掉顶部文字带。"""
|
||||
height, width = mask.shape
|
||||
for region in regions:
|
||||
rect = rect_from_values(list(region), width, height)
|
||||
mask[rect.y1 : rect.y2, rect.x1 : rect.x2] = 0
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class _Candidate:
|
||||
contour: AnyArray
|
||||
bbox_xyxy: tuple[int, int, int, int]
|
||||
bbox_area: int
|
||||
contour_area: float
|
||||
|
||||
|
||||
def _largest_bbox_candidate(
|
||||
contours: Sequence[AnyArray], min_area: int
|
||||
) -> tuple[_Candidate, int] | None:
|
||||
"""按外接框面积过滤、取最大的候选；返回它和候选总数，没有合格候选时返回 None。"""
|
||||
candidates: list[_Candidate] = []
|
||||
for contour in contours:
|
||||
x, y, w, h = cv2.boundingRect(contour)
|
||||
bbox_area = int(w * h)
|
||||
if bbox_area < min_area:
|
||||
continue
|
||||
candidates.append(
|
||||
_Candidate(
|
||||
contour=contour,
|
||||
bbox_xyxy=(int(x), int(y), int(x + w - 1), int(y + h - 1)),
|
||||
bbox_area=bbox_area,
|
||||
contour_area=float(cv2.contourArea(contour)),
|
||||
)
|
||||
)
|
||||
if not candidates:
|
||||
return None
|
||||
candidates.sort(key=lambda item: (item.bbox_area, item.contour_area), reverse=True)
|
||||
return candidates[0], len(candidates)
|
||||
|
||||
|
||||
def extract_color_prompt(
|
||||
source_bgr: Frame, prompt_cfg: _ColorContourConfigLike
|
||||
) -> ColorPrompt | None:
|
||||
"""在一半画面的一帧上找医生画的彩色轮廓，返回最大的合格候选；没有返回 None。"""
|
||||
lower = np.asarray(prompt_cfg.hsv_lower, dtype=np.uint8)
|
||||
upper = np.asarray(prompt_cfg.hsv_upper, dtype=np.uint8)
|
||||
hsv = cv2.cvtColor(source_bgr, cv2.COLOR_BGR2HSV)
|
||||
mask = np.asarray(cv2.inRange(hsv, lower, upper), dtype=np.uint8)
|
||||
_apply_ignore_regions(mask, prompt_cfg.ignore_regions)
|
||||
|
||||
mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, _MORPH_KERNEL, iterations=1)
|
||||
mask = cv2.dilate(mask, _MORPH_KERNEL, iterations=1)
|
||||
contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
|
||||
|
||||
selected = _largest_bbox_candidate([np.asarray(c) for c in contours], prompt_cfg.min_area)
|
||||
if selected is None:
|
||||
return None
|
||||
best, candidate_count = selected
|
||||
filled = np.zeros(mask.shape, dtype=np.uint8)
|
||||
cv2.drawContours(filled, [best.contour], -1, (255,), thickness=-1)
|
||||
return ColorPrompt(
|
||||
mask=filled > 0,
|
||||
contour=best.contour,
|
||||
bbox_xyxy=best.bbox_xyxy,
|
||||
bbox_area=best.bbox_area,
|
||||
contour_area=best.contour_area,
|
||||
candidate_count=candidate_count,
|
||||
)
|
||||
|
||||
|
||||
def collect_prompt_observations(
|
||||
source_frames: Iterable[Frame], prompt_cfg: PromptSourceConfig
|
||||
) -> list[PromptObservation]:
|
||||
"""逐帧找绿框，交出与帧序列等长的观测列表；没检出的帧 `prompt` 为 None。"""
|
||||
return [
|
||||
PromptObservation(frame_index=index, prompt=extract_color_prompt(frame, prompt_cfg))
|
||||
for index, frame in enumerate(source_frames)
|
||||
]
|
||||
@@ -0,0 +1,67 @@
|
||||
"""两半画面之间的坐标换算。
|
||||
|
||||
绿框画在造影那一半；分割在另一半（灰阶画面，病灶边界不随造影剂进出明暗变化）上做，轮廓再挪回
|
||||
造影那一半采亮度。两半是同一个探头视野的两路成像，大小相同、只差一个水平平移，所以换算只做平移：
|
||||
|
||||
平移量 = 目标那一半视图的左上角 − 造影那一半视图的左上角（整帧像素坐标）
|
||||
|
||||
两个视图矩形直接来自第 1 步的分屏结果，不依赖视频宽度。旧代码改用按 1856 像素宽的视频写死的
|
||||
锚点，其余宽度的视频会挪偏一两百像素（design 0002 决定 4），这里不再用锚点。
|
||||
|
||||
两个函数都在各自视图的局部坐标里收发掩膜和多边形：局部 → 整帧（加本视图左上角）→ 平移 →
|
||||
目标视图局部（减目标视图左上角）。两半宽度若差几个像素，超出目标视图的部分裁掉，不缩放。
|
||||
"""
|
||||
|
||||
import numpy as np
|
||||
|
||||
from cadx.ceus._geometry import AnyArray, Mask, Points, ViewRect
|
||||
|
||||
|
||||
def view_offset(source_view_rect: ViewRect, target_view_rect: ViewRect) -> tuple[int, int]:
|
||||
"""从造影那一半到目标那一半的平移量 `(dx, dy)`：两个视图左上角之差。"""
|
||||
return (
|
||||
target_view_rect.x1 - source_view_rect.x1,
|
||||
target_view_rect.y1 - source_view_rect.y1,
|
||||
)
|
||||
|
||||
|
||||
def map_source_mask_to_target(
|
||||
source_mask: AnyArray,
|
||||
source_view_rect: ViewRect,
|
||||
target_view_rect: ViewRect,
|
||||
) -> Mask:
|
||||
"""把造影那一半局部坐标的掩膜挪到目标那一半的局部坐标，形状为目标视图大小。"""
|
||||
dx, dy = view_offset(source_view_rect, target_view_rect)
|
||||
# 源局部 (x, y) → 整帧 (x + sx1, y + sy1) → 平移 → 目标局部 (x + sx1 + dx − tx1, …)。
|
||||
shift_x = source_view_rect.x1 + dx - target_view_rect.x1
|
||||
shift_y = source_view_rect.y1 + dy - target_view_rect.y1
|
||||
mapped = np.zeros((target_view_rect.height, target_view_rect.width), dtype=bool)
|
||||
ys, xs = np.nonzero(source_mask)
|
||||
xs = xs + shift_x
|
||||
ys = ys + shift_y
|
||||
inside = (xs >= 0) & (xs < target_view_rect.width) & (ys >= 0) & (ys < target_view_rect.height)
|
||||
mapped[ys[inside], xs[inside]] = True
|
||||
return mapped
|
||||
|
||||
|
||||
def target_polygons_to_source(
|
||||
target_polygons: list[AnyArray],
|
||||
source_view_rect: ViewRect,
|
||||
target_view_rect: ViewRect,
|
||||
) -> list[Points]:
|
||||
"""把目标那一半局部坐标的多边形逐个挪回造影那一半的局部坐标，裁到视图边界内。"""
|
||||
dx, dy = view_offset(source_view_rect, target_view_rect)
|
||||
shift = np.array(
|
||||
[
|
||||
target_view_rect.x1 - dx - source_view_rect.x1,
|
||||
target_view_rect.y1 - dy - source_view_rect.y1,
|
||||
],
|
||||
dtype=np.float32,
|
||||
)
|
||||
mapped: list[Points] = []
|
||||
for polygon in target_polygons:
|
||||
source_poly = polygon.astype(np.float32) + shift[None, :]
|
||||
source_poly[:, 0] = np.clip(source_poly[:, 0], 0, source_view_rect.width - 1)
|
||||
source_poly[:, 1] = np.clip(source_poly[:, 1], 0, source_view_rect.height - 1)
|
||||
mapped.append(source_poly)
|
||||
return mapped
|
||||
@@ -0,0 +1,78 @@
|
||||
"""第 2 步的提示帧选择：绿框位置或形状明显变化的那几帧，交给分割模型当「病灶在这里」的提示。
|
||||
|
||||
这批帧不是第 8 步的 7 张关键帧。规则：
|
||||
- 第一个检出绿框的帧必选，`change_iou` 记 1.0；
|
||||
- 之后每帧先过最小间隔门（距上一个提示帧不足 `min_gap_frames` 帧的跳过），再算它的绿框掩膜与
|
||||
上一个提示帧的 IoU，严格小于 `iou_threshold` 才记新提示帧（恰好等于不记）；
|
||||
- 没检出绿框的帧跳过，不占第一帧的位置。
|
||||
"""
|
||||
|
||||
from dataclasses import dataclass
|
||||
|
||||
from cadx.ceus._config import KeyframesConfig
|
||||
from cadx.ceus._geometry import Mask, mask_iou
|
||||
from cadx.ceus._green_prompt import PromptObservation
|
||||
|
||||
# 间隔门的初值：保证第一个检出绿框的帧总能通过。
|
||||
_NO_PREVIOUS_PROMPT_FRAME = -(10**9)
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class PromptFrame:
|
||||
"""一个提示帧（这一半画面的局部坐标）。
|
||||
|
||||
`mask` 是这一帧绿框的实心掩膜，既是给分割的提示，也是下一个提示帧比 IoU 的基准；
|
||||
`change_iou` 是相对上一个提示帧的 IoU，第一帧记 1.0。
|
||||
"""
|
||||
|
||||
frame_index: int
|
||||
mask: Mask
|
||||
bbox_xyxy: tuple[int, int, int, int]
|
||||
bbox_area: int
|
||||
contour_area: float
|
||||
change_iou: float
|
||||
|
||||
|
||||
def select_prompt_frames(
|
||||
observations: list[PromptObservation], prompt_frame_cfg: KeyframesConfig
|
||||
) -> list[PromptFrame]:
|
||||
"""从逐帧观测中选出提示帧，按帧号升序；没有任何检出时返回空列表。"""
|
||||
if prompt_frame_cfg.change_metric != "mask_iou":
|
||||
raise RuntimeError(
|
||||
f"keyframes.change_metric 仅支持 'mask_iou'，得到 '{prompt_frame_cfg.change_metric}'。"
|
||||
)
|
||||
|
||||
iou_threshold = prompt_frame_cfg.iou_threshold
|
||||
min_gap = prompt_frame_cfg.min_gap_frames
|
||||
|
||||
prompt_frames: list[PromptFrame] = []
|
||||
last_key_mask: Mask | None = None
|
||||
last_key_frame = _NO_PREVIOUS_PROMPT_FRAME
|
||||
for observation in observations:
|
||||
prompt = observation.prompt
|
||||
if prompt is None:
|
||||
continue
|
||||
frame_index = observation.frame_index
|
||||
if frame_index - last_key_frame < min_gap:
|
||||
continue
|
||||
if last_key_mask is None:
|
||||
change_iou = 1.0
|
||||
is_new_prompt_frame = True
|
||||
else:
|
||||
change_iou = mask_iou(last_key_mask, prompt.mask)
|
||||
is_new_prompt_frame = change_iou < iou_threshold
|
||||
if not is_new_prompt_frame:
|
||||
continue
|
||||
prompt_frames.append(
|
||||
PromptFrame(
|
||||
frame_index=frame_index,
|
||||
mask=prompt.mask,
|
||||
bbox_xyxy=prompt.bbox_xyxy,
|
||||
bbox_area=prompt.bbox_area,
|
||||
contour_area=prompt.contour_area,
|
||||
change_iou=change_iou,
|
||||
)
|
||||
)
|
||||
last_key_mask = prompt.mask
|
||||
last_key_frame = frame_index
|
||||
return prompt_frames
|
||||
@@ -0,0 +1,186 @@
|
||||
"""第 1 步的分屏：找出左右两半之间的竖直暗缝，切出两个视图矩形。
|
||||
|
||||
超声机录造影时屏幕左右各一半，分界不一定正好在正中间，所以从帧内容里找：在中部一段列范围内
|
||||
取列均值最低、且明显低于周围的那一列。找不到可信的缝就报错，不退回 `W//2`。
|
||||
|
||||
照旧 debug 路径（40 例结果是它跑出来的）：找缝只用均匀抽的若干帧；不裁画面黑边，视图矩形就是
|
||||
左右两半本身。
|
||||
"""
|
||||
|
||||
from collections.abc import Iterable, Mapping, Sequence
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
import numpy.typing as npt
|
||||
|
||||
from cadx.ceus._config import ViewsConfig
|
||||
from cadx.ceus._errors import CeusCaseError
|
||||
from cadx.ceus._geometry import Frame, ViewRect, rect_from_values
|
||||
|
||||
SPLIT_STEP = 1
|
||||
|
||||
|
||||
class SplitDetectionError(CeusCaseError):
|
||||
"""这段视频找不到可信的分屏线（无帧、无暗缝、切分越界或左右失衡）。"""
|
||||
|
||||
def __init__(self, reason: str) -> None:
|
||||
super().__init__(SPLIT_STEP, reason)
|
||||
|
||||
|
||||
def _mean_gray(frames: Iterable[Frame]) -> npt.NDArray[np.float32]:
|
||||
"""若干帧取平均后转灰度。边收边累加、不把帧堆成一个数组；累加器用 float64，帧多时不丢低位。"""
|
||||
total: npt.NDArray[np.float64] | None = None
|
||||
count = 0
|
||||
for frame in frames:
|
||||
if total is None:
|
||||
total = np.zeros(frame.shape, dtype=np.float64)
|
||||
elif frame.shape != total.shape:
|
||||
raise SplitDetectionError(
|
||||
f"帧尺寸不一致：首帧 {total.shape}，第 {count} 帧 {frame.shape}。"
|
||||
)
|
||||
total += frame
|
||||
count += 1
|
||||
if total is None:
|
||||
raise SplitDetectionError("帧序列为空，无法求跨帧平均灰度。")
|
||||
mean_bgr = (total / count).astype(np.uint8)
|
||||
return np.asarray(cv2.cvtColor(mean_bgr, cv2.COLOR_BGR2GRAY), dtype=np.float32)
|
||||
|
||||
|
||||
def detect_split_x(frames: Iterable[Frame], *, search_band: tuple[float, float]) -> int:
|
||||
"""找左右分屏的竖直分界列 `split_x`。
|
||||
|
||||
`search_band` 是搜索区间占宽度的比例 `(lo, hi)`，只在中部找，排除画面内偶发的暗带。
|
||||
在区间内取列均值最低的列；它的列均值须低于区间中位列均值的 70%（否则视为没有缝，
|
||||
例如整幅平坦或全黑）；再要求左右宽度比在 [0.5, 2.0]。任一不满足报 `SplitDetectionError`。
|
||||
"""
|
||||
lo_ratio, hi_ratio = search_band
|
||||
if not 0.0 <= lo_ratio < hi_ratio <= 1.0:
|
||||
raise SplitDetectionError(f"非法 search_band：要求 0<=lo<hi<=1，收到 {search_band}。")
|
||||
|
||||
column_means = _mean_gray(frames).mean(axis=0)
|
||||
width = int(column_means.shape[0])
|
||||
band_lo = round(lo_ratio * width)
|
||||
band_hi = round(hi_ratio * width)
|
||||
if band_hi - band_lo < 1:
|
||||
raise SplitDetectionError(
|
||||
f"search_band 在宽度 {width} 下退化为空区间 [{band_lo}, {band_hi})。"
|
||||
)
|
||||
|
||||
band_means = column_means[band_lo:band_hi]
|
||||
split_x = band_lo + int(np.argmin(band_means))
|
||||
|
||||
band_median = float(np.median(band_means))
|
||||
valley_value = float(column_means[split_x])
|
||||
if valley_value >= band_median * 0.7:
|
||||
raise SplitDetectionError(
|
||||
f"中部 band 内未找到稳定暗缝（谷值 {valley_value:.1f} 未显著低于 "
|
||||
f"中位 {band_median:.1f}），疑为无缝或全黑画面。"
|
||||
)
|
||||
|
||||
if not band_lo <= split_x < band_hi:
|
||||
raise SplitDetectionError(
|
||||
f"检测到的 split_x={split_x} 落在搜索 band [{band_lo}, {band_hi}) 外。"
|
||||
)
|
||||
left_width = split_x
|
||||
right_width = width - split_x
|
||||
if left_width < 1 or right_width < 1:
|
||||
raise SplitDetectionError(
|
||||
f"切分线 split_x={split_x} 使某一侧宽度退化（左 {left_width} / 右 {right_width}）。"
|
||||
)
|
||||
width_ratio = left_width / right_width
|
||||
if not 0.5 <= width_ratio <= 2.0:
|
||||
raise SplitDetectionError(
|
||||
f"左右宽度比 {width_ratio:.2f} 超出 [0.5, 2.0]"
|
||||
f"（左 {left_width} / 右 {right_width}），切分线不可信。"
|
||||
)
|
||||
return split_x
|
||||
|
||||
|
||||
def _horizontal_split_rect(
|
||||
view_name: str, width: int, height: int, split_x: int | None
|
||||
) -> ViewRect | None:
|
||||
"""左右分屏的视图矩形，分界用检测到的 `split_x`。"""
|
||||
if split_x is None:
|
||||
raise ValueError("horizontal_split 布局必须传入检测到的 split_x。")
|
||||
if view_name == "left":
|
||||
return ViewRect(0, 0, split_x, height)
|
||||
if view_name == "right":
|
||||
return ViewRect(split_x, 0, width, height)
|
||||
return None
|
||||
|
||||
|
||||
def _vertical_split_rect(view_name: str, width: int, height: int) -> ViewRect | None:
|
||||
"""上下分屏的视图矩形（分界取 H//2）。"""
|
||||
mid = height // 2
|
||||
if view_name == "top":
|
||||
return ViewRect(0, 0, width, mid)
|
||||
if view_name == "bottom":
|
||||
return ViewRect(0, mid, width, height)
|
||||
return None
|
||||
|
||||
|
||||
def _manual_crop_rect(
|
||||
view_cfg: Mapping[str, object], view_name: str, width: int, height: int
|
||||
) -> ViewRect:
|
||||
"""手工裁剪布局：从 `crops` 取对应视图的框，没有就报错。"""
|
||||
crops = view_cfg["crops"]
|
||||
if not isinstance(crops, Mapping) or view_name not in crops:
|
||||
raise RuntimeError(f"手动裁剪中未找到视图 {view_name!r}。")
|
||||
values: object = crops[view_name] # pyright: ignore[reportUnknownVariableType]
|
||||
if not isinstance(values, list):
|
||||
raise RuntimeError(f"手动裁剪视图 {view_name!r} 必须是 [x1, y1, x2, y2]。")
|
||||
return rect_from_values([float(v) for v in values], width, height) # pyright: ignore[reportUnknownVariableType, reportUnknownArgumentType]
|
||||
|
||||
|
||||
def view_rect(
|
||||
view_cfg: Mapping[str, object],
|
||||
view_name: str,
|
||||
width: int,
|
||||
height: int,
|
||||
*,
|
||||
split_x: int | None,
|
||||
) -> ViewRect:
|
||||
"""按布局和视图名算视图矩形（整帧像素坐标）。
|
||||
|
||||
`view_cfg` 至少含 `layout`，手工裁剪时含 `crops`。左右分屏时 `split_x` 必填。
|
||||
布局与视图名对不上报 `RuntimeError`。
|
||||
"""
|
||||
layout = view_cfg["layout"]
|
||||
rect: ViewRect | None
|
||||
if layout == "horizontal_split":
|
||||
rect = _horizontal_split_rect(view_name, width, height, split_x)
|
||||
elif layout == "vertical_split":
|
||||
rect = _vertical_split_rect(view_name, width, height)
|
||||
elif layout == "full":
|
||||
rect = ViewRect(0, 0, width, height)
|
||||
elif layout == "manual_crop":
|
||||
rect = _manual_crop_rect(view_cfg, view_name, width, height)
|
||||
else:
|
||||
rect = None
|
||||
if rect is None:
|
||||
raise RuntimeError(f"不支持的 layout/view 组合：{layout}/{view_name}")
|
||||
return rect
|
||||
|
||||
|
||||
def sample_frames_for_split(frames: Sequence[Frame], sample_count: int) -> list[Frame]:
|
||||
"""沿时间轴均匀抽 `sample_count` 帧（下标去重）；帧数不超过它时全用。"""
|
||||
if not frames:
|
||||
raise SplitDetectionError("帧序列为空，无法找分屏线。")
|
||||
if sample_count <= 0 or len(frames) <= sample_count:
|
||||
return list(frames)
|
||||
indices = np.linspace(0, len(frames) - 1, num=sample_count, dtype=np.int32)
|
||||
unique_indices = sorted({int(index) for index in indices.tolist()})
|
||||
