基于OpenCV实现对图片及视频中感兴趣区域颜色识别

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         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)     # 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

基于OpenCV实现对图片及视频中感兴趣区域颜色识别

示例图片2

基于OpenCV实现对图片及视频中感兴趣区域颜色识别

示例图片3

基于OpenCV实现对图片及视频中感兴趣区域颜色识别

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