commit e2d8be035665d8fc0653368dbbe849849476cf9b
Author: grtsinry43 <grtsinry43@outlook.com>
Date:   Sat Jan 17 02:33:38 2026 +0800

    first commit

diff --git a/main.py b/main.py
new file mode 100644
index 0000000..ce3cc84
--- /dev/null
+++ b/main.py
@@ -0,0 +1,77 @@
+import cv2
+from ultralytics import YOLO
+import time
+
+
+def detect_bikes_modern(video_path, output_path=None):
+    # 1. 加载模型
+    # yolov8n.pt 是 "Nano" 版本，速度最快，适合 CPU 运行
+    # 第一次运行时会自动从互联网下载该文件 (约 6MB)
+    print("正在加载 YOLOv8 模型...")
+    model = YOLO('yolov8n.pt')
+
+    # 2. 打开视频
+    cap = cv2.VideoCapture(video_path)
+    if not cap.isOpened():
+        print(f"错误: 无法打开视频 {video_path}")
+        return
+
+    # 获取视频参数
+    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    fps = cap.get(cv2.CAP_PROP_FPS)
+
+    # 3. 设置保存 (可选)
+    out = None
+    if output_path:
+        # 现代环境通常使用 mp4v 或 avc1
+        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+        out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
+        print(f"结果将保存到: {output_path}")
+
+    print("开始检测... 按 'q' 退出")
+
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        # 4. 核心检测逻辑
+        # stream=True 让处理更流畅
+        # classes=[1] 意思是我们只检测 COCO 数据集中的第 1 类 (0是人, 1是自行车, 2是汽车...)
+        # conf=0.3 意思是置信度大于 0.3 才算检测到
+        results = model.predict(frame, conf=0.2, classes=[1], verbose=False)
+
+        # 5. 在图上画框
+        # result.plot() 会自动把检测框画在 frame 上
+        annotated_frame = results[0].plot()
+
+        # 显示计数
+        bike_count = len(results[0].boxes)
+        cv2.putText(annotated_frame, f"Bikes: {bike_count}", (20, 40),
+                    cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
+
+        # 显示画面
+        cv2.imshow("Modern Bike Detection", annotated_frame)
+
+        # 保存画面
+        if out:
+            out.write(annotated_frame)
+
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
+
+    cap.release()
+    if out:
+        out.release()
+    cv2.destroyAllWindows()
+    print("处理完成。")
+
+
+if __name__ == "__main__":
+    # 替换成你的视频路径
+    # 注意：这里我们不再需要那个 lowerFAR.xml 文件了，直接扔掉它
+    video_source = "intersection.avi"
+    output_file = "result_modern.mp4"
+
+    detect_bikes_modern(video_source, output_file)
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diff --git a/main2.py b/main2.py
new file mode 100644
index 0000000..3b16b08
--- /dev/null
+++ b/main2.py
@@ -0,0 +1,82 @@
+import cv2
+import numpy as np
+
+
+def run_optical_flow(video_path):
+    # 1. 打开视频
+    cap = cv2.VideoCapture(video_path)
+    if not cap.isOpened():
+        print(f"错误: 无法打开视频 {video_path}")
+        return
+
+    # 2. 读取第一帧并转换为灰度
+    ret, first_frame = cap.read()
+    if not ret:
+        print("错误: 视频无法读取")
+        return
+    prev_gray = cv2.cvtColor(first_frame, cv2.COLOR_BGR2GRAY)
+
+    # 3. 设置可视化参数
+    step = 16  # 步长：不需要每个像素都画箭头，每隔 16 个像素画一个
+    scale = 1  # 缩放因子：控制箭头的长度，如果箭头太短看不清，把这个改大 (例如 2 或 3)
+    color = (0, 255, 0)  # 绿色箭头
+
+    print("按 'q' 退出...")
+
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        # 转换为灰度图
+        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+
+        # 4. 计算稠密光流 (Farneback 算法)
+        # flow 是一个 (h, w, 2) 的数组，flow[..., 0] 是水平移动(dx)，flow[..., 1] 是垂直移动(dy)
+        flow = cv2.calcOpticalFlowFarneback(prev_gray, gray, None,
+                                            pyr_scale=0.5, levels=3, winsize=15,
+                                            iterations=3, poly_n=5, poly_sigma=1.2, flags=0)
+
+        # 5. 可视化：在原图上画箭头
+        # 创建网格点 (只在 step 的倍数位置画)
+        h, w = gray.shape
+        y, x = np.mgrid[step / 2:h:step, step / 2:w:step].reshape(2, -1).astype(int)
+
+        # 获取这些网格点对应的 dx, dy
+        fx, fy = flow[y, x].T
+
+        # 创建画线的图层
+        vis_frame = frame.copy()
+
+        # 绘制线条
+        lines = np.vstack([x, y, x + fx * scale, y + fy * scale]).T.reshape(-1, 2, 2)
+        lines = np.int32(lines + 0.5)
+
+        # 遍历绘制箭头 (cv2.arrowedLine)
+        for (x1, y1), (x2, y2) in lines:
+            # 只有当运动幅度超过一定阈值才画，减少噪点 (对应 MATLAB 的 NoiseThreshold)
+            if np.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2) > 1:
+                cv2.arrowedLine(vis_frame, (x1, y1), (x2, y2), color, 1, tipLength=0.3)
+
+        # 6. 显示结果
+        cv2.imshow('Optical Flow (Dense)', vis_frame)
+
+        # 更新上一帧
+        prev_gray = gray
+
+        # 按 'q' 退出
+        if cv2.waitKey(20) & 0xFF == ord('q'):  # waitKey(20) 控制播放速度
+            break
+
+    cap.release()
+    cv2.destroyAllWindows()
+
+
+if __name__ == "__main__":
+    # 请确保目录下有 thumb.avi，或者改成 0 使用摄像头测试
+    video_file = "thumb.avi"
+
+    # 如果没有视频文件，想用摄像头测试，取消下面这行的注释：
+    # video_file = 0
+
+    run_optical_flow(video_file)
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