def compare_histograms(imageA, imageB): # 将图像转换为HSV颜色空间 imageA = cv2.cvtColor(imageA, cv2.COLOR_BGR2HSV) imageB = cv2.cvtColor(imageB, cv2.COLOR_BGR2HSV) # 计算图像直方图 histA = cv2.calcHist([imageA], [0, 1], None, [50, 60], [0, 180, 0, 256]) histB = cv2....
img2 = cv2.imread('image2.jpg', 0) similarity = compare_histograms(img1, img2) print(f"Histogram similarity: {similarity}") 二、结构相似性指数(SSIM) SSIM 是衡量图像相似度的常用指标之一。它通过比较图像的亮度、对比度和结构来判断两个图像的相似度。 from skimage.metrics import structural_similar...
# 使用OpenCV进行图像相似度比较importcv2importnumpyasnp# 读取图片imageA=cv2.imread('imageA.jpg')imageB=cv2.imread('imageB.jpg')# 计算SSIMscore,diff=cv2.compare_ssim(imageA,imageB,full=True)# 使用TensorFlow进行特征提取和相似度计算importtensorflowastf model=tf.keras.applications.ResNet50(weights=...
# ssim_val = cv2.SSIM(imageA, imageB) # ssim_val = structural_similarity(imageA, imageB, data_range=255, multichannel=True) ssim_val = structural_similarity(imageA, imageB, data_range=255, channel_axis=1) return ssim_val # 方法三、归一化互相关(NCC) def ncc(imageA, imageB): """...
import numpy as np from PIL import Image from skimage.metrics import structural_similarity as ssim def compare_images(img1_path, img2_path): # 加载图片并转换为灰度图 img1 = Image.open(img1_path).convert('L') img2 = Image.open(img2_path).convert('L') # 转换为NumPy数组 img1_np =...
fromimagededup.methodsimportPHashdefcompare_image_similarity(photo_id, photo_path, encoding_map: dict):"""比较图片相似度 :param photo_id: :param photo_path: :param encoding_map: 哈希值map 首次传空 {} :return:"""encoding=""try: phasher=PHash()#生成图像的二值hash编码encoding =phasher.enc...
h2 = image2.histogram() rms = math.sqrt(reduce(operator.add, list(map(lambdaa,b:(a-b)**2, h1, h2)))/len(h1) )returnrms print pil_image_similarity('/Users/apple/Desktop/git/Vimi_API_Test/Compare_image_test/output.jpg','/Users/apple/Desktop/git/Vimi_API_Test/Compare_image_test/...
cmd输入:pip install scikit-image 安装成功后如下图显示: 三、python计算两张图片的相似率 from skimage.metrics import structural_similarity as sk_cpt_ssim import cv2 def compare_image(): # 传入图片路径,读取图片 image_a = cv2.imread(r'path1') image_b = cv2.imread(r'path1') # 使用色彩空间...
cmd 输入:pip install scikit-image 安装成功后如下图显示: 三、python 计算两张图片的相似率 fromskimage.metricsimportstructural_similarityassk_cpt_ssimimportcv2defcompare_image():# 传入图片路径,读取图片image_a=cv2.imread(r'path1')image_b=cv2.imread(r'path1')# 使用色彩空间转化函数 cv2.cvtColor( ...
在Python中,可以使用scikit-image库中的compare_ssim函数来计算SSIM。 from skimage.metrics import structural_similarity as ssim import cv2 加载两张图片 imageA = cv2.imread('path_to_imageA') imageB = cv2.imread('path_to_imageB') 将图片转换为灰度图 ...