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"""
Video Processor - Handles video I/O, frame processing, and overlay generation
Manages the complete video processing pipeline
"""
import cv2
import numpy as np
from pathlib import Path
from typing import Optional, Callable, Dict, Any, List, Tuple
import logging
from app.config import Config
from app.core.pose_analyzer import PoseAnalyzer, PoseKeypoints
from app.core.movement_classifier import MovementClassifier, MovementMetrics
from app.utils.helpers import timing_decorator
from app.utils.file_utils import format_file_size
logger = logging.getLogger(__name__)
import inspect
import asyncio
class VideoProcessor:
"""
Manages video loading, processing, and output generation
Coordinates between pose analysis and movement classification
"""
def __init__(self):
"""Initialize video processor with analyzer components"""
self.pose_analyzer = PoseAnalyzer()
self.movement_classifier = MovementClassifier()
self.current_video_path: Optional[Path] = None
self.video_info: Dict[str, Any] = {}
logger.info("VideoProcessor initialized")
def _safe_callback(self, callback, *args, **kwargs):
"""Safely handle async or sync progress callbacks."""
if inspect.iscoroutinefunction(callback):
asyncio.create_task(callback(*args, **kwargs))
else:
callback(*args, **kwargs)
def load_video(self, video_path: Path) -> Dict[str, Any]:
"""
Load video and extract metadata
Args:
video_path: Path to video file
Returns:
Dictionary with video information
Raises:
ValueError: If video cannot be loaded
"""
if not video_path.exists():
raise ValueError(f"Video file not found: {video_path}")
# Open video with OpenCV
cap = cv2.VideoCapture(str(video_path))
if not cap.isOpened():
raise ValueError(f"Cannot open video file: {video_path}")
# Extract video properties
fps = cap.get(cv2.CAP_PROP_FPS)
frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
duration = frame_count / fps if fps > 0 else 0
cap.release()
# Validate video duration
if duration > Config.MAX_VIDEO_DURATION:
raise ValueError(
f"Video too long: {duration:.1f}s (max: {Config.MAX_VIDEO_DURATION}s)"
)
# Store video info
self.current_video_path = video_path
size_bytes = video_path.stat().st_size
self.video_info = {
"path": str(video_path),
"filename": video_path.name,
"fps": fps,
"frame_count": frame_count,
"width": width,
"height": height,
"duration": duration,
"size_bytes": size_bytes,
"size": format_file_size(size_bytes)
}
logger.info(f"Loaded video: {video_path.name} ({width}x{height}, "
f"{fps:.1f} fps, {duration:.1f}s)")
return self.video_info
@timing_decorator
def process_video(self, video_path: Path, output_path: Path,
progress_callback: Optional[Callable[[float, str], None]] = None) -> Dict[str, Any]:
"""
Process video with pose detection and movement analysis
Args:
video_path: Input video path
output_path: Output video path
progress_callback: Optional callback for progress updates (progress, message)
Returns:
Dictionary with processing results and analysis
"""
# Load video info
video_info = self.load_video(video_path)
# Reset analyzers for fresh processing
self.pose_analyzer.reset()
self.movement_classifier.reset()
# Open video for reading
cap = cv2.VideoCapture(str(video_path))
# Setup video writer with codec fallback
codecs_to_try = [
('avc1', 'H.264'), # Try H.264 first
('mp4v', 'MPEG-4'), # Fallback to MPEG-4
('XVID', 'Xvid'),
('MJPG', 'Motion JPEG')
]
out = None
last_error = None
for codec_code, codec_name in codecs_to_try:
try:
logger.info(f"Trying codec: {codec_name} ({codec_code})")
fourcc = cv2.VideoWriter_fourcc(*codec_code)
out = cv2.VideoWriter(
str(output_path),
fourcc,
video_info['fps'],
(video_info['width'], video_info['height'])
)
if out.isOpened():
logger.info(f"✅ Successfully initialized VideoWriter with {codec_name} codec")
break
else:
logger.warning(f"Failed to open VideoWriter with {codec_name}")
out.release()
out = None
except Exception as e:
logger.warning(f"Error with {codec_name} codec: {e}")
last_error = e
if out:
out.release()
out = None
if out is None or not out.isOpened():
error_msg = f"Cannot create output video with any codec. Last error: {last_error}"
logger.error(error_msg)
raise ValueError(error_msg)
frame_number = 0
processed_frames = 0
all_keypoints: List[PoseKeypoints] = []
try:
while True:
ret, frame = cap.read()
if not ret:
break
# Calculate timestamp
timestamp = frame_number / video_info['fps']
# Process frame with pose detection
pose_keypoints = self.pose_analyzer.process_frame(
frame, frame_number, timestamp
)
if pose_keypoints:
all_keypoints.append(pose_keypoints)
processed_frames += 1
# Draw skeleton overlay
annotated_frame = self.pose_analyzer.draw_skeleton_overlay(
frame, pose_keypoints, draw_confidence=True
)
# Add processing status box
annotated_frame = self._add_status_box(
annotated_frame, frame_number, video_info, pose_keypoints
)
# Write frame to output
out.write(annotated_frame)
# Update progress
if progress_callback:
progress = (frame_number + 1) / video_info['frame_count']
message = f"Processing frame {frame_number + 1}/{video_info['frame_count']}"
# progress_callback(progress, message)
self._safe_callback(progress_callback, progress, message)
frame_number += 1
finally:
cap.release()
out.release()
# Analyze movement patterns
if progress_callback:
progress_callback(0.95, "Analyzing movements...")
