{"id":1086639,"date":"2025-01-08T13:27:34","date_gmt":"2025-01-08T05:27:34","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1086639.html"},"modified":"2025-01-08T13:27:36","modified_gmt":"2025-01-08T05:27:36","slug":"python%e5%a6%82%e4%bd%95%e5%88%a4%e6%96%ad%e4%b8%a4%e4%b8%aa%e5%9b%be%e5%83%8f%e7%9b%b8%e4%bc%bc-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1086639.html","title":{"rendered":"python\u5982\u4f55\u5224\u65ad\u4e24\u4e2a\u56fe\u50cf\u76f8\u4f3c"},"content":{"rendered":"

\"python\u5982\u4f55\u5224\u65ad\u4e24\u4e2a\u56fe\u50cf\u76f8\u4f3c\"<\/p>\n

Python\u5224\u65ad\u4e24\u4e2a\u56fe\u50cf\u76f8\u4f3c\u7684\u65b9\u6cd5\u6709\uff1a\u76f4\u65b9\u56fe\u6bd4\u8f83\u3001\u7ed3\u6784\u76f8\u4f3c\u6027\u6bd4\u8f83\u3001\u7279\u5f81\u70b9\u5339\u914d\u3001\u611f\u77e5\u54c8\u5e0c\u7b97\u6cd5\u3002\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u7ed3\u6784\u76f8\u4f3c\u6027\u6bd4\u8f83\uff08SSIM\uff09\u6765\u8be6\u7ec6\u63cf\u8ff0\u3002<\/strong><\/p>\n<\/p>\n

\u7ed3\u6784\u76f8\u4f3c\u6027\u6bd4\u8f83\uff08SSIM\uff09<\/strong>\u662f\u4e00\u79cd\u8861\u91cf\u4e24\u5e45\u56fe\u50cf\u76f8\u4f3c\u5ea6\u7684\u6307\u6807\u3002SSIM\u8003\u8651\u4e86\u4eae\u5ea6\u3001\u5bf9\u6bd4\u5ea6\u548c\u7ed3\u6784\u4fe1\u606f\uff0c\u662f\u4e00\u79cd\u66f4\u7b26\u5408\u4eba\u773c\u89c6\u89c9\u611f\u77e5\u7684\u76f8\u4f3c\u5ea6\u8bc4\u4f30\u65b9\u6cd5\u3002\u901a\u8fc7\u8ba1\u7b97SSIM\u503c\uff0c\u6211\u4eec\u53ef\u4ee5\u5f97\u5230\u4e24\u4e2a\u56fe\u50cf\u7684\u76f8\u4f3c\u5ea6\u5206\u6570\uff0c\u5206\u6570\u8d8a\u9ad8\u8868\u793a\u56fe\u50cf\u8d8a\u76f8\u4f3c\u3002<\/p>\n<\/p>\n

\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Python\u8fdb\u884c\u56fe\u50cf\u76f8\u4f3c\u5ea6\u5224\u65ad\uff1a<\/p>\n<\/p>\n

\u4e00\u3001\u76f4\u65b9\u56fe\u6bd4\u8f83<\/p>\n<\/p>\n

\u76f4\u65b9\u56fe\u6bd4\u8f83\u662f\u4e00\u79cd\u7ecf\u5178\u7684\u56fe\u50cf\u76f8\u4f3c\u5ea6\u8bc4\u4f30\u65b9\u6cd5\u3002\u5b83\u901a\u8fc7\u8ba1\u7b97\u56fe\u50cf\u7684\u989c\u8272\u76f4\u65b9\u56fe\uff0c\u5e76\u6bd4\u8f83\u76f4\u65b9\u56fe\u4e4b\u95f4\u7684\u5dee\u5f02\u6765\u5224\u65ad\u56fe\u50cf\u7684\u76f8\u4f3c\u5ea6\u3002\u5e38\u89c1\u7684\u76f4\u65b9\u56fe\u6bd4\u8f83\u65b9\u6cd5\u6709\u5df4\u6c0f\u8ddd\u79bb\u3001\u76f8\u5173\u6027\u6bd4\u8f83\u548c\u5361\u65b9\u6bd4\u8f83\u7b49\u3002<\/p>\n<\/p>\n

