3h精通OpenCV（七）-颜色检测

ゞ浴缸里的玫瑰 2023-10-02 19:17 7阅读 0赞

右击新建的项目，选择Python File，新建一个Python文件，然后在开头`import cv2`导入cv2库，`import numpy`并且重命名为`np`。

import cv2
    import numpy as np

![在这里插入图片描述][watermark_type_d3F5LXplbmhlaQ_shadow_50_text_Q1NETiBA5oyv5Y2OT1BQTw_size_20_color_FFFFFF_t_70_g_se_x_16]  
我们还要知道在OpenCV中，坐标轴的方向是x轴向右，y轴向下，坐标原点在左上角，比如下面这张长为640像素，宽为480像素的图片。OK，下面开始本节的学习吧。  
![在这里插入图片描述][watermark_type_d3F5LXplbmhlaQ_shadow_50_text_Q1NETiBA5oyv5Y2OT1BQTw_size_20_color_FFFFFF_t_70_g_se_x_16 1]

*  stackImages()是我们上一节就使用的自己写的库函数，下面几行createTrackbar()是在定义窗口和滑动条。`createTrackbar("Hue Min","TrackBars",0,179,empty)`中0是滑动条的默认值，179就是它的值域，empty是为空就pass不管它。
 *  Hue是色相，Sat是饱和度，Val是像素值。图像会显示它这三个属性最小值和最大值之间的像素点。
 *  while循环就是在滑动条进行拖动时，图像也会改变显示，同时数值也会更新输出。

def empty(a):
        pass
    
    def stackImages(scale,imgArray):
        rows = len(imgArray)
        cols = len(imgArray[0])
        rowsAvailable = isinstance(imgArray[0], list)
        width = imgArray[0][0].shape[1]
        height = imgArray[0][0].shape[0]
        if rowsAvailable:
            for x in range ( 0, rows):
                for y in range(0, cols):
                    if imgArray[x][y].shape[:2] == imgArray[0][0].shape [:2]:
                        imgArray[x][y] = cv2.resize(imgArray[x][y], (0, 0), None, scale, scale)
                    else:
                        imgArray[x][y] = cv2.resize(imgArray[x][y], (imgArray[0][0].shape[1], imgArray[0][0].shape[0]), None, scale, scale)
                    if len(imgArray[x][y].shape) == 2: imgArray[x][y]= cv2.cvtColor( imgArray[x][y], cv2.COLOR_GRAY2BGR)
            imageBlank = np.zeros((height, width, 3), np.uint8)
            hor = [imageBlank]*rows
            hor_con = [imageBlank]*rows
            for x in range(0, rows):
                hor[x] = np.hstack(imgArray[x])
            ver = np.vstack(hor)
        else:
            for x in range(0, rows):
                if imgArray[x].shape[:2] == imgArray[0].shape[:2]:
                    imgArray[x] = cv2.resize(imgArray[x], (0, 0), None, scale, scale)
                else:
                    imgArray[x] = cv2.resize(imgArray[x], (imgArray[0].shape[1], imgArray[0].shape[0]), None,scale, scale)
                if len(imgArray[x].shape) == 2: imgArray[x] = cv2.cvtColor(imgArray[x], cv2.COLOR_GRAY2BGR)
            hor= np.hstack(imgArray)
            ver = hor
        return ver
    
    
    path="Resources/lambo.png"
    cv2.namedWindow("TrackBars")
    cv2.resizeWindow("TrackBars",640,240)
    cv2.createTrackbar("Hue Min","TrackBars",0,179,empty)
    cv2.createTrackbar("Hue Max","TrackBars",19,179,empty)
    cv2.createTrackbar("Sat Min","TrackBars",110,255,empty)
    cv2.createTrackbar("Sat Max","TrackBars",240,255,empty)
    cv2.createTrackbar("Val Min","TrackBars",153,255,empty)
    cv2.createTrackbar("Val Max","TrackBars",255,255,empty)
    
    while True:
        img = cv2.imread(path)
        imgHSV = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
        h_min = cv2.getTrackbarPos("Hue Min","TrackBars")
        h_max = cv2.getTrackbarPos("Hue Max", "TrackBars")
        s_min = cv2.getTrackbarPos("Sat Min", "TrackBars")
        s_max = cv2.getTrackbarPos("Sat Max", "TrackBars")
        v_min = cv2.getTrackbarPos("Val Min", "TrackBars")
        v_max = cv2.getTrackbarPos("Val Max", "TrackBars")
        print(h_min,h_max,s_min,s_max,v_min,v_max)
        lower = np.array([h_min,s_min,v_min])
        upper = np.array([h_max,s_max,v_max])
        mask = cv2.inRange(img,lower,upper)
        imgResult = cv2.bitwise_and(img,img,mask=mask)
    
        imgStack = stackImages(0.6,([img,imgHSV],[mask,imgResult]))
        cv2.imshow("Stacked Images", imgStack)
    
        cv2.waitKey(1)

运行看下效果，刚开始都是默认值，我们将色相最小值和最大值都改为0，那肯定黑漆漆的一片。  
![在这里插入图片描述][watermark_type_d3F5LXplbmhlaQ_shadow_50_text_Q1NETiBA5oyv5Y2OT1BQTw_size_20_color_FFFFFF_t_70_g_se_x_16 2]  
我们将色相的Max拖到116，发现车辆的颜色显示出来了。  
![在这里插入图片描述][watermark_type_d3F5LXplbmhlaQ_shadow_50_text_Q1NETiBA5oyv5Y2OT1BQTw_size_20_color_FFFFFF_t_70_g_se_x_16 3]  
我们再来改下饱和度的Min，调到了110，图像有很多颜色点都消失了。  
![在这里插入图片描述][watermark_type_d3F5LXplbmhlaQ_shadow_50_text_Q1NETiBA5oyv5Y2OT1BQTw_size_20_color_FFFFFF_t_70_g_se_x_16 4]  
我们将饱和度的Max调到最大，这时候图像又全部显示出来了。  
![在这里插入图片描述][watermark_type_d3F5LXplbmhlaQ_shadow_50_text_Q1NETiBA5oyv5Y2OT1BQTw_size_20_color_FFFFFF_t_70_g_se_x_16 5]  
最后调整像素值，小车颜色显示的很全面，说明橙色和白色都在这个区间中。  
![在这里插入图片描述][watermark_type_d3F5LXplbmhlaQ_shadow_50_text_Q1NETiBA5oyv5Y2OT1BQTw_size_20_color_FFFFFF_t_70_g_se_x_16 6]  
降低像素值范围，小车只会显示该范围内的颜色。  
![在这里插入图片描述][watermark_type_d3F5LXplbmhlaQ_shadow_50_text_Q1NETiBA5oyv5Y2OT1BQTw_size_20_color_FFFFFF_t_70_g_se_x_16 7]

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