4c9b48e057
- src/calibration: 3D camera calibration (solvePnP, homography) - src/physics: trajectory model with gravity/drag, close call event detector - src/streaming: camera reader + ring buffer for VAR clips - src/web: Flask app with 3-tab UI (Detection, Court, Trajectory) + Three.js - jetson/main.py: unified entry point wiring all components Court tab: one-click calibration button, 3D scene with camera positions Trajectory tab: accumulated ball path, VAR panel with snapshot + timer Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
390 lines
14 KiB
Python
390 lines
14 KiB
Python
#!/usr/bin/env python3
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"""
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Pickle Vision - Referee System main entry point for Jetson.
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Dual CSI cameras, real-time YOLO detection, trajectory tracking, VAR triggers.
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"""
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import sys
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import os
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import cv2
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import time
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import base64
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import argparse
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import threading
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import numpy as np
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# Add project root to path
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from ultralytics import YOLO
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from src.streaming.camera_reader import CameraReader
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from src.streaming.ring_buffer import FrameRingBuffer
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from src.physics.trajectory import TrajectoryModel
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from src.physics.event_detector import EventDetector
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from src.calibration.camera_calibrator import (
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CameraCalibrator, get_half_court_3d_points,
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COURT_LENGTH, COURT_WIDTH, HALF_COURT_LENGTH
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)
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from src.web.app import app, state
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BALL_CLASS_ID = 32 # sports ball in COCO
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# Global references set in main()
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_cam_readers = {}
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_args = None
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def auto_calibrate():
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"""One-click calibration: detect court lines from current frames,
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compute camera pose, save to config.
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Each camera sees one half of the court from the net position.
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We use the court lines visible in each frame to build correspondences.
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For now: use a simple approach — detect the 4 most prominent lines
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(baseline, two sidelines, kitchen line) and map to known 3D coords.
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Falls back to estimated corners based on frame geometry if detection fails.
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"""
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results = {}
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for sensor_id, reader in _cam_readers.items():
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frame = reader.grab()
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if frame is None:
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results[str(sensor_id)] = {'ok': False, 'error': 'No frame available'}
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continue
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h, w = frame.shape[:2]
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side = 'left' if sensor_id == 0 else 'right'
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# Try to detect court lines using edge detection + Hough
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corners_pixel = _detect_court_corners(frame, side)
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if corners_pixel is None:
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# Fallback: estimate corners from typical camera-at-net perspective
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corners_pixel = _estimate_corners_from_net(w, h, side)
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# Get known 3D coordinates for this half
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corners_3d = get_half_court_3d_points(side)
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# Calibrate
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cal = CameraCalibrator()
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ok = cal.calibrate(
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np.array(corners_pixel, dtype=np.float32),
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corners_3d,
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w, h
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)
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if ok:
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# Save to config
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cal_path = os.path.join(_args.calibration_dir,
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f'cam{sensor_id}_calibration.json')
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os.makedirs(os.path.dirname(cal_path), exist_ok=True)
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cal.save(cal_path)
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state['calibrators'][sensor_id] = cal
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# Get camera position for 3D scene
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cam_pos = (-cal.rotation_matrix.T @ cal.translation_vec).flatten()
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results[str(sensor_id)] = {
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'ok': True,
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'camera_position': cam_pos.tolist(),
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'corners_pixel': corners_pixel.tolist() if isinstance(corners_pixel, np.ndarray)
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else corners_pixel,
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}
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print(f"[CAM {sensor_id}] Calibrated! Camera at "
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f"({cam_pos[0]:.1f}, {cam_pos[1]:.1f}, {cam_pos[2]:.1f})")
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else:
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results[str(sensor_id)] = {'ok': False, 'error': 'Calibration failed'}
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return results
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def _detect_court_corners(frame, side):
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"""Detect court corners from frame using edge detection.
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Returns 4 corner points as numpy array, or None if detection fails.
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"""
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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blur = cv2.GaussianBlur(gray, (5, 5), 0)
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edges = cv2.Canny(blur, 50, 150)
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# Detect lines
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lines = cv2.HoughLinesP(edges, 1, np.pi / 180, threshold=80,
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minLineLength=100, maxLineGap=20)
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if lines is None or len(lines) < 4:
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return None
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# Classify lines into horizontal and vertical
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horizontals = []
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verticals = []
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for line in lines:
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x1, y1, x2, y2 = line[0]
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angle = abs(np.arctan2(y2 - y1, x2 - x1) * 180 / np.pi)
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if angle < 30 or angle > 150:
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horizontals.append(line[0])
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elif 60 < angle < 120:
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verticals.append(line[0])
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if len(horizontals) < 2 or len(verticals) < 2:
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return None
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# Cluster lines by position to find the dominant ones
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h_positions = sorted(horizontals, key=lambda l: (l[1] + l[3]) / 2)
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v_positions = sorted(verticals, key=lambda l: (l[0] + l[2]) / 2)
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# Take the most separated horizontal pair (top and bottom court lines)
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top_line = h_positions[0]
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bottom_line = h_positions[-1]
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# Take the most separated vertical pair (left and right sidelines)
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left_line = v_positions[0]
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right_line = v_positions[-1]
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# Find intersections as corner points
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def line_intersection(l1, l2):
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x1, y1, x2, y2 = l1
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x3, y3, x4, y4 = l2
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denom = (x1 - x2) * (y3 - y4) - (y1 - y2) * (x3 - x4)
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if abs(denom) < 1e-6:
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return None
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t = ((x1 - x3) * (y3 - y4) - (y1 - y3) * (x3 - x4)) / denom
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ix = x1 + t * (x2 - x1)
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iy = y1 + t * (y2 - y1)
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return [ix, iy]
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corners = [
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line_intersection(top_line, left_line), # TL
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line_intersection(top_line, right_line), # TR
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line_intersection(bottom_line, right_line), # BR
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line_intersection(bottom_line, left_line), # BL
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]
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if any(c is None for c in corners):
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return None
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return np.array(corners, dtype=np.float32)
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def _estimate_corners_from_net(w, h, side):
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"""Estimate court corner positions for a camera mounted at the net.
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Camera looks down the half-court. Perspective: far baseline is smaller
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(top of frame), near net line is wider (bottom of frame).
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"""
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# Near edge (bottom of frame, close to camera = at the net)
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near_left = [w * 0.05, h * 0.85]
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near_right = [w * 0.95, h * 0.85]
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# Far edge (top of frame, baseline = far from camera)
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far_left = [w * 0.20, h * 0.15]
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far_right = [w * 0.80, h * 0.15]
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# For camera at net looking at court half:
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# - bottom of frame = net line (near)
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# - top of frame = baseline (far)
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# Order must match get_half_court_3d_points output
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if side == 'left':
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# 3D order: baseline near-left, baseline near-right, net far-right, net far-left
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return np.array([far_left, far_right, near_right, near_left], dtype=np.float32)
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else:
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return np.array([far_left, far_right, near_right, near_left], dtype=np.float32)
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def _capture_var_snapshot(frame, event):
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"""Create a snapshot for VAR event and store it in state."""
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# Draw VAR overlay on frame
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snapshot = frame.copy()
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cv2.putText(snapshot, "VAR DETECT", (10, 40),
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cv2.FONT_HERSHEY_SIMPLEX, 1.2, (0, 0, 255), 3)
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cv2.putText(snapshot, f"Line: {event['line']} Dist: {event['distance_m']*100:.0f}cm",
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(10, 80), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2)
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_, jpeg = cv2.imencode('.jpg', snapshot, [cv2.IMWRITE_JPEG_QUALITY, 85])
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b64 = base64.b64encode(jpeg.tobytes()).decode('ascii')
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state['last_var'] = {
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'event': event,
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'snapshot_b64': b64,
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}
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def detection_loop(cam_readers, model, conf_threshold, ring_buffer):
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"""Main detection loop: alternate cameras, run YOLO, update state."""
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frame_counts = {sid: 0 for sid in cam_readers}
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start_times = {sid: time.time() for sid in cam_readers}
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trajectory = state['trajectory']
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event_detector = state['event_detector']
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calibrators = state['calibrators']
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while True:
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for sensor_id, reader in cam_readers.items():
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cam = state['cameras'][sensor_id]
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frame = reader.grab()
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if frame is None:
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continue
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now = time.time()
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# Store raw frame in ring buffer for VAR
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ring_buffer.push(frame, now, sensor_id)
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# YOLO detection
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results = model(frame, verbose=False, classes=[BALL_CLASS_ID], conf=conf_threshold)
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det_count = 0
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best_detection = None
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best_conf = 0
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for r in results:
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for box in r.boxes:
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x1, y1, x2, y2 = map(int, box.xyxy[0])
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conf = float(box.conf[0])
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cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 3)
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label = f"Ball {conf:.0%}"
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(tw, th), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.7, 2)
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cv2.rectangle(frame, (x1, y1 - th - 10), (x1 + tw, y1), (0, 255, 0), -1)
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cv2.putText(frame, label, (x1, y1 - 5),