\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528OpenCV\u5e93\u6765\u8ba1\u7b97\u548c\u6bd4\u8f83\u56fe\u50cf\u76f4\u65b9\u56fe\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n

import cv2<\/p>\n
import numpy as np<\/p>\n
def calculate_histogram(image):<\/p>\n
    # \u5c06\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf<\/p>\n
    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)<\/p>\n
    # \u8ba1\u7b97\u56fe\u50cf\u7684\u76f4\u65b9\u56fe<\/p>\n
    histogram = cv2.calcHist([gray_image], [0], None, [256], [0, 256])<\/p>\n
    return histogram<\/p>\n
def compare_histograms(hist1, hist2):<\/p>\n
    # \u4f7f\u7528\u5df4\u6c0f\u8ddd\u79bb\u6bd4\u8f83\u76f4\u65b9\u56fe<\/p>\n
    score = cv2.compareHist(hist1, hist2, cv2.HISTCMP_BHATTACHARYYA)<\/p>\n
    return score<\/p>\n
\u8bfb\u53d6\u4e24\u5e45\u56fe\u50cf<\/strong><\/h2>\n
image1 = cv2.imread('image1.jpg')<\/p>\n
image2 = cv2.imread('image2.jpg')<\/p>\n
\u8ba1\u7b97\u4e24\u5e45\u56fe\u50cf\u7684\u76f4\u65b9\u56fe<\/strong><\/h2>\n
hist1 = calculate_histogram(image1)<\/p>\n
hist2 = calculate_histogram(image2)<\/p>\n
\u6bd4\u8f83\u4e24\u5e45\u56fe\u50cf\u7684\u76f4\u65b9\u56fe<\/strong><\/h2>\n
score = compare_histograms(hist1, hist2)<\/p>\n
print("Histogram comparison score:", score)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u5c06\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf\uff0c\u7136\u540e\u8ba1\u7b97\u5176\u76f4\u65b9\u56fe\u3002\u63a5\u7740\uff0c\u6211\u4eec\u4f7f\u7528\u5df4\u6c0f\u8ddd\u79bb\u6765\u6bd4\u8f83\u4e24\u5e45\u56fe\u50cf\u7684\u76f4\u65b9\u56fe\uff0c\u5e76\u8f93\u51fa\u6bd4\u8f83\u5f97\u5206\u3002\u5f97\u5206\u8d8a\u4f4e\u8868\u793a\u56fe\u50cf\u8d8a\u76f8\u4f3c\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u7ed3\u6784\u76f8\u4f3c\u6027\u6bd4\u8f83\uff08SSIM\uff09<\/p>\n<\/p>\n
\u7ed3\u6784\u76f8\u4f3c\u6027\u6bd4\u8f83\uff08SSIM\uff09\u662f\u4e00\u79cd\u66f4\u7b26\u5408\u4eba\u773c\u89c6\u89c9\u611f\u77e5\u7684\u76f8\u4f3c\u5ea6\u8bc4\u4f30\u65b9\u6cd5\u3002\u5b83\u901a\u8fc7\u8003\u8651\u56fe\u50cf\u7684\u4eae\u5ea6\u3001\u5bf9\u6bd4\u5ea6\u548c\u7ed3\u6784\u4fe1\u606f\u6765\u8861\u91cf\u56fe\u50cf\u7684\u76f8\u4f3c\u5ea6\u3002<\/p>\n<\/p>\n
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528scikit-image\u5e93\u4e2d\u7684ssim\u51fd\u6570\u6765\u8ba1\u7b97\u56fe\u50cf\u7684SSIM\u503c\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
from skimage.metrics import structural_similarity as ssim<\/p>\n
import cv2<\/p>\n
def calculate_ssim(image1, image2):<\/p>\n
    # \u5c06\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf<\/p>\n
    gray_image1 = cv2.cvtColor(image1, cv2.COLOR_BGR2GRAY)<\/p>\n
    gray_image2 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY)<\/p>\n
    # \u8ba1\u7b97\u56fe\u50cf\u7684SSIM\u503c<\/p>\n
    score, _ = ssim(gray_image1, gray_image2, full=True)<\/p>\n
    return score<\/p>\n
\u8bfb\u53d6\u4e24\u5e45\u56fe\u50cf<\/strong><\/h2>\n
image1 = cv2.imread('image1.jpg')<\/p>\n
image2 = cv2.imread('image2.jpg')<\/p>\n
\u8ba1\u7b97\u4e24\u5e45\u56fe\u50cf\u7684SSIM\u503c<\/strong><\/h2>\n
