Python笔记-使用SSIM找两张图不同及使用Opencv显示

川长思鸟来 2022-08-28 08:45 164阅读 0赞

运行截图如下：

![watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBASVQxOTk1_size_20_color_FFFFFF_t_70_g_se_x_16][]

这里有几点要注意的：

①对比两张图片的函数SSIM具体是structural\_similarity：

Ⅰ：第一个参数和第二个参数是要对比的两张图片；

Ⅱ：参数中有个full，默认为True，如果为True会把两张图的相似部分返回，如果为False就不返回了；

Ⅳ：返回值full为False的时候有2个，一个是mssim浮点型的相似度，1代表2张图一模一样。0代表完全不一样，grad返回两张图片不同的地方类型为ndarray；

②SSIM返回的ndarray里面的值为\[0, 1\]的float型，而OpenCV的\[0, 255\]为uint8型，可以通过这样的方式转换：

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

③cv2.threshold函数，设置一个阈值和一个颜色，将传入的图片没有达到这个阈值的，转换为这个颜色。

源码如下：

from skimage.metrics import structural_similarity
    import cv2
    import numpy as np
    
    
    if __name__ == '__main__':
    
    
        before = cv2.imread('./pic/demo1.png')
        after = cv2.imread('./pic/demo2.png')
    
        # Convert images to grayscale
        before_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 OpenCV
        diff = (diff * 255).astype("uint8")
    
        # Threshold the difference image, followed by finding contours to
        # obtain the regions of the two input images that differ
        thresh = 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)

[watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBASVQxOTk1_size_20_color_FFFFFF_t_70_g_se_x_16]: /images/20220828/347b057868bb4d808ef8cd79de3536b7.png