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cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 0), 2)
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det_count += 1
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if conf > best_conf:
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best_conf = conf
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cx = (x1 + x2) / 2
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cy = (y1 + y2) / 2
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diameter = max(x2 - x1, y2 - y1)
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best_detection = (cx, cy, diameter, conf)
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# Update trajectory if we have calibration and a detection
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if best_detection and sensor_id in calibrators:
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cal = calibrators[sensor_id]
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if cal.calibrated:
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px, py, diam, conf = best_detection
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pos_3d = cal.pixel_to_3d(px, py, diam)
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if pos_3d:
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trajectory.add_observation(
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pos_3d[0], pos_3d[1], pos_3d[2],
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now, frame_counts[sensor_id], sensor_id, conf
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)
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# Check for close calls
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event = event_detector.check(trajectory)
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if event:
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print(f"[VAR] Close call! Line: {event['line']}, "
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f"Distance: {event['distance_m']*100:.0f}cm")
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state['events'].append(event)
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_capture_var_snapshot(frame, event)
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# FPS tracking
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frame_counts[sensor_id] += 1
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elapsed = time.time() - start_times[sensor_id]
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fps_actual = frame_counts[sensor_id] / elapsed if elapsed > 0 else 0
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# HUD overlay
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cv2.putText(frame, f"CAM {sensor_id} | FPS: {fps_actual:.1f}", (10, 30),
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cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 255), 2)
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if det_count > 0:
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cv2.putText(frame, f"Balls: {det_count}", (10, 60),
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cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)
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# Encode JPEG and update shared state
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_, jpeg = cv2.imencode('.jpg', frame, [cv2.IMWRITE_JPEG_QUALITY, 80])
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with cam['lock']:
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cam['frame'] = jpeg.tobytes()
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cam['fps'] = fps_actual
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cam['detections'] = det_count
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if frame_counts[sensor_id] % 150 == 0:
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speed = trajectory.get_speed()
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speed_str = f"{speed:.1f} m/s" if speed else "N/A"
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print(f"[CAM {sensor_id}] Frame {frame_counts[sensor_id]}, "
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f"FPS: {fps_actual:.1f}, Det: {det_count}, Speed: {speed_str}")
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def main():
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global _cam_readers, _args
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parser = argparse.ArgumentParser(description='Pickle Vision Referee System')
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parser.add_argument('--width', type=int, default=1280)
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parser.add_argument('--height', type=int, default=720)
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parser.add_argument('--fps', type=int, default=30)
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parser.add_argument('--model', type=str, default='yolov8n.pt')
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parser.add_argument('--conf', type=float, default=0.25)
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parser.add_argument('--port', type=int, default=8080)
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parser.add_argument('--buffer-seconds', type=int, default=10,
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help='Ring buffer size in seconds for VAR clips')
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parser.add_argument('--calibration-dir', type=str,
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default=os.path.join(os.path.dirname(__file__), 'config'),
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help='Directory with calibration JSON files')
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args = parser.parse_args()
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_args = args
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# Load YOLO model
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print(f"Loading YOLO model: {args.model}")
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model = YOLO(args.model)
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try:
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model.to("cuda")
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print("Inference on CUDA")
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except Exception:
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print("CUDA unavailable, using CPU")
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# Initialize shared state
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state['trajectory'] = TrajectoryModel(fps=args.fps)
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state['event_detector'] = EventDetector(trigger_distance_m=0.3)
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state['calibration_dir'] = args.calibration_dir
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state['calibrate_fn'] = auto_calibrate
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ring_buffer = FrameRingBuffer(max_seconds=args.buffer_seconds, fps=args.fps)
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# Load calibrations if available
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os.makedirs(args.calibration_dir, exist_ok=True)
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for sensor_id in [0, 1]:
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cal = CameraCalibrator()
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cal_path = os.path.join(args.calibration_dir, f'cam{sensor_id}_calibration.json')
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if os.path.exists(cal_path):
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if cal.load(cal_path):
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print(f"[CAM {sensor_id}] Loaded calibration from {cal_path}")
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state['calibrators'][sensor_id] = cal
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# Start camera readers
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cam_readers = {}
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for sensor_id in [0, 1]:
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state['cameras'][sensor_id] = {
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'frame': None, 'lock': threading.Lock(), 'fps': 0, 'detections': 0
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}
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cam_readers[sensor_id] = CameraReader(sensor_id, args.width, args.height, args.fps)
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_cam_readers = cam_readers
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# Wait for at least one camera
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print("Waiting for cameras...")
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for _ in range(100):
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if any(r.grab() is not None for r in cam_readers.values()):
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break
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time.sleep(0.1)
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# Start detection loop
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det_thread = threading.Thread(
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target=detection_loop,
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args=(cam_readers, model, args.conf, ring_buffer),
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daemon=True
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)
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det_thread.start()
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time.sleep(2)
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print(f"\n{'=' * 50}")
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print(f" Pickle Vision Referee System")
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print(f" Open in browser: http://192.168.1.253:{args.port}")
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print(f"{'=' * 50}\n")
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app.run(host='0.0.0.0', port=args.port, threaded=True)
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if __name__ == '__main__':
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main()
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