Python+OpenCV實現圖片及視頻中選定區域顏色識別

近期，需要實現檢測攝像頭中指定坐標區域內得主體顏色，通過查閱大量相關得內容，最終實現代碼及效果如下，具體得實現步驟在代碼中都詳細注釋，代碼還可以進一步優化，但提升有限。

主要實現過程：按不同顏色得取值范圍，對圖像進行循環遍歷，轉換為灰度圖，將本次遍歷得顏色像素轉換為白色，對白色部分進行膨脹處理，使其更加連續，計算白色部分外輪廓包圍得面積累加求和，比較每種顏色圍起來面積，保存最大值及其顏色，所有顏色遍歷完后，返回最大值對應得顏色，顯示在圖像上

如果有類似得顏色識別得任務，可參考以下代碼修改后實現具體需求

colorList.py

import numpy as npimport collections# 將rgb圖像轉換為hsv圖像后，確定不同顏色得取值范圍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

import cv2import colorList# 實現對圖片中目標區域顏色得識別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])  # 在后兩個參數范圍內得值變成255        binary = cv2.threshold(mask, 127, 255, cv2.THRESH_BINARY)[1]  # 在灰度圖片中，像素值大于127得都變成255，[1]表示調用圖像，也就是該函數第二個返回值        # cv2.imshow("0",binary)        # cv2.waitKey(0)        # count+=1        binary = cv2.dilate(binary, None, iterations=2)  # 使用默認內核進行膨脹操作，操作兩次，使縫隙變小，圖像更連續        cnts = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2]  # 獲取該函數倒數第二個返回值輪廓        sum = 0        for c in cnts:            sum += cv2.contourArea(c)  # 獲取該顏色所有輪廓圍成得面積得和        # 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順序    color = get_color(frame)    # 繪制文本    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)  # 設置顯示窗口可調節    cv2.imshow('frame',frame)    cv2.waitKey(0)

video_color_realize.py

import cv2import xf_color# 對視頻或攝像頭獲取得影像目標區域顏色進行識別cap = cv2.VideoCapture("C:/Users/admin/Desktop/water_samples/01.mp4")# cap = cv2.VideoCapture(0)cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1100)  # 這里窗口大小調節只對攝像頭有效cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 750)while cap.isOpened():    ret, frame0 = cap.read()    # 對圖像幀進行翻轉（因為opencv圖像和我們正常是反著得） 視頻是正常得，攝像頭是反轉得    # 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]  # 設置檢測顏色得區域，四個頂點坐標    frame = frame0    # frame=cv2.resize(src=frame,dsize=(750,600))    hsv_frame = cv2.cvtColor(src=frame, code=cv2.COLOR_BGR2HSV)    # 獲取讀取得幀得高寬    height, width, channel = frame.shape    color = xf_color.get_color(hsv_frame)    # 繪制文本    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：

示例圖片1

示例圖片2

示例圖片3

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