使用 OpenCV Python 检测和可视化两个图像之间的差异 [英] Detect and visualize differences between two images with OpenCV Python

问题描述

我有两张图片，我想说明不同之处.我想为这两个图像添加颜色，以便用户可以在一两秒钟内清楚地发现所有差异.

例如，这里有两张有一些不同的图像:

leftImage.jpg:

rightImage.jpg:

我目前使差异明显的方法是创建一个蒙版(两个图像之间的差异)，将其着色为红色，然后将其添加到图像中.目标是用强烈的红色清楚地标记所有差异.这是我当前的代码:

导入 cv2# 加载图片image1 = cv2.imread("leftImage.jpg")image2 = cv2.imread("rightImage.jpg")# 计算差异差异 = cv2.subtract(image1, image2)# 将面具涂成红色Conv_hsv_Gray = cv2.cvtColor(差异，cv2.COLOR_BGR2GRAY)ret, mask = cv2.threshold(Conv_hsv_Gray, 0, 255,cv2.THRESH_BINARY_INV |cv2.THRESH_OTSU)差异[掩码！= 255] = [0, 0, 255]# 为图像添加红色蒙版，使差异明显image1[mask != 255] = [0, 0, 255]image2[mask != 255] = [0, 0, 255]# 存储图像cv2.imwrite('diffOverImage1.png', image1)cv2.imwrite('diffOverImage2.png', image1)cv2.imwrite('diff.png', 差异)

diff.png:

diffOverImage1.png

diffOverImage2.png

当前代码的问题:计算出的掩码显示了一些差异，但不是全部差异(例如，请参见右上角的小块，或蓝色包上的绳索).这些差异在计算的掩码中只显示得很轻微，但它们应该像其他差异一样清晰地呈现红色.

输入: 2 张有一些差异的图像.

预期输出: 3 个图像:两个输入图像，但突出显示差异(以可配置的颜色清晰突出显示)，第三张图像仅包含差异(蒙版).

解决方案

为了可视化两个图像之间的差异，我们可以使用，该功能仍然存在，但现在在新的skimage.metrics 不同名称的子模块.新的更新函数是 skimage.metrics.structural_similarity

I have two images and would like to make it obvious where the differences are. I want to add color to the two images such that a user can clearly spot all the differences within a second or two.

For example, here are two images with a few differences:

leftImage.jpg:

rightImage.jpg:

My current approach to make the differences obvious, is to create a mask (difference between the two images), color it red, and then add it to the images. The goal is to clearly mark all differences with a strong red color. Here is my current code:

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

diffOverImage2.png

Problem with the current code: The computed mask shows some differences but not all of them (see for example the tiny piece in the upper right corner, or the rope thingy on the blue packet). These differences are shown only very lightly in the computed mask, but they should be clearly red like the other differences.

Input: 2 images with some differences.

Expected Output: 3 images: the two input images but with the differences highlighted (clearly highlighted in a configurable color), and a third image containing only the differences (the mask).

解决方案

To visualize differences between two images, we can take a quantitative approach to determine the exact discrepancies between images using the Structural Similarity Index (SSIM) which was introduced in Image Quality Assessment: From Error Visibility to Structural Similarity. This method is already implemented in the scikit-image library for image processing. You can install scikit-image with pip install scikit-image.

Using the structural_similarity() function from scikit-image, it returns a score and a difference image, diff. The score represents the structural similarity index between the two input images and can fall between the range [-1,1] with values closer to one representing higher similarity. But since you're only interested in where the two images differ, the diff image is what we'll focus on. Specifically, the diff image contains the actual image differences with darker regions having more disparity. Larger areas of disparity are highlighted in black while smaller differences are in gray.

The gray noisy areas are probably due to .jpg lossy compression. We would obtain a cleaner result if we used a lossless compression image format. The SSIM score after comparing the two images show that they are very similar.

Image similarity 0.9198863419190031

Now we filter through the diff image since we only want to find the large differences between the images. We iterate through each contour, filter using a minimum threshold area to remove the gray noise, and highlight the differences with a bounding box. Here's the result.

To visualize the exact differences, we fill the contours onto a mask and on the original image.

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)

Note: scikit-image version used is 0.18.1. In previous versions, the function was skimage.measure.compare_ssim but has been depreciated and removed in 0.18.1. According to the docs, the functionality still exists but is now under the new skimage.metrics submodule under different names. The new updated function is skimage.metrics.structural_similarity

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