score = calculate_ssim(image1, image2)<\/p>\n
print("SSIM score:", score)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u5c06\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf\uff0c\u7136\u540e\u4f7f\u7528ssim\u51fd\u6570\u8ba1\u7b97\u56fe\u50cf\u7684SSIM\u503c\uff0c\u5e76\u8f93\u51fa\u76f8\u4f3c\u5ea6\u5206\u6570\u3002\u5206\u6570\u8d8a\u9ad8\u8868\u793a\u56fe\u50cf\u8d8a\u76f8\u4f3c\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u7279\u5f81\u70b9\u5339\u914d<\/p>\n<\/p>\n
\u7279\u5f81\u70b9\u5339\u914d\u662f\u4e00\u79cd\u57fa\u4e8e\u56fe\u50cf\u7279\u5f81\u70b9\u8fdb\u884c\u76f8\u4f3c\u5ea6\u8bc4\u4f30\u7684\u65b9\u6cd5\u3002\u5b83\u901a\u8fc7\u68c0\u6d4b\u56fe\u50cf\u4e2d\u7684\u7279\u5f81\u70b9\uff0c\u5e76\u6bd4\u8f83\u7279\u5f81\u70b9\u4e4b\u95f4\u7684\u5339\u914d\u7a0b\u5ea6\u6765\u5224\u65ad\u56fe\u50cf\u7684\u76f8\u4f3c\u5ea6\u3002\u5e38\u89c1\u7684\u7279\u5f81\u70b9\u68c0\u6d4b\u7b97\u6cd5\u6709SIFT\u3001SURF\u548cORB\u7b49\u3002<\/p>\n<\/p>\n
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528OpenCV\u5e93\u4e2d\u7684ORB\u7b97\u6cd5\u8fdb\u884c\u7279\u5f81\u70b9\u5339\u914d\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import cv2<\/p>\n
def match_features(image1, image2):<\/p>\n
    # \u521b\u5efaORB\u7279\u5f81\u68c0\u6d4b\u5668<\/p>\n
    orb = cv2.ORB_create()<\/p>\n
    # \u68c0\u6d4b\u56fe\u50cf\u7684\u7279\u5f81\u70b9\u548c\u63cf\u8ff0\u7b26<\/p>\n
    keypoints1, descriptors1 = orb.detectAndCompute(image1, None)<\/p>\n
    keypoints2, descriptors2 = orb.detectAndCompute(image2, None)<\/p>\n
    # \u521b\u5efaBFMatcher\u8fdb\u884c\u7279\u5f81\u70b9\u5339\u914d<\/p>\n
    bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)<\/p>\n
    matches = bf.match(descriptors1, descriptors2)<\/p>\n
    # \u6839\u636e\u7279\u5f81\u70b9\u5339\u914d\u7684\u8ddd\u79bb\u8fdb\u884c\u6392\u5e8f<\/p>\n
    matches = sorted(matches, key=lambda x: x.distance)<\/p>\n
    return matches<\/p>\n
\u8bfb\u53d6\u4e24\u5e45\u56fe\u50cf<\/strong><\/h2>\n
image1 = cv2.imread('image1.jpg')<\/p>\n
image2 = cv2.imread('image2.jpg')<\/p>\n
\u8fdb\u884c\u7279\u5f81\u70b9\u5339\u914d<\/strong><\/h2>\n
matches = match_features(image1, image2)<\/p>\n
print("Number of matched features:", len(matches))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528ORB\u7b97\u6cd5\u68c0\u6d4b\u56fe\u50cf\u7684\u7279\u5f81\u70b9\u548c\u63cf\u8ff0\u7b26\uff0c\u7136\u540e\u4f7f\u7528BFMatcher\u8fdb\u884c\u7279\u5f81\u70b9\u5339\u914d\uff0c\u5e76\u8f93\u51fa\u5339\u914d\u7684\u7279\u5f81\u70b9\u6570\u91cf\u3002\u5339\u914d\u7684\u7279\u5f81\u70b9\u6570\u91cf\u8d8a\u591a\u8868\u793a\u56fe\u50cf\u8d8a\u76f8\u4f3c\u3002<\/p>\n<\/p>\n
\u56db\u3001\u611f\u77e5\u54c8\u5e0c\u7b97\u6cd5<\/p>\n<\/p>\n
\u611f\u77e5\u54c8\u5e0c\u7b97\u6cd5\u662f\u4e00\u79cd\u57fa\u4e8e\u56fe\u50cf\u611f\u77e5\u7279\u6027\u7684\u76f8\u4f3c\u5ea6\u8bc4\u4f30\u65b9\u6cd5\u3002\u5b83\u901a\u8fc7\u5bf9\u56fe\u50cf\u8fdb\u884c\u54c8\u5e0c\u7f16\u7801\uff0c\u5e76\u6bd4\u8f83\u54c8\u5e0c\u503c\u7684\u5dee\u5f02\u6765\u5224\u65ad\u56fe\u50cf\u7684\u76f8\u4f3c\u5ea6\u3002\u5e38\u89c1\u7684\u611f\u77e5\u54c8\u5e0c\u7b97\u6cd5\u6709\u5dee\u5f02\u54c8\u5e0c\u3001\u5e73\u5747\u54c8\u5e0c\u548c\u611f\u77e5\u54c8\u5e0c\u7b49\u3002<\/p>\n<\/p>\n
