from skimage.metrics import structural_similarity as compare_ssim
import cv2

# 加载两张图片并将他们转换为灰度
imageA = cv2.imread(r"/Users/dcc/Desktop/333.JPG")
imageB = cv2.imread(r"/Users/dcc/Desktop/4444.JPG")

grayA = cv2.cvtColor(imageA, cv2.COLOR_BGR2GRAY)
grayB = cv2.cvtColor(imageB, cv2.COLOR_BGR2GRAY)

# 计算两个灰度图像之间的结构相似度指数
(score, diff) = compare_ssim(grayA, grayB, full=True)
diff = (diff * 255).astype("uint8")
print("SSIM:{}".format(score))

#找到不同点的轮廓以致于我们可以在被标识为“不同”的区域周围放置矩形
thresh = cv2.threshold(diff, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
contours, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

# #找到一系列区域，在区域周围放置矩形
for c in contours:
    (x, y, w, h) = cv2.boundingRect(c)
    cv2.rectangle(imageA, (x,y), (x+w,y+h), (0,0,255), 2)
    cv2.rectangle(imageB, (x,y), (x+w,y+h), (0,0,255), 2)
#用cv2.imshow 展现最终对比之后的图片， cv2.imwrite 保存最终的结果图片
cv2.imshow("Modified", imageB)
cv2.imwrite(r"/Users/dcc/Desktop/99999999999.png", imageB)
cv2.waitKey(0)