8a4bad99d3
- 新增 app/baked/algorithm|pipeline，非部署参数不再走 env；Settings 保留 DB/HTTP/RTSP/海康/百度/MinIO/Demo - 移除 init_db_schema 与 reload 配置；main 仅 check_database；start*.sh 在 uvicorn 前执行 alembic upgrade head - 依赖 psycopg[binary] 供 Alembic 同步 URL；alembic/env 注释与预发清单更新 - 撕段门控消费管线、各视频/语音/归档调用改为 baked - 百度环境变量仅 BAIDU_APP_ID、BAIDU_API_KEY、BAIDU_SECRET_KEY 与 BAIDU_* 超时/ASR；人脸脚本与 baidu_speech 文案同步 - 全量单测与 .env.example 更新；.gitignore 忽略 refs/（本地权重/视频不入库） Made-with: Cursor
93 lines
2.5 KiB
Python
93 lines
2.5 KiB
Python
"""手部框、撕动作几何与概率（从离线 tear 脚本抽离，不依赖 OpenCV 绘制）。"""
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from __future__ import annotations
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from itertools import combinations
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from typing import Any
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import numpy as np
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from ultralytics import YOLO
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def union_boxes(boxes: list[list[float]]) -> list[float]:
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x1 = min(b[0] for b in boxes)
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y1 = min(b[1] for b in boxes)
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x2 = max(b[2] for b in boxes)
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y2 = max(b[3] for b in boxes)
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return [x1, y1, x2, y2]
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def pad_box(
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xyxy: list[float],
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img_w: int,
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img_h: int,
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pad_ratio: float = 0.30,
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) -> tuple[int, int, int, int]:
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x1, y1, x2, y2 = xyxy
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bw, bh = x2 - x1, y2 - y1
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px, py = bw * pad_ratio, bh * pad_ratio
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return (
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max(0, int(x1 - px)),
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max(0, int(y1 - py)),
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min(img_w, int(x2 + px)),
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min(img_h, int(y2 + py)),
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)
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def collect_hand_boxes(det_model: YOLO, boxes) -> list[list[float]]:
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names = det_model.names
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out: list[list[float]] = []
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for box in boxes:
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cid = int(box.cls[0])
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label = names.get(cid, "")
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if label == "hand":
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out.append(box.xyxy[0].tolist())
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return out
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def box_edge_distance(a: list[float], b: list[float]) -> float:
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dx = max(0, max(a[0], b[0]) - min(a[2], b[2]))
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dy = max(0, max(a[1], b[1]) - min(a[3], b[3]))
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return float((dx**2 + dy**2) ** 0.5)
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def box_avg_width(boxes: list[list[float]]) -> float:
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if not boxes:
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return 0.0
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return sum(b[2] - b[0] for b in boxes) / len(boxes)
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def find_tearing_pair(
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hand_boxes: list[list[float]],
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gap_ratio: float = 1.5,
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) -> tuple[list[float], list[float]] | None:
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if len(hand_boxes) < 2:
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return None
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avg_w = box_avg_width(hand_boxes)
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threshold = avg_w * gap_ratio
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best_dist = float("inf")
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best_pair: tuple[list[float], list[float]] | None = None
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for a, b in combinations(hand_boxes, 2):
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d = box_edge_distance(a, b)
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if d < best_dist:
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best_dist = d
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best_pair = (a, b)
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if best_pair is not None and best_dist <= threshold:
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return best_pair
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return None
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def prob_tearing(tprobs, tear_names: dict[Any, str]) -> float:
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if tprobs is None:
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return 0.0
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data = tprobs.data
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if data is None:
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return 0.0
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d = data.detach().float().cpu().numpy().ravel()
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for i, name in tear_names.items():
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if name == "tearing":
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ii = int(i)
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if 0 <= ii < len(d):
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return float(d[ii])
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return 0.0
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