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主要实现的功能是能实时识别视频中的绿色圆,并返回圆心位置,这既是对前面所学知识的总结,也是为下一步摄像头的追踪打下基础。
前期准备
摄像头为原装摄像头(非USB外接),环境为RaspberryPi,python2,代码如下:
import numpy as npimport cv2cap = cv2.VideoCapture(0)font = cv2.FONT_HERSHEY_SIMPLEX # 设置字体样式kernel = np.ones((5, 5), np.uint8) # 卷积核if cap.isOpened() is True: # 检查摄像头是否正常启动 while(True): ret, frame = cap.read() frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) # 转换为RGB通道 gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY) # 转换为灰色通道 hsv = cv2.cvtColor(frame, cv2.COLOR_RGB2HSV) # 转换为HSV空间 lower_green = np.array([30, 100, 100]) # 设定绿色的阈值下限 upper_green = np.array([80, 255, 255]) # 设定绿色的阈值上限 # 消除噪声 mask = cv2.inRange(hsv, lower_green, upper_green) # 设定掩膜取值范围 bila = cv2.bilateralFilter(mask, 10, 200, 200) # 双边滤波消除噪声 opening = cv2.morphologyEx(bila, cv2.MORPH_OPEN, kernel) # 形态学开运算 closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel) # 形态学开运算 edges = cv2.Canny(closing, 50, 100) # 边缘识别 # 识别圆形 circles = cv2.HoughCircles( edges, cv2.cv.CV_HOUGH_GRADIENT, 1, 100, param1=100, param2=10, minRadius=10, maxRadius=500) if circles is not None: # 如果识别出圆 for circle in circles[0]: # 获取圆的坐标与半径 x = int(circle[0]) y = int(circle[1]) r = int(circle[2]) cv2.circle(frame, (x, y), r, (0, 0, 255), 3) # 标记圆 cv2.circle(frame, (x, y), 3, (255, 255, 0), -1) # 标记圆心 text = 'x: '+str(x)+' y: '+str(y) cv2.putText(frame, text, (10, 30), font, 1, (0, 255, 0), 2) # 显示圆心位置 else: # 如果识别不出,显示圆心不存在 cv2.putText(frame, 'x: None y: None', (10, 30), font, 1, (0, 255, 0), 2) cv2.imshow('frame', frame) cv2.imshow('mask', mask) cv2.imshow('edges', edges) k = cv2.waitKey(5) & 0xFF if k == 27: break cap.release() cv2.destroyAllWindows()else: print('cap is not opened!')
结果如下: