5、 Image pixel statistics

Image area pixel statistics

It is suitable for statistical image with obvious pixel gap , When the pixel value is calculated, it can be compared .

Here are some Inline code slice .

// A code block
var foo = 'bar';

import cv2 as cv,cv2
import matplotlib.pyplot as plt
import numpy as np,time,os
# # The difference between different operators 
def cv_show(img,name):
    cv2.imshow(name,img)
    cv2.waitKey()
    cv2.destroyAllWindows()

def sobel(img):
    sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=3)
    sobely = cv2.Sobel(img,cv2.CV_64F,0,1,ksize=3)
    sobelx = cv2.convertScaleAbs(sobelx)
    sobely = cv2.convertScaleAbs(sobely)
    sobelxy =  cv2.addWeighted(sobelx,0.5,sobely,0.5,0)

    return sobelxy
def scharr(img):
    scharrx = cv2.Scharr(img,cv2.CV_64F,1,0)
    scharry = cv2.Scharr(img,cv2.CV_64F,0,1)
    scharrx = cv2.convertScaleAbs(scharrx)
    scharry = cv2.convertScaleAbs(scharry)
    scharrxy =  cv2.addWeighted(scharrx,0.5,scharry,0.5,0)

    return scharrxy


def laplacian(img):
    laplacian = cv2.Laplacian(img,cv2.CV_64F)
    laplacian = cv2.convertScaleAbs(laplacian)
    return laplacian

def statistics():
    f= open(r'./id_pv.txt', "w")
    img_path=r'/data2/enducation/answer_card/answer-card-recognition/id_img'
    len_=len(os.listdir(img_path))
    for j in range(len_):
        img_path_=os.path.join(img_path,f"{j}.jpg")
        # img_path_=r"/data2/enducation/answer_card/answer-card-recognition/id_img/21.jpg"
        print(img_path_)
        src = cv.imread(img_path_)
        # cv.imshow("q",src)
        h,w,ch = np.shape(src)

        scharrx = cv.Scharr(src,cv.CV_64F,1,0)
        scharry = cv.Scharr(src,cv.CV_64F,0,1)
        scharrx = cv.convertScaleAbs(scharrx)
        scharry = cv.convertScaleAbs(scharry)
        scharrxy =  cv.addWeighted(scharrx,0.5,scharry,0.5,0)


        pw=int(w/9)
        one_id_pv=[]
        for i in range(9):
            x1 = i * pw
            y1 = 66
            x2 = (i + 1) * pw
            y2 = h
            image = scharrxy[y1:y2, x1:x2,:]
            h1,w1,c=image.shape
            cv.rectangle(src, (x1, y1), (x2, y2), (100, 108, 255), 1)
            #??????
            gray = cv.cvtColor(image,cv.COLOR_BGR2GRAY)
            print(gray.shape)
            # exit()
            #??????????
            # cv.imshow("gray",gray)
            hest = np.zeros([256],dtype = np.int32)
            #??????
            pvs = []
            for row in range(h1):
                for col in range(w1):
                    pv = gray[row,col]
                    pvs.append(pv)
                    hest[pv] +=1
                    #????????????
            x=np.mean(pvs)
            one_id_pv.append(int(x))
            print(x)
            # plt.plot(hest,color = "r")
            # plt.xlim([0,256])
            # plt.show()
            # #?????
            # cv.waitKey(0)
            # cv.destroyAllWindows()
        # card_logger.info(f"{one_id_pv}\n")

            f.write(f"{one_id_pv}+{j}\n")
        f.write(f"{img_path_}\n\n")
if __name__ == '__main__':
    path=r""
    img=cv.imread(path)
    # sobelxy=sobel(img)
    # scharrxy=scharr(img)
    # laplacian=laplacian(img)
    # res = np.hstack((sobelxy,scharrxy,laplacian))
    # cv_show(res, 'res')


    statistics()