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import tempfile
import gradio as gr
import tensorflow as tf
from moviepy.editor import VideoFileClip
from moviepy.video.io.ImageSequenceClip import ImageSequenceClip
from configuration import Config
from model import load_classifier, load_detector
from inference import format_frame, detect_object, classify_action, draw_boxes
config = Config()
print(f'TensorFlow {tf.__version__}')
print(f'Load classifier from {config.classifier_path}')
classifier = load_classifier(config)
classifier.summary()
print('Load detector.')
detector = load_detector(config)
def fn(video: gr.Video):
print('Process video.')
with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as f:
output = f.name
clip = VideoFileClip(video)
processed_frames = []
frames = []
actions = []
detections = ([], [])
for i, frame in enumerate(clip.iter_frames()):
if i % config.classify_action_frame_steps == 0:
frames.append(format_frame(frame, config))
if i % config.detect_object_frame_steps == 0:
print(f'Detect object: Frame {i}')
detections = detect_object(detector, frame)
if len(frames) == config.classify_action_num_frames:
print(f'Classify action: Until frame {i}')
actions = classify_action(classifier, frames, config.id_to_name)
frames = []
frame = draw_boxes(frame, detections, actions)
processed_frames.append(frame)
if i % config.yield_frame_steps == 0:
yield frame, None
processed_clip = ImageSequenceClip(processed_frames, clip.fps)
processed_clip.audio = clip.audio
processed_clip.write_videofile(output, fps=clip.fps, audio_codec='aac', logger=None)
yield frame, output
inputs = gr.Video(sources=['upload'], label='Input Video')
outputs = [
gr.Image(interactive=False, label='Last Frame Processed'),
gr.Video(interactive=False, label='Aeroplane Position and Action Marked')]
examples = [
['examples/ZFLFDfovqls_001310_001320.mp4'], # cspell: disable-line
['examples/Zv7GyH-fpEY_2023.0_2033.0.mp4']]
iface = gr.Interface(
title='Aeroplane Position and Action Detection',
description='Detect aeroplane position and action in a video.',
theme='soft',
fn=fn,
inputs=inputs,
outputs=outputs,
examples=examples,
cache_examples=False)
iface.launch()
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