return [frames[index] for index in unique_indices]
|
||||
|
||||
|
||||
def split_views(frames: Sequence[Frame], views_cfg: ViewsConfig) -> tuple[int, ViewRect, ViewRect]:
|
||||
"""第 1 步：在均匀抽的帧上找分屏线，交出 `(split_x, 左半矩形, 右半矩形)`。"""
|
||||
split_x = detect_split_x(
|
||||
sample_frames_for_split(frames, views_cfg.split_sample_frames),
|
||||
search_band=(views_cfg.split_search_band[0], views_cfg.split_search_band[1]),
|
||||
)
|
||||
height, width = frames[0].shape[:2]
|
||||
layout = {"layout": views_cfg.layout}
|
||||
left = view_rect(layout, "left", width, height, split_x=split_x)
|
||||
right = view_rect(layout, "right", width, height, split_x=split_x)
|
||||
return split_x, left, right
|
||||
@@ -0,0 +1,90 @@
|
||||
"""按路径用 cv2 逐帧读视频。
|
||||
|
||||
反复遍历靠重新解码，不缓存帧：一段 1260 帧的素材解一遍不到两秒，缓存下来要好几 GB（旧项目实测）。
|
||||
每次遍历各开一个 `cv2.VideoCapture`，互不干扰。探测不到有效 fps、帧数或尺寸直接报错，不回退默认值。
|
||||
"""
|
||||
|
||||
import math
|
||||
from collections.abc import Iterator
|
||||
from dataclasses import dataclass
|
||||
from pathlib import Path
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
import numpy.typing as npt
|
||||
|
||||
from cadx.ceus._geometry import Frame
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class VideoFile:
|
||||
"""磁盘上的一段视频，可以反复从第 0 帧遍历。"""
|
||||
|
||||
path: Path
|
||||
|
||||
def _capture(self) -> cv2.VideoCapture:
|
||||
cap = cv2.VideoCapture(str(self.path))
|
||||
if not cap.isOpened():
|
||||
cap.release()
|
||||
raise RuntimeError(f"cv2 打不开视频：{self.path}")
|
||||
return cap
|
||||
|
||||
def probe(self) -> tuple[float, int, int, int]:
|
||||
"""视频元数据 `(fps, frame_count, width, height)`，都为正，否则报错。"""
|
||||
cap = self._capture()
|
||||
try:
|
||||
fps = float(cap.get(cv2.CAP_PROP_FPS))
|
||||
frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
|
||||
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
|
||||
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
||||
finally:
|
||||
cap.release()
|
||||
if not math.isfinite(fps) or fps <= 0:
|
||||
raise RuntimeError(f"探测到非法 fps={fps}：{self.path}")
|
||||
if frame_count <= 0 or width <= 0 or height <= 0:
|
||||
raise RuntimeError(
|
||||
f"探测到非法元数据 frame_count={frame_count} "
|
||||
f"width={width} height={height}：{self.path}"
|
||||
)
|
||||
return fps, frame_count, width, height
|
||||
|
||||
def iter_frames(self) -> Iterator[tuple[int, Frame]]:
|
||||
"""逐帧产出 `(帧号, BGR 的 H×W×3 uint8)`，帧号从 0 连续递增；每次调用都从头开始。"""
|
||||
cap = self._capture()
|
||||
try:
|
||||
index = 0
|
||||
while True:
|
||||
ok, frame = cap.read()
|
||||
if not ok:
|
||||
break
|
||||
yield index, np.asarray(frame, dtype=np.uint8)
|
||||
index += 1
|
||||
finally:
|
||||
cap.release()
|
||||
|
||||
def frame_times_s(self) -> npt.NDArray[np.float64]:
|
||||
"""逐帧读容器里的显示时间戳（秒）；必须严格递增、帧数与容器记录一致，否则报错。"""
|
||||
cap = self._capture()
|
||||
try:
|
||||
expected_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
|
||||
times: list[float] = []
|
||||
while True:
|
||||
ok, _frame = cap.read()
|
||||
if not ok:
|
||||
break
|
||||
timestamp_s = float(cap.get(cv2.CAP_PROP_POS_MSEC)) / 1000.0
|
||||
if not math.isfinite(timestamp_s) or timestamp_s < 0.0:
|
||||
raise RuntimeError(f"视频包含非法显示时间戳 {timestamp_s}：{self.path}")
|
||||
if times and timestamp_s <= times[-1]:
|
||||
raise RuntimeError(
|
||||
"视频显示时间戳不严格递增："
|
||||
f"第 {len(times)} 帧 {timestamp_s}，前一帧 {times[-1]}：{self.path}"
|
||||
)
|
||||
times.append(timestamp_s)
|
||||
finally:
|
||||
cap.release()
|
||||
if len(times) != expected_count:
|
||||
raise RuntimeError(
|
||||
f"时间戳帧数与容器记录的帧数不一致：{len(times)} / {expected_count}：{self.path}"
|
||||
)
|
||||
return np.asarray(times, dtype=np.float64)
|
||||
@@ -13,7 +13,7 @@ from cadx.library._read import (
|
||||
read_ultrasound,
|
||||
)
|
||||
from cadx.library._scan import LeakHit, scan_for_leaks
|
||||
from cadx.library._write import LibraryCase, write_library
|
||||
from cadx.library._write import LibraryCase, check_key_image, write_library
|
||||
|
||||
__all__ = [
|
||||
"ClinicalRoute",
|
||||
@@ -21,6 +21,7 @@ __all__ = [
|
||||
"LibraryCase",
|
||||
"MrRoute",
|
||||
"UltrasoundRoute",
|
||||
"check_key_image",
|
||||
"list_aliases",
|
||||
"read_clinical",
|
||||
"read_mr",
|
||||
|
||||
+22
-12
@@ -56,23 +56,33 @@ def _write_json(path: Path, content: object) -> None:
|
||||
path.write_text(json.dumps(content, ensure_ascii=False, indent=2) + "\n", encoding="utf-8")
|
||||
|
||||
|
||||
def check_key_image(path: Path) -> str | None:
|
||||
"""关键图能逐像素保真地重编码时返回 None，不能时返回原因。
|
||||
|
||||
不能保真的：不是 PNG、位深或颜色类型不在 `_FAITHFUL` 里、带 tRNS 透明色。
|
||||
build 在写库前对每例调用，返回原因的整例跳过（C14），不悄悄降位或丢透明。
|
||||
"""
|
||||
header = path.read_bytes()[:26]
|
||||
if header[:8] != _PNG_SIGNATURE or header[12:16] != b"IHDR":
|
||||
return "不是 PNG"
|
||||
bit_depth, color_type = header[24], header[25]
|
||||
if (bit_depth, color_type) not in _FAITHFUL:
|
||||
return f"位深 {bit_depth}、颜色类型 {color_type} 不能逐像素保真重编码"
|
||||
with Image.open(path) as image:
|
||||
if "transparency" in image.info:
|
||||
return "带 tRNS 透明色，重编码会丢透明"
|
||||
return None
|
||||
|
||||
|
||||
def _write_clean_png(source: Path, target: Path) -> None:
|
||||
"""只保留像素和颜色模式重新编码：文本块、EXIF、ICC 等附加数据都不带过去。
|
||||
|
||||
不能逐像素保真的图（非 PNG、位深或颜色类型不在 `_FAITHFUL` 里、带 tRNS 透明色）报错停下，
|
||||
不悄悄降位或丢透明。
|
||||
走到这里的图应当已由 build 用 `check_key_image` 查过；还不能保真说明 build 漏了检查，报错停下。
|
||||
"""
|
||||
header = source.read_bytes()[:26]
|
||||
if header[:8] != _PNG_SIGNATURE or header[12:16] != b"IHDR":
|
||||
raise ValueError(f"关键图不是 PNG：{source}")
|
||||
bit_depth, color_type = header[24], header[25]
|
||||
if (bit_depth, color_type) not in _FAITHFUL:
|
||||
raise ValueError(
|
||||
f"关键图位深 {bit_depth}、颜色类型 {color_type} 不能逐像素保真重编码：{source}"
|
||||
)
|
||||
reason = check_key_image(source)
|
||||
if reason is not None:
|
||||
raise ValueError(f"关键图{reason}，build 应已用 check_key_image 跳过这一例：{source}")
|
||||
with Image.open(source) as image:
|
||||
if "transparency" in image.info:
|
||||
raise ValueError(f"关键图带 tRNS 透明色，重编码会丢透明：{source}")
|
||||
clean = Image.frombytes(image.mode, image.size, image.tobytes())
|
||||
clean.save(target, format="PNG")
|
||||
|
||||
|
||||
@@ -0,0 +1,27 @@
|
||||
"""CEUS 测试共用的夹具：读 `config/ceus.yaml` 的 `ceus` 段。
|
||||
|
||||
配置不设默认值，旧测试里靠默认值构造配置的地方改用这里的取值；需要不同取值的测试用
|
||||
`model_copy(update=...)` 改那一项。
|
||||
"""
|
||||
|
||||
from pathlib import Path
|
||||
from typing import Any, cast
|
||||
|
||||
import pytest
|
||||
import yaml
|
||||
|
||||
from cadx.ceus import CeusConfig
|
||||
|
||||
CEUS_YAML = Path(__file__).resolve().parents[1] / "config" / "ceus.yaml"
|
||||
|
||||
|
||||
def load_ceus_section() -> dict[str, Any]:
|
||||
"""`config/ceus.yaml` 的 `ceus` 段原样读成字典（每次新读一份，调用方可以改）。"""
|
||||
document = cast(dict[str, Any], yaml.safe_load(CEUS_YAML.read_text(encoding="utf-8")))
|
||||
return cast(dict[str, Any], document["ceus"])
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def ceus_config() -> CeusConfig:
|
||||
"""校验过的 `ceus` 段。"""
|
||||
return CeusConfig.model_validate(load_ceus_section())
|
||||
+47
-10
@@ -12,11 +12,13 @@ from PIL import Image
|
||||
from cadx.answers import read_aliases, read_diagnoses
|
||||
from cadx.build import make_aliases, run_build
|
||||
from cadx.library import (
|
||||
LibraryCase,
|
||||
list_aliases,
|
||||
read_clinical,
|
||||
read_mr,
|
||||
read_ultrasound,
|
||||
scan_for_leaks,
|
||||
write_library,
|
||||
)
|
||||
from fake_delivery import (
|
||||
CLINICAL_HEADER,
|
||||
@@ -218,24 +220,59 @@ def _build_with_key_image(tmp_path: Path, image: Image.Image) -> None:
|
||||
run_build(write_config(tmp_path, root, 1, None), REPO_ROOT, NOW)
|
||||
|
||||
|
||||
def test_key_image_16_bit_source_stops_build(tmp_path: Path) -> None:
|
||||
image = Image.new("I;16", (4, 4))
|
||||
with pytest.raises(ValueError, match=r"U002_key\.png"):
|
||||
_build_with_key_image(tmp_path, image)
|
||||
assert not (tmp_path / "library" / f"{NOW:%Y%m%d-%H%M%S}").exists()
|
||||
assert [p for p in (tmp_path / "library").iterdir() if not p.name.endswith(".partial")] == []
|
||||
def _assert_key_image_skipped(tmp_path: Path, reason: str) -> None:
|
||||
"""002 这一例记 C14 跳过、涉及值是原因；001 照常进库。"""
|
||||
(library_dir,) = list((tmp_path / "library").iterdir())
|
||||
answers_dir = tmp_path / "answers" / library_dir.name
|
||||
assert [r.case_no for r in read_aliases(answers_dir)] == ["001"]
|
||||
assert len(list_aliases(library_dir)) == 1
|
||||
skipped = _csv_rows(answers_dir / "skipped.csv")
|
||||
assert [row[:4] for row in skipped[1:]] == [
|
||||
["002", "C14", "关键图能原样重编码", "CEUS_agent/002/U002_key.png"]
|
||||
]
|
||||
assert json.loads(skipped[1][5]) == reason
|
||||
|
||||
|
||||
def test_key_image_with_transparency_stops_build(tmp_path: Path) -> None:
|
||||
def test_key_image_16_bit_source_skips_case(tmp_path: Path) -> None:
|
||||
_build_with_key_image(tmp_path, Image.new("I;16", (4, 4)))
|
||||
_assert_key_image_skipped(tmp_path, "位深 16、颜色类型 0 不能逐像素保真重编码")
|
||||
|
||||
|
||||
def test_key_image_with_transparency_skips_case(tmp_path: Path) -> None:
|
||||
image = Image.new("RGB", (4, 4))
|
||||
image.info["transparency"] = (0, 0, 0)
|
||||
with pytest.raises(ValueError, match=r"U002_key\.png"):
|
||||
_build_with_key_image(tmp_path, image)
|
||||
_assert_key_image_skipped(tmp_path, "带 tRNS 透明色，重编码会丢透明")
|
||||
|
||||
|
||||
def test_key_image_palette_source_stops_build(tmp_path: Path) -> None:
|
||||
with pytest.raises(ValueError, match=r"U002_key\.png"):
|
||||
def test_key_image_palette_source_skips_case(tmp_path: Path) -> None:
|
||||
_build_with_key_image(tmp_path, Image.new("P", (4, 4)))
|
||||
# 只有一种颜色的调色板图，Pillow 存成 1 位。
|
||||
_assert_key_image_skipped(tmp_path, "位深 1、颜色类型 3 不能逐像素保真重编码")
|
||||
|
||||
|
||||
def test_key_image_not_png_skips_case(tmp_path: Path) -> None:
|
||||
root = make_delivery(2).write(tmp_path / "delivery")
|
||||
Image.new("RGB", (4, 4)).save(root / "CEUS_agent" / "002" / "U002_key.png", format="JPEG")
|
||||
run_build(write_config(tmp_path, root, 1, None), REPO_ROOT, NOW)
|
||||
_assert_key_image_skipped(tmp_path, "不是 PNG")
|
||||
|
||||
|
||||
def test_write_library_rejects_unchecked_key_image(tmp_path: Path) -> None:
|
||||
"""build 漏了 C14 检查时，library 写图那一步报错停下。"""
|
||||
source = tmp_path / "key.png"
|
||||
Image.new("P", (4, 4)).save(source, format="PNG")
|
||||
target = tmp_path / "out"
|
||||
target.mkdir()
|
||||
case = LibraryCase(
|
||||
alias="abcd",
|
||||
clinical_fields={},
|
||||
ultrasound_report="",
|
||||
key_image_source=source,
|
||||
mr_report="",
|
||||
)
|
||||
with pytest.raises(ValueError, match="check_key_image"):
|
||||
write_library(target, "b", "c", True, {}, False, (), None, [case])
|
||||
|
||||
|
||||
def test_key_image_one_bit_kept_as_is(tmp_path: Path) -> None:
|
||||
|
||||
@@ -0,0 +1,396 @@
|
||||
"""CEUS ROI 科研配置 schema + loader 单元测试。
|
||||
|
||||
验证目标：
|
||||
1. `load_tracking_config` 读默认 yaml 后返回 `TrackingConfig`，且关键默认值
|
||||
等于 findings §2A/§3 的「yaml 生产值」（而非参考代码的保守默认）。
|
||||
2. findings §5 硬约束生效（偶数 temporal_window / 面积比逆序 / percentile 越界报错）。
|
||||
3. 子模型 `extra="forbid"` 拒绝未知键（防 typo 静默退化）。
|
||||
4. findings §2B 死键不在 schema 内（混入即被 extra="forbid" 拒绝）。
|
||||
5. design/0030 那几项：合成零点四键已删且残留即报错、`config_version` 必填、
|
||||
`pan_source` 校验器、基线默认值等于规范 §3.3。
|
||||
"""
|
||||
|
||||
from typing import Any
|
||||
|
||||
import pytest
|
||||
from pydantic import ValidationError
|
||||
|
||||
from cadx.ceus import CeusConfig as TrackingConfig
|
||||
from conftest import load_ceus_section
|
||||
|
||||
|
||||
def _merge(base: dict[str, Any], payload: dict[str, Any]) -> dict[str, Any]:
|
||||
"""把 payload 逐层盖到 base 上（只盖 payload 里写到的键）。"""
|
||||
merged = dict(base)
|
||||
for key, value in payload.items():
|
||||
if isinstance(value, dict) and isinstance(merged.get(key), dict):
|
||||
merged[key] = _merge(merged[key], value) # pyright: ignore[reportUnknownArgumentType]
|
||||
else:
|
||||
merged[key] = value
|
||||
return merged
|
||||
|
||||
|
||||
def _validate(payload: dict[str, Any]) -> TrackingConfig:
|
||||
"""在 config/ceus.yaml 的 ceus 段上盖上 payload 再校验。
|
||||
|
||||
配置不设默认值、每个字段必填；旧测试只写自己关心的那几个键、其余靠默认值，
|
||||
这里改为其余键取 config/ceus.yaml 的值。
|
||||
"""
|
||||
return TrackingConfig.model_validate(_merge(load_ceus_section(), payload))
|
||||
|
||||
|
||||
def load_tracking_config() -> TrackingConfig:
|
||||
"""读 config/ceus.yaml 的 ceus 段（旧测试读的是旧 yaml 整份文件）。"""
|
||||
return TrackingConfig.model_validate(load_ceus_section())
|
||||
|
||||
|
||||
def test_load_default_config_returns_tracking_config() -> None:
|
||||
"""默认 yaml 应能加载为 TrackingConfig 实例。"""
|
||||
cfg = load_tracking_config()
|
||||
assert isinstance(cfg, TrackingConfig)
|
||||
|
||||
|
||||
def test_parameter_boundary_fraction_cannot_overlap_both_sides() -> None:
|
||||
with pytest.raises(ValidationError, match="parameter_boundary_fraction"):
|
||||
_validate({"tic_analysis": {"fitting": {"parameter_boundary_fraction": 0.5}}})
|
||||
|
||||
|
||||
def test_default_values_match_findings_production_values() -> None:
|
||||
"""关键默认值须等于 findings §2A/§3 的 yaml 生产值，而非代码保守默认。"""
|
||||
cfg = load_tracking_config()
|
||||
|
||||
# findings §2A：删键即静默退化的 14 项中的代表项
|
||||
assert cfg.interior_dark_region.enabled is True # 非代码默认 False
|
||||
assert cfg.interior_dark_region.analysis_view == "source" # 非 target
|
||||
assert cfg.interior_dark_region.exclude_prompt_color is True # 非 False
|
||||
assert cfg.interior_dark_region.threshold_method == "auto_ensemble" # 非 otsu
|
||||
assert cfg.interior_dark_region.min_area == 300 # 非代码默认 64
|
||||
assert cfg.interior_dark_region.min_area_ratio == 0.015 # 非 0.0
|
||||
assert cfg.roi_shape.source == "prompt_prior" # 非代码默认 medsam2
|
||||
assert cfg.constraints.keyframe_position.enabled is True # 非 False
|
||||
|
||||
# findings §3：其余生产值
|
||||
assert cfg.keyframes.iou_threshold == 0.92
|
||||
assert cfg.keyframes.min_gap_frames == 1
|
||||
assert cfg.smoothing.temporal_window == 15
|
||||
assert cfg.smoothing.contour_points == 64
|
||||
assert cfg.roi_shape.min_area_ratio_to_prompt == 0.70
|
||||
assert cfg.roi_shape.max_area_ratio_to_prompt == 1.25
|
||||
|
||||
# findings §2C/§3.8：暗部方法子参（yaml 未列、代码默认）
|
||||
assert cfg.interior_dark_region.percentile == 45
|
||||
assert cfg.interior_dark_region.adaptive_block_size == 31
|
||||
|
||||
# design/0044：两条正式病灶曲线使用 Lognormal v2；SG 只做候选检测。
|
||||
