68d7d6a3cb18578616ae2f3b445b6ba87fd6baf3,dvalib/facerecognition.py,,represent,#Any#Any#Any#,109

Before Change

            logging.info("Forward pass took avg of %.3f[seconds/image] for %d images\n" % (time_avg_forward_pass, nrof_images))
            logging.info("Finally saving embeddings and gallery to: %s" % (output_dir))
            np.save(os.path.join(output_dir, "facenet.npy"), emb_array)
            with open(os.path.join(output_dir, "facenet.framelist"),"w") as fh:
                for c in range(count):
                    fh.write("{}\n".format(path_count[c]))
            return path_count,emb_array

After Change

            emb_array = np.zeros((nrof_images, embedding_size))
            count = 0
            path_count = {}
            fh = open(os.path.join(output_dir, "facenet.framelist"),"w")
            for i in xrange(nrof_batches):
                start_index = i * batch_size
                end_index = min((i + 1) * batch_size, nrof_images)
                paths_batch = paths[start_index:end_index]
                for fname in paths_batch:
                    count += 1
                    fh.write("{}_{}\n".format(fname,paths_to_pk[fname]))
                images = facenet.load_data(paths_batch, do_random_crop=False, do_random_flip=False,image_size=image_size, do_prewhiten=True)
                feed_dict = {images_placeholder: images, phase_train_placeholder: False}
                emb_array[start_index:end_index, :] = sess.run(embeddings, feed_dict=feed_dict)
            time_avg_forward_pass = (time.time() - start_time) / float(nrof_images)
            logging.info("Forward pass took avg of %.3f[seconds/image] for %d images\n" % (time_avg_forward_pass, nrof_images))
            logging.info("Finally saving embeddings and gallery to: %s" % (output_dir))
            np.save(os.path.join(output_dir, "facenet.npy"), emb_array)
            fh.close()
            return path_count,emb_array

Instances