def like_or_dislike_users(self, users): # automatically like or dislike users based on your previously trained # model on your historical preference. # facenet settings from export_embeddings.... model_dir = '20170512-110547' data_dir = 'temp_images_aligned' embeddings_name = 'temp_embeddings.npy' labels_name = 'temp_labels.npy' labels_strings_name = 'temp_label_strings.npy' is_aligned = True image_size = 160 margin = 44 gpu_memory_fraction = 1.0 image_batch = 1000 with tf.Graph().as_default(): with tf.Session() as sess: # Load the facenet model facenet.load_model(model_dir) for user in users: clean_temp_images() urls = user.get_photos(width='640') image_list = download_url_photos(urls, user.id, is_temp=True) # align the database tindetheus_align.main(input_dir='temp_images', output_dir='temp_images_aligned') # export the embeddinggs from the aligned database train_set = facenet.get_dataset(data_dir) image_list_temp, label_list = facenet.get_image_paths_and_labels( train_set) label_strings = [ name for name in os.listdir(os.path.expanduser(data_dir)) if os.path.isdir( os.path.join(os.path.expanduser(data_dir), name)) ] # Get input and output tensors images_placeholder = tf.get_default_graph( ).get_tensor_by_name("input:0") embeddings = tf.get_default_graph().get_tensor_by_name( "embeddings:0") phase_train_placeholder = tf.get_default_graph( ).get_tensor_by_name("phase_train:0") # Run forward pass to calculate embeddings nrof_images = len(image_list_temp) print('Number of images: ', nrof_images) batch_size = image_batch if nrof_images % batch_size == 0: nrof_batches = nrof_images // batch_size else: nrof_batches = (nrof_images // batch_size) + 1 print('Number of batches: ', nrof_batches) embedding_size = embeddings.get_shape()[1] emb_array = np.zeros((nrof_images, embedding_size)) start_time = time.time() for i in range(nrof_batches): if i == nrof_batches - 1: n = nrof_images else: n = i * batch_size + batch_size # Get images for the batch if is_aligned is True: images = facenet.load_data( image_list_temp[i * batch_size:n], False, False, image_size) else: images = load_and_align_data( image_list_temp[i * batch_size:n], image_size, margin, gpu_memory_fraction) feed_dict = { images_placeholder: images, phase_train_placeholder: False } # Use the facenet model to calcualte embeddings embed = sess.run(embeddings, feed_dict=feed_dict) emb_array[i * batch_size:n, :] = embed print('Completed batch', i + 1, 'of', nrof_batches) run_time = time.time() - start_time print('Run time: ', run_time) # export emedings and labels label_list = np.array(label_list) np.save(embeddings_name, emb_array) if emb_array.size > 0: # calculate the 128 average embedding per profiles X = calc_avg_emb_temp(emb_array) # ealuate on the model yhat = self.model.predict(X) if yhat[0] == 1: didILike = 'Like' else: didILike = 'Dislike' else: # there were no faces in this profile didILike = 'Dislike' print( '********************************************************' ) print(user.name, user.age, didILike) print( '********************************************************' ) dbase_names = move_images_temp(image_list, user.id) if didILike == 'Like': print(user.like()) self.likes_left -= 1 else: print(user.dislike()) userList = [ user.id, user.name, user.age, user.bio, user.distance_km, user.jobs, user.schools, user.get_photos(width='640'), dbase_names, didILike ] self.al_database.append(userList) np.save('al_database.npy', self.al_database) clean_temp_images_aligned()
def main(model_dir='20170512-110547', data_dir='database_aligned', is_aligned=True, image_size=160, margin=44, gpu_memory_fraction=1.0, image_batch=1000, embeddings_name='embeddings.npy', labels_name='labels.npy', labels_strings_name='label_strings.npy', return_image_list=False): train_set = facenet.get_dataset(data_dir) image_list, label_list = facenet.get_image_paths_and_labels(train_set) # fetch the classes (labels as strings) exactly as it's done in get_dataset path_exp = os.path.expanduser(data_dir) classes = [ path for path in os.listdir(path_exp) if os.path.isdir(os.path.join(path_exp, path)) ] # get the label strings label_strings = [ name for name in classes if os.path.isdir(os.path.join(path_exp, name)) ] with tf.Graph().as_default(): with tf.Session() as sess: # Load the model facenet.load_model(model_dir) # Get input and output tensors images_placeholder = tf.get_default_graph().get_tensor_by_name( "input:0") # noqa: E501 embeddings = tf.get_default_graph().get_tensor_by_name( "embeddings:0") # noqa: E501 phase_train_placeholder = tf.get_default_graph( ).get_tensor_by_name("phase_train:0") # noqa: E501 # Run forward pass to calculate embeddings nrof_images = len(image_list) print('Number of images: ', nrof_images) batch_size = image_batch if nrof_images % batch_size == 0: nrof_batches = nrof_images // batch_size else: nrof_batches = (nrof_images // batch_size) + 1 print('Number of batches: ', nrof_batches) embedding_size = embeddings.get_shape()[1] emb_array = np.zeros((nrof_images, embedding_size)) start_time = time.time() for i in range(nrof_batches): if i == nrof_batches - 1: n = nrof_images else: n = i * batch_size + batch_size # Get images for the batch if is_aligned is True: images = facenet.load_data(image_list[i * batch_size:n], False, False, image_size) else: images = load_and_align_data(image_list[i * batch_size:n], image_size, margin, gpu_memory_fraction) feed_dict = { images_placeholder: images, phase_train_placeholder: False } # Use the facenet model to calculate embeddings embed = sess.run(embeddings, feed_dict=feed_dict) emb_array[i * batch_size:n, :] = embed print('Completed batch', i + 1, 'of', nrof_batches) run_time = time.time() - start_time print('Run time: ', run_time) # export embeddings and labels label_list = np.array(label_list) np.save(embeddings_name, emb_array) if emb_array.size > 0: labels_final = (label_list) - np.min(label_list) np.save(labels_name, labels_final) label_strings = np.array(label_strings) np.save(labels_strings_name, label_strings[labels_final]) np.save('image_list.npy', image_list) if return_image_list: np.save('validation_image_list.npy', image_list) return image_list, emb_array
def like_or_dislike_users(self, users): # automatically like or dislike users based on your previously trained # model on your historical preference. # facenet settings from export_embeddings.... data_dir = 'temp_images_aligned' embeddings_name = 'temp_embeddings.npy' # labels_name = 'temp_labels.npy' # labels_strings_name = 'temp_label_strings.npy' is_aligned = True image_size = 160 margin = 44 gpu_memory_fraction = 1.0 image_batch = 1000 prev_user = None for user in users: clean_temp_images() urls = user.get_photos(width='640') image_list = download_url_photos(urls, user.id, is_temp=True) # align the database tindetheus_align.main(input_dir='temp_images', output_dir='temp_images_aligned') # export the embeddings from the aligned database train_set = facenet.get_dataset(data_dir) image_list_temp, label_list = facenet.get_image_paths_and_labels(train_set) # noqa: E501 # Get input and output tensors images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0") # noqa: E501 embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0") # noqa: E501 phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0") # noqa: E501 # Run forward pass to calculate embeddings nrof_images = len(image_list_temp) print('Number of images: ', nrof_images) batch_size = image_batch if nrof_images % batch_size == 0: nrof_batches = nrof_images // batch_size else: nrof_batches = (nrof_images // batch_size) + 1 print('Number of batches: ', nrof_batches) embedding_size = embeddings.get_shape()[1] emb_array = np.zeros((nrof_images, embedding_size)) start_time = time.time() for i in range(nrof_batches): if i == nrof_batches - 1: n = nrof_images else: n = i*batch_size + batch_size # Get images for the batch if is_aligned is True: images = facenet.load_data(image_list_temp[i*batch_size:n], # noqa: E501 False, False, image_size) else: images = load_and_align_data(image_list_temp[i*batch_size:n], # noqa: E501 image_size, margin, gpu_memory_fraction) feed_dict = {images_placeholder: images, phase_train_placeholder: False} # Use the facenet model to calculate embeddings embed = self.sess.run(embeddings, feed_dict=feed_dict) emb_array[i*batch_size:n, :] = embed print('Completed batch', i+1, 'of', nrof_batches) run_time = time.time() - start_time print('Run time: ', run_time) # export embeddings and labels label_list = np.array(label_list) np.save(embeddings_name, emb_array) if emb_array.size > 0: # calculate the n average embedding per profiles X = calc_avg_emb_temp(emb_array) # evaluate on the model yhat = self.model.predict(X) if yhat[0] == 1: didILike = 'Like' # check to see if this is the same user as before if prev_user == user.id: clean_temp_images_aligned() print('\n\n You have already liked this user!!! \n \n') print('This typically means you have used all of your' ' free likes. Exiting program!!! \n\n') self.likes_left = -1 return else: prev_user = user.id else: didILike = 'Dislike' else: # there were no faces in this profile didILike = 'Dislike' print('**************************************************') print(user.name, user.age, didILike) print('**************************************************') dbase_names = move_images_temp(image_list, user.id) if didILike == 'Like': print(user.like()) self.likes_left -= 1 else: print(user.dislike()) userList = [user.id, user.name, user.age, user.bio, user.distance_km, user.jobs, user.schools, user.get_photos(width='640'), dbase_names, didILike] self.al_database.append(userList) np.save('al_database.npy', self.al_database) clean_temp_images_aligned()