from skimage.metrics import structural_similarity as ssim def compare_ssim(img1, img2): """ 使用SSIM来计算两张图片的相似度。 """ score, diff = ssim(np.array(img1), np.array(img2), full=True) return score, diff score, diff = compare_ssim(img1, img2) print(f"SSIM: {score}, Di...
cv2.COLOR_BGR2RGB)# 计算直方图histogram,_=np.histogram(image.ravel(),bins=256,range=[0,256])# 归一化histogram=histogram.astype('float')/histogram.sum()returnhistogramdefcompare_images(image1_path,image2_path):# 计算直方图hist1=calculate_histogram...
return ncc_val def compare_images(imageA, imageB, title): # 分别计算输入图片的MSE和SSIM指标值的大小 m = mse(imageA, imageB) s = ssim(imageA, imageB) # 创建figure fig = plt.figure(title) plt.suptitle("MSE: %.2f, SSIM: %.2f" % (m, s)) # plt.suptitle("MSE: %.2f, SSIM:...
is_img.append(k)elifknotinrepeat_img: is_img.append(k) repeat_img.extend(v)else: repeat_img.extend(v)print(len(is_img)) 单张图片调用方法 fromimagededup.methodsimportPHashdefcompare_image_similarity(photo_id, photo_path, encoding_map: dict):"""比较图片相似度 :param photo_id: :param pho...
imread("image1.jpg") image2 = cv2.imread("image2.jpg") # 比较图像 compare_images(image1, image2) 这段代码使用OpenCV库中的cv2.cvtColor()函数将图像转换为灰度图像,然后使用structural_similarity()函数计算图像的相似度。最后,使用cv2.imshow()函数显示比较结果。
方法添加到base_page.py中。 def compare_image_with_screenshot(self，image_name: str)：os.chdir(...
("两张图片不一样")# convert the images to grayscalegrayA=cv2.cvtColor(imageA,cv2.COLOR_BGR2GRAY)grayB=cv2.cvtColor(imageB,cv2.COLOR_BGR2GRAY)# compute the Structural Similarity Index (SSIM) between the two# images, ensuring that the difference image is returned(score,diff)=compare_ssim(...
使用scikit-image中的compare_ssim函数,我们计算得分和差异图像diff。 分数表示两个输入图像之间的结构相似性指数。 该值在[-1,1]范围内,值为1是“完美匹配”。 差异图像包含我们希望可视化的两个输入图像之间的实际图像差异。 差异图像当前表示为[0,1]范围内的浮点数据类型,因此我们首先将数组转换为[0,255]范围...
err/= float(imageA.shape[0] * imageA.shape[1])#return the MSE, the lower the error, the more "similar"#the two images arereturnerrdefcompare_images(imageA, imageB, title):#compute the mean squared error and structural similarity#index for the imagesm =mse(imageA, imageB) ...
ssim = compare_ssim(img1, img2, multichannel=True) print(ssim) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 如果skimage版本比较高的话,用下面代码, import cv2 from skimage.metrics import structural_similarity import time ...