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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
# Kích thước ảnh đầu vào và số lượng đặc trưng
IMG_SIZE = 224
NUM_FEATURES = 2048
# Tải mô hình CNN-RNN từ HuggingFace
model = from_pretrained_keras("keras-io/video-classification-cnn-rnn")
# Tạo danh sách video ví dụ từ thư mục Samples
samples = []
for file in os.listdir("Samples"):
tag = file.split("_")[1]
samples.append([f"samples/{file}"])
# Cắt phần hình vuông ở trung tâm frame
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]
# Đọc video và xử lý từng frame
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)
# Xây dựng mô hình trích xuất đặc trưng (InceptionV3)
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")
# Tạo feature extractor 1 lần
feature_extractor = build_feature_extractor()
# Trích xuất đặc trưng cho từng frame của video
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
# Dự đoán hành động từ video
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
# HTML mô tả bên dưới app
article = article = "<div style='text-align: center;'><a href='https://github.com/ChainYo' target='_blank'>Space by Thomas Chaigneau</a><br><a href='https://keras.io/examples/vision/video_classification/' target='_blank'>Keras example by Sayak Paul</a></div>"
# Tạo giao diện Gradio
app = gr.Interface(
fn=sequence_prediction,
inputs=[gr.Video(label="Video")],
outputs=gr.Label(label="Prediction"),
title="Keras Video Classification with CNN-RNN",
description="Video classification demo using CNN-RNN based model.",
article=article,
examples=samples
)
# Khởi chạy ứng dụng
app.launch()
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