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# -*- coding: utf-8 -*-
"""🎬 Keras Video Classification CNN-RNN model
Spaces for showing the model usage.
Author:
- Thomas Chaigneau @ChainYo
"""
import os
import cv2
import gradio as gr
import numpy as np
from tensorflow import keras
from tensorflow_docs.vis import embed
from huggingface_hub import from_pretrained_keras
IMG_SIZE = 224
NUM_FEATURES = 2048
model = from_pretrained_keras("keras-io/video-classification-cnn-rnn")
samples = []
for file in os.listdir("samples"):
tag = file.split("_")[0]
samples.append([f"samples/{file}"])
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(path, max_frames=0, resize=(IMG_SIZE, IMG_SIZE)):
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()
return np.array(frames)
def build_feature_extractor():
feature_extractor = keras.applications.InceptionV3(
weights="imagenet",
include_top=False,
pooling="avg",
input_shape=(IMG_SIZE, IMG_SIZE, 3),
)
preprocess_input = keras.applications.inception_v3.preprocess_input
inputs = keras.Input((IMG_SIZE, IMG_SIZE, 3))
preprocessed = preprocess_input(inputs)
outputs = feature_extractor(preprocessed)
return keras.Model(inputs, outputs, name="feature_extractor")
feature_extractor = build_feature_extractor()
def prepare_video(frames, max_seq_length: int = 20):
frames = frames[None, ...]
frame_mask = np.zeros(shape=(1, max_seq_length,), dtype="bool")
frame_features = np.zeros(shape=(1, max_seq_length, NUM_FEATURES), dtype="float32")
for i, batch in enumerate(frames):
video_length = batch.shape[0]
length = min(max_seq_length, video_length)
for j in range(length):
frame_features[i, j, :] = feature_extractor.predict(batch[None, j, :])
frame_mask[i, :length] = 1 # 1 = not masked, 0 = masked
return frame_features, frame_mask
def sequence_prediction(path):
class_vocab = ["CricketShot", "PlayingCello", "Punch", "ShavingBeard", "TennisSwing"]
frames = load_video(path)
frame_features, frame_mask = prepare_video(frames)
probabilities = model.predict([frame_features, frame_mask])[0]
preds = {}
for i in np.argsort(probabilities)[::-1]:
preds[class_vocab[i]] = float(probabilities[i])
return preds
article = article = "<p style='text-align: center'><a href='http://allaideas.com/index.html' target='_blank'>Video Segmentación: Link para mas info</a> </p>"
app = gr.Interface(
sequence_prediction,
inputs=[gr.inputs.Video(label="Video", type="mp4")],
outputs=[gr.outputs.Label(label="Prediction", type="confidences")],
title=" Video Classification ",
description="""
<p>
<center>
Demo de clasificador de video, el objetivo es reconocer un segemento y recortarlo .
<img src="https://raw.githubusercontent.com/All-Aideas/sea_apirest/main/logo.png" alt="logo" width="250"/>
</center>
</p>
""",
article=article,
examples=samples
).launch(enable_queue=True, cache_examples=True)
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