基于OpenCV实现图片及视频中选定区域颜色识别
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
import numpy as np import 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 dict if __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
import cv2 import 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 # mask = cv2.medianBlur(mask, 3) # 中值滤波 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 color if __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
import cv2 import 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) color = xf_color.get_color(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: break cap.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
