如何使用Python比较一个图像与多个其他图像的SSIM？

Question

我使用这个很棒的页面：https://www.pyimagesearch.com/2014/09/15/python-compare-two-images/，我能够找到三张图片之间的SSIM值。

# import the necessary packages
from skimage.measure import structural_similarity as ssim
import matplotlib.pyplot as plt
import numpy as np
import cv2 as cv

def mse(imageA, imageB):
    # the 'Mean Squared Error' between the two images is the
    # sum of the squared difference between the two images;
    # NOTE: the two images must have the same dimension
    err = np.sum((imageA.astype("float") - imageB.astype("float")) ** 2)
    err /= float(imageA.shape[0] * imageA.shape[1])
    
    # return the MSE, the lower the error, the more "similar"
    # the two images are
    return err

def compare_images(imageA, imageB, title):
    # compute the mean squared error and structural similarity
    # index for the images
    m = mse(imageA, imageB)
    s = ssim(imageA, imageB)

    # setup the figure
    fig = plt.figure(title)
    plt.suptitle("MSE: %.2f, SSIM: %.2f" % (m, s))

    # show first image
    ax = fig.add_subplot(1, 2, 1)
    plt.imshow(imageA, cmap = plt.cm.gray)
    plt.axis("off")

    # show the second image
    ax = fig.add_subplot(1, 2, 2)
    plt.imshow(imageB, cmap = plt.cm.gray)
    plt.axis("off")

    # show the images
    plt.show()

# load the images -- the original, the original + contrast,
# and the original + photoshop
original = cv.imread("images/jp_gates_original.png")
contrast = cv.imread("images/jp_gates_contrast.png")
shopped = cv.imread("images/jp_gates_photoshopped.png")

# convert the images to grayscale
original = cv.cvtColor(original, cv.COLOR_BGR2GRAY)
contrast = cv.cvtColor(contrast, cv.COLOR_BGR2GRAY)
shopped = cv.cvtColor(shopped, cv.COLOR_BGR2GRAY)

# initialize the figure
fig = plt.figure("Images")
images = ("Original", original), ("Contrast", contrast), ("Photoshopped", 
shopped)

# loop over the images
for (i, (name, image)) in enumerate(images):
    # show the image
    ax = fig.add_subplot(1, 3, i + 1)
    ax.set_title(name)
    plt.imshow(image, cmap = plt.cm.gray)
    plt.axis("off")

# show the figure
plt.show()

# compare the images
compare_images(original, original, "Original vs. Original")
compare_images(original, contrast, "Original vs. Contrast")
compare_images(original, shopped, "Original vs. Photoshopped")

不过，我不太确定如何将其应用于许多图像。特别是，我如何从数百张图片的文件夹中取出一张图像（测试图像），并计算测试图像与所有其他图像之间的MSE / SSIM？

谢谢！

- T. Grove

Answer 1

你只需要在不同的目录之间循环。这将比较目录：first_path和second_path以及它们之间的所有文件。

import os
import cv2

results = []
first_dir = os.fsencode(first_path)
second_dir = os.fsencode(second_path)

# Loop through all files in first directory
for first_file in os.listdir(first_dir):
    first_filename = os.fsdecodoe(first_file)
    first_filepath = os.path.join(os.fsdecode(first_dir), first_filename))
    if first_filename.endswith(".your_extension"):

        # Compare each file in second directory to each file in first directory
        for second_file in os.listdir(second_dir):
            second_filename = os.fsdecode(second_file)
            second_filepath = os.path.join(os.fsdecode(second_dir), second_filename)
            if second_filename.endswith(".your_extension"):
                imageA = cv2.imread(first_filepath)
                imageB = cv2.imread(second_filepath)
                (score, diff) = ssim(imageA, imageB, full=True)
                results.append((first_filepath, second_filepath, score))

代码没有运行，但是它应该可以帮助你得到你需要的结果。如果你只想处理一个文件，那么可以去掉第一个循环，并将imageA = cv2.imread移到前面。

- Hair of Slytherin