python-条形码检测脚本

缺乏、安全感 2022-09-04 10:49 193阅读 0赞

import cv2
    import numpy as  np
    import math
    from pyzbar.pyzbar import decode
    
    #读图
    img=cv2.imread(r'XX.png',1) #读取图片
    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)  #将图片变为灰度图片
    
    #使用Sobel算子,求得水平和垂直方向灰度图像的梯度差
    gradX = cv2.Sobel(gray,ddepth = cv2.CV_32F,dx = 1,dy = 0,ksize = -1)
    gradY = cv2.Sobel(gray,ddepth = cv2.CV_32F,dx = 0,dy = 1,ksize = -1)
    gradient = cv2.subtract(gradX, gradY)
    gradient = cv2.convertScaleAbs(gradient)
    images = cv2.resize(gradient,(480,640))
    cv2.imshow("closed1", images)
    cv2.waitKey(0)
    #均值滤波，消除高频噪声 (8*8)像素块
    blurred = cv2.blur(gradient,(8,8))
    #二值化
    ret,thresh = cv2.threshold(blurred, 225, 255, cv2.THRESH_BINARY)
    #闭运算 填充条码空隙 （参数自己调整）
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(25,25))
    closed1 = cv2.morphologyEx(thresh,cv2.MORPH_CLOSE,kernel, iterations = 2)
    # images = cv2.resize(closed1,(480,640))
    # cv2.imshow("closed1", images)
    # cv2.waitKey(0)
    #4次腐蚀 再4次膨胀：消除小斑点
    closed2 = cv2.erode(closed1, None, iterations = 4)
    closed3 = cv2.dilate(closed2, None, iterations = 4)
    # images = cv2.resize(closed3,(480,640))
    # cv2.imshow("closed3", images)
    # cv2.waitKey(0)
    #开运算
    ret,th2=cv2.threshold(closed3,0.1,255,cv2.THRESH_BINARY)
    kernel = np.ones((10,10),np.uint8)
    opening = cv2.morphologyEx(th2, cv2.MORPH_OPEN, kernel,iterations = 2)
    images = cv2.resize(opening,(480,640))
    cv2.imshow("contours", images)
    cv2.waitKey(0)
    #腐蚀
    kernel = np.ones((5,5),np.uint8)
    erosion = cv2.erode(opening,kernel,iterations = 2)
    opening = cv2.resize(erosion ,(480,640))
    cv2.imshow("contours",opening )
    cv2.waitKey(0)
    
    #找出边界
    contours, hierarchy = cv2.findContours(erosion.copy(),cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
    
    #获取最大轮廓
    c = sorted(contours, key = cv2.contourArea, reverse = True)[0]
    
    #图形旋转矫正 并截取最大边界区域
    #修改最小框大小 放大50
    rect = cv2.minAreaRect(c)
    b = list(rect)
    b[1] = list(b[1])
    b[1][0] = b[1][0] + 50
    b[1][1] = b[1][1] + 50
    new_rect = tuple(b)
    box = np.int0(cv2.boxPoints(new_rect))                     
    #draw_img = cv2.drawContours(img.copy(), [box], -1, (0, 0, 255), 3)
    # 获取画框宽高(x=orignal_W,y=orignal_H)
    orignal_W = math.ceil(np.sqrt((box[3][1] - box[2][1])**2 + (box[3][0] - box[2][0])**2))
    orignal_H= math.ceil(np.sqrt((box[3][1] - box[0][1])**2 + (box[3][0] - box[0][0])**2))
    # 原图中的四个顶点,与变换矩阵
    pts1 = np.float32([box[0], box[1], box[2], box[3]])
    pts2 = np.float32([[int(orignal_W+1),int(orignal_H+1)], [0, int(orignal_H+1)], [0, 0], [int(orignal_W+1), 0]])
    # 生成透视变换矩阵；进行透视变换
    M = cv2.getPerspectiveTransform(pts1, pts2)
    result_img = cv2.warpPerspective(img, M*1.5, (int(orignal_W+20),int(orignal_H+20)))
    cv2.imshow("JIAOZHEN",result_img)
    cv2.waitKey(0)
    #二值化 筛掉一些错误
    ret,thresh = cv2.threshold(result_img,128, 255, cv2.THRESH_BINARY)
    #二维码编码检测
    barcodes = decode(thresh)
    result = []
    for barcode in barcodes:
        barcodeData = barcode.data.decode("utf-8")
        result.append(barcodeData)
    print(result)