删除 2.py

2.py

@@ -1,105 +0,0 @@
-import numpy as np
-import cv2
-from ultralytics import YOLO
-
-def point_to_line_distance(point, line_start, line_end):
-    # 线的方向向量
-    line_dir = np.array(line_end) - np.array(line_start)
-    # 点到线起点的向量
-    point_to_start = np.array(point) - np.array(line_start)
-
-    # 计算叉积（只适用于二维空间）
-    cross_product = line_dir[0] * point_to_start[1] - line_dir[1] * point_to_start[0]
-
-    # 叉积的符号会表明点在直线的哪一侧
-    return cross_product
-
-def check_crossing_detection_box(box, start_point, end_point):
-    x1, y1, x2, y2 = box
-    points = [(x1, y1), (x2, y1), (x1, y2), (x2, y2)]
-    distances = [point_to_line_distance(p, start_point, end_point) for p in points]
-
-    # 检查是否有正负距离，即检测框的点分别位于斜线两侧
-    crossing = any(dist < 0 for dist in distances) and any(dist > 0 for dist in distances)
-    return crossing
-
-def process_frame(frame):
-    height, width = frame.shape[:2]
-    desired_height = 500
-    scale = desired_height / height
-    resized_frame = cv2.resize(frame, (int(width * scale), desired_height))
-    frame = resized_frame
-
-    height, width = frame.shape[:2]
-    start_point = (0, height // 2)
-    end_point = (width, height // 3)
-
-    cv2.line(frame, start_point, end_point, (255, 0, 0), 3)
-
-    results = model(frame, verbose=False)
-    for result in results:
-        boxes = result.boxes
-        classes = model.names
-        for box in boxes:
-            x1, y1, x2, y2 = box.xyxy[0].tolist()
-            class_id = int(box.cls.item())
-            if classes[class_id] == 'person':
-                cv2.rectangle(frame, (int(x1), int(y1)), (int(x2), int(y2)), (0, 255, 0), 2)
-                if check_crossing_detection_box((x1, y1, x2, y2), start_point, end_point):
-                    cv2.rectangle(frame, (int(x1), int(y1)), (int(x2), int(y2)), (0, 0, 255), 2)
-
-    return frame
-
-def main():
-    global model
-    try:
-        model = YOLO('yolov8s.pt')
-        print("模型加载成功。")
-    except Exception as e:
-        print(f"加载模型时出现错误: {e}")
-        model = None
-
-    if model is not None:
-        video_path = "./image/a.mp4"
-        print("开始视频捕获...")
-        cap = cv2.VideoCapture(video_path)
-        if not cap.isOpened():
-            print("打开视频流或文件时出错")
-        else:
-            print("视频流已成功打开。")
-
-            # 获取视频的帧率和尺寸
-            fps = cap.get(cv2.CAP_PROP_FPS)
-            width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-            height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-
-            # 创建VideoWriter对象
-            fourcc = cv2.VideoWriter_fourcc(*'mp4v')  # 使用mp4v编码器
-            out = cv2.VideoWriter('output.mp4', fourcc, fps, (width, height))  # 输出文件名为output.mp4
-
-            while cap.isOpened():
-                ret, frame = cap.read()
-                if not ret:
-                    break
-
-                processed_frame = process_frame(frame)
-
-                # 将处理后的帧写入视频文件
-                out.write(processed_frame)
-
-                cv2.imshow('Processed Video', processed_frame)
-                key = cv2.waitKey(1)
-                if key & 0xFF == ord('q'):
-                    break
-
-            # 释放VideoWriter资源
-            out.release()
-
-            cap.release()
-            cv2.destroyAllWindows()
-            print("视频处理完成。")
-    else:
-        print("模型未加载，无法继续处理。")
-
-if __name__ == '__main__':
-    main()