基于内容的图像检索（CBIR）——以图搜图@⽬录在CBIR中，图像通过其视觉内容(例如颜⾊，纹理，形状)来索引。
⼀、实现原理⾸先从图像数据库中提取特征并存储它。
然后我们计算与查询图像相关的特征。
最后，我们检索具有最近特征的图像⼆、基于内容的图像检索的特征提取三、代码实现import numpy as npfrom keras.models import Modelfrom keras.datasets import mnistimport cv2from keras.models import load_modelfrom sklearn.metrics import label_ranking_average_precision_scoreimport timeprint('Loading mnist dataset')t0 = time.time()(x_train, y_train), (x_test, y_test) = mnist.load_data()x_train = x_train.astype('float32') / 255.x_test = x_test.astype('float32') / 255.x_train = np.reshape(x_train, (len(x_train), 28, 28, 1)) # adapt this if using `channels_first` image data formatx_test = np.reshape(x_test, (len(x_test), 28, 28, 1)) # adapt this if using `channels_first` image data formatnoise_factor = 0.5x_train_noisy = x_train + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_train.shape)x_test_noisy = x_test + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_test.shape)x_train_noisy = np.clip(x_train_noisy, 0., 1.)x_test_noisy = np.clip(x_test_noisy, 0., 1.)t1 = time.time()print('mnist dataset loaded in: ', t1-t0)print('Loading model :')t0 = time.time()autoencoder = load_model('autoencoder.h5')encoder = Model(inputs=autoencoder.input, outputs=autoencoder.get_layer('encoder').output)t1 = time.time()print('Model loaded in: ', t1-t0)def retrieve_closest_images(test_element, test_label, n_samples=10):learned_codes = encoder.predict(x_train) # 提取数据库图像的特征向量# 转换成⼀维向量learned_codes = learned_codes.reshape(learned_codes.shape[0],learned_codes.shape[1] * learned_codes.shape[2] * learned_codes.shape[3])learned_codes.shape[1] * learned_codes.shape[2] * learned_codes.shape[3])test_code = encoder.predict(np.array([test_element]))test_code = test_code.reshape(test_code.shape[1] * test_code.shape[2] * test_code.shape[3])distances = []# 计算输⼊图像和数据库所有图像的距离for code in learned_codes:distance = np.linalg.norm(code - test_code)distances.append(distance)# 排序取出距离最⼩的图像nb_elements = learned_codes.shape[0]distances = np.array(distances)learned_code_index = np.arange(nb_elements)labels = np.copy(y_train).astype('float32')labels[labels != test_label] = -1labels[labels == test_label] = 1labels[labels == -1] = 0distance_with_labels = np.stack((distances, labels, learned_code_index), axis=-1)sorted_distance_with_labels = distance_with_labels[distance_with_labels[:, 0].argsort()]sorted_distances = 28 - sorted_distance_with_labels[:, 0]sorted_labels = sorted_distance_with_labels[:, 1]sorted_indexes = sorted_distance_with_labels[:, 2]kept_indexes = sorted_indexes[:n_samples]score = label_ranking_average_precision_score(np.array([sorted_labels[:n_samples]]), np.array([sorted_distances[:n_samples]])) print("Average precision ranking score for tested element is {}".format(score))original_image = x_test[0]cv2.imshow('original_image', original_image)retrieved_images = x_train[int(kept_indexes[0]), :]for i in range(1, n_samples):retrieved_images = np.hstack((retrieved_images, x_train[int(kept_indexes[i]), :]))cv2.imshow('Results', retrieved_images)cv2.waitKey(0)cv2.imwrite('test_results/original_image.jpg', 255 * cv2.resize(original_image, (0,0), fx=3, fy=3))cv2.imwrite('test_results/retrieved_results.jpg', 255 * cv2.resize(retrieved_images, (0,0), fx=2, fy=2))# To retrieve closest imageretrieve_closest_images(x_test[0], y_test[0])打赏如果对您有帮助，就打赏⼀下吧O(∩_∩)O。
