def run_typing_test(topics): """Measure typing speed and accuracy on the command line.""" paragraphs = lines_from_file('data/sample_paragraphs.txt') select = lambda p: True if topics: select = about(topics) i = 0 while True: reference = choose(paragraphs, select, i) if not reference: print('No more paragraphs about', topics, 'are available.') return print('Type the following paragraph and then press enter/return.') print( 'If you only type part of it, you will be scored only on that part.\n' ) print(reference) print() start = datetime.now() typed = input() if not typed: print('Goodbye.') return print() elapsed = (datetime.now() - start).total_seconds() print("Nice work!") print('Words per minute:', wpm(typed, elapsed)) print('Accuracy: ', accuracy(typed, reference)) print('\nPress enter/return for the next paragraph or type q to quit.') if input().strip() == 'q': return i += 1
# Training image_size = 112 batch_size = 32 num_epochs = 20 epoch_size = 28747 train_enqueue_steps = 50 save_steps = 200 # Number of steps to perform saving checkpoints test_steps = 20 # Number of times to test for test accuracy start_test_step = 50 max_checkpoints_to_keep = 2 save_dir = "/home/aiteam/quan/checkpoints/ucf101" train_data_reader = lines_from_file(train_txt, repeat=True) image_paths_placeholder = tf.placeholder(tf.string, shape=(None, num_frames), name='image_paths') labels_placeholder = tf.placeholder(tf.int64, shape=(None, ), name='labels') train_input_queue = data_flow_ops.FIFOQueue(capacity=10000, dtypes=[tf.string, tf.int64], shapes=[(num_frames, ), ()]) train_enqueue_op = train_input_queue.enqueue_many( [image_paths_placeholder, labels_placeholder]) frames_batch, labels_batch = input_pipeline(train_input_queue, batch_size=batch_size,
return resized_image def visualize_frame_batches(frame_batches, num_videos, num_frames): for i in range(num_videos): print("Visualize video ", i) video = frame_batches[i, :, :, :, :] for j in range(num_frames): frame = video[j, :, :, :] frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR) cv2.imshow("frame", frame) cv2.waitKey(100) if __name__ == "__main__": num_frames = 10 root_folder = "/home/ubuntu/datasets/ucf101/train/" data_reader = lines_from_file("/home/ubuntu/datasets/ucf101/train.txt", repeat=True) # image_paths, labels = sample_videos(data_reader, root_folder=root_folder, # num_samples=3, num_frames=num_frames) # # for i in range(3): # print(image_paths[i], labels[i]) image_paths_placeholder = tf.placeholder(tf.string, shape=(None, num_frames), name='image_paths') labels_placeholder = tf.placeholder(tf.int64, shape=(None, ), name='labels') input_queue = data_flow_ops.FIFOQueue(capacity=10000, dtypes=[tf.string, tf.int64], shapes=[(num_frames,), ()]) enqueue_op = input_queue.enqueue_many([image_paths_placeholder, labels_placeholder])