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import cv2
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import numpy as np
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# 视频源设置为电脑摄像头，0通常代表默认摄像头
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cap = cv2.VideoCapture(0)
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# 定义要追踪的颜色范围
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# 注意：根据实际情况调整HSV值以精确匹配想要追踪的颜色
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greenLower = np.array([(35, 46, 120)])
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greenUpper = np.array([60, 255, 255])
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while True:
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# 从摄像头读取一帧
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ret, frame = cap.read()
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if not ret:
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break
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# 将BGR图像转换为HSV，因为HSV在颜色分割上更为直观
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hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
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# 根据定义的HSV范围创建掩码
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mask = cv2.inRange(hsv, greenLower, greenUpper)
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# 对原图像和掩码应用位运算，提取出指定颜色部分
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result = cv2.bitwise_and(frame, frame, mask=mask)
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# 使用开运算和闭运算去除噪声
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kernel = np.ones((5, 5), np.uint8)
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mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
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mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
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# 查找轮廓
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contours, _ = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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# 遍历轮廓，画出边界框
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for contour in contours:
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if cv2.contourArea(contour) > 100: # 过滤小面积噪点
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x, y, w, h = cv2.boundingRect(contour)
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cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
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# 显示原图、掩码和结果图像
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cv2.imshow('Original', frame)
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cv2.imshow('Mask', mask)
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cv2.imshow('Result', result)
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# 按'q'键退出循环
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if cv2.waitKey(1) & 0xFF == ord('q'):
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break
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# 释放资源并关闭所有窗口
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cap.release()
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cv2.destroyAllWindows()
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