movement_metrics = None
if all_keypoints:
movement_metrics = self.movement_classifier.analyze_sequence(all_keypoints)
# Get rhythm analysis
rhythm_analysis = {}
if all_keypoints:
rhythm_analysis = self.movement_classifier.detect_rhythm_patterns(
all_keypoints, video_info['fps']
)
# Calculate smoothness
smoothness = 0.0
if all_keypoints:
smoothness = self.movement_classifier.calculate_movement_smoothness(
all_keypoints
)
# Compile results
results = {
"video_info": video_info,
"processing": {
"total_frames": frame_number,
"frames_with_pose": processed_frames,
"detection_rate": processed_frames / frame_number if frame_number > 0 else 0,
"output_path": str(output_path)
},
"pose_analysis": {
"average_confidence": self.pose_analyzer.get_average_confidence(),
"keypoints_detected": len(all_keypoints)
},
"movement_analysis": self._format_movement_metrics(movement_metrics) if movement_metrics else {},
"rhythm_analysis": rhythm_analysis,
"smoothness_score": smoothness,
"summary": self.movement_classifier.get_movement_summary()
}
if progress_callback:
progress_callback(1.0, "Processing complete!")
logger.info(f"Video processing complete: {output_path.name}")
return results
def _add_status_box(self, frame: np.ndarray, frame_number: int,
video_info: Dict[str, Any],
pose_keypoints: Optional[PoseKeypoints]) -> np.ndarray:
"""
Add status information box to frame
Args:
frame: Video frame
frame_number: Current frame number
video_info: Video metadata
pose_keypoints: Detected pose keypoints (if any)
Returns:
Frame with status box
"""
# Create semi-transparent overlay
overlay = frame.copy()
h, w = frame.shape[:2]
# Status box dimensions
box_height = 120
box_width = 300
box_x = w - box_width - 10
box_y = 10
# Draw semi-transparent rectangle
cv2.rectangle(
overlay,
(box_x, box_y),
(box_x + box_width, box_y + box_height),
(0, 0, 0),
-1
)
# Blend with original frame
alpha = 0.6
frame = cv2.addWeighted(overlay, alpha, frame, 1 - alpha, 0)
# Add text information
text_x = box_x + 10
text_y = box_y + 25
line_height = 25
# Frame info
frame_text = f"Frame: {frame_number}/{video_info['frame_count']}"
cv2.putText(frame, frame_text, (text_x, text_y),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
# FPS info
fps_text = f"FPS: {video_info['fps']:.1f}"
cv2.putText(frame, fps_text, (text_x, text_y + line_height),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
# Pose detection status
if pose_keypoints:
status_text = "Pose: DETECTED"
status_color = (0, 255, 0)
conf_text = f"Conf: {pose_keypoints.confidence:.2f}"
else:
status_text = "Pose: NOT DETECTED"
status_color = (0, 0, 255)
conf_text = "Conf: N/A"
cv2.putText(frame, status_text, (text_x, text_y + line_height * 2),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, status_color, 1)
cv2.putText(frame, conf_text, (text_x, text_y + line_height * 3),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
return frame
def _format_movement_metrics(self, metrics: MovementMetrics) -> Dict[str, Any]:
"""
Format movement metrics for JSON serialization
Args:
metrics: MovementMetrics object
Returns:
Dictionary with formatted metrics
"""
return {
"movement_type": metrics.movement_type.value,
"intensity": round(metrics.intensity, 2),
"velocity": round(metrics.velocity, 4),
"body_part_activity": {
part: round(activity, 2)
for part, activity in metrics.body_part_activity.items()
},
"frame_range": metrics.frame_range
}
def extract_frame(self, video_path: Path, frame_number: int) -> Optional[np.ndarray]:
"""
Extract a specific frame from video
Args:
video_path: Path to video file
frame_number: Frame index to extract
Returns:
Frame as numpy array, or None if extraction fails
"""
cap = cv2.VideoCapture(str(video_path))
if not cap.isOpened():
return None
cap.set(cv2.CAP_PROP_POS_FRAMES, frame_number)
ret, frame = cap.read()
cap.release()
return frame if ret else None
def create_thumbnail(self, video_path: Path, output_path: Path,
timestamp: float = 0.0) -> bool:
"""
Create thumbnail image from video
Args:
video_path: Path to video file
output_path: Output image path
timestamp: Timestamp in seconds for thumbnail
Returns:
True if successful
"""
cap = cv2.VideoCapture(str(video_path))
if not cap.isOpened():
return False
# Seek to timestamp
cap.set(cv2.CAP_PROP_POS_MSEC, timestamp * 1000)
ret, frame = cap.read()
cap.release()
if not ret:
return False
# Save thumbnail
success = cv2.imwrite(str(output_path), frame)
return success |