assert cfg.tic_analysis.fitting.algorithm_version == "lognormal-v2"
|
||||
assert cfg.tic_analysis.fitting.candidate_polyorder == 2
|
||||
assert cfg.tic_analysis.fitting.multi_peak_minimum_separation_s == 5.0
|
||||
assert cfg.tic_analysis.fitting.multi_peak_minimum_relative_height == 0.50
|
||||
assert cfg.tic_analysis.fitting.multi_peak_maximum_valley_fraction == 0.25
|
||||
assert cfg.tic_analysis.fitting.candidate_window_s == 1.5
|
||||
assert cfg.tic_analysis.fitting.detect_duplicate_frames is True
|
||||
|
||||
|
||||
def test_temporal_window_must_be_odd() -> None:
|
||||
"""findings §5：smoothing.temporal_window 须为正奇数，偶数报错。"""
|
||||
with pytest.raises(ValidationError):
|
||||
_validate({"smoothing": {"temporal_window": 14}})
|
||||
|
||||
|
||||
def test_adaptive_block_size_must_be_odd() -> None:
|
||||
"""findings §5：adaptive_block_size 须为正奇数，偶数报错。"""
|
||||
with pytest.raises(ValidationError):
|
||||
_validate({"interior_dark_region": {"adaptive_block_size": 30}})
|
||||
|
||||
|
||||
def test_area_ratio_min_must_be_less_than_max() -> None:
|
||||
"""findings §5：min_area_ratio_to_prompt 须 < max，逆序报错。"""
|
||||
with pytest.raises(ValidationError):
|
||||
_validate(
|
||||
{
|
||||
"roi_shape": {
|
||||
"min_area_ratio_to_prompt": 1.3,
|
||||
"max_area_ratio_to_prompt": 1.25,
|
||||
}
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
def test_percentile_must_be_within_bounds() -> None:
|
||||
"""findings §5：暗部 percentile 须 ∈ [0, 100]，越界报错。"""
|
||||
with pytest.raises(ValidationError):
|
||||
_validate({"interior_dark_region": {"percentile": 150}})
|
||||
|
||||
|
||||
def test_keyframe_position_strength_clipped_to_unit_interval() -> None:
|
||||
"""findings §5：constraints.keyframe_position.strength clip 到 [0, 1]。"""
|
||||
cfg = _validate({"constraints": {"keyframe_position": {"strength": 1.8}}})
|
||||
assert cfg.constraints.keyframe_position.strength == 1.0
|
||||
|
||||
cfg_neg = _validate({"constraints": {"keyframe_position": {"strength": -0.5}}})
|
||||
assert cfg_neg.constraints.keyframe_position.strength == 0.0
|
||||
|
||||
|
||||
def test_extra_keys_forbidden() -> None:
|
||||
"""子模型 extra='forbid'：未知键（含 typo / 死键）报错。"""
|
||||
with pytest.raises(ValidationError):
|
||||
_validate(
|
||||
{"interior_dark_region": {"temporal_window": 9}} # findings §2B 死键
|
||||
)
|
||||
with pytest.raises(ValidationError):
|
||||
_validate({"unknown_top_level_key": 1})
|
||||
|
||||
|
||||
def test_reference_region_manual_requires_center_xy() -> None:
|
||||
"""findings §3.11：reference_region.mode=manual 时 center_xy 必填，缺则报错。"""
|
||||
with pytest.raises(ValidationError):
|
||||
_validate({"tic_analysis": {"reference_region": {"mode": "manual"}}})
|
||||
|
||||
|
||||
def test_reference_region_manual_with_center_xy_passes() -> None:
|
||||
"""findings §3.11：manual 模式提供 center_xy 即合法。"""
|
||||
cfg = _validate(
|
||||
{"tic_analysis": {"reference_region": {"mode": "manual", "center_xy": [100.0, 200.0]}}}
|
||||
)
|
||||
assert cfg.tic_analysis.reference_region.mode == "manual"
|
||||
assert cfg.tic_analysis.reference_region.center_xy == [100.0, 200.0]
|
||||
|
||||
|
||||
def test_invalid_layout_rejected() -> None:
|
||||
"""闭集枚举：非法 views.layout 应被 Literal 拒绝（防 typo 穿透）。"""
|
||||
with pytest.raises(ValidationError):
|
||||
_validate({"views": {"layout": "diagonal"}})
|
||||
|
||||
|
||||
def test_invalid_threshold_method_rejected() -> None:
|
||||
"""闭集枚举：非法 interior_dark_region.threshold_method 应被 Literal 拒绝。"""
|
||||
with pytest.raises(ValidationError):
|
||||
_validate({"interior_dark_region": {"threshold_method": "foobar"}})
|
||||
|
||||
|
||||
def test_invalid_reference_region_mode_rejected() -> None:
|
||||
"""闭集枚举：非法 reference_region.mode 应被 Literal 拒绝。"""
|
||||
with pytest.raises(ValidationError):
|
||||
_validate({"tic_analysis": {"reference_region": {"mode": "random"}}})
|
||||
|
||||
|
||||
# ---------- design/0030 决策二：合成零点那四个键已删，残留即报错 ----------
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"payload",
|
||||
[
|
||||
{"add_zero_timepoint": True},
|
||||
{"shift_frame_times_after_zero": True},
|
||||
{"zero_time_sec": 0.0},
|
||||
{"smoothing": {"force_zero_start": True}},
|
||||
],
|
||||
)
|
||||
def test_zero_timepoint_keys_are_gone(payload: dict[str, Any]) -> None:
|
||||
"""design/0030 决策二：合成零点那四个键已删，残留即被 extra='forbid' 拒掉。
|
||||
|
||||
理由写在 `TicAnalysisConfig` 的 docstring 上（CLAUDE.md §1.6）。
|
||||
这条守的只是名字，换个名字把同一个概念加回来它照样绿，所以那两段 docstring 是不可省
|
||||
的另一半。
|
||||
"""
|
||||
with pytest.raises(ValidationError):
|
||||
_validate({"tic_analysis": payload})
|
||||
|
||||
|
||||
def test_anchor_frames_defaults_mirror_contract() -> None:
|
||||
"""anchor_frames 段的默认值镜像自《临床口径契约》（2026-08-25 版，同步 2026-09-01）。
|
||||
|
||||
这批数不来自 findings 也不来自工程直觉——动任何一个之前先去 Linear 那边核对。
|
||||
钉在测试里是为了防手滑改配置时漏看注释：改了数这条就红，红的含义是「抄本动过了，
|
||||
去核对契约」。
|
||||
"""
|
||||
cfg = load_tracking_config()
|
||||
a = cfg.tic_analysis.anchor_frames
|
||||
assert a.arrival.baseline_sd_multiplier == 3.0
|
||||
assert a.arrival.relative_peak_threshold == 0.10
|
||||
assert a.arrival.persistence_s == 0.5
|
||||
# 突出度倍数与到达判据倍数是两个独立的键（契约明写改一个不应连带改另一个）。
|
||||
assert a.peak_prominence_baseline_sd_multiplier == 3.0
|
||||
assert a.baseline_frame_backoff_s == 0.3
|
||||
assert a.d1_window_after_s == 0.5
|
||||
assert a.d2_window_s == 0.3
|
||||
assert a.d3_window_s == 0.5
|
||||
# D4/B0 两档契约空着、第 12 题委托工程侧，取曲线锚点档里较宽的一档。
|
||||
assert a.d4_window_s == 0.5
|
||||
assert a.b0_window_s == 0.5
|
||||
assert a.late_phase.preferred_time_s == 60.0
|
||||
assert a.late_phase.tolerance_s == 2.0
|
||||
assert a.f1_anchor_s == 20.0
|
||||
|
||||
|
||||
def test_anchor_frames_unknown_key_rejected() -> None:
|
||||
"""schema 拒绝未知键：照抄契约时拼错键名在加载配置这一步就报错，不进流水线。"""
|
||||
with pytest.raises(ValidationError):
|
||||
_validate({"tic_analysis": {"anchor_frames": {"arrival_threshold": 3.0}}})
|
||||
|
||||
|
||||
def test_yaml_with_leftover_zero_key_fails_to_load() -> None:
|
||||
"""yaml 里留着 `add_zero_timepoint` 时加载当场失败，不留兼容路径。"""
|
||||
section = load_ceus_section()
|
||||
section["tic_analysis"]["add_zero_timepoint"] = True
|
||||
with pytest.raises(ValidationError):
|
||||
TrackingConfig.model_validate(section)
|
||||
|
||||
|
||||
# ---------- design/0030 决策十：config_version ----------
|
||||
|
||||
|
||||
def test_config_version_is_required() -> None:
|
||||
"""design/0030 决策十：`config_version` 无默认，缺了直接报错。
|
||||
|
||||
有默认值等于「yaml 里删掉这行也能起来」，那时两批不同参数跑出来的结果落在库里
|
||||
长得一模一样，没有任何东西能把它们分开。
|
||||
"""
|
||||
section = load_ceus_section()
|
||||
del section["config_version"]
|
||||
with pytest.raises(ValidationError, match="config_version"):
|
||||
TrackingConfig.model_validate(section)
|
||||
|
||||
|
||||
def test_config_version_rejects_empty_string() -> None:
|
||||
"""空串在**加载配置这一步**就得被拒，不能等到整条流水线跑完才炸。
|
||||
|
||||
实体 ``CeusRoiTracking`` 上有同一道判据，但那是落库前的最后一关：解码、分割、六趟
|
||||
遍历全跑完，走到写库才报错，而这一趟是白跑的。
|
||||
"""
|
||||
with pytest.raises(ValidationError, match="config_version"):
|
||||
_validate({"config_version": ""})
|
||||
|
||||
|
||||
def test_default_yaml_declares_config_version() -> None:
|
||||
"""默认 yaml 顶层给了 `config_version`。
|
||||
|
||||
"4" 起于双曲线 Lognormal v2（design/0044）：拟合与锚点语义发生变化，
|
||||
两批产物靠这个号分开（design/0030 决策十）。
|
||||
"""
|
||||
cfg = load_tracking_config()
|
||||
assert cfg.config_version == "4"
|
||||
|
||||
|
||||
# ---------- design/0030 决策二：基线区间默认值 ----------
|
||||
|
||||
|
||||
def test_baseline_defaults_match_spec_section_3_3() -> None:
|
||||
"""基线两个时长的默认值等于规范 §3.3 给的首选 2 秒 / 最短 1 秒。"""
|
||||
cfg = load_tracking_config()
|
||||
assert cfg.tic_analysis.baseline.preferred_duration_s == 2.0
|
||||
assert cfg.tic_analysis.baseline.minimum_duration_s == 1.0
|
||||
# 规范 §3.3 没给稳定性判据，这个数是我们定的占位。
|
||||
assert cfg.tic_analysis.baseline.stability_sd_multiplier == 3.0
|
||||
|
||||
|
||||
def test_baseline_minimum_must_not_exceed_preferred() -> None:
|
||||
"""最短时长超过首选时长即报错——那样「退到最短」比首选还长，等于没有退路。"""
|
||||
with pytest.raises(ValidationError):
|
||||
_validate(
|
||||
{
|
||||
"tic_analysis": {
|
||||
"baseline": {
|
||||
"preferred_duration_s": 1.0,
|
||||
"minimum_duration_s": 2.0,
|
||||
}
|
||||
}
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
def test_baseline_durations_must_be_positive() -> None:
|
||||
"""非正时长即报错（0 秒的基线段算不出 B 与 σ0）。"""
|
||||
with pytest.raises(ValidationError):
|
||||
_validate({"tic_analysis": {"baseline": {"minimum_duration_s": 0.0}}})
|
||||
|
||||
|
||||
# ---------- design/0030 决策四：pan_source（紫色轮廓检测器） ----------
|
||||
|
||||
|
||||
def test_pan_source_defaults_match_default_yaml() -> None:
|
||||
"""默认 yaml 里的紫色占位值原样加载（这一组待真实素材标定）。"""
|
||||
cfg = load_tracking_config()
|
||||
assert cfg.pan_source.hsv_lower == [125, 60, 60]
|
||||
assert cfg.pan_source.hsv_upper == [165, 255, 255]
|
||||
assert cfg.pan_source.min_area == 2000
|
||||
assert cfg.pan_source.min_detected_frame_fraction == 0.9
|
||||
# 顶部文字带同绿框那条：紫色轮廓和绿框画在同一画面上。
|
||||
assert cfg.pan_source.ignore_regions == cfg.prompt_source.ignore_regions
|
||||
|
||||
|
||||
@pytest.mark.parametrize("bound", ["hsv_lower", "hsv_upper"])
|
||||
def test_pan_source_hsv_must_have_three_components(bound: str) -> None:
|
||||
"""HSV 阈值分量数不对即报错（否则要到 cv2.inRange 里才炸）。"""
|
||||
with pytest.raises(ValidationError):
|
||||
_validate({"pan_source": {bound: [125, 60]}})
|
||||
|
||||
|
||||
def test_pan_source_hsv_upper_must_not_be_below_lower() -> None:
|
||||
"""上下界写反即报错——inRange 不会报错，只会恒返回空掩膜，静默变成「检不到紫色」。"""
|
||||
with pytest.raises(ValidationError):
|
||||
_validate({"pan_source": {"hsv_lower": [125, 60, 60], "hsv_upper": [120, 255, 255]}})
|
||||
|
||||
|
||||
def test_pan_source_min_area_must_be_positive() -> None:
|
||||
"""min_area 须 ≥1。"""
|
||||
with pytest.raises(ValidationError):
|
||||
_validate({"pan_source": {"min_area": 0}})
|
||||
|
||||
|
||||
@pytest.mark.parametrize("fraction", [0.0, -0.1, 1.5])
|
||||
def test_pan_source_detected_fraction_out_of_bounds_rejected(fraction: float) -> None:
|
||||
"""检出帧占比须 ∈ (0, 1]：取 0 这道门形同虚设，>1 则永远判不出有效。"""
|
||||
with pytest.raises(ValidationError):
|
||||
_validate({"pan_source": {"min_detected_frame_fraction": fraction}})
|
||||
|
||||
|
||||
def test_pan_source_detected_fraction_accepts_one() -> None:
|
||||
"""1.0 合法（虽然默认不取它——一帧漏检就整段作废太脆）。"""
|
||||
cfg = _validate({"pan_source": {"min_detected_frame_fraction": 1.0}})
|
||||
assert cfg.pan_source.min_detected_frame_fraction == 1.0
|
||||
|
||||
|
||||
# ---------- 任务书 02：配置不设默认值，删掉的旧字段不再接受 ----------
|
||||
|
||||
|
||||
def _leaf_paths(node: dict[str, Any], prefix: tuple[str, ...]) -> list[tuple[str, ...]]:
|
||||
paths: list[tuple[str, ...]] = []
|
||||
for key, value in node.items():
|
||||
if isinstance(value, dict):
|
||||
paths.extend(_leaf_paths(value, (*prefix, key))) # pyright: ignore[reportUnknownArgumentType]
|
||||
paths.append((*prefix, key))
|
||||
return paths
|
||||
|
||||
|
||||
@pytest.mark.parametrize("path", _leaf_paths(load_ceus_section(), ()), ids="/".join)
|
||||
def test_every_field_is_required(path: tuple[str, ...]) -> None:
|
||||
"""ceus 段删掉任何一个键（含整个子段）都报错：没有一个字段有默认值。"""
|
||||
section = load_ceus_section()
|
||||
node = section
|
||||
for key in path[:-1]:
|
||||
node = node[key]
|
||||
del node[path[-1]]
|
||||
with pytest.raises(ValidationError):
|
||||
TrackingConfig.model_validate(section)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"payload",
|
||||
[
|
||||
{"views": {"source_view": "left"}},
|
||||
{"views": {"target_view": "right"}},
|
||||
{"views": {"effective_region": {"type": "full"}}},
|
||||
{"mapping": {"type": "normalized_view_coordinates", "anchor_points": []}},
|
||||
],
|
||||
)
|
||||
def test_removed_fields_are_rejected(payload: dict[str, Any]) -> None:
|
||||
"""造影在哪一半、裁黑边、映射锚点这几个旧字段已删，写了就报错。"""
|
||||
with pytest.raises(ValidationError):
|
||||
_validate(payload)
|
||||
|
||||
|
||||
def test_config_is_frozen() -> None:
|
||||
cfg = load_tracking_config()
|
||||
with pytest.raises(ValidationError):
|
||||
cfg.keyframes.iou_threshold = 0.5 # pyright: ignore[reportAttributeAccessIssue]
|
||||
@@ -0,0 +1,93 @@
|
||||
"""造影画面在哪一半：两半各自选提示帧，恰好一半有才定（任务书 02 第一阶段第 2 条）。
|
||||
|
||||
合成 400×200 的分屏视频，分屏线在 x=200；绿框是 120×100 的实心框（外接框面积 12000，
|
||||
大于 config/ceus.yaml 的 min_area 10000），画在顶部文字带之下。预期值按画框位置手算。
|
||||
"""
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from cadx.ceus import (
|
||||
CeusCaseError,
|
||||
CeusConfig,
|
||||
ContrastSideError,
|
||||
ViewRect,
|
||||
locate_contrast_side,
|
||||
map_source_mask_to_target,
|
||||
split_views,
|
||||
)
|
||||
from cadx.ceus._geometry import Frame
|
||||
|
||||
_WIDTH, _HEIGHT, _SPLIT = 400, 200, 200
|
||||
_GREEN = (0, 255, 0)
|
||||
|
||||
|
||||
def _frames(green_boxes_x1: list[int]) -> list[Frame]:
|
||||
"""10 帧分屏画面；每个 x1 画一个整帧坐标 [x1, x1+119] × [60, 159] 的绿框。"""
|
||||
frame = np.zeros((_HEIGHT, _WIDTH, 3), dtype=np.uint8)
|
||||
frame[:, :_SPLIT] = 120
|
||||
frame[:, _SPLIT + 1 :] = 90
|
||||
for x1 in green_boxes_x1:
|
||||
cv2.rectangle(frame, (x1, 60), (x1 + 119, 159), _GREEN, thickness=-1)
|
||||
return [frame.copy() for _ in range(10)]
|
||||
|
||||
|
||||
def test_green_box_in_left_half(ceus_config: CeusConfig) -> None:
|
||||
frames = _frames([40])
|
||||
_, left, right = split_views(frames, ceus_config.views)
|
||||
assert (left, right) == (ViewRect(0, 0, 200, 200), ViewRect(200, 0, 400, 200))
|
||||
|
||||
side = locate_contrast_side(
|
||||
frames, left, right, ceus_config.prompt_source, ceus_config.keyframes
|
||||
)
|
||||
|
||||
assert side.side == "left"
|
||||
assert side.contrast_view == left
|
||||
assert side.target_view == right
|
||||
# 10 帧绿框不变：只有第一帧是提示帧。
|
||||
assert [p.frame_index for p in side.prompt_frames] == [0]
|
||||
x1, y1, _, _ = side.prompt_frames[0].bbox_xyxy
|
||||
assert abs(x1 - 40) <= 3 and abs(y1 - 60) <= 3
|
||||
|
||||
|
||||
def test_green_box_in_right_half(ceus_config: CeusConfig) -> None:
|
||||
frames = _frames([250])
|
||||
_, left, right = split_views(frames, ceus_config.views)
|
||||
|
||||
side = locate_contrast_side(
|
||||
frames, left, right, ceus_config.prompt_source, ceus_config.keyframes
|
||||
)
|
||||
|
||||
assert side.side == "right"
|
||||
assert side.contrast_view == right
|
||||
assert side.target_view == left
|
||||
# 整帧 x=250 在右半局部是 250 − 200 = 50。
|
||||
prompt = side.prompt_frames[0]
|
||||
assert abs(prompt.bbox_xyxy[0] - 50) <= 3
|
||||
# 挪到左半（灰阶那一半）：整帧 x = 250 − 200 = 50，局部也是 50。
|
||||
mapped = map_source_mask_to_target(prompt.mask, side.contrast_view, side.target_view)
|
||||
xs = np.where(mapped)[1]
|
||||
assert abs(int(xs.min()) - 50) <= 3
|
||||
assert abs(int(xs.max()) - 169) <= 3
|
||||
|
||||
|
||||
def test_green_box_in_both_halves_fails_case(ceus_config: CeusConfig) -> None:
|
||||
frames = _frames([40, 250])
|
||||
_, left, right = split_views(frames, ceus_config.views)
|
||||
|
||||
with pytest.raises(ContrastSideError, match="两半都找到绿框") as raised:
|
||||
locate_contrast_side(frames, left, right, ceus_config.prompt_source, ceus_config.keyframes)
|
||||
|
||||
assert isinstance(raised.value, CeusCaseError)
|
||||
assert raised.value.step == 2
|
||||
|
||||
|
||||
def test_no_green_box_fails_case(ceus_config: CeusConfig) -> None:
|
||||
frames = _frames([])
|
||||
_, left, right = split_views(frames, ceus_config.views)
|
||||
|
||||
with pytest.raises(ContrastSideError, match="两半都没有找到绿框") as raised:
|
||||
locate_contrast_side(frames, left, right, ceus_config.prompt_source, ceus_config.keyframes)
|
||||
|
||||
assert raised.value.step == 2
|
||||
@@ -0,0 +1,278 @@
|
||||
"""B1 geometry 纯函数库单元测试。
|
||||
|
||||
钉死几何/重采样/平滑工具的关键性质：IoU 边界、等弧长重采样闭合性、
|
||||
三角平滑的恒等与偶窗拒绝、奇窗化与可选正整数解析、多边形栅格化 dtype，
|
||||
以及 ViewRect 的不变量校验。测试用真实数据的二次构造（合成 mask / 正方形轮廓 /
|
||||
常量轨迹），边缘场景靠实现里的 assertion/raise 保证。
|
||||
"""
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from cadx.ceus import (
|
||||
ViewRect,
|
||||
crop_frame,
|
||||
local_rect_within,
|
||||
mask_iou,
|
||||
odd_window,
|
||||
optional_positive_int,
|
||||
polygon_area,
|
||||
polygon_center,
|
||||
polygon_to_mask,
|
||||
polygons_to_mask,
|
||||
rect_from_values,
|
||||
resample_closed_contour,
|
||||
triangular_smooth,
|
||||
)
|
||||
|
||||
|
||||
# Phase 1: mask_iou —— 交并比边界
|
||||
def test_mask_iou_identical_is_one() -> None:
|
||||
mask = np.zeros((10, 10), dtype=bool)
|
||||
mask[2:6, 3:7] = True
|
||||
assert mask_iou(mask, mask) == 1.0
|
||||
|
||||
|
||||
def test_mask_iou_disjoint_is_zero() -> None:
|
||||
a = np.zeros((10, 10), dtype=bool)
|
||||
a[0:3, 0:3] = True
|
||||
b = np.zeros((10, 10), dtype=bool)
|
||||
b[6:9, 6:9] = True
|
||||
assert mask_iou(a, b) == 0.0
|
||||
|
||||
|
||||
def test_mask_iou_empty_union_is_zero() -> None:
|
||||
empty = np.zeros((5, 5), dtype=bool)
|
||||
assert mask_iou(empty, empty) == 0.0
|
||||
|
||||
|
||||
def test_mask_iou_partial_overlap() -> None:
|
||||
a = np.zeros((10, 10), dtype=bool)
|
||||
a[0:4, 0:4] = True # 16 像素
|
||||
b = np.zeros((10, 10), dtype=bool)
|
||||
b[2:6, 2:6] = True # 16 像素，交集 2x2=4，并集 28
|
||||
assert mask_iou(a, b) == pytest.approx(4 / 28)
|
||||
|
||||
|
||||
# Phase 2: resample_closed_contour —— 等弧长重采样
|
||||
def _unit_square() -> np.ndarray:
|
||||
return np.array([[0.0, 0.0], [10.0, 0.0], [10.0, 10.0], [0.0, 10.0]], dtype=np.float32)
|
||||
|
||||
|
||||
def test_resample_shape_is_num_points_by_two() -> None:
|
||||
out = resample_closed_contour(_unit_square(), 64)
|
||||
assert out is not None
|
||||
assert out.shape == (64, 2)
|
||||
|
||||
|
||||
def test_resample_equal_arc_length_spacing() -> None:
|
||||
out = resample_closed_contour(_unit_square(), 64)
|
||||
assert out is not None
|
||||
closed = np.vstack([out, out[0]])
|
||||
seg = np.sqrt((np.diff(closed, axis=0) ** 2).sum(axis=1))
|
||||
# 等弧长：所有相邻采样段长应近似一致
|
||||
assert seg.std() < 1e-3
|
||||
|
||||
|
||||
def test_resample_too_few_points_returns_none() -> None:
|
||||
assert resample_closed_contour(np.array([[0.0, 0.0], [1.0, 1.0]]), 64) is None
|
||||
assert resample_closed_contour(None, 64) is None
|
||||
|
||||
|
||||
# Phase 3: triangular_smooth —— 时间轴三角加权
|
||||
def test_triangular_smooth_even_window_raises() -> None:
|
||||
values = np.zeros((5, 4, 2), dtype=np.float32)
|
||||
with pytest.raises(ValueError):
|
||||
triangular_smooth(values, 4)
|
||||
|
||||
|
||||
def test_triangular_smooth_window_one_is_identity() -> None:
|
||||
rng = np.random.default_rng(0)
|
||||
values = rng.standard_normal((6, 8, 2)).astype(np.float32)
|
||||
out = triangular_smooth(values, 1)
|
||||
assert np.array_equal(out, values)
|
||||
|
||||
|
||||
def test_triangular_smooth_constant_track_unchanged() -> None:
|
||||
const = np.full((7, 5, 2), 3.5, dtype=np.float32)
|
||||
out = triangular_smooth(const, 3)
|
||||
assert np.allclose(out, 3.5)
|
||||
|
||||
|
||||
# Phase 4: odd_window —— 奇窗化
|
||||
def test_odd_window_even_increments() -> None:
|
||||
assert odd_window(10) == 11
|
||||
|
||||
|
||||
def test_odd_window_odd_unchanged() -> None:
|
||||
assert odd_window(7) == 7
|
||||
|
||||
|
||||
def test_odd_window_floor_at_one() -> None:
|
||||
assert odd_window(0) == 1
|
||||
assert odd_window(-2) == 1
|
||||
|
||||
|
||||
# Phase 5: optional_positive_int —— 可选正整数解析
|
||||
def test_optional_positive_int_auto_is_none() -> None:
|
||||
assert optional_positive_int("auto") is None
|
||||
|
||||
|
||||
def test_optional_positive_int_none_is_none() -> None:
|
||||
assert optional_positive_int(None) is None
|
||||
|
||||
|
||||
def test_optional_positive_int_negative_is_none() -> None:
|
||||
assert optional_positive_int(-3) is None
|
||||
assert optional_positive_int(0) is None
|
||||
|
||||
|
||||
def test_optional_positive_int_positive_parsed() -> None:
|
||||
assert optional_positive_int(5) == 5
|
||||
assert optional_positive_int("8") == 8
|
||||
|
||||
|
||||
# Phase 6: polygon_to_mask —— 栅格化
|
||||
def test_polygon_to_mask_dtype_is_bool() -> None:
|
||||
poly = _unit_square()
|
||||
mask = polygon_to_mask(poly, 12, 12)
|
||||
assert mask.dtype == bool
|
||||
assert mask.shape == (12, 12)
|
||||
assert mask.any()
|
||||
|
||||
|
||||
# Phase 6a: polygons_to_mask —— even-odd 一次填充挖洞
|
||||
def test_polygons_to_mask_carves_hole_via_even_odd() -> None:
|
||||
"""外框 + 内洞一次 fillPoly：洞中心为 False、外框环上为 True（even-odd 挖洞）。"""
|
||||
outer = np.array([[5.0, 5.0], [45.0, 5.0], [45.0, 45.0], [5.0, 45.0]])
|
||||
hole = np.array([[20.0, 20.0], [30.0, 20.0], [30.0, 30.0], [20.0, 30.0]])
|
||||
mask = polygons_to_mask([outer, hole], 50, 50)
|
||||
assert mask.dtype == bool
|
||||
assert not mask[25, 25] # 洞中心：被挖空
|
||||
assert mask[10, 10] # 外框环上：填充
|
||||
# 甜甜圈面积 = 外框 40x40 - 洞 10x10（栅格边界差几像素，放宽）
|
||||
assert mask.sum() == pytest.approx(40 * 40 - 10 * 10, abs=200)
|
||||
|
||||
|
||||
def test_polygons_to_mask_empty_is_all_false() -> None:
|
||||
"""空列表 / 全退化多边形：返回全 False 掩膜（不报错）。"""
|
||||
assert not polygons_to_mask([], 20, 20).any()
|
||||
degenerate = np.array([[1.0, 1.0], [2.0, 2.0]]) # < 3 顶点，跳过
|
||||
assert not polygons_to_mask([degenerate], 20, 20).any()
|
||||
|
||||
|
||||
# Phase 6b: polygon_area —— 鞋带公式精确面积
|
||||
def test_polygon_area_square_exact() -> None:
|
||||
"""边长 10 的正方形面积精确为 100.0（鞋带浮点精确，不取整）。"""
|
||||
square = np.array([[0.0, 0.0], [10.0, 0.0], [10.0, 10.0], [0.0, 10.0]], dtype=np.float32)
|
||||
assert polygon_area(square) == pytest.approx(100.0, abs=1e-6)
|
||||
|
||||
|
||||
def test_polygon_area_triangle_exact() -> None:
|
||||
"""直角三角形（底 8 高 6）面积精确为 24.0。"""
|
||||
triangle = np.array([[0.0, 0.0], [8.0, 0.0], [0.0, 6.0]], dtype=np.float32)
|
||||
assert polygon_area(triangle) == pytest.approx(24.0, abs=1e-6)
|
||||
|
||||
|
||||
def test_polygon_area_orientation_independent() -> None:
|
||||
"""顺时针 / 逆时针同一多边形面积一致（取绝对值消朝向符号）。"""
|
||||
ccw = np.array([[0.0, 0.0], [10.0, 0.0], [10.0, 10.0], [0.0, 10.0]], dtype=np.float32)
|
||||
cw = ccw[::-1].copy()
|
||||
assert polygon_area(ccw) == pytest.approx(polygon_area(cw), abs=1e-6)
|
||||
assert polygon_area(cw) == pytest.approx(100.0, abs=1e-6)
|
||||
|
||||
|
||||
def test_polygon_area_non_integer_vertices_not_quantized() -> None:
|
||||
"""半整数顶点不被取整：边长 10.5 正方形面积 = 110.25（不会被 round 量化）。"""
|
||||
square = np.array([[0.0, 0.0], [10.5, 0.0], [10.5, 10.5], [0.0, 10.5]], dtype=np.float32)
|
||||
assert polygon_area(square) == pytest.approx(110.25, abs=1e-4)
|
||||
|
||||
|
||||
# Phase 6c: polygon_center —— 质心（退化时回退顶点均值）
|
||||
def test_polygon_center_square_is_geometric_center() -> None:
|
||||
"""正方形质心在几何中心 (5, 5)。"""
|
||||
square = np.array([[0.0, 0.0], [10.0, 0.0], [10.0, 10.0], [0.0, 10.0]], dtype=np.float32)
|
||||
center = polygon_center(square)
|
||||
assert center == pytest.approx(np.array([5.0, 5.0]), abs=1e-3)
|
||||
|
||||
|
||||
def test_polygon_center_rectangle_is_geometric_center() -> None:
|
||||
"""矩形质心在几何中心 (10, 3)。"""
|
||||
rect = np.array([[0.0, 0.0], [20.0, 0.0], [20.0, 6.0], [0.0, 6.0]], dtype=np.float32)
|
||||
center = polygon_center(rect)
|
||||
assert center == pytest.approx(np.array([10.0, 3.0]), abs=1e-3)
|
||||
|
||||
|
||||
def test_polygon_center_degenerate_collinear_falls_back_to_vertex_mean() -> None:
|
||||
"""共线顶点 m00≈0：退化路径回退到顶点算术均值，不崩、不除零。"""
|
||||
collinear = np.array([[0.0, 0.0], [2.0, 2.0], [4.0, 4.0], [6.0, 6.0]], dtype=np.float32)
|
||||
center = polygon_center(collinear)
|
||||
assert center == pytest.approx(collinear.mean(axis=0), abs=1e-3)
|
||||
|
||||
|
||||
def test_polygon_center_degenerate_coincident_falls_back_to_vertex_mean() -> None:
|
||||
"""所有点重合 m00≈0：退化路径回退到该点本身（顶点均值），不除零。"""
|
||||
coincident = np.full((5, 2), 7.0, dtype=np.float32)
|
||||
center = polygon_center(coincident)
|
||||
assert center == pytest.approx(np.array([7.0, 7.0]), abs=1e-3)
|
||||
|
||||
|
||||
# Phase 7: ViewRect / crop_frame / local_rect_within / rect_from_values
|
||||
def test_view_rect_valid() -> None:
|
||||
rect = ViewRect(0, 0, 4, 6)
|
||||
assert (rect.x1, rect.y1, rect.x2, rect.y2) == (0, 0, 4, 6)
|
||||
|
||||
|
||||
def test_view_rect_rejects_inverted() -> None:
|
||||
with pytest.raises(ValueError):
|
||||
ViewRect(5, 0, 3, 6)
|
||||
with pytest.raises(ValueError):
|
||||
ViewRect(0, 6, 4, 2)
|
||||
with pytest.raises(ValueError):
|
||||
ViewRect(-1, 0, 4, 6)
|
||||
|
||||
|
||||
def test_crop_frame_slices_subimage() -> None:
|
||||
frame = np.arange(100).reshape(10, 10)
|
||||
rect = ViewRect(2, 1, 5, 4)
|
||||
sub = crop_frame(frame, rect)
|
||||
assert sub.shape == (3, 3)
|
||||
assert np.array_equal(sub, frame[1:4, 2:5])
|
||||
|
||||
|
||||
def test_local_rect_within_translates_to_parent_origin() -> None:
|
||||
parent = ViewRect(10, 20, 110, 220)
|
||||
child = ViewRect(30, 50, 60, 90)
|
||||
local = local_rect_within(parent, child)
|
||||
assert (local.x1, local.y1, local.x2, local.y2) == (20, 30, 50, 70)
|
||||
|
||||
|
||||
def test_rect_from_values_pixel() -> None:
|
||||
rect = rect_from_values([3, 4, 8, 9], width=20, height=20)
|
||||
assert (rect.x1, rect.y1, rect.x2, rect.y2) == (3, 4, 8, 9)
|
||||
|
||||
|
||||
def test_rect_from_values_normalized() -> None:
|
||||
rect = rect_from_values([0.0, 0.0, 0.5, 0.5], width=100, height=80)
|
||||
assert (rect.x1, rect.y1, rect.x2, rect.y2) == (0, 0, 50, 40)
|
||||
|
||||
|
||||
def test_rect_from_values_clamps_and_keeps_min_extent() -> None:
|
||||
# x2==x1 时应被推到 x1+1，避免退化
|
||||
rect = rect_from_values([5, 5, 5, 5], width=20, height=20)
|
||||
assert rect.x2 >= rect.x1 + 1
|
||||
assert rect.y2 >= rect.y1 + 1
|
||||
|
||||
|
||||
def test_rect_from_values_wrong_length_raises() -> None:
|
||||
with pytest.raises(ValueError):
|
||||
rect_from_values([1, 2, 3], width=10, height=10)
|
||||
|
||||
|
||||
def test_viewrect_width_height():
|
||||
"""ViewRect.width/height 与参考接口对齐，供下游消费。"""
|
||||
from cadx.ceus import ViewRect
|
||||
|
||||
r = ViewRect(10, 20, 110, 220)
|
||||
assert r.width == 100
|
||||
assert r.height == 200
|
||||
@@ -0,0 +1,291 @@
|
||||
"""B3 green_prompt 绿框提示检测单元测试。
|
||||
|
||||
钉死四件事的关键性质（参考 ``references/ROI/roi_tracking_pipeline.py`` P:241-332/989-1004）：
|
||||
|
||||
- ``extract_color_prompt``：合成在已知位置画绿色多边形的 BGR 左视图帧 →
|
||||
返回该绿框 bbox（含端点 ``x2=x1+w-1`` / ``y2=y1+h-1``）+ 充满轮廓的布尔 mask；
|
||||
纯灰度帧（无绿）→ ``None``。
|
||||
- ``ignore_regions``：把绿框画在顶部文字带内，配置抹除顶部带后该绿不计 → ``None``；
|
||||
绿框在带外则正常返回。
|
||||
- ``min_area``：按 **bbox 面积 w×h**（findings §3.2，非轮廓面积）过滤——
|
||||
bbox 面积 < min_area 的候选被丢弃。
|
||||
- ``collect_prompt_observations``：对多帧序列返回逐帧观测列表，
|
||||
无绿帧以 ``None`` 占位（``observation.prompt is None``）。
|
||||
- ``prompt_color_mask``：TIC 端"排除绿框色"掩膜——``exclude_prompt_color=False`` 全 False；
|
||||
``prompt_exclude_dilate=1`` 不膨胀（mask == 原始 inRange）；增大膨胀核则 mask 更大。
|
||||
|
||||
测试用真实数据的二次构造（合成定位绿框 / 纯灰度帧），边缘场景靠实现 raise/None 保证。
|
||||
"""
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
||||
from cadx.ceus import (
|
||||
CeusConfig,
|
||||
ColorPrompt,
|
||||
PanSourceConfig,
|
||||
PromptObservation,
|
||||
PromptSourceConfig,
|
||||
TicAnalysisConfig,
|
||||
collect_prompt_observations,
|
||||
extract_color_prompt,
|
||||
prompt_color_mask,
|
||||
)
|
||||
from conftest import load_ceus_section
|
||||
|
||||
# 配置不设默认值：旧测试靠默认值构造的配置，改为在 config/ceus.yaml 的取值上 model_copy。
|
||||
_CONFIG = CeusConfig.model_validate(load_ceus_section())
|
||||
|
||||
|
||||
# 纯绿（BGR）：H≈60、S=255、V=255，落在 inRange [35,60,60]~[90,255,255] 内。
|
||||
_GREEN_BGR = (0, 255, 0)
|
||||
_GRAY_BGR = 120
|
||||
# 紫（BGR）：H≈145、S=255、V=255，落在 PanSourceConfig 默认阈 [125,60,60]~[165,255,255] 内。
|
||||
_PURPLE_BGR = (255, 0, 212)
|
||||
|
||||
|
||||
def _gray_frame(height: int, width: int) -> np.ndarray:
|
||||
"""合成一帧纯灰度 BGR 图（三通道等值），不含任何绿色像素。"""
|
||||
return np.full((height, width, 3), _GRAY_BGR, dtype=np.uint8)
|
||||
|
||||
|
||||
def _frame_with_green_rect(
|
||||
height: int,
|
||||
width: int,
|
||||
rect_xyxy: tuple[int, int, int, int],
|
||||
*,
|
||||
thickness: int = -1,
|
||||
) -> np.ndarray:
|
||||
"""在灰底帧上画一个绿色矩形（默认实心填充）。
|
||||
|
||||
``rect_xyxy`` 为 ``(x1, y1, x2, y2)`` 含端点像素坐标。
|
||||
"""
|
||||
frame = _gray_frame(height, width)
|
||||
x1, y1, x2, y2 = rect_xyxy
|
||||
cv2.rectangle(frame, (x1, y1), (x2, y2), _GREEN_BGR, thickness=thickness)
|
||||
return frame
|
||||
|
||||
|
||||
def _cfg(**overrides: object) -> PromptSourceConfig:
|
||||
"""构造 PromptSourceConfig，默认无 ignore_regions、min_area=1，便于钉死定位。"""
|
||||
base: dict[str, object] = {"ignore_regions": [], "min_area": 1}
|
||||
base.update(overrides)
|
||||
return _CONFIG.prompt_source.model_copy(update=base)
|
||||
|
||||
|
||||
def _tic_cfg(**overrides: object) -> TicAnalysisConfig:
|
||||
"""构造 TicAnalysisConfig，默认 exclude_prompt_color=True、prompt_exclude_dilate=1。
|
||||
|
||||
把 dilate 默认置 1 便于钉死"原始 inRange == mask"（无膨胀外扩），
|
||||
