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!pip install -q opencv-python
import os
import tensorflow.compat.v2 as tf
import tensorflow_hub as hub
import numpy as np
import cv2
from IPython import display
import math
Importa modello TF-Hub
Questo tutorial mostra come utilizzare il modello di S3D MIL-NCE da tensorflow Hub per fare il recupero text-to-video per trovare il maggior numero di video simili per una determinata query di testo.
Il modello dispone di 2 firme, una per incastri di video di generazione e uno per la generazione di immersioni di testo. Useremo questi incorporamenti per trovare i vicini più vicini nello spazio di incorporamento.
# Load the model once from TF-Hub.
hub_handle = 'https://tfhub.dev/deepmind/mil-nce/s3d/1'
hub_model = hub.load(hub_handle)
def generate_embeddings(model, input_frames, input_words):
"""Generate embeddings from the model from video frames and input words."""
# Input_frames must be normalized in [0, 1] and of the shape Batch x T x H x W x 3
vision_output = model.signatures['video'](tf.constant(tf.cast(input_frames, dtype=tf.float32)))
text_output = model.signatures['text'](tf.constant(input_words))
return vision_output['video_embedding'], text_output['text_embedding']
# @title Define video loading and visualization functions { display-mode: "form" }
# Utilities to open video files using CV2
def crop_center_square(frame):
y, x = frame.shape[0:2]
min_dim = min(y, x)
start_x = (x // 2) - (min_dim // 2)
start_y = (y // 2) - (min_dim // 2)
return frame[start_y:start_y+min_dim,start_x:start_x+min_dim]
def load_video(video_url, max_frames=32, resize=(224, 224)):
path = tf.keras.utils.get_file(os.path.basename(video_url)[-128:], video_url)
cap = cv2.VideoCapture(path)
frames = []
try:
while True:
ret, frame = cap.read()
if not ret:
break
frame = crop_center_square(frame)
frame = cv2.resize(frame, resize)
frame = frame[:, :, [2, 1, 0]]
frames.append(frame)
if len(frames) == max_frames:
break
finally:
cap.release()
frames = np.array(frames)
if len(frames) < max_frames:
n_repeat = int(math.ceil(max_frames / float(len(frames))))
frames = frames.repeat(n_repeat, axis=0)
frames = frames[:max_frames]
return frames / 255.0
def display_video(urls):
html = '<table>'
html += '<tr><th>Video 1</th><th>Video 2</th><th>Video 3</th></tr><tr>'
for url in urls:
html += '<td>'
html += '<img src="{}" height="224">'.format(url)
html += '</td>'
html += '</tr></table>'
return display.HTML(html)
def display_query_and_results_video(query, urls, scores):
"""Display a text query and the top result videos and scores."""
sorted_ix = np.argsort(-scores)
html = ''
html += '<h2>Input query: <i>{}</i> </h2><div>'.format(query)
html += 'Results: <div>'
html += '<table>'
html += '<tr><th>Rank #1, Score:{:.2f}</th>'.format(scores[sorted_ix[0]])
html += '<th>Rank #2, Score:{:.2f}</th>'.format(scores[sorted_ix[1]])
html += '<th>Rank #3, Score:{:.2f}</th></tr><tr>'.format(scores[sorted_ix[2]])
for i, idx in enumerate(sorted_ix):
url = urls[sorted_ix[i]];
html += '<td>'
html += '<img src="{}" height="224">'.format(url)
html += '</td>'
html += '</tr></table>'
return html
# @title Load example videos and define text queries { display-mode: "form" }
video_1_url = 'https://upload.wikimedia.org/wikipedia/commons/b/b0/YosriAirTerjun.gif' # @param {type:"string"}
video_2_url = 'https://upload.wikimedia.org/wikipedia/commons/e/e6/Guitar_solo_gif.gif' # @param {type:"string"}
video_3_url = 'https://upload.wikimedia.org/wikipedia/commons/3/30/2009-08-16-autodrift-by-RalfR-gif-by-wau.gif' # @param {type:"string"}
video_1 = load_video(video_1_url)
video_2 = load_video(video_2_url)
video_3 = load_video(video_3_url)
all_videos = [video_1, video_2, video_3]
query_1_video = 'waterfall' # @param {type:"string"}
query_2_video = 'playing guitar' # @param {type:"string"}
query_3_video = 'car drifting' # @param {type:"string"}
all_queries_video = [query_1_video, query_2_video, query_3_video]
all_videos_urls = [video_1_url, video_2_url, video_3_url]
display_video(all_videos_urls)
Downloading data from https://upload.wikimedia.org/wikipedia/commons/b/b0/YosriAirTerjun.gif 1212416/1207385 [==============================] - 0s 0us/step 1220608/1207385 [==============================] - 0s 0us/step Downloading data from https://upload.wikimedia.org/wikipedia/commons/e/e6/Guitar_solo_gif.gif 1024000/1021622 [==============================] - 0s 0us/step 1032192/1021622 [==============================] - 0s 0us/step Downloading data from https://upload.wikimedia.org/wikipedia/commons/3/30/2009-08-16-autodrift-by-RalfR-gif-by-wau.gif 1507328/1506603 [==============================] - 1s 1us/step 1515520/1506603 [==============================] - 1s 1us/step
Dimostra il recupero dal testo al video
# Prepare video inputs.
videos_np = np.stack(all_videos, axis=0)
# Prepare text input.
words_np = np.array(all_queries_video)
# Generate the video and text embeddings.
video_embd, text_embd = generate_embeddings(hub_model, videos_np, words_np)
# Scores between video and text is computed by dot products.
all_scores = np.dot(text_embd, tf.transpose(video_embd))
# Display results.
html = ''
for i, words in enumerate(words_np):
html += display_query_and_results_video(words, all_videos_urls, all_scores[i, :])
html += '<br>'
display.HTML(html)