Python 在多幅图像中匹配ROI或形状以进行分析

我有很多图片要用。但是这个问题，我将使用第一个和最后一个图像

图1/83

图83/83

我通过这段代码运行了图1

import numpy as np
import cv2

def process(filename, key):
   gwash = cv2.imread(filename)
   gwashBW = cv2.cvtColor(gwash, cv2.COLOR_BGR2GRAY)

   ret,thresh1 = cv2.threshold(gwashBW,179,255,cv2.THRESH_BINARY) 
   kernel = np.ones((1,1),np.uint8)
   erosion = cv2.erode(thresh1, kernel,iterations = 1) 
   opening = cv2.morphologyEx(erosion, cv2.MORPH_OPEN, kernel)
   closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel) 
   _, contours ,_ = cv2.findContours(closing,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) 

   areas = [cv2.contourArea(c) for c in contours]
   idx = np.argmax(areas)
   cnt = contours[idx]

   mask = np.zeros_like(gwash)
   cv2.drawContours(mask, contours, idx, (255,255,255), -1) 
   out = np.zeros_like(gwash) 
   out[mask == 255] = gwash[mask == 255]

   cv2.imwrite('img/out{}.jpg'.format(key),out)
   print idx

这一个结果我得到了更好和最坏的结果

这是我使用的轮廓代码和结果

import cv2
    import numpy as np
    import imutils
    from matplotlib import pyplot as plt

    def process(filename, key):
        image = cv2.imread(filename)

        resized = imutils.resize(image, width=600)
        ratio = image.shape[0] / float(resized.shape[0])
        blurred = cv2.GaussianBlur(resized, (5, 5), 0)
        gray = cv2.cvtColor(blurred, cv2.COLOR_BGR2GRAY)
        lab = cv2.cvtColor(resized, cv2.COLOR_BGR2LAB)
        thresh = cv2.threshold(gray, 180, 255, cv2.THRESH_BINARY)[1]
        imagem = cv2.bitwise_not(thresh)
        th4 = cv2.threshold(gray,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)[1]
        th3 = cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY,11,2)
        gray_2 = cv2.cvtColor(resized, cv2.COLOR_BGR2GRAY)
        gray_blur = cv2.GaussianBlur(gray, (15, 15), 0)
        thresh_2 = cv2.adaptiveThreshold(gray_blur, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY_INV, 11, 1)
        kernel = np.ones((1, 1), np.uint8)
        closing = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel, iterations=500)
        cnts = cv2.findContours(closing.copy(), cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
        cnts2 = cv2.findContours(closing.copy(), cv2.RETR_EXTERNAL ,cv2.CHAIN_APPROX_SIMPLE)


        cnts = cnts[0] if imutils.is_cv2() else cnts[1]
        cnts2 = cnts2[0] if imutils.is_cv2() else cnts2[1]

            # loop over the contours
        for c in cnts:
              # compute the center of the contour
              M = cv2.moments(c)
              if M["m00"] != 0:
                  cX = int(M["m10"] / M["m00"])
                  cY = int(M["m01"] / M["m00"])

              else:
                  cX, cY = 0, 0

              # multiply the contour (x, y)-coordinates by the resize ratio,
              # then draw the contours and the name of the shape and labeled
              # color on the image
              c = c.astype("float")
              c *= ratio
              c = c.astype("int")
              cX *= ratio
              cY *= ratio
              cv2.drawContours(image, [c], -1, (0, 255, 0), 5)
              cv2.circle(image, (int(cX),int(cY)),5,300,3)  

        #r = 100.0 / image.shape[1]
        #dim = (100, int(image.shape[0] *r))

        #imageresized = cv2.resize(image,(2048,2048),dim,interpolation = cv2.INTER_AREA)
        cv2.imwrite( 'i/image_{}.jpg'.format(key) ,QR_final )
        print 'image_{}.jpg'.format(key) 

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因此，我的问题是，使用python在所有照片中准确找到画布形状的最佳方法是什么？

这在很大程度上取决于其他81幅图像的外观…@barny word有意义，那么展示的2幅图像呢？谢谢你的意思是你还没有解决。是的，看起来很难，但是图片周围有一个白色的边框。用那个？不，我没有。我正在考虑使用法兰。你能更具体地描述一下桌子的白色边框吗？谢谢去争取吧我指的是图片周围的白色边框。