各测试再按需覆盖以验证条件膨胀语义。
|
||||
"""
|
||||
base: dict[str, object] = {"prompt_exclude_dilate": 1}
|
||||
base.update(overrides)
|
||||
return _CONFIG.tic_analysis.model_copy(update=base)
|
||||
|
||||
|
||||
def _pan_cfg() -> PanSourceConfig:
|
||||
"""紫圈检测配置：config/ceus.yaml 的取值，去掉忽略区、min_area=1。"""
|
||||
return _CONFIG.pan_source.model_copy(update={"ignore_regions": [], "min_area": 1})
|
||||
|
||||
|
||||
# Phase 1: extract_color_prompt —— 定位绿框 / 无绿返回 None
|
||||
def test_extract_returns_bbox_with_inclusive_endpoints() -> None:
|
||||
# 在 (40,30)-(120,90) 画实心绿框（含端点）。
|
||||
rect = (40, 30, 120, 90)
|
||||
frame = _frame_with_green_rect(120, 200, rect)
|
||||
prompt = extract_color_prompt(frame, _cfg())
|
||||
assert isinstance(prompt, ColorPrompt)
|
||||
x1, y1, x2, y2 = prompt.bbox_xyxy
|
||||
# 形态学 CLOSE+dilate 会外扩几像素，允许 ±3 容差，钉死端点语义。
|
||||
assert abs(x1 - 40) <= 3 and abs(y1 - 30) <= 3
|
||||
assert abs(x2 - 120) <= 3 and abs(y2 - 90) <= 3
|
||||
# 含端点：width = x2 - x1 + 1。
|
||||
assert prompt.width == x2 - x1 + 1
|
||||
assert prompt.height == y2 - y1 + 1
|
||||
# mask 是布尔、与帧同高宽、绿框区域为 True。
|
||||
assert prompt.mask.dtype == np.bool_
|
||||
assert prompt.mask.shape == (120, 200)
|
||||
assert bool(prompt.mask[60, 80]) # 框中心点在 mask 内
|
||||
assert prompt.candidate_count >= 1
|
||||
|
||||
|
||||
def test_extract_gray_frame_returns_none() -> None:
|
||||
assert extract_color_prompt(_gray_frame(120, 200), _cfg()) is None
|
||||
|
||||
|
||||
# Phase 2: ignore_regions —— 顶部文字带抹除
|
||||
def test_ignore_regions_suppresses_top_band_green() -> None:
|
||||
# 绿框完全落在顶部 8.34% 带内（120*0.0834≈10px），抹除后不计。
|
||||
top_rect = (40, 1, 120, 8)
|
||||
frame = _frame_with_green_rect(120, 200, top_rect)
|
||||
cfg = _cfg(ignore_regions=[[0.0, 0.0, 1.0, 0.0834]])
|
||||
assert extract_color_prompt(frame, cfg) is None
|
||||
|
||||
|
||||
def test_ignore_regions_keeps_below_band_green() -> None:
|
||||
# 绿框在顶部带之下，抹除顶部带不影响它。
|
||||
body_rect = (40, 40, 120, 90)
|
||||
frame = _frame_with_green_rect(120, 200, body_rect)
|
||||
cfg = _cfg(ignore_regions=[[0.0, 0.0, 1.0, 0.0834]])
|
||||
prompt = extract_color_prompt(frame, cfg)
|
||||
assert prompt is not None
|
||||
assert abs(prompt.bbox_xyxy[1] - 40) <= 3
|
||||
|
||||
|
||||
# Phase 3: min_area —— 按 bbox 面积 w×h 过滤
|
||||
def test_min_area_filters_by_bbox_area() -> None:
|
||||
# 绿框 bbox≈ 21×21=441；min_area=2000 应过滤掉 → None。
|
||||
small_rect = (50, 50, 70, 70)
|
||||
frame = _frame_with_green_rect(120, 200, small_rect)
|
||||
assert extract_color_prompt(frame, _cfg(min_area=2000)) is None
|
||||
# 放宽 min_area 后能检出。
|
||||
assert extract_color_prompt(frame, _cfg(min_area=100)) is not None
|
||||
|
||||
|
||||
def test_min_area_boundary_equal_passes() -> None:
|
||||
# 钉死边界：bbox 面积"恰等于"min_area 应通过（实现是 `< min_area` 过滤，
|
||||
# 非 `<= min_area`；与参考 P:270 一致）。用细绿框确保 bbox 面积可精确预测，
|
||||
# 不被形态学外扩干扰（CLOSE+dilate 只让外轮廓更大、bbox 不会缩小）。
|
||||
# 实心绿框 (50,50)-(70,70)：bbox = (70-50+1)*(70-50+1) = 21*21 = 441。
|
||||
rect = (50, 50, 70, 70)
|
||||
expected_area = (70 - 50 + 1) * (70 - 50 + 1)
|
||||
assert expected_area == 441
|
||||
frame = _frame_with_green_rect(120, 200, rect)
|
||||
# 形态学会外扩，故实际 bbox 面积 >= 441；取一个明显小于实际面积、
|
||||
# 但用于证伪 `<=` 误写的下界即可——这里用恰等于"检出框的真实面积"。
|
||||
detected = extract_color_prompt(frame, _cfg(min_area=1))
|
||||
assert detected is not None
|
||||
area_eq = detected.bbox_area
|
||||
# min_area 恰等于检出 bbox 面积 → 应通过（area >= min_area）；
|
||||
# 若误写成 `<= min_area` 过滤，则此处会被丢弃返回 None，从而证伪。
|
||||
on_boundary = extract_color_prompt(frame, _cfg(min_area=area_eq))
|
||||
assert on_boundary is not None
|
||||
assert on_boundary.bbox_area == area_eq
|
||||
# 反向：min_area 比真实面积大 1 → 被过滤为 None（确认过滤确实生效）。
|
||||
assert extract_color_prompt(frame, _cfg(min_area=area_eq + 1)) is None
|
||||
|
||||
|
||||
def test_hollow_green_rect_closes_to_single_filled_contour() -> None:
|
||||
# 覆盖参考 P:261-288 真实服务路径：医生画的是空心绿框（细描边，非实心）。
|
||||
# 用 thickness=3 的空心矩形，验证 CLOSE+dilate 后 findContours 连通成单个外轮廓，
|
||||
# 且 drawContours 实心填充使 mask 覆盖框内区域（含框中心，原图中心是灰底非绿）。
|
||||
rect = (40, 30, 140, 100)
|
||||
frame = _frame_with_green_rect(120, 200, rect, thickness=3)
|
||||
# 前置：中心点在原图是灰底（空心框内部无绿），证明确为空心框。
|
||||
cx, cy = (40 + 140) // 2, (30 + 100) // 2
|
||||
assert tuple(int(c) for c in frame[cy, cx]) == (_GRAY_BGR, _GRAY_BGR, _GRAY_BGR)
|
||||
prompt = extract_color_prompt(frame, _cfg(min_area=100))
|
||||
assert prompt is not None
|
||||
# 单个外轮廓：候选数为 1（空心框经形态学连通后只剩一条外轮廓）。
|
||||
assert prompt.candidate_count == 1
|
||||
# 实心填充 mask 覆盖框内中心区域（findContours 外轮廓 + drawContours thickness=-1）。
|
||||
assert bool(prompt.mask[cy, cx])
|
||||
# bbox 端点贴合空心框外缘（±5 容差吸收核 (5,5) CLOSE+dilate 的外扩）。
|
||||
assert abs(prompt.bbox_xyxy[0] - 40) <= 5 and abs(prompt.bbox_xyxy[1] - 30) <= 5
|
||||
assert abs(prompt.bbox_xyxy[2] - 140) <= 5 and abs(prompt.bbox_xyxy[3] - 100) <= 5
|
||||
|
||||
|
||||
def test_min_area_picks_largest_bbox_candidate() -> None:
|
||||
# 两个绿框：小框 (10,10)-(30,30)，大框 (80,60)-(170,110)；选大框。
|
||||
frame = _frame_with_green_rect(120, 200, (10, 10, 30, 30))
|
||||
cv2.rectangle(frame, (80, 60), (170, 110), _GREEN_BGR, thickness=-1)
|
||||
prompt = extract_color_prompt(frame, _cfg(min_area=1))
|
||||
assert prompt is not None
|
||||
assert prompt.candidate_count == 2
|
||||
# 选中的应是大框（左上角接近 80,60）。
|
||||
assert abs(prompt.bbox_xyxy[0] - 80) <= 3
|
||||
assert abs(prompt.bbox_xyxy[1] - 60) <= 3
|
||||
|
||||
|
||||
# Phase 4: collect_prompt_observations —— 多帧观测列表（None 占位）
|
||||
def test_collect_observations_none_placeholder_for_no_green_frames() -> None:
|
||||
frames = [
|
||||
_frame_with_green_rect(120, 200, (40, 40, 120, 90)), # 有绿
|
||||
_gray_frame(120, 200), # 无绿 → None 占位
|
||||
_frame_with_green_rect(120, 200, (50, 40, 130, 95)), # 有绿
|
||||
]
|
||||
observations = collect_prompt_observations(frames, _cfg())
|
||||
assert len(observations) == 3
|
||||
assert all(isinstance(o, PromptObservation) for o in observations)
|
||||
assert [o.frame_index for o in observations] == [0, 1, 2]
|
||||
assert observations[0].prompt is not None
|
||||
assert observations[1].prompt is None # 无绿帧占位
|
||||
assert observations[2].prompt is not None
|
||||
assert observations[1].detected is False
|
||||
assert observations[0].detected is True
|
||||
|
||||
|
||||
# Phase 5: prompt_color_mask —— TIC 端"排除绿框色"掩膜（受 tic_cfg 门控）
|
||||
def test_prompt_color_mask_marks_green_pixels() -> None:
|
||||
frame = _frame_with_green_rect(120, 200, (40, 30, 120, 90))
|
||||
mask = prompt_color_mask(frame, _cfg(), _tic_cfg())
|
||||
assert mask.dtype == np.bool_
|
||||
assert mask.shape == (120, 200)
|
||||
assert bool(mask[60, 80]) # 绿框内为 True
|
||||
assert not bool(mask[5, 5]) # 灰底为 False
|
||||
|
||||
|
||||
def test_prompt_color_mask_all_gray_is_empty() -> None:
|
||||
mask = prompt_color_mask(_gray_frame(120, 200), _cfg(), _tic_cfg())
|
||||
assert not mask.any()
|
||||
|
||||
|
||||
def test_prompt_color_mask_exclude_disabled_returns_all_false() -> None:
|
||||
# exclude_prompt_color=False → 直接返回全 False（语义："不排除任何绿框色像素"）。
|
||||
frame = _frame_with_green_rect(120, 200, (40, 30, 120, 90))
|
||||
mask = prompt_color_mask(frame, _cfg(), _tic_cfg(exclude_prompt_color=False))
|
||||
assert mask.dtype == np.bool_
|
||||
assert mask.shape == (120, 200)
|
||||
assert not mask.any()
|
||||
|
||||
|
||||
def test_prompt_color_mask_dilate_one_equals_raw_inrange() -> None:
|
||||
# prompt_exclude_dilate=1 → 不膨胀，mask 应恰等于原始 inRange 二值。
|
||||
frame = _frame_with_green_rect(120, 200, (40, 30, 120, 90))
|
||||
mask = prompt_color_mask(frame, _cfg(), _tic_cfg(prompt_exclude_dilate=1))
|
||||
lower = np.asarray(_cfg().hsv_lower, dtype=np.uint8)
|
||||
upper = np.asarray(_cfg().hsv_upper, dtype=np.uint8)
|
||||
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
|
||||
raw = cv2.inRange(hsv, lower, upper) > 0
|
||||
assert np.array_equal(mask, raw)
|
||||
|
||||
|
||||
def test_prompt_color_mask_larger_dilate_grows_mask() -> None:
|
||||
# prompt_exclude_dilate=5 应比 =1 时膨胀出更多 True 像素（外扩绿框边缘）。
|
||||
frame = _frame_with_green_rect(120, 200, (40, 30, 120, 90))
|
||||
mask_one = prompt_color_mask(frame, _cfg(), _tic_cfg(prompt_exclude_dilate=1))
|
||||
mask_five = prompt_color_mask(frame, _cfg(), _tic_cfg(prompt_exclude_dilate=5))
|
||||
assert int(mask_five.sum()) > int(mask_one.sum())
|
||||
# 膨胀只增不减：dilate=1 的 True 像素应全被 dilate=5 覆盖。
|
||||
assert bool((mask_five | mask_one == mask_five).all())
|
||||
|
||||
|
||||
# Phase 5: 换一组颜色范围就是另一个检测器（design/0030 决策四）
|
||||
def test_extract_color_prompt_detects_purple_with_pan_config() -> None:
|
||||
"""同一个 ``extract_color_prompt``，喂 ``PanSourceConfig`` 检的是紫色轮廓。
|
||||
|
||||
紫色轮廓是医生画的邻近正常胰腺实质（``ROI_pan``），和绿框走同一套 HSV 机制、
|
||||
同一个函数——所以这里断言的是「函数认颜色配置，不认死绿色」这个行为。
|
||||
"""
|
||||
rect = (40, 30, 120, 90)
|
||||
frame = _gray_frame(120, 200)
|
||||
cv2.rectangle(frame, rect[:2], rect[2:], _PURPLE_BGR, thickness=-1)
|
||||
|
||||
pan_cfg = _pan_cfg()
|
||||
prompt = extract_color_prompt(frame, pan_cfg)
|
||||
assert isinstance(prompt, ColorPrompt)
|
||||
assert bool(prompt.mask[60, 80])
|
||||
# 同一帧喂绿框那组配置一个候选都没有——两组颜色范围互不命中。
|
||||
assert extract_color_prompt(frame, _cfg()) is None
|
||||
|
||||
|
||||
def test_prompt_color_mask_takes_hsv_from_the_config_it_is_given() -> None:
|
||||
"""排除色掩膜的 HSV 来源是入参：喂紫色配置就出紫色掩膜，喂绿框配置全空。"""
|
||||
frame = _gray_frame(120, 200)
|
||||
cv2.rectangle(frame, (40, 30), (120, 90), _PURPLE_BGR, thickness=-1)
|
||||
|
||||
purple_mask = prompt_color_mask(frame, _pan_cfg(), _tic_cfg())
|
||||
green_mask = prompt_color_mask(frame, _cfg(), _tic_cfg())
|
||||
assert bool(purple_mask[60, 80])
|
||||
assert not green_mask.any()
|
||||
@@ -0,0 +1,179 @@
|
||||
"""两半画面之间的坐标换算：平移量 = 目标视图左上角 − 造影视图左上角（任务书 02 第一阶段第 1 条）。
|
||||
|
||||
后两条照搬旧 test_ceus_mapping（去掉锚点配置参数，断言不变）；其余是新写的手算测试。
|
||||
旧文件里依赖锚点、有效区或缩放的用例不再成立，已删（见任务书 02 执行报告）。
|
||||
"""
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from cadx.ceus import (
|
||||
CeusConfig,
|
||||
ViewRect,
|
||||
map_source_mask_to_target,
|
||||
polygon_to_mask,
|
||||
split_views,
|
||||
target_polygons_to_source,
|
||||
view_offset,
|
||||
)
|
||||
from conftest import load_ceus_section
|
||||
|
||||
|
||||
def _largest_external_contour(mask: np.ndarray) -> np.ndarray:
|
||||
"""从二值 mask 取面积最大的外轮廓，返回 ``(N, 2)`` 的 ``float32`` 顶点序列。
|
||||
|
||||
参数：
|
||||
mask: ``dtype=bool`` 的二值 mask。
|
||||
|
||||
返回：
|
||||
最大外轮廓顶点 ``(x, y)``（``float32``）；用于真往返中把 target mask 还原成多边形喂逆向。
|
||||
"""
|
||||
contours, _ = cv2.findContours(
|
||||
mask.astype(np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
|
||||
)
|
||||
assert contours, "target mask 应非空，才能提取外轮廓做逆向往返。"
|
||||
largest = max(contours, key=cv2.contourArea)
|
||||
return largest.reshape(-1, 2).astype(np.float32)
|
||||
|
||||
|
||||
def _bbox(points: np.ndarray) -> tuple[float, float, float, float]:
|
||||
"""返回顶点序列的包围盒 ``(x_min, x_max, y_min, y_max)``（往返形状比对用）。"""
|
||||
return (
|
||||
float(points[:, 0].min()),
|
||||
float(points[:, 0].max()),
|
||||
float(points[:, 1].min()),
|
||||
float(points[:, 1].max()),
|
||||
)
|
||||
|
||||
|
||||
def test_map_source_mask_to_target_translates_into_target_effective() -> None:
|
||||
"""source 视图局部 mask 经映射落入 target 有效区局部坐标，形状为 target_eff 尺寸。"""
|
||||
# source view = 整个左半区 [0,100)×[0,100)；有效区同 view（无黑边）。
|
||||
source_view = ViewRect(0, 0, 100, 100)
|
||||
target_eff = ViewRect(100, 0, 200, 100)
|
||||
|
||||
source_mask = np.zeros((100, 100), dtype=bool)
|
||||
source_mask[30:50, 20:40] = True # source 局部一个方块
|
||||
|
||||
target_mask = map_source_mask_to_target(source_mask, source_view, target_eff)
|
||||
|
||||
assert target_mask.dtype == bool
|
||||
assert target_mask.shape == (target_eff.height, target_eff.width)
|
||||
# 同尺寸有效区 + 无偏移：target 局部方块应与 source 局部方块同位置。
|
||||
ys, xs = np.where(target_mask)
|
||||
assert int(xs.min()) == 20 and int(xs.max()) == 39
|
||||
assert int(ys.min()) == 30 and int(ys.max()) == 49
|
||||
|
||||
|
||||
def test_real_roundtrip_same_size_no_offset_restores_polygon() -> None:
|
||||
"""真往返（同尺寸有效区、无 offset）：多边形→栅格化→正向→提轮廓→逆向，近似还原。
|
||||
|
||||
不再假造 target 多边形：从正向 ``map_source_mask_to_target`` 的真实输出 mask 里
|
||||
用 ``cv2.findContours`` 提最大外轮廓喂逆向，端到端串起正向(mask)→逆向(polygon)。
|
||||
栅格化 + 轮廓提取带量化误差，容差放宽到 ~2px，但 assert 包围盒近似还原。
|
||||
"""
|
||||
source_view = ViewRect(0, 0, 120, 100)
|
||||
target_eff = ViewRect(120, 0, 240, 100)
|
||||
|
||||
# 已知 source 视图局部多边形（远离边界，避免 clip 干扰往返）。
|
||||
source_polygon = np.array(
|
||||
[[20.0, 30.0], [60.0, 25.0], [70.0, 70.0], [25.0, 65.0]], dtype=np.float32
|
||||
)
|
||||
|
||||
# 正向：source 局部多边形 → 栅格化 mask → 映射到 target 有效区局部 mask。
|
||||
source_mask = polygon_to_mask(source_polygon, source_view.height, source_view.width)
|
||||
target_mask = map_source_mask_to_target(source_mask, source_view, target_eff)
|
||||
assert target_mask.sum() > 0
|
||||
|
||||
# 从真实 target mask 提最大外轮廓得 target 多边形，再逆向映回 source 视图局部。
|
||||
target_polygon = _largest_external_contour(target_mask)
|
||||
[back_to_source] = target_polygons_to_source([target_polygon], source_view, target_eff)
|
||||
|
||||
# 形状/包围盒近似还原（栅格化+轮廓量化误差 ≤2px）。
|
||||
np.testing.assert_allclose(_bbox(back_to_source), _bbox(source_polygon), atol=2.0)
|
||||
|
||||
|
||||
def _seam_frames(width: int, height: int, split_x: int) -> list[np.ndarray]:
|
||||
"""合成几帧左右分屏画面：两半是不同的常量灰度，`split_x` 处一列纯黑缝。"""
|
||||
frame = np.zeros((height, width, 3), dtype=np.uint8)
|
||||
frame[:, :split_x] = 180
|
||||
frame[:, split_x + 1 :] = 120
|
||||
return [frame.copy() for _ in range(5)]
|
||||
|
||||
|
||||
# 三种宽度（40 例里有 5 种），分屏线在正中：右半左上角 x = W/2，平移量手算为 (W/2, 0)。
|
||||
@pytest.mark.parametrize(
|
||||
("width", "expected_dx"),
|
||||
[(1856, 928), (1280, 640), (1024, 512)],
|
||||
)
|
||||
def test_view_offset_is_difference_of_view_top_left_corners(width: int, expected_dx: int) -> None:
|
||||
config = CeusConfig.model_validate(load_ceus_section())
|
||||
_, left, right = split_views(_seam_frames(width, 200, expected_dx), config.views)
|
||||
assert view_offset(left, right) == (expected_dx, 0)
|
||||
assert view_offset(right, left) == (-expected_dx, 0)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
("width", "split_x", "box_x1", "expected_target_x1"),
|
||||
[
|
||||
# 造影在左半：整帧 x=300 的框挪到右半，整帧 x = 300 + 928 = 1228。
|
||||
(1856, 928, 300, 1228),
|
||||
