【学习笔记】opencv的python接口轮廓特征值滚动条控制阈值参数

女爷i 2022-11-04 11:09 181阅读 0赞

## 滚动条控制阈值和阈值的处理方式 ##

控制thres中的方式和阈值  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMzU4NTc0_size_16_color_FFFFFF_t_70]

import cv2
    
    
    img = cv2.imread("p7.jpg")
    #img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    type = 0#阈值的处理方式
    value = 0#使用的阈值
    def ontype(a):
        type = cv2.getTrackbarPos("Type","j")
        value = cv2.getTrackbarPos("tValue","j")
        ret,dst = cv2.threshold(img,value,255,type)
        cv2.imshow("j",dst)
    
    def onvalue(a):
        type = cv2.getTrackbarPos("Type", "j")
        value = cv2.getTrackbarPos("tValue", "j")
        ret, dst = cv2.threshold(img, value, 255, type)
        cv2.imshow("j", dst)
    
    cv2.imshow("j",img)
    cv2.createTrackbar("Type","j",0,4,ontype)
    cv2.createTrackbar("tValue","j",0,255,onvalue)
    if cv2.waitKey(0) == 27:
        cv2.destroyWindow()

## 轮廓特征值 ##

*  minMaxLoc的参数：图片须为单通道，mask为掩膜。可以用它得到指定感兴趣区域的最值。
 *  findNonZero:获取一个轮廓点的位置（一般来说轮廓的像素点为非零值）
 *

img = cv2.imread("deng.png")
    img2 = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
    rst,dst = cv2.threshold(img2,250,255,0)
    conters = cv2.findContours(dst,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)[0]
    img3 = cv2.drawContours(img.copy(),conters,-1,(100,222,255),3)
    mask = np.zeros(img2.shape,dtype=np.uint8)
    mask = cv2.drawContours(mask,conters,-1,(100,222,255),3)
    loc2 = cv2.bitwise_and(mask,img2)
    loc = cv2.bitwise_and(img,img3)
    minval,maxval,minloc,maxloc = cv2.minMaxLoc(img2,mask=mask)
    print(maxloc)
    cv2.imshow("and",loc)
    cv2.imshow("and2",loc2)
    cv2.imshow("j",img3)

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMzU4NTc0_size_16_color_FFFFFF_t_70 1]

## 椭圆拟合 ##

之前都是矩形，现在用椭圆拟合  
上面的代码不变，加上这个：

ellipse = cv2.fitEllipse(conters[1])
    cv2.ellipse(loc2,ellipse,(0,0,255),2)
    cv2.ellipse(img2,ellipse,(0,0,255),2)
    cv2.imshow(str(0),img2)
    # cv2.imshow("and",loc)
    cv2.imshow("and2",loc2)

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMzU4NTc0_size_16_color_FFFFFF_t_70 2]

## 提取颜色，用掩膜控制参数: ##

进行了一些开闭运算，不过感觉对这个图片来说没必要![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMzU4NTc0_size_16_color_FFFFFF_t_70 3]

img = cv2.imread("deng.png")
    cv2.imshow("i",img)
    kenal = np.ones((10,10),np.uint8)
    img = cv2.morphologyEx(img,cv2.MORPH_OPEN,kenal)
    img = cv2.morphologyEx(img,cv2.MORPH_CLOSE,kenal)
    hsvimg = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)
    lower_red = np.array((150, 23, 70))
    
    upper_red = np.array([180, 255, 255])
    mask1 = cv2.inRange(hsvimg,lower_red,upper_red)
    lower_red2 = np.array((0, 43, 35))
    
    upper_red2 = np.array([11, 255, 255])
    mask2 = cv2.inRange(hsvimg,lower_red2,upper_red2)
    mask = mask1+mask2
    img0 = cv2.bitwise_and(img,img,mask=mask)
    
    cv2.imshow(str(0),img0)

## 获取一个特定点的阈值 ##

今天才想到可以获取一个特定点的阈值，天哪，之前还试试试好久，浪费好多时间阿

img = cv2.imread("cv.png")
    cv2.imshow("i",img)
    kenal = np.ones((10,10),np.uint8)
    hsvimg = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)
    def gethsv(event,x,y,flags,params):
        if event == cv2.EVENT_LBUTTONDOWN:
            print(hsvimg[x,y])
    
    
    cv2.imshow("hi",hsvimg)
    cv2.setMouseCallback("hi",gethsv)

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMzU4NTc0_size_16_color_FFFFFF_t_70 4]  
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存在下标溢出报错。

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