用OpenCV Python实现两幅图像差异的检测和可视化

Question

我有两个图像，想让它明显的差异在哪里。我想添加颜色的两个图像，这样用户可以清楚地发现所有的差异在一两秒钟内。

例如，以下是两个有一些不同之处的图像：

jpg：

我目前的方法使差异明显，是创建一个蒙版（两个图像之间的差异），把它涂成红色，然后把它添加到图像中。目标是用强烈的红色清晰地标记所有的差异。下面是我当前的代码：

import cv2

# load images
image1 = cv2.imread("leftImage.jpg")
image2 = cv2.imread("rightImage.jpg")

# compute difference
difference = cv2.subtract(image1, image2)

# color the mask red
Conv_hsv_Gray = cv2.cvtColor(difference, cv2.COLOR_BGR2GRAY)
ret, mask = cv2.threshold(Conv_hsv_Gray, 0, 255,cv2.THRESH_BINARY_INV |cv2.THRESH_OTSU)
difference[mask != 255] = [0, 0, 255]

# add the red mask to the images to make the differences obvious
image1[mask != 255] = [0, 0, 255]
image2[mask != 255] = [0, 0, 255]

# store images
cv2.imwrite('diffOverImage1.png', image1)
cv2.imwrite('diffOverImage2.png', image1)
cv2.imwrite('diff.png', difference)

diff.png：

DiffoverImage1.PNG

Answer 1

为了可视化两幅图像之间的差异，我们可以利用图像质量评价中引入的结构相似度指数(SSIM)来定量地确定图像之间的准确差异：从误差可见性到结构相似度。该方法已经在用于图像处理的scikit-image库中实现。您可以使用PIP安装scikit-image安装scikit-image。

使用scikit-image中的structural_simality()函数，它返回score和差异图像diff。score表示两个输入图像之间的结构相似性指数，可以在[-1,1]范围内，其值更接近一，表示更高的相似性。但是由于您只对这两个图像的不同之处感兴趣，所以我们将重点关注diff图像。具体地说，diff图像包含实际的图像差异，较暗的区域具有更大的差异。较大的差异区域用黑色突出，而较小的差异用灰色突出。

灰色噪声区域可能是由于。jpg有损压缩。如果我们使用无损压缩图像格式，我们将获得更干净的结果。两幅图像比较后的SSIM评分显示它们非常相似。

图像相似度0.9198863419190031

现在我们通过diff图像进行过滤，因为我们只想找出图像之间的巨大差异。我们迭代每个轮廓，用一个最小阈值区域滤波去除灰度噪声，并用一个包围盒突出差异。结果是这样的。

为了显示确切的差异，我们将轮廓填充到蒙版和原始图像上。

from skimage.metrics import structural_similarity
import cv2
import numpy as np

before = cv2.imread('left.jpg')
after = cv2.imread('right.jpg')

# 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)