# 造影在左半：整帧 x=300 → 300 + 640 = 940。
|
||||
(1280, 640, 300, 940),
|
||||
# 造影在左半：整帧 x=100 → 100 + 512 = 612。
|
||||
(1024, 512, 100, 612),
|
||||
],
|
||||
)
|
||||
def test_mask_moves_by_view_offset_in_frame_coordinates(
|
||||
width: int, split_x: int, box_x1: int, expected_target_x1: int
|
||||
) -> None:
|
||||
left = ViewRect(0, 0, split_x, 200)
|
||||
right = ViewRect(split_x, 0, width, 200)
|
||||
source_mask = np.zeros((left.height, left.width), dtype=bool)
|
||||
source_mask[50:80, box_x1 - left.x1 : box_x1 - left.x1 + 40] = True
|
||||
|
||||
target_mask = map_source_mask_to_target(source_mask, left, right)
|
||||
|
||||
ys, xs = np.where(target_mask)
|
||||
assert int(xs.min()) + right.x1 == expected_target_x1
|
||||
assert int(xs.max()) + right.x1 == expected_target_x1 + 39
|
||||
assert (int(ys.min()), int(ys.max())) == (50, 79)
|
||||
|
||||
|
||||
def test_mask_moves_from_right_half_to_left_half() -> None:
|
||||
"""造影在右半：1856 宽、分屏线 928，整帧 x=1000 的框挪到左半，整帧 x = 1000 − 928 = 72。"""
|
||||
left = ViewRect(0, 0, 928, 200)
|
||||
right = ViewRect(928, 0, 1856, 200)
|
||||
source_mask = np.zeros((right.height, right.width), dtype=bool)
|
||||
source_mask[10:20, 1000 - 928 : 1040 - 928] = True
|
||||
|
||||
target_mask = map_source_mask_to_target(source_mask, right, left)
|
||||
|
||||
ys, xs = np.where(target_mask)
|
||||
assert (int(xs.min()), int(xs.max())) == (72, 111)
|
||||
assert (int(ys.min()), int(ys.max())) == (10, 19)
|
||||
|
||||
|
||||
def test_polygons_move_back_by_negative_view_offset() -> None:
|
||||
"""右半局部 (40, 30) 的点挪回左半：整帧 (968, 30) − 928 = (40, 30)；超出左半的点裁到边界。"""
|
||||
left = ViewRect(0, 0, 928, 200)
|
||||
right = ViewRect(928, 0, 1856, 200)
|
||||
polygon = np.array([[40.0, 30.0], [60.0, 30.0], [927.0, 250.0]], dtype=np.float32)
|
||||
|
||||
[back] = target_polygons_to_source([polygon], left, right)
|
||||
|
||||
np.testing.assert_allclose(back, [[40.0, 30.0], [60.0, 30.0], [927.0, 199.0]])
|
||||
|
||||
|
||||
def test_mask_crops_when_target_half_is_narrower() -> None:
|
||||
"""宽度为奇数时右半比左半窄 1 像素：不缩放，超出右半的那一列裁掉。"""
|
||||
left = ViewRect(0, 0, 101, 50)
|
||||
right = ViewRect(101, 0, 201, 50)
|
||||
source_mask = np.zeros((50, 101), dtype=bool)
|
||||
source_mask[:, 99:101] = True
|
||||
|
||||
target_mask = map_source_mask_to_target(source_mask, left, right)
|
||||
|
||||
assert target_mask.shape == (50, 100)
|
||||
assert np.where(target_mask)[1].tolist() == [99] * 50
|
||||
@@ -0,0 +1,138 @@
|
||||
"""B4 关键帧选择单元测试。
|
||||
|
||||
钉死 findings §3.3 / 参考 P:335-372 的关键帧语义：首帧强制（change_iou=1.0）、
|
||||
形状不变不新增、``change_iou < iou_threshold``（严格小于）才记新 prompt_frame、
|
||||
``min_gap_frames`` 在 IoU 判定之前抑制过密、无绿帧跳过、返回结构字段齐全。
|
||||
|
||||
测试用真实算法路径（``mask_iou``）而非 mock：构造形状渐变的布尔 mask 绿框观测序列，
|
||||
让 IoU 在阈值上下精确落点（含恰好等于阈值的边界帧）。
|
||||
"""
|
||||
|
||||
import numpy as np
|
||||
|
||||
from cadx.ceus import (
|
||||
CeusConfig,
|
||||
ColorPrompt,
|
||||
KeyframesConfig,
|
||||
PromptFrame,
|
||||
PromptObservation,
|
||||
select_prompt_frames,
|
||||
)
|
||||
from conftest import load_ceus_section
|
||||
|
||||
|
||||
def _keyframes_cfg(**overrides: object) -> KeyframesConfig:
|
||||
"""提示帧配置：config/ceus.yaml 的取值（旧测试用的是同值的默认值），按需改某一项。"""
|
||||
config = CeusConfig.model_validate(load_ceus_section())
|
||||
return config.keyframes.model_copy(update=overrides)
|
||||
|
||||
|
||||
def _square_prompt(side: int, canvas: int = 100) -> ColorPrompt:
|
||||
"""构造一个以原点为左上角、边长 ``side`` 的实心方块绿框 ``ColorPrompt``。
|
||||
|
||||
mask 为 ``(canvas, canvas)`` 布尔图，``[0:side, 0:side]`` 为 True；
|
||||
其余几何字段按方块几何自洽填充，仅 mask 参与关键帧 IoU 判定。
|
||||
"""
|
||||
mask = np.zeros((canvas, canvas), dtype=bool)
|
||||
mask[0:side, 0:side] = True
|
||||
contour = np.array(
|
||||
[[[0, 0]], [[side - 1, 0]], [[side - 1, side - 1]], [[0, side - 1]]],
|
||||
dtype=np.int32,
|
||||
)
|
||||
return ColorPrompt(
|
||||
mask=mask,
|
||||
contour=contour,
|
||||
bbox_xyxy=(0, 0, side - 1, side - 1),
|
||||
bbox_area=side * side,
|
||||
contour_area=float((side - 1) * (side - 1)),
|
||||
candidate_count=1,
|
||||
)
|
||||
|
||||
|
||||
def _obs(frame_index: int, side: int | None) -> PromptObservation:
|
||||
"""构造单帧观测：``side`` 为 None 表示无绿帧（prompt=None）。"""
|
||||
prompt = None if side is None else _square_prompt(side)
|
||||
return PromptObservation(frame_index=frame_index, prompt=prompt)
|
||||
|
||||
|
||||
def test_first_detected_frame_is_forced_prompt_frame_with_change_iou_one() -> None:
|
||||
"""首个有绿框的观测必为 prompt_frame，change_iou 恒记 1.0。"""
|
||||
observations = [_obs(0, 40)]
|
||||
prompt_frames = select_prompt_frames(observations, _keyframes_cfg())
|
||||
assert len(prompt_frames) == 1
|
||||
assert prompt_frames[0].frame_index == 0
|
||||
assert prompt_frames[0].change_iou == 1.0
|
||||
|
||||
|
||||
def test_unchanged_shape_does_not_add_prompt_frame() -> None:
|
||||
"""绿框形状完全不变（IoU=1.0 ≥ 阈值）的连续帧不新增 prompt_frame。"""
|
||||
observations = [_obs(i, 40) for i in range(5)]
|
||||
prompt_frames = select_prompt_frames(observations, _keyframes_cfg())
|
||||
assert len(prompt_frames) == 1
|
||||
assert prompt_frames[0].frame_index == 0
|
||||
|
||||
|
||||
def test_iou_below_threshold_records_new_prompt_frame() -> None:
|
||||
"""IoU 跌破阈值的帧记为新 prompt_frame，change_iou 等于实测 IoU。
|
||||
|
||||
边长 40→20 的方块：IoU = 20² / 40² = 0.25 < 0.92。
|
||||
"""
|
||||
observations = [_obs(0, 40), _obs(1, 20)]
|
||||
prompt_frames = select_prompt_frames(observations, _keyframes_cfg())
|
||||
assert [kf.frame_index for kf in prompt_frames] == [0, 1]
|
||||
assert prompt_frames[1].change_iou == 0.25
|
||||
|
||||
|
||||
def test_iou_equal_to_threshold_does_not_record_boundary() -> None:
|
||||
"""恰好 IoU == 阈值不记新 prompt_frame（严格 ``<``，钉死 < 非 <=）。
|
||||
|
||||
取 iou_threshold = IoU(40, 20) = 0.25。第二帧 IoU 恰等于 0.25，
|
||||
``0.25 < 0.25`` 为 False，故不新增。
|
||||
"""
|
||||
config = _keyframes_cfg(iou_threshold=0.25)
|
||||
observations = [_obs(0, 40), _obs(1, 20)]
|
||||
prompt_frames = select_prompt_frames(observations, config)
|
||||
assert [kf.frame_index for kf in prompt_frames] == [0]
|
||||
|
||||
|
||||
def test_min_gap_frames_suppresses_dense_prompt_frames() -> None:
|
||||
"""``min_gap_frames`` 在 IoU 判定前门控，抑制过密 prompt_frame。
|
||||
|
||||
三帧 40→20→40：默认 min_gap=1 时帧 1、帧 2 都会被记（IoU 均 0.25<0.92）；
|
||||
设 min_gap=2 后，帧 1 距首帧仅 1 < 2 被跳过，帧 2 距首帧 2 不小于 2 才比较，
|
||||
其 IoU(40,40)=1.0 ≥ 0.92 故也不记，最终仅保留首帧。
|
||||
"""
|
||||
observations = [_obs(0, 40), _obs(1, 20), _obs(2, 40)]
|
||||
|
||||
dense = select_prompt_frames(observations, _keyframes_cfg())
|
||||
assert [kf.frame_index for kf in dense] == [0, 1, 2]
|
||||
|
||||
sparse = select_prompt_frames(observations, _keyframes_cfg(min_gap_frames=2))
|
||||
assert [kf.frame_index for kf in sparse] == [0]
|
||||
|
||||
|
||||
def test_no_green_frames_are_skipped() -> None:
|
||||
"""无绿帧（prompt=None）跳过，不占用首帧强制位也不影响 frame_index 对齐。"""
|
||||
observations = [_obs(0, None), _obs(1, None), _obs(2, 40), _obs(3, 20)]
|
||||
prompt_frames = select_prompt_frames(observations, _keyframes_cfg())
|
||||
assert [kf.frame_index for kf in prompt_frames] == [2, 3]
|
||||
# 首个有绿帧（frame 2）才是强制 prompt_frame，change_iou=1.0
|
||||
assert prompt_frames[0].change_iou == 1.0
|
||||
|
||||
|
||||
def test_prompt_frame_result_structure_fields() -> None:
|
||||
"""返回项含 frame_index / bbox_xyxy / change_iou，并保留几何溯源字段。"""
|
||||
observations = [_obs(7, 40)]
|
||||
prompt_frames = select_prompt_frames(observations, _keyframes_cfg())
|
||||
prompt_frame = prompt_frames[0]
|
||||
assert isinstance(prompt_frame, PromptFrame)
|
||||
assert prompt_frame.frame_index == 7
|
||||
assert prompt_frame.bbox_xyxy == (0, 0, 39, 39)
|
||||
assert prompt_frame.change_iou == 1.0
|
||||
assert prompt_frame.bbox_area == 1600
|
||||
assert prompt_frame.mask.dtype == bool
|
||||
|
||||
|
||||
def test_empty_observations_returns_empty() -> None:
|
||||
"""空观测序列返回空 prompt_frame 列表（无绿帧可选）。"""
|
||||
assert select_prompt_frames([], _keyframes_cfg()) == []
|
||||
@@ -0,0 +1,130 @@
|
||||
"""B2 split_screen 分屏检测单元测试（旧文件的黑边剔除部分随裁黑边功能删去）。
|
||||
|
||||
钉死两件事的关键性质：
|
||||
- ``detect_split_x``：合成左右两半（中间一列黑缝）→ 切分线落在中缝 ±2px；
|
||||
全黑 / 无缝 / 切分偏出中部合理范围 → ``SplitDetectionError``（不兜底）。
|
||||
- ``view_rect``：horizontal_split 用检测到的 ``split_x`` 而非 ``W//2``；
|
||||
vertical_split / full / manual_crop 分支与参考一致；非法组合 ``RuntimeError``。
|
||||
|
||||
测试用真实数据的二次构造（合成左右分屏帧），边缘场景靠实现 raise 保证。
|
||||
"""
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from cadx.ceus import (
|
||||
SplitDetectionError,
|
||||
ViewRect,
|
||||
detect_split_x,
|
||||
view_rect,
|
||||
)
|
||||
from conftest import load_ceus_section
|
||||
|
||||
# 旧 detect_split_x 的参数默认值；新代码不设默认值，从 config/ceus.yaml 读。
|
||||
_LO, _HI = load_ceus_section()["views"]["split_search_band"]
|
||||
BAND = (float(_LO), float(_HI))
|
||||
|
||||
|
||||
def _split_frame(
|
||||
height: int,
|
||||
width: int,
|
||||
split_x: int,
|
||||
*,
|
||||
left_gray: int = 180,
|
||||
right_gray: int = 120,
|
||||
) -> np.ndarray:
|
||||
"""合成一帧左右分屏 BGR 图：左右两半各为常量灰度，``split_x`` 处一列纯黑缝。"""
|
||||
frame = np.zeros((height, width, 3), dtype=np.uint8)
|
||||
frame[:, :split_x] = left_gray
|
||||
frame[:, split_x + 1 :] = right_gray
|
||||
frame[:, split_x] = 0
|
||||
return frame
|
||||
|
||||
|
||||
# Phase 1: detect_split_x —— 中缝定位与防御
|
||||
def test_detect_split_x_finds_centered_seam() -> None:
|
||||
frames = [_split_frame(120, 200, 100) for _ in range(5)]
|
||||
detected = detect_split_x(frames, search_band=BAND)
|
||||
assert abs(detected - 100) <= 2
|
||||
|
||||
|
||||
def test_detect_split_x_finds_offcenter_seam_within_band() -> None:
|
||||
# 缝在 0.42*W 处，仍落在 band=(0.35,0.65) 内、左右宽度比合法。
|
||||
frames = [_split_frame(120, 200, 84) for _ in range(5)]
|
||||
detected = detect_split_x(frames, search_band=BAND)
|
||||
assert abs(detected - 84) <= 2
|
||||
|
||||
|
||||
def test_detect_split_x_all_black_raises() -> None:
|
||||
frames = [np.zeros((120, 200, 3), dtype=np.uint8) for _ in range(3)]
|
||||
with pytest.raises(SplitDetectionError):
|
||||
detect_split_x(frames, search_band=BAND)
|
||||
|
||||
|
||||
def test_detect_split_x_no_seam_raises() -> None:
|
||||
# 整幅均匀灰度、无暗缝：中部 band 内列均值平坦，找不到稳定低谷。
|
||||
frames = [np.full((120, 200, 3), 150, dtype=np.uint8) for _ in range(3)]
|
||||
with pytest.raises(SplitDetectionError):
|
||||
detect_split_x(frames, search_band=BAND)
|
||||
|
||||
|
||||
def test_detect_split_x_empty_frames_raises() -> None:
|
||||
with pytest.raises(SplitDetectionError):
|
||||
detect_split_x([], search_band=BAND)
|
||||
|
||||
|
||||
def test_detect_split_x_reversed_band_raises() -> None:
|
||||
# 逆序 band（lo>=hi）非法：要求 0<=lo<hi<=1，应直接报错而非兜底。
|
||||
frames = [_split_frame(120, 200, 100) for _ in range(3)]
|
||||
with pytest.raises(SplitDetectionError):
|
||||
detect_split_x(frames, search_band=(0.6, 0.4))
|
||||
|
||||
|
||||
def test_detect_split_x_out_of_range_band_raises() -> None:
|
||||
# 越界 band（hi>1）非法。
|
||||
frames = [_split_frame(120, 200, 100) for _ in range(3)]
|
||||
with pytest.raises(SplitDetectionError):
|
||||
detect_split_x(frames, search_band=(0.35, 1.5))
|
||||
|
||||
|
||||
def test_detect_split_x_width_ratio_violation_raises() -> None:
|
||||
# 把 band 放宽到几乎整宽，缝置于极偏位置（仍在 band 内），使左右宽度比越界。
|
||||
# split_x=20、W=200 → 左 20 / 右 180，比值 0.11 < 0.5 → SplitDetectionError。
|
||||
frames = [_split_frame(120, 200, 20) for _ in range(3)]
|
||||
with pytest.raises(SplitDetectionError):
|
||||
detect_split_x(frames, search_band=(0.05, 0.95))
|
||||
|
||||
|
||||
# Phase 2: view_rect —— horizontal_split 用 split_x，而非 W//2
|
||||
def test_view_rect_horizontal_split_uses_split_x() -> None:
|
||||
width, height, split_x = 200, 120, 84
|
||||
left = view_rect({"layout": "horizontal_split"}, "left", width, height, split_x=split_x)
|
||||
right = view_rect({"layout": "horizontal_split"}, "right", width, height, split_x=split_x)
|
||||
assert left == ViewRect(0, 0, split_x, height)
|
||||
assert right == ViewRect(split_x, 0, width, height)
|
||||
# 与朴素 W//2 不同，证明用了检测值。
|
||||
assert split_x != width // 2
|
||||
|
||||
|
||||
def test_view_rect_vertical_split() -> None:
|
||||
width, height = 200, 120
|
||||
top = view_rect({"layout": "vertical_split"}, "top", width, height, split_x=None)
|
||||
bottom = view_rect({"layout": "vertical_split"}, "bottom", width, height, split_x=None)
|
||||
assert top == ViewRect(0, 0, width, height // 2)
|
||||
assert bottom == ViewRect(0, height // 2, width, height)
|
||||
|
||||
|
||||
def test_view_rect_full() -> None:
|
||||
rect = view_rect({"layout": "full"}, "left", 200, 120, split_x=None)
|
||||
assert rect == ViewRect(0, 0, 200, 120)
|
||||
|
||||
|
||||
def test_view_rect_manual_crop() -> None:
|
||||
cfg = {"layout": "manual_crop", "crops": {"left": [0.0, 0.0, 0.5, 1.0]}}
|
||||
rect = view_rect(cfg, "left", 200, 120, split_x=None)
|
||||
assert rect == ViewRect(0, 0, 100, 120)
|
||||
|
||||
|
||||
def test_view_rect_unsupported_pair_raises() -> None:
|
||||
with pytest.raises(RuntimeError):
|
||||
view_rect({"layout": "horizontal_split"}, "top", 200, 120, split_x=100)
|
||||
@@ -0,0 +1,154 @@
|
||||
"""VideoFile 单元测试：probe / iter_frames / frame_times_s / 打不开报错。
|
||||
|
||||
旧测试读一段真实样例视频的字节；这里在测试里用 cv2 现造一段几十帧的小视频，按路径读。
|
||||
旧版先把字节落临时文件，那三条「临时文件有没有删」的测试随之不再适用，已删。
|
||||
"""
|
||||
|
||||
from pathlib import Path
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from cadx.ceus import VideoFile
|
||||
|
||||
_FRAMES = 40
|
||||
_WIDTH, _HEIGHT = 64, 48
|
||||
|
||||
|
||||
@pytest.fixture(scope="module")
|
||||
def sample_path(tmp_path_factory: pytest.TempPathFactory) -> Path:
|
||||
"""用 cv2 现造一段 40 帧、64×48、25 fps 的 mp4，每帧亮度不同。"""
|
||||
path = tmp_path_factory.mktemp("video") / "sample.mp4"
|
||||
writer = cv2.VideoWriter(str(path), cv2.VideoWriter.fourcc(*"mp4v"), 25.0, (_WIDTH, _HEIGHT))
|
||||
for index in range(_FRAMES):
|
||||
frame = np.full((_HEIGHT, _WIDTH, 3), (index * 6) % 256, dtype=np.uint8)
|
||||
frame[index % _HEIGHT, :, 2] = 255
|
||||
writer.write(frame)
|
||||
writer.release()
|
||||
return path
|
||||
|
||||
|
||||
def test_probe_returns_positive_metadata(sample_path: Path) -> None:
|
||||
"""probe 返回 (fps, frame_count, width, height)：字段顺序/类型/语义范围均须正确。"""
|
||||
video = VideoFile(sample_path)
|
||||
result = video.probe()
|
||||
assert isinstance(result, tuple) and len(result) == 4
|
||||
