近期，需要实现检测摄像头中指定坐标区域内的主体颜色，通过查阅大量相关的内容，最终实现代码及效果如下，具体的实现步骤在代码中都详细注释，代码还可以进一步优化，但提升有限。
主要实现过程：按不同颜色的取值范围，对图像进行循环遍历，转换为灰度图，将本次遍历的颜色像素转换为白色，对白色部分进行膨胀处理，使其更加连续，计算白色部分外轮廓包围的面积累加求和，比较每种颜色围起来面积，保存最大值及其颜色，所有颜色遍历完后，返回最大值对应的颜色，显示在图像上
如果有类似的颜色识别的任务，可参考以下代码修改后实现具体需求
colorList.py
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import numpy as npimport collections# 将rgb图像转换为hsv图像后，确定不同颜色的取值范围def getColorList(): dict = collections.defaultdict(list) # black lower_black = np.array([0, 0, 0]) upper_black = np.array([180, 255, 46]) color_list_black = [] color_list_black.append(lower_black) color_list_black.append(upper_black) dict['black'] = color_list_black # gray lower_gray = np.array([0, 0, 46]) upper_gray = np.array([180, 43, 220]) color_list_gray= [] color_list_gray.append(lower_gray) color_list_gray.append(upper_gray) dict['gray'] = color_list_gray # white lower_white = np.array([0, 0, 221]) upper_white = np.array([180, 30, 255]) color_list_white = [] color_list_white.append(lower_white) color_list_white.append(upper_white) dict['white'] = color_list_white # red lower_red = np.array([156, 43, 46]) upper_red = np.array([180, 255, 255]) color_list_red = [] color_list_red.append(lower_red) color_list_red.append(upper_red) dict['red'] = color_list_red # red2 lower_red = np.array([0, 43, 46]) upper_red = np.array([10, 255, 255]) color_list_red2 = [] color_list_red2.append(lower_red) color_list_red2.append(upper_red) dict['red2'] = color_list_red2 # orange lower_orange = np.array([11, 43, 46]) upper_orange = np.array([25, 255, 255]) color_list_orange = [] color_list_orange.append(lower_orange) color_list_orange.append(upper_orange) dict['orange'] = color_list_orange # yellow lower_yellow = np.array([26, 43, 46]) upper_yellow = np.array([34, 255, 255]) color_list_yellow = [] color_list_yellow.append(lower_yellow) color_list_yellow.append(upper_yellow) dict['yellow'] = color_list_yellow # green lower_green = np.array([35, 43, 46]) upper_green = np.array([77, 255, 255]) color_list_green = [] color_list_green.append(lower_green) color_list_green.append(upper_green) dict['green'] = color_list_green # cyan lower_cyan = np.array([78, 43, 46]) upper_cyan = np.array([99, 255, 255]) color_list_cyan = [] color_list_cyan.append(lower_cyan) color_list_cyan.append(upper_cyan) dict['cyan'] = color_list_cyan # blue lower_blue = np.array([100, 43, 46]) upper_blue = np.array([124, 255, 255]) color_list_blue = [] color_list_blue.append(lower_blue) color_list_blue.append(upper_blue) dict['blue'] = color_list_blue # purple lower_purple = np.array([125, 43, 46]) upper_purple = np.array([155, 255, 255]) color_list_purple = [] color_list_purple.append(lower_purple) color_list_purple.append(upper_purple) dict['purple'] = color_list_purple return dictif __name__ == '__main__': color_dict = getColorList() print(color_dict) num = len(color_dict) print('num=', num) for d in color_dict: print('key=', d) print('value=', color_dict[d][1]) |
image_color_realize.py
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import cv2import colorList# 实现对图片中目标区域颜色的识别def get_color(frame): print('go in get_color') hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) maxsum = 0 color = None color_dict = colorList.getColorList() # count = 0 for d in color_dict: mask = cv2.inRange(hsv, color_dict[d][0], color_dict[d][1]) # 在后两个参数范围内的值变成255 binary = cv2.threshold(mask, 127, 255, cv2.THRESH_BINARY)[1] # 在灰度图片中，像素值大于127的都变成255，[1]表示调用图像，也就是该函数第二个返回值 # cv2.imshow("0",binary) # cv2.waitKey(0) # count+=1 binary = cv2.dilate(binary, None, iterations=2) # 使用默认内核进行膨胀操作，操作两次，使缝隙变小，图像更连续 cnts = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2] # 获取该函数倒数第二个返回值轮廓 sum = 0 for c in cnts: sum += cv2.contourArea(c) # 获取该颜色所有轮廓围成的面积的和 # print("%s , %d" %(d, sum )) if sum > maxsum: maxsum = sum color = d if color == 'red2': color = 'red' elif color == 'orange': color = 'yellow' elif color == 'purple' or color == 'blue' or color == 'cyan' or color == 'white' or color == 'green': color = 'normal' return colorif __name__ == '__main__': filename = "C:/Users/admin/Desktop/water_samples/live01.jpg" frame = cv2.imread(filename) # frame = frame[180:280, 180:380] # [y:y+h, x:x+w] 注意x,y顺序 color = get_color(frame) # 绘制文本 cv2.putText(img=frame,text=color,org=(20,50),fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=1.0,color=(0,255,0),thickness=2) # cv2.namedWindow('frame',cv2.WINDOW_NORMAL) # 设置显示窗口可调节 cv2.imshow('frame',frame) cv2.waitKey(0) |
video_color_realize.py
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import cv2import xf_color# 对视频或摄像头获取的影像目标区域颜色进行识别cap = cv2.VideoCapture("C:/Users/admin/Desktop/water_samples/01.mp4")# cap = cv2.VideoCapture(0)cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1100) # 这里窗口大小调节只对摄像头有效cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 750)while cap.isOpened(): ret, frame0 = cap.read() # 对图像帧进行翻转（因为opencv图像和我们正常是反着的） 视频是正常的，摄像头是反转的 # frame0 = cv2.flip(src=frame0, flipCode=2) # frame = frame[180:280, 180:380] # [y:y+h, x:x+w] # frame = frame0[200:400, 100:300] # 设置检测颜色的区域，四个顶点坐标 frame = frame0 # frame=cv2.resize(src=frame,dsize=(750,600)) hsv_frame = cv2.cvtColor(src=frame, code=cv2.COLOR_BGR2HSV) # 获取读取的帧的高宽 height, width, channel = frame.shape color = xf_color.get_color(hsv_frame) # 绘制文本 cv2.putText(img=frame0, text=color, org=(20, 50), fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=1.0, color=(0, 255, 0), thickness=2) cv2.imshow('frame', frame0) key = cv2.waitKey(1) if key == 27: breakcap.release()cv2.destroyAllWindows()if __name__ == '__main__': print('Pycharm') |
效果如下：
示例图片1
示例图片2
示例图片3
到此这篇关于Python+OpenCV实现图片及视频中选定区域颜色识别的文章就介绍到这了,更多相关Python OpenCV颜色识别内容请搜索服务器之家以前的文章或继续浏览下面的相关文章希望大家以后多多支持服务器之家！
原文链接：https://www.cnblogs.com/wydilearn/p/16505887.html
