運行截圖如下：

?

這里有幾點要注意的：

①對比兩張圖片的函數SSIM具體是structural_similarity：

? ? ? ? ? Ⅰ：第一個參數和第二個參數是要對比的兩張圖片；

? ? ? ? ? Ⅱ：參數中有個full，默認為True，如果為True會把兩張圖的相似部分返回，如果為False就不返回了；

? ? ? ? ? Ⅳ：返回值full為False的時候有2個，一個是mssim浮點型的相似度，1代表2張圖一模一樣。0代表完全不一樣，grad返回兩張圖片不同的地方類型為ndarray；

②SSIM返回的ndarray里面的值為[0, 1]的float型，而OpenCV的[0, 255]為uint8型，可以通過這樣的方式轉換：

? ? ? ? ? Ⅰ：diff = (diff * 255).astype("uint8")

③cv2.threshold函數，設置一個閾值和一個顏色，將傳入的圖片沒有達到這個閾值的，轉換為這個顏色。

源碼如下：

from skimage.metrics import structural_similarity import cv2 import numpy as npif __name__ == '__main__':before = cv2.imread('./pic/demo1.png')after = cv2.imread('./pic/demo2.png')# Convert images to grayscalebefore_gray = cv2.cvtColor(before, cv2.COLOR_BGR2GRAY)after_gray = cv2.cvtColor(after, cv2.COLOR_BGR2GRAY)# Compute SSIM between two images(score, diff) = structural_similarity(before_gray, after_gray, full=True)print("Image similarity", score)# The diff image contains the actual image differences between the two images# and is represented as a floating point data type in the range [0,1]# so we must convert the array to 8-bit unsigned integers in the range# [0,255] before we can use it with OpenCVdiff = (diff * 255).astype("uint8")# Threshold the difference image, followed by finding contours to# obtain the regions of the two input images that differthresh = cv2.threshold(diff, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]contours = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)contours = contours[0] if len(contours) == 2 else contours[1]mask = np.zeros(before.shape, dtype='uint8')filled_after = after.copy()for c in contours:area = cv2.contourArea(c)if area > 40:x, y, w, h = cv2.boundingRect(c)cv2.rectangle(before, (x, y), (x + w, y + h), (36, 255, 12), 2)cv2.rectangle(after, (x, y), (x + w, y + h), (36, 255, 12), 2)cv2.drawContours(mask, [c], 0, (0, 255, 0), -1)cv2.drawContours(filled_after, [c], 0, (0, 255, 0), -1)cv2.imshow('before', before)cv2.imshow('after', after)cv2.imshow('diff', diff)cv2.imshow('mask', mask)cv2.imshow('filled after', filled_after)cv2.waitKey(0)

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