def prepare_full_feature(): image_base_path = "../images/kyoto/" model_path = "/home/ge/tests/vgg16_weights.h5" FeatureExtractor.initialize(model_path) images = np.zeros((500, 4096)) for i in range(500): img = imread(image_base_path + str(i) + ".jpg") feature = FeatureExtractor.feature(img) images[i, :] = feature np.save("../mid-data/full_feature.npy", images)
refined_proposals = [] test_image_path = "../test_images/kyoto2/" image_base_path = "../images/kyoto/" model_path = "/home/ge/tests/vgg16_weights.h5" FeatureExtractor.initialize(model_path) for iter in range(1): if iter == 0: KNN.initialize("../mid-data/full_feature.npy") else: distance_mat = np.zeros((dataset_size, feature_length)) for i in range(dataset_size): image = imread(image_base_path + str(i) + ".jpg") if len(refined_proposals[0][i]) != 0: t_refined_box = refined_proposals[0][i] image = image[t_refined_box[0]:t_refined_box[2], t_refined_box[1]:t_refined_box[3]] distance_mat[i, :] = FeatureExtractor.feature(image) KNN.set_distance_mat(distance_mat) refined_proposals = main_iterate() print str(iter) + " th iteration finished" refined_box = refined_proposals[0] candidate_box = refined_proposals[1] np.save("bbox.npy", refined_proposals) for i in range(dataset_size): image = imread(image_base_path + str(i) + ".jpg") t_refined_box = refined_box[i] if len(t_refined_box) == 0: continue for j in range(len(candidate_box[i])): proposal = candidate_box[i][j] if proposal[2] >= image.shape[1]: