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Web API Wrapper for Team Analysis - Uses main.py as the core engine
This module provides the async/web interface to the core team analysis logic
defined in main.py. It handles:
- Progress updates via database
- Jersey color customization
- Stub management options
"""
import os
import sys
import time
import threading
from typing import Dict, Any, Optional
# Add parent directory for imports
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from main import run_team_analysis as core_run_team_analysis
async def run_team_analysis(
video_path: str,
options: Optional[Dict[str, Any]] = None,
video_id: Optional[str] = None
) -> Dict[str, Any]:
"""
Async wrapper for team analysis (Web API interface).
Uses the core analysis from main.py with web-specific features:
- Progress updates to database
- Jersey color customization
- Stub management options
Args:
video_path: Path to input video
options: Analysis options dict with keys:
- our_team_jersey: Team jersey description
- opponent_jersey: Opponent jersey description
- our_team_id: Team ID (1 or 2)
- read_from_stub: Use cached detections
- clear_stubs_after: Clear stubs after analysis
video_id: Video UUID for progress tracking in database
Returns:
Dictionary with analysis results
"""
import anyio
from app.services.supabase_client import get_supabase_service
supabase = get_supabase_service()
# Shared state for progress updates between threads
progress_state = {"step": "Initializing", "percent": 0, "finished": False}
progress_lock = threading.Lock()
def sync_progress_callback(step: str, percent: int):
"""
Synchronous callback for progress updates - stores in shared state.
The async updater will periodically read and push to database.
"""
# Cap at 99% until the very end to avoid updater exiting early
db_percent = min(percent, 99)
with progress_lock:
progress_state["step"] = step
progress_state["percent"] = db_percent
print(f"[{percent}%] {step}")
async def background_progress_updater():
"""
Periodically reads progress state and updates the database.
Runs in parallel with the analysis thread.
"""
if not (video_id and supabase):
return
last_update = 0
while True:
await anyio.sleep(0.5) # Check every 500ms
with progress_lock:
current_step = progress_state["step"]
current_percent = progress_state["percent"]
is_finished = progress_state["finished"]
# Update database if progress changed
if current_percent > last_update:
try:
await supabase.update("videos", video_id, {
"current_step": current_step,
"progress_percent": current_percent
})
last_update = current_percent
except Exception as e:
print(f"⚠️ Error updating progress: {e}")
# Exit when signaled
if is_finished:
break
try:
# Parse options with defaults - MUST come from user input (web form)
options = options or {}
# Jersey colors MUST be provided by user
our_team_jersey = str(options.get("our_team_jersey") or "").strip()
opponent_jersey = str(options.get("opponent_jersey") or "").strip()
if not our_team_jersey or not opponent_jersey:
raise ValueError("Jersey colors are required - user must select team colors in the web form")
try:
our_team_id = int(options.get("our_team_id") or 1)
except Exception:
our_team_id = 1
our_team_id = 1 if our_team_id not in (1, 2) else our_team_id
read_from_stub = bool(options.get("read_from_stub", False))
clear_stubs_after = bool(options.get("clear_stubs_after", True))
save_annotated_video = bool(options.get("save_annotated_video", True))
# Detection parameters (from user selections)
player_confidence = float(options.get("player_confidence", 0.3))
ball_confidence = float(options.get("ball_confidence", 0.15))
detection_batch_size = int(options.get("detection_batch_size", 10))
image_size = int(options.get("image_size", 1080))
max_players_on_court = int(options.get("max_players_on_court", 10))
# Display parameters (from user preferences)
render_speed_text = bool(options.get("render_speed_text", True))
render_distance_text = bool(options.get("render_distance_text", True))
render_tactical_view = bool(options.get("render_tactical_view", True))
render_court_keypoints = bool(options.get("render_court_keypoints", True))
# Output paths (use absolute paths to avoid working directory issues)
from configs import STUBS_DEFAULT_PATH
stub_root = os.path.join(STUBS_DEFAULT_PATH, "api", str(video_id or "no-id"))
# Create absolute path for output video
backend_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
annotated_dir = os.path.join(backend_dir, "output_videos", "annotated")
os.makedirs(annotated_dir, exist_ok=True)
output_path = os.path.join(annotated_dir, f"{video_id or 'latest'}.mp4")
# Start progress updater task and analysis in parallel
async with anyio.create_task_group() as tg:
# Start the background progress updater
tg.start_soon(background_progress_updater)
# Run the analysis in a thread
def run_sync():
"""Synchronous wrapper for core analysis."""
return core_run_team_analysis(
video_path=video_path,
output_path=output_path,
stub_path=stub_root,
our_team_jersey=our_team_jersey,
opponent_jersey=opponent_jersey,
our_team_id=our_team_id,
read_from_stub=read_from_stub,
clear_stubs_after=clear_stubs_after,
save_annotated_video=save_annotated_video,
progress_callback=sync_progress_callback,
player_confidence=player_confidence,
ball_confidence=ball_confidence,
detection_batch_size=detection_batch_size,
image_size=image_size,
max_players_on_court=max_players_on_court,
render_speed_text=render_speed_text,
render_distance_text=render_distance_text,
render_tactical_view=render_tactical_view,
render_court_keypoints=render_court_keypoints,
)
# Wait for analysis to complete
result = await anyio.to_thread.run_sync(run_sync)
# Run advanced analytics if requested
if result.get("status") == "completed" and options.get("enable_advanced_analytics", False):
try:
sync_progress_callback("Running advanced analytics", 95)
from analytics_engine import AnalyticsCoordinator
from utils import read_video
# Re-read frames for advanced analytics
video_frames = await anyio.to_thread.run_sync(read_video, video_path)
coordinator = AnalyticsCoordinator()
advanced_results = await anyio.to_thread.run_sync(
coordinator.process_all,
video_frames,
[], # player_tracks
[], # ball_tracks
[], # tactical_positions
[], # player_assignment
[], # ball_possession
result.get("events", []),
[], # shots
[], # court_keypoints
[], # speeds
video_path,
result.get("fps", 30.0)
)
result["advanced_analytics"] = advanced_results
sync_progress_callback("Advanced analytics complete", 98)
except Exception as e:
print(f"⚠️ Advanced analytics failed: {e}")
# Signal background updater to finish
with progress_lock:
progress_state["finished"] = True
# Final progress update
if video_id and supabase:
await supabase.update("videos", video_id, {
"current_step": "Analysis complete",
"progress_percent": 100,
"status": result.get("status", "completed")
})
# Ensure all required fields are present (never null)
if result.get("status") == "completed":
required_fields = {
"total_frames": lambda r: r.get("total_frames") or 0,
"duration_seconds": lambda r: r.get("duration_seconds") or 0.0,
"players_detected": lambda r: r.get("players_detected") or 0,
"team_1_possession_percent": lambda r: r.get("team_1_possession_percent") or 50.0,
"team_2_possession_percent": lambda r: r.get("team_2_possession_percent") or 50.0,
"total_passes": lambda r: r.get("total_passes") or 0,
"team_1_passes": lambda r: r.get("team_1_passes") or 0,
"team_2_passes": lambda r: r.get("team_2_passes") or 0,
"total_interceptions": lambda r: r.get("total_interceptions") or 0,
"team_1_interceptions": lambda r: r.get("team_1_interceptions") or 0,
"team_2_interceptions": lambda r: r.get("team_2_interceptions") or 0,
"defensive_actions": lambda r: r.get("defensive_actions") or 0,
"overall_shooting_percentage": lambda r: r.get("overall_shooting_percentage") or 0.0,
"total_distance_meters": lambda r: r.get("total_distance_meters") or 0.0,
"avg_speed_kmh": lambda r: r.get("avg_speed_kmh") or 0.0,
"max_speed_kmh": lambda r: r.get("max_speed_kmh") or 0.0,
"processing_time_seconds": lambda r: r.get("processing_time_seconds") or 0.0,
"annotated_video_path": lambda r: r.get("annotated_video_path") or "",
}
for field, getter in required_fields.items():
if field not in result or result[field] is None:
result[field] = getter(result)
return result
except Exception as e:
print(f"❌ Team analysis failed: {e}")
import traceback
traceback.print_exc()
# Update status to failed
if video_id and supabase:
try:
await supabase.update("videos", video_id, {
"status": "failed",
"error": str(e)
})
except:
pass
return {
"status": "failed",
"error": str(e)
}
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