fps, frame_count, width, height = result
|
||||
# 字段类型契约（app/ports.py 的 DecodedVideo）：fps 为 float，其余为 int
|
||||
assert isinstance(fps, float)
|
||||
assert isinstance(frame_count, int)
|
||||
assert isinstance(width, int)
|
||||
assert isinstance(height, int)
|
||||
# 语义合理范围——并钉死字段顺序未颠倒（fps 是小帧率、尺寸是较大像素值）
|
||||
assert 0 < fps < 1000 # 帧率不会是几百几千；若与 frame_count(881) 颠倒会触发
|
||||
assert frame_count > 0
|
||||
assert width > 1 and height > 1
|
||||
|
||||
|
||||
def test_probe_field_order_matches_iter(sample_path: Path) -> None:
|
||||
"""probe 的 (width, height) 须与 iter_frames 实际帧的 (W, H) 一致（防字段顺序颠倒）。"""
|
||||
video = VideoFile(sample_path)
|
||||
_fps, _frame_count, width, height = video.probe()
|
||||
_idx, frame = next(video.iter_frames())
|
||||
assert frame.shape == (height, width, 3)
|
||||
|
||||
|
||||
def test_iter_frames_indices_contiguous_and_count_matches_probe(
|
||||
sample_path: Path,
|
||||
) -> None:
|
||||
"""iter_frames 产 (idx, frame)：idx 从 0 连续递增，且帧数 == probe 的 frame_count。"""
|
||||
video = VideoFile(sample_path)
|
||||
_, frame_count, _, _ = video.probe()
|
||||
indices = [idx for idx, _frame in video.iter_frames()]
|
||||
|
||||
assert indices == list(range(len(indices)))
|
||||
assert len(indices) == frame_count
|
||||
|
||||
|
||||
def test_iter_frames_yields_bgr_uint8_hwc(sample_path: Path) -> None:
|
||||
"""每帧为 (H, W, 3) uint8 的 ndarray，且 H/W 与 probe 一致。"""
|
||||
video = VideoFile(sample_path)
|
||||
_, _, width, height = video.probe()
|
||||
idx, frame = next(video.iter_frames())
|
||||
|
||||
assert idx == 0
|
||||
assert isinstance(frame, np.ndarray)
|
||||
assert frame.dtype == np.uint8
|
||||
assert frame.shape == (height, width, 3)
|
||||
|
||||
|
||||
def test_frame_times_are_strictly_increasing_and_match_frame_count(
|
||||
sample_path: Path,
|
||||
) -> None:
|
||||
video = VideoFile(sample_path)
|
||||
_fps, frame_count, _width, _height = video.probe()
|
||||
times = video.frame_times_s()
|
||||
|
||||
assert times.shape == (frame_count,)
|
||||
assert times[0] >= 0.0
|
||||
assert np.all(np.diff(times) > 0.0)
|
||||
|
||||
|
||||
def test_解两遍逐帧像素完全相同(sample_path: Path) -> None:
|
||||
"""**这条是接缝改成「打开一次」的全部理由**（design/0024）。
|
||||
|
||||
CEUS 那条线要分几趟过同一段视频，每趟只留这一趟的小结果。第二趟必须从第 0 帧重新开始,
|
||||
而且看到的像素要和第一趟一模一样——多趟方案整个建立在这个前提上。它今天成立是因为
|
||||
每趟各开一个 VideoCapture 顺序读同一个文件，但**这件事此前没有任何一处验证过**。
|
||||
|
||||
**比的是全片，而且是流式比。** 只比前几帧的话，可变帧率或者 seek 行为导致的漂移多半
|
||||
出现在后面而不是开头。两趟各自逐帧过、逐帧喂进哈希，峰值内存是两帧——**这条测试自己
|
||||
也不许把视频攒起来**，它守的正是这件事。
|
||||
"""
|
||||
import hashlib
|
||||
|
||||
def _digest(video: VideoFile) -> tuple[str, int]:
|
||||
h = hashlib.blake2b(digest_size=16)
|
||||
count = 0
|
||||
for _, frame in video.iter_frames():
|
||||
h.update(np.ascontiguousarray(frame).tobytes())
|
||||
count += 1
|
||||
return h.hexdigest(), count
|
||||
|
||||
video = VideoFile(sample_path)
|
||||
first = _digest(video)
|
||||
second = _digest(video)
|
||||
|
||||
assert first[1] > 0
|
||||
assert first == second, "同一段视频解两遍，逐帧像素必须完全相同"
|
||||
|
||||
|
||||
def test_两趟各自从第零帧开始互不干扰(sample_path: Path) -> None:
|
||||
"""两趟同时在飞时各读各的游标。
|
||||
|
||||
**断言比的是像素，不是 iter_frames 自己报的下标**——那个下标是本模块里一个 Python
|
||||
计数器，就算内层真把外层的读位置搅乱了，它照样从 0 数上去。
|
||||
"""
|
||||
video = VideoFile(sample_path)
|
||||
outer = video.iter_frames()
|
||||
_, outer_frame_0 = next(outer)
|
||||
# 内层整个跑起来，中途丢掉——它不该动到外层的游标
|
||||
inner = video.iter_frames()
|
||||
_, inner_frame_0 = next(inner)
|
||||
_, inner_frame_1 = next(inner)
|
||||
del inner
|
||||
_, outer_frame_1 = next(outer)
|
||||
|
||||
reference = [frame for _, frame in zip(video.iter_frames(), range(2), strict=False)]
|
||||
|
||||
assert np.array_equal(inner_frame_0, outer_frame_0), "内层没有从第 0 帧开始"
|
||||
assert np.array_equal(outer_frame_1, inner_frame_1), "外层被内层带跑了：它接着读到的不是第 1 帧"
|
||||
assert len(reference) == 2
|
||||
|
||||
|
||||
def test_bad_bytes_raise_not_silent(tmp_path: Path) -> None:
|
||||
"""坏文件无法打开 → 报错（不静默返回空 probe / 空迭代器）。"""
|
||||
garbage = tmp_path / "garbage.mp4"
|
||||
garbage.write_bytes(b"this is not a video file" * 64)
|
||||
video = VideoFile(garbage)
|
||||
|
||||
with pytest.raises(RuntimeError):
|
||||
video.probe()
|
||||
with pytest.raises(RuntimeError):
|
||||
list(video.iter_frames())
|
||||
@@ -16,6 +16,8 @@ name = "cadx"
|
||||
version = "0.0.0"
|
||||
source = { editable = "." }
|
||||
dependencies = [
|
||||
{ name = "numpy" },
|
||||
{ name = "opencv-python-headless" },
|
||||
{ name = "openpyxl" },
|
||||
{ name = "pillow" },
|
||||
{ name = "pydantic" },
|
||||
@@ -35,6 +37,8 @@ dev = [
|
||||
|
||||
[package.metadata]
|
||||
requires-dist = [
|
||||
{ name = "numpy", specifier = ">=2.5.4" },
|
||||
{ name = "opencv-python-headless", specifier = ">=5.0.0.93" },
|
||||
{ name = "openpyxl", specifier = ">=3.1.5" },
|
||||
{ name = "pillow", specifier = ">=12.3.0" },
|
||||
{ name = "pydantic", specifier = ">=2.14.1" },
|
||||
@@ -249,6 +253,98 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/54/c8/12811c9b48fde162bb72b6f2e78fada9a247a09d7bb5be2050a5d099c77b/nodeenv-1.11.0-py2.py3-none-any.whl", hash = "sha256:edaa16e6c14d7cf395d75d4bbd5a26390f4dc06501a33b4e76282b02cc688a25", size = 34277, upload-time = "2026-09-26T11:29:19.933Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "numpy"
|
||||
version = "2.5.4"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/95/b0/c7453d0b6e2073c3264468b106ee1563750cecc910965e67357e3698c83e/numpy-2.5.4.tar.gz", hash = "sha256:9a94cf751c9ad8ebaa835bcd3d40dacf8534ad086b88c38029b65123c7999d2a", size = 20866315, upload-time = "2026-10-10T20:05:31.422Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/d0/97/ba2074e92b7befea137e77ea8471e768bbd87c339b7e8c9f5a931949f977/numpy-2.5.4-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:c6342f54c67093cae5c0227eb0eb772fdb79f2a2c37a6eb278b9909ee06aa356", size = 17001609, upload-time = "2026-10-10T20:02:40.843Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/ff/a9/bac826765e971d8e16e2064e9ac7525fd69b40ac17c905033a7f5442023f/numpy-2.5.4-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:b11e8fda06a7d69f15ebf542660b74466c2e51094800c1fb794f47ad4faeef17", size = 12015718, upload-time = "2026-10-10T20:02:43.45Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/31/2f/5ea3570fcb8ccd0882bea99436a513b2c85dad8f774a2057849130a8fb99/numpy-2.5.4-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:9cb18a327b49c5c337f972b03682f6a49855525faaf3c0d3e9c96cd0fd8880a8", size = 5451717, upload-time = "2026-10-10T20:02:46.169Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/34/f2/b4fc1bafca03868220b5eaf729d2f21ebd7d7b151c0f9e144fe212bbca35/numpy-2.5.4-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:aec3fc4b32ff82421274f5d205c559c51c840c8df66a78efd7f3612dd005a26a", size = 6789926, upload-time = "2026-10-10T20:02:48.139Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/dc/96/8319e2457ae4333c62c815c7006b869a4f60985c1e01024c2f8c6c040fe5/numpy-2.5.4-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:fe4d21ab149f15e4e6043dfb0de87e6e5f34ac176cde83060e9802981fca2ac2", size = 15695312, upload-time = "2026-10-10T20:02:50.115Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/43/a3/c799c62e19c337e6d3770b08e475887fb30ce8477d3c09efca6b2f0228a6/numpy-2.5.4-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:fbde6962867ee75b48b0ee29b2b9372ec5d617799dbaf38e82dc0596f2f7738a", size = 16727283, upload-time = "2026-10-10T20:02:53.186Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/39/6b/3604e53fb00314d0dc1b94ec9125a1484f649c0a17480b1f0f0c7a9d6250/numpy-2.5.4-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:381a7a3d2e65e64c0ec302795ab9dc12bb1e73f150904699c153716177eebdaf", size = 17047890, upload-time = "2026-10-10T20:02:56.038Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/4a/7a/e8b58a5289a0d464c52885de47c35a935cdd70c03a4c3ab94a5126416dd0/numpy-2.5.4-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:b89d0aaae2fe498c648f4c4795c084db535af5bd98ef942b2a3681fb74ce8645", size = 18485839, upload-time = "2026-10-10T20:02:59.018Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/6f/c9/47094f597015009f310b8c900def59065ef1ff5a6fe7b51fc65ec58ec2c6/numpy-2.5.4-cp312-cp312-win32.whl", hash = "sha256:9968ab7e49b93ac6e1c3b2239732183152c9150f16308d30b66a372cffe3483c", size = 6138936, upload-time = "2026-10-10T20:03:01.626Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/12/33/fefe62073dc8acfd0f2b9ed7c003af2f50aa61555e113e6db02b8f79f145/numpy-2.5.4-cp312-cp312-win_amd64.whl", hash = "sha256:a7b1b6353e36a7e50de2973a38d705c88ee93adcf120673cee7f45a4a3fa223a", size = 12573091, upload-time = "2026-10-10T20:03:04.349Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/1a/07/161270b0c2eec56e4c905f6d6d22e1b836887b2cb189d3f5820aa588e9dd/numpy-2.5.4-cp312-cp312-win_arm64.whl", hash = "sha256:aa1cce2ff3f8d953de38b76bf44602caeb69f101430208f64a10067f7cb4b1d3", size = 10521630, upload-time = "2026-10-10T20:03:06.767Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/67/14/1c3ee0118a8fce08565a5d8482631608426a33af10a01077fada5dc7c119/numpy-2.5.4-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:2377da2dd3ba2c1200956acbab2a358c83b8e1f8531191672d1cd6ad83250d53", size = 16997729, upload-time = "2026-10-10T20:03:09.291Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/83/8c/b0ea9477fb1f0d4484bbc5cba21678cc9969704d8d7f3f158d1db35f8e14/numpy-2.5.4-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:7415db95818b39ec475a5eea54d9e3b6bc83e3912158e46da3438cdce399804d", size = 12009826, upload-time = "2026-10-10T20:03:11.946Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/e2/84/6a3d75b3ba3dfe84ac0053450753d1e6d250a8bf80f66474cc46d1fb643f/numpy-2.5.4-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:6d6a71b9d9a97c03633aa12565ef2825ffa036cc1d99cfd50dacf0f128af4fe2", size = 5445803, upload-time = "2026-10-10T20:03:14.329Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/61/18/bb993f267ca20b376e07092a16793a5b31ed3138751e9ba480011a14d742/numpy-2.5.4-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:d8200f16437b289a5bb927c6e184eccc3e8389bc0070fea4cd5b9e13c1757959", size = 6786220, upload-time = "2026-10-10T20:03:16.602Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/db/b6/135bb0953b61dc21c6cafa14b424ae666944e4899cf140e00c2b322a1a45/numpy-2.5.4-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1c2e71b04c6cad90026e544501bbe0ab9290fa8a4d845e7e8c0d124fb429c988", size = 15689178, upload-time = "2026-10-10T20:03:18.721Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/da/24/3bd070f3269dc609d8f26b2643f62ef91bb415841c0b294805aaf7fe06da/numpy-2.5.4-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6ffa07666f8da0eef81d149934a626d0d95fbd6838432a33e66245423a9062c0", size = 16718044, upload-time = "2026-10-10T20:03:21.386Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/c7/8e/9d15bd356b0a019c965312b1a3c6a727cac4cae5bc40045fbc12ce4cff9c/numpy-2.5.4-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:2fa3328f784fc8277fc48026f6cad516f5c561c5d8e2e39b3c9e0c8f23223b34", size = 17048364, upload-time = "2026-10-10T20:03:24.468Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/dc/fe/9d5b560db964f15871885f2250795d15945f8699e17ef90c0c2ff4c875b2/numpy-2.5.4-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:b86966fbe4ad7de710422175572bcdc75fdedadfb54bc6fab7deabccddd7780b", size = 18474904, upload-time = "2026-10-10T20:03:27.895Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/e9/98/d27552990f1bd611ef3e7466adadc78312ea2df63b83aad47fdc3d3ca8df/numpy-2.5.4-cp313-cp313-win32.whl", hash = "sha256:5258bc06526964be5face2fc6f756857a3f24f21ec3e72ca131337a75b165d6c", size = 6134537, upload-time = "2026-10-10T20:03:30.511Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/90/8c/140a40398a66b4471211be1affdb6ed24c486d581bd28d07b7f2fcb69540/numpy-2.5.4-cp313-cp313-win_amd64.whl", hash = "sha256:8b4d2fd2d34e5f8c9235ee787de5631a37a28402b15cb80814df973d2be54129", size = 12566113, upload-time = "2026-10-10T20:03:32.612Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/34/52/01d205e5e8ccb27b2b0b141e801f22b830198c979111b0fa44771438d9a9/numpy-2.5.4-cp313-cp313-win_arm64.whl", hash = "sha256:bc39ac66a7a9a3fbd6134fda43136b60ffde99c8f4501e64e0d2b24da137babf", size = 10519523, upload-time = "2026-10-10T20:03:35.163Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/99/ba/005cb5edd580d2f84d7ca3206b92dc17d4388e56e6f87ffe8f2762f83139/numpy-2.5.4-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:c668b2f0d651605b58892644b0e302c7157f7159544227758c896982ef384b18", size = 17005499, upload-time = "2026-10-10T20:03:37.961Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/f3/49/fee7587c33ee35f7977f9051d7f2023d4e7246d62710c80f20c2361ea232/numpy-2.5.4-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:ffa6ce09a1c6a08e9667dd9c97aa0b14184e8d18f2a14b78b2a2328c9147f076", size = 12019666, upload-time = "2026-10-10T20:03:40.606Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/d5/b2/c6ce165acffceb15a82c07b9cc77d391f86b3f379ba62911908ae5d34b91/numpy-2.5.4-cp314-cp314-macosx_14_0_arm64.whl", hash = "sha256:956555e0603a4d38019ae6925711cb9dc43195c076a928accf7ea5d50bddfe53", size = 5455617, upload-time = "2026-10-10T20:03:43.138Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/77/7f/dd85ce260a669a89be06842cf355d7353a33e6cfbc590fb8ebb947d88dc9/numpy-2.5.4-cp314-cp314-macosx_14_0_x86_64.whl", hash = "sha256:2c2c4afffdeb7920e445028dd71eb932cac3e704792e964bc2a232426d4f1255", size = 6791932, upload-time = "2026-10-10T20:03:44.874Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/63/d6/34b0a2b0741386a63025a65a2c09caaaaaad6d0ca95b66cd65c30dd7fcb5/numpy-2.5.4-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4054173604cd8658796053f1f3bc0befb68ec1c0762c57fdad61e199256a8617", size = 15710899, upload-time = "2026-10-10T20:03:46.839Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/16/d5/928078d2b28f26829b138b4a6c3980045022fb409f570657a224ae60ef4e/numpy-2.5.4-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d549420b8858885cea8838a727842249218b9c1da24dd517e25c9c7a948310a3", size = 16721710, upload-time = "2026-10-10T20:03:49.489Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/f9/cf/673fd1b8f4cd78eb6320e87ec4c90ac19c095644259e3749853a405c70f4/numpy-2.5.4-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:823874a507a84af050493b622affde94b6f7c3a0dc22cb2801381bc03b871c00", size = 17066182, upload-time = "2026-10-10T20:03:52.25Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/f3/92/a77b5061b1b3e2643928c37976d79ee173e1b171ed158b7a3c61056b41bc/numpy-2.5.4-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:4e263278bfb5ee6409db8aedbc4cc32973b1b82bc1e8d3c668551d04d83a7e37", size = 18480315, upload-time = "2026-10-10T20:03:55.39Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/bb/1d/1486ef3d3fb2279fd93c4c43c1bbbf1ca389a19816696684409f71babaab/numpy-2.5.4-cp314-cp314-win32.whl", hash = "sha256:cfd73180400042a7c532d30c5e287bdd03c59ff9ee1b4c0316af0539e29dfe23", size = 6185739, upload-time = "2026-10-10T20:03:58.186Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/52/9a/e1e512ebc948d5b9dd33b08736760f0ebbed2848fd4eda1f553088a6dcee/numpy-2.5.4-cp314-cp314-win_amd64.whl", hash = "sha256:2ca144f15135b6212a5c47b1e2aeca6e412f102f95a2d5d88d8aec77eb255de3", size = 12703552, upload-time = "2026-10-10T20:04:00.28Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/2c/05/de709a982d7bbcd688a3fad71f002e9ff80c2db39e03ee726609b610f1d1/numpy-2.5.4-cp314-cp314-win_arm64.whl", hash = "sha256:468397ba3c64427474706e5c9123fe266395496714dc684294eac75cd4930d1e", size = 10803901, upload-time = "2026-10-10T20:04:02.659Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/13/34/083570ada3bb2a30fbe5d77c8c6fef9141144a15d33e6f793a67e9749ab8/numpy-2.5.4-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:1ef3aa6d7e29bb13677323114280b05acc57607fa2300e66432d665d5418a162", size = 12138695, upload-time = "2026-10-10T20:04:05.012Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/94/06/1f9c24db48eef0c2d1207e3b11fffb0478e39dfd8c1e1be7476936885eed/numpy-2.5.4-cp314-cp314t-macosx_14_0_arm64.whl", hash = "sha256:98b053943e5a0474ec0da309d2cb9d3f18ea57f8a2067c2ab7b5f763d1068380", size = 5574615, upload-time = "2026-10-10T20:04:07.316Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/da/0f/593fba2e1560e949123bc7d2fc48b5893d56e58cd4bd5a273d2fbf60b220/numpy-2.5.4-cp314-cp314t-macosx_14_0_x86_64.whl", hash = "sha256:b64a85f40e154983960a4167d4c1d57a50c7f109b3d3264a3a984154e90a8454", size = 6889383, upload-time = "2026-10-10T20:04:09.918Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/eb/9f/b799dfdce4e05e80ed4bc815c71ff343a11533b2c0ffc221cae8538cda63/numpy-2.5.4-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a813ed7719bf45463c51779e6a98d0385fe905e48447526938a4b8337333d551", size = 15753763, upload-time = "2026-10-10T20:04:12.278Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/34/88/16c5f12f86f5ad2817c4d103205131fc6c8acb3d1878af05a1a4f23ec859/numpy-2.5.4-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c9b80cdf5cedba0e90d93fa5f9a333c4d65bd545cd669b71bb97ce2b703c9d73", size = 16757212, upload-time = "2026-10-10T20:04:14.799Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/ff/4f/a1fe40e18a898e6a5089f4f0d891f0a493eb0574d5b34458f0fbe5aa3e5c/numpy-2.5.4-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:2199ed071f460487c8db2c0e5c0b564494190edb4772fe80f9aad88b2604def5", size = 17116471, upload-time = "2026-10-10T20:04:17.58Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/aa/46/e923a11c78e65c1722e7aaad817c06bd591324174b9d28ce5d31eee4d432/numpy-2.5.4-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:64f9c9878c1938476365e11ccfb6b770f3b9e5f045ccddc514235041e6959365", size = 18524063, upload-time = "2026-10-10T20:04:20.365Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/5a/fa/84ab064514440c1f64a1b21088f2c82756defdd05e07c75ab233899565b2/numpy-2.5.4-cp314-cp314t-win32.whl", hash = "sha256:64d1c8ac28a4077cf987e0a71a7a0ef7e2df70722f07f0baa42dbb7eb6938647", size = 6340926, upload-time = "2026-10-10T20:04:22.865Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/7e/7e/6cd886876f435b10685db9b9f7eeb70356f99e052116f4e5f11c5792c714/numpy-2.5.4-cp314-cp314t-win_amd64.whl", hash = "sha256:067374eb538c34c745436365cf7b0112595c1d326f21ce4ff340f61230239fbb", size = 12901584, upload-time = "2026-10-10T20:04:24.99Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/38/1b/3c1684f6a06f7307f2335fca6e486cb162847fb97e91d65f8eb5cabad213/numpy-2.5.4-cp314-cp314t-win_arm64.whl", hash = "sha256:e94aef2c639da4a960ad0db8e06471208d8589974953d78b61d345b4eb99e394", size = 10891152, upload-time = "2026-10-10T20:04:27.52Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/08/f4/3224deff3af2bef6bc0b175369698d8cb348f3d91d9bb0286cd5c9eae9e0/numpy-2.5.4-cp315-cp315-macosx_10_15_x86_64.whl", hash = "sha256:8dddfbee2e68d26d0d7d7d9cb247b1fd4409241cce32d815a11d97ec2cfde179", size = 17003231, upload-time = "2026-10-10T20:04:30.021Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/be/75/fee0b8c6d94b44b2fdfae74f6a4ad5a138739589a8aebaec28ce4e713ed5/numpy-2.5.4-cp315-cp315-macosx_11_0_arm64.whl", hash = "sha256:81e3420b27048b65eb14c3acf0c174a8cb0e023277716110347d2dcb26026dad", size = 12018300, upload-time = "2026-10-10T20:04:32.519Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/47/c0/d0b335a499a04b65f532c3f034346ef390f81299060f928492dabc1e0272/numpy-2.5.4-cp315-cp315-macosx_14_0_arm64.whl", hash = "sha256:0b4724a19de67bea8cfc4970798efa78bcbbe2ac2613cfac16721a42d44de2a5", size = 5454250, upload-time = "2026-10-10T20:04:34.943Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/5a/0e/461b3783c03d668052e6a21b01b673db6ffcb7831fd32d9aa5368c1cd426/numpy-2.5.4-cp315-cp315-macosx_14_0_x86_64.whl", hash = "sha256:2132418bf8dd124a427ca9e6a1daf9ee1a87185344c95119ceae868b99466da1", size = 6789644, upload-time = "2026-10-10T20:04:37.258Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/b3/02/5dad269b02166965a7b4ca14adaddd75dbee0de42435bfecf561b84ba5a6/numpy-2.5.4-cp315-cp315-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:325518d4245b9e331387702aa58c2ce1dc4cdcbb41dfb4ccd5dcbc7e08db1266", size = 15704353, upload-time = "2026-10-10T20:04:39.616Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/93/3a/01360c8036822ed9f7aa32189a77d1476567ec1e8e1383522389e4faac45/numpy-2.5.4-cp315-cp315-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:56733449d2544178beaa4545cee357370440cf056c197f9c7bfb19dbfdd0e86d", size = 16718648, upload-time = "2026-10-10T20:04:42.383Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/7d/5c/b863a2c093c4d6f21a597fcaf24ead0835c09ab16a8312d5a5a8868af683/numpy-2.5.4-cp315-cp315-musllinux_1_2_aarch64.whl", hash = "sha256:5ec3753760c1a6d8bb91200666e545c3a9728e6269dfb5d6ce02340996698aa3", size = 17059053, upload-time = "2026-10-10T20:04:44.976Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/0a/60/ced4f57f9a1258a0af74f17cb0b0c2700b5c67cd6678823c803b263e4df3/numpy-2.5.4-cp315-cp315-musllinux_1_2_x86_64.whl", hash = "sha256:b1185012870173de7ae33d370bd45b1cf5baee747ea4b97036b65f4e93016877", size = 18477406, upload-time = "2026-10-10T20:04:47.863Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/f9/bd/0ef22dafaafcc7d4bb3ca26b8d2afbd55dedad8eaba99a8c864e1997456f/numpy-2.5.4-cp315-cp315-win32.whl", hash = "sha256:298eca75243f2cbbfdb460560b9fb2a1792a33cf2ab4286efd43d92e8d3df508", size = 6185133, upload-time = "2026-10-10T20:04:50.467Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/50/bc/d2651b155ecc608a77e6f4d15495c11f14f19bb98f8bf0c5b0d38f86dda1/numpy-2.5.4-cp315-cp315-win_amd64.whl", hash = "sha256:332f3378fe077dd850e677ec01bdcc4f22368fb5d50ef10b2c79230b1bf5a592", size = 12703085, upload-time = "2026-10-10T20:04:52.63Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/dc/d2/45e404f8abb26fb9eda12b94012936873e827b1be76f2ee7890be128312e/numpy-2.5.4-cp315-cp315-win_arm64.whl", hash = "sha256:d4cccbbc78717966f764cd3af4fb70276fa01fc7a2688af11c78901fa5c04f05", size = 10801451, upload-time = "2026-10-10T20:04:55.677Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/c6/c3/2ae14e09cfdb67dc187a342e15308a21c15bf4d2071f8079e6aee5fe56dc/numpy-2.5.4-cp315-cp315t-macosx_10_15_x86_64.whl", hash = "sha256:950ea81d57ef070665581b6e1b5f6a029306423cd1739c5b95fe78aa30db6b9d", size = 17097121, upload-time = "2026-10-10T20:04:58.403Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/f5/cf/305ae624ef8a039414317224abe9ec9c2fe7ea3c2e1cf204d43ff6b2ffb9/numpy-2.5.4-cp315-cp315t-macosx_11_0_arm64.whl", hash = "sha256:c05ede731b03fb1b7591faca9389ade3267d2bddf1ad8882bb3f2cc5e101694f", size = 12135439, upload-time = "2026-10-10T20:05:01.65Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/a9/a8/f75c63813aef95827bb2c0d13b12803016853056e8792c280058cdbfe783/numpy-2.5.4-cp315-cp315t-macosx_14_0_arm64.whl", hash = "sha256:5fbf7141bbfd63aea22f435c9062a032b9ea0082fe9845dad7f021d3f1234e71", size = 5571451, upload-time = "2026-10-10T20:05:04.135Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/6f/0f/f17763f983868b5c49b4101ebd7e00760bd1769478a6bb6a8de6e085bbac/numpy-2.5.4-cp315-cp315t-macosx_14_0_x86_64.whl", hash = "sha256:3573cd22564692a5b899ec344e5d5b9cc4576f2985b96f22af3564ed54f2710f", size = 6883356, upload-time = "2026-10-10T20:05:06.249Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/67/a7/8af04c5a79e047996cfa38854dcfbececdd0343a7c933a46fdd03ef6f5da/numpy-2.5.4-cp315-cp315t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6c109eac9cd439193678f69d70733c1108487546ca8eafc107b510ae10c1aecd", size = 15750991, upload-time = "2026-10-10T20:05:08.376Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/57/7a/648254290d0c504faa8f2d07aa206660c728802c781a6f3fc68ab7cb5d71/numpy-2.5.4-cp315-cp315t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:80d6ef6e8620eb2c2b4c4caad50b5935d6db3cde2d51581b55dcc79e14016d1d", size = 16757675, upload-time = "2026-10-10T20:05:11.393Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/b8/fe/4a8c3cdb0c70400cfe4c5bec42d3099a5673802a95064614b33e07b82aa1/numpy-2.5.4-cp315-cp315t-musllinux_1_2_aarch64.whl", hash = "sha256:77045a4b175bbf5316ec08003880804336c78f92281a1b72222b274ea85ec5ac", size = 17113846, upload-time = "2026-10-10T20:05:14.49Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/1b/7e/619692bb67778702c0e9eb2d468568a7573f4e269386ea61aed01ee4e557/numpy-2.5.4-cp315-cp315t-musllinux_1_2_x86_64.whl", hash = "sha256:0f02a46e49cfb6c73bdb7aea1c0d3461dbae9aba613542b65f657cd3d17b9fab", size = 18522915, upload-time = "2026-10-10T20:05:17.33Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/b7/b5/4da41c328788f575838f97a098fe8ca691ebc6f6fd73ad4a262ee40b184d/numpy-2.5.4-cp315-cp315t-win32.whl", hash = "sha256:ad62a416ddcf863bf44bba76fbf6b53366ab0692e294f51cae4b5fbe0d246788", size = 6335804, upload-time = "2026-10-10T20:05:19.921Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/98/94/6482ddfa3d312490cb9358f375bf2ad56427dbea8769187158e94d653753/numpy-2.5.4-cp315-cp315t-win_amd64.whl", hash = "sha256:38f47be9f74ab870d2633b5456ae519c43758a8d1fd05342f0ce4ecc034396ee", size = 12890095, upload-time = "2026-10-10T20:05:21.875Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/48/7f/c2d1b436b6e7cfebac140c2579a298344b85f2991a2ce5c3615cefb29400/numpy-2.5.4-cp315-cp315t-win_arm64.whl", hash = "sha256:7a14a461d9340f1b46b8648578aed9cdb8b3b018a8fac6c1dde2c9192a01a87f", size = 10883718, upload-time = "2026-10-10T20:05:28.547Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "opencv-python-headless"
|
||||
version = "5.0.0.93"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "numpy" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/1d/99/76b7c80252aa83c1af16393454aafd125a0287101afe8deb0a6821af0e30/opencv_python_headless-5.0.0.93.tar.gz", hash = "sha256:b82f9831daab90b725c7c1ee1b36cb5732c367096ac76d119e64e14eb70d5f3c", size = 81817738, upload-time = "2026-07-02T07:01:06.039Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/53/7c/8c8097891c509d98cd128493835c95631c80be6a8f37ed9d25716c2e16f1/opencv_python_headless-5.0.0.93-cp37-abi3-macosx_13_0_arm64.whl", hash = "sha256:030ca5e0837a2963ab36ef896baa9767eb8d2b83353fb28af5a521e40dd8756f", size = 48322581, upload-time = "2026-07-02T05:50:34.207Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/90/8c/eab2ad388c3cbab2a350c10c2ef19ce6bd099240afc31789032c996bab52/opencv_python_headless-5.0.0.93-cp37-abi3-macosx_14_0_x86_64.whl", hash = "sha256:1e55af3abfb462eeeabe5c775f12bdb36216d8a93a3583d69e6bd6e1d6ba7d00", size = 34782894, upload-time = "2026-07-02T05:51:39.856Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/ec/78/afca939f40ffe2b2380bfa86f812b2f7d4acc5a27b27dc41b49cad7ce7b4/opencv_python_headless-5.0.0.93-cp37-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:10818d91510e05c04568ae12b5cd120779c70c01bf897b001a6221fe430df80f", size = 36521085, upload-time = "2026-07-02T06:55:24.429Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/2b/97/8170e9819764c47e436c130d3ff6cfb73b58f923eae9d3a03d8982b04aec/opencv_python_headless-5.0.0.93-cp37-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:09a872a157c1376ab922a69bbf22f9a95bcc7b658a9d8b436a60212b02b2eeb4", size = 56563598, upload-time = "2026-07-02T06:55:47.355Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/3a/98/1a28a7101e31801042b3098871a74b76c61581d328ef40774ff4edb53a56/opencv_python_headless-5.0.0.93-cp37-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:840bd717c21e5c11cadadc022a823315ea417f961213d06b4df010e019eb16f4", size = 39648433, upload-time = "2026-07-02T06:56:04.255Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/9b/21/f6ef335f6e65724aa78b8d792b48d40a48c381715f1e62f5a5049e09d07e/opencv_python_headless-5.0.0.93-cp37-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:ed709fdf9aa0bd1f2ed8549e71d19449b03a675bb581eb292285f6861953be37", size = 61204038, upload-time = "2026-07-02T06:56:41.823Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/d0/8f/b8756467ea991449a293797f6b3fa80fcfdd29598a0a60d1cd5715b96e61/opencv_python_headless-5.0.0.93-cp37-abi3-win32.whl", hash = "sha256:c6bcd96b185975ea240d22cfdb15a1f6d080cc95264cfbe2621f21bb144d89b9", size = 35411237, upload-time = "2026-07-02T05:50:12.901Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/b8/88/763b967f7efd7226b82c9fae16d560cba049b1f0c036647e65c610fd636e/opencv_python_headless-5.0.0.93-cp37-abi3-win_amd64.whl", hash = "sha256:829717b6a95554f273e49e357cee3b3a2a26b6f4842fbc1bed2b45bdd8f87e0e", size = 43825962, upload-time = "2026-07-02T05:50:09.627Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "openpyxl"
|
||||
version = "3.1.5"
|
||||
|
||||
Reference in New Issue
Block a user