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528imagehash\u5e93\u4e2d\u7684\u611f\u77e5\u54c8\u5e0c\u7b97\u6cd5\u6765\u8ba1\u7b97\u56fe\u50cf\u7684\u54c8\u5e0c\u503c\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
from PIL import Image<\/p>\n
import imagehash<\/p>\n
def calculate_hash(image_path):<\/p>\n
    # \u8bfb\u53d6\u56fe\u50cf<\/p>\n
    image = Image.open(image_path)<\/p>\n
    # \u8ba1\u7b97\u56fe\u50cf\u7684\u611f\u77e5\u54c8\u5e0c\u503c<\/p>\n
    hash_value = imagehash.phash(image)<\/p>\n
    return hash_value<\/p>\n
\u8ba1\u7b97\u4e24\u5e45\u56fe\u50cf\u7684\u611f\u77e5\u54c8\u5e0c\u503c<\/strong><\/h2>\n
hash1 = calculate_hash('image1.jpg')<\/p>\n
hash2 = calculate_hash('image2.jpg')<\/p>\n
\u8ba1\u7b97\u54c8\u5e0c\u503c\u4e4b\u95f4\u7684\u6c49\u660e\u8ddd\u79bb<\/strong><\/h2>\n
hamming_distance = hash1 - hash2<\/p>\n
print("Hamming distance:", hamming_distance)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528imagehash\u5e93\u8ba1\u7b97\u56fe\u50cf\u7684\u611f\u77e5\u54c8\u5e0c\u503c\uff0c\u7136\u540e\u8ba1\u7b97\u54c8\u5e0c\u503c\u4e4b\u95f4\u7684\u6c49\u660e\u8ddd\u79bb\uff0c\u5e76\u8f93\u51fa\u76f8\u4f3c\u5ea6\u5206\u6570\u3002\u6c49\u660e\u8ddd\u79bb\u8d8a\u5c0f\u8868\u793a\u56fe\u50cf\u8d8a\u76f8\u4f3c\u3002<\/p>\n<\/p>\n
\u7efc\u4e0a\u6240\u8ff0\uff0c\u4f7f\u7528Python\u5224\u65ad\u4e24\u4e2a\u56fe\u50cf\u7684\u76f8\u4f3c\u5ea6\u6709\u591a\u79cd\u65b9\u6cd5\uff0c\u5305\u62ec\u76f4\u65b9\u56fe\u6bd4\u8f83\u3001\u7ed3\u6784\u76f8\u4f3c\u6027\u6bd4\u8f83\u3001\u7279\u5f81\u70b9\u5339\u914d\u548c\u611f\u77e5\u54c8\u5e0c\u7b97\u6cd5\u7b49\u3002\u5176\u4e2d\uff0c\u7ed3\u6784\u76f8\u4f3c\u6027\u6bd4\u8f83\uff08SSIM\uff09\u662f\u4e00\u79cd\u66f4\u7b26\u5408\u4eba\u773c\u89c6\u89c9\u611f\u77e5\u7684\u76f8\u4f3c\u5ea6\u8bc4\u4f30\u65b9\u6cd5\uff0c\u9002\u7528\u4e8e\u5927\u90e8\u5206\u573a\u666f\u3002\u901a\u8fc7\u5408\u7406\u9009\u62e9\u548c\u7ec4\u5408\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u53ef\u4ee5\u66f4\u51c6\u786e\u5730\u5224\u65ad\u56fe\u50cf\u7684\u76f8\u4f3c\u5ea6\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u4f7f\u7528Python\u6bd4\u8f83\u4e24\u5e45\u56fe\u50cf\u7684\u76f8\u4f3c\u5ea6\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u591a\u79cd\u5e93\u6765\u6bd4\u8f83\u56fe\u50cf\u7684\u76f8\u4f3c\u5ea6\u3002\u5e38\u89c1\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528OpenCV\u5e93\u7684\u7ed3\u6784\u76f8\u4f3c\u6027\u6307\u6570\uff08SSIM\uff09\u6216\u5747\u65b9\u8bef\u5dee\uff08MSE\uff09\u6765\u91cf\u5316\u56fe\u50cf\u4e4b\u95f4\u7684\u5dee\u5f02\u3002\u6b64\u5916\uff0cPIL\u5e93\u4e5f\u53ef\u4ee5\u7528\u6765\u52a0\u8f7d\u548c\u5904\u7406\u56fe\u50cf\u3002\u901a\u8fc7\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u53ef\u4ee5\u5b9e\u73b0\u5bf9\u56fe\u50cf\u76f8\u4f3c\u5ea6\u7684\u7cbe\u786e\u6d4b\u91cf\u3002<\/p>\n
\u54ea\u4e9b\u5e93\u9002\u5408\u7528\u4e8e\u56fe\u50cf\u76f8\u4f3c\u5ea6\u7684\u8ba1\u7b97\uff1f<\/strong>
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