使用scikit-image库中的compare_ssim函数来计算两张图片的SSIM: from skimage.metrics import structural_similarity as compare_ssim (score, diff) = compare_ssim(gray_image1, gray_image2, full=True) 五、解释SSIM得分 SSIM得分范围在-1到1
print(combined_compare('image1.png', 'image2.png')) 4.2、可调参数 在综合比较方法中,可以根据实际需求调整哈希值算法和SSIM阈值,以提高比较的准确性和灵活性。 import imagehash from PIL import Image from skimage.metrics import structural_similarity as ssim import cv2 def combined_compare(img1_path, ...
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:...
print(img1.shape) 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 # 读取图像 img1 = cv2.imread(r'...
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 =...
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 photo_path: :param encoding_map: 哈希值map 首次传空 {} ...
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。
64.b64encode(img.read().decode(‘ascii’) second_image = self._driver.get_images_similarity(...
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(grayA, grayB, full=True) diff = (diff *255).astype("uint8")print("SSIM: {}".format...
from skimage.metrics import structural_similarity as ssim import cv2 def compare_images(imageA, imageB): # 将图像转换为灰度 grayA = cv2.cvtColor(imageA, cv2.COLOR_BGR2GRAY) grayB = cv2.cvtColor(imageB, cv2.COLOR_BGR2GRAY) # 计算SSIM指数 ...