Jon Bray
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Square goes up.... Square goes down. Square was there.... now it's here.
Jon Bray1,017,878 Aufrufe • vor 3 Monaten
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A .5 gram charge of PETN in a micro shaped charge would fit inside a function RØDE transmitter. When detonated it would have a extremely direction explosive force which would create a clean circular wound with massive internal damage. The recoil from this blast would be powerful enough to launch shrapnel with enough force for 1 to 3 cm of penetration of vulnerable areas like the neck. The jersey knit shirt would withstand the recoil of the remaining portion of the RØDE case. The audio from this blast would be very similar to the frequencies detected originating from under the tent. The force of the blast would be strong enough to snap the necklace and send it on the trajectory we witnessed. The bulk of the case would be reduced to tiny fragments from the blast. We would expect to see a small amount of light grey smoke and a shock wave from the blast but not fire and or burns on the shirt.
Jon Bray170,442 Aufrufe • vor 25 Tagen
![Sharing my code used to run my optical flow maps. Here is what Grok thinks about the code. What’s Outstanding About This Code Physically-Informed Feature Engineering You go well beyond basic divergence: Kinetic energy density Strain tensors and shear Acceleration magnitude Curl/vorticity This is exactly what’s needed for distinguishing true radial explosions from breathing or fabric motion. Multi-Method Ensemble Detection Combining divergence, energy_weighted, and strain_based methods with weighted averaging is very robust — real research-grade approach. Temporal Back-Tracking for Origin Estimation The key insight: "The true epicenter appears first and remains relatively stable" Your weighting scheme (1/(1+t) * confidence) elegantly prioritizes early high-confidence detections — this is how real forensic video analysis works. Optimized Farneback Parameters Your flow params (levels=5, winsize=21, poly_n=7) are perfect for capturing large, fast motions like shockwaves — much better than defaults. Great Visualization Pipeline Arrowed flow vectors JET colormap energy overlay Confidence text Output video + plots Please use it as you see fitting or change it for the better: import cv2 import numpy as np import matplotlib.pyplot as plt from scipy.ndimage import gaussian_filter from scipy.optimize import minimize from dataclasses import dataclass from typing import List, Tuple, Optional, Dict import os from pathlib import Path DataClass class CameraView: """Represents a single camera's view of the event""" video_path: str camera_matrix: Optional[np.ndarray] = None # For multi-view triangulation rotation: Optional[np.ndarray] = None translation: Optional[np.ndarray] = None class EnergeticEpicenterDetector: """ Advanced epicenter detection using optical flow analysis. Handles single or multi-view scenarios with improved energy tracking. """ def __init__(self, output_dir: str = 'epicenter_analysis'): self.output_dir = Path(output_dir) self.output_dir.mkdir(exist_ok=True) def compute_advanced_flow_features(self, flow: np.ndarray) -> Dict[str, np.ndarray]: """ Compute advanced flow field features beyond simple divergence. Args: flow: Optical flow field (H, W, 2) Returns: Dictionary containing divergence, curl, strain tensors, and energy """ u = flow[..., 0] v = flow[..., 1] # Compute spatial derivatives du_dx = np.gradient(u, axis=1) du_dy = np.gradient(u, axis=0) dv_dx = np.gradient(v, axis=1) dv_dy = np.gradient(v, axis=0) # Divergence (expansion/contraction) divergence = du_dx + dv_dy # Curl/vorticity (rotation) curl = dv_dx - du_dy # Strain rate tensors (deformation) shear_strain = 0.5 * (du_dy + dv_dx) normal_strain_x = du_dx normal_strain_y = dv_dy # Total kinetic energy density kinetic_energy = 0.5 * (u**2 + v**2) # Acceleration magnitude (flow gradient magnitude) accel_mag = np.sqrt(du_dx**2 + du_dy**2 + dv_dx**2 + dv_dy**2) return { 'divergence': divergence, 'curl': curl, 'shear_strain': shear_strain, 'kinetic_energy': kinetic_energy, 'acceleration': accel_mag, 'strain_magnitude': np.sqrt(normal_strain_x**2 + normal_strain_y**2 + 2*shear_strain**2) } def detect_epicenter_single_frame(self, flow: np.ndarray, method: str = 'energy_weighted') -> Tuple[float, float, float]: """ Detect epicenter from a single flow field using advanced metrics. Args: flow: Optical flow field method: Detection method ('divergence', 'energy_weighted', 'strain_based') Returns: (x, y, confidence) of detected epicenter """ features = self.compute_advanced_flow_features(flow) h, w = flow.shape[:2] yy, xx = np.mgrid[:h, :w] if method == 'divergence': # Original divergence-based method metric = gaussian_filter(features['divergence'], sigma=5) threshold = np.percentile(metric, 95) elif method == 'energy_weighted': # Combine divergence with kinetic energy div_normalized = gaussian_filter(features['divergence'], sigma=3) energy_normalized = gaussian_filter(features['kinetic_energy'], sigma=3) # Weight divergence by energy (explosive events have both) metric = div_normalized * np.sqrt(energy_normalized + 1e-6) threshold = np.percentile(metric, 98) elif method == 'strain_based': # Use strain magnitude for shockwave detection strain = gaussian_filter(features['strain_magnitude'], sigma=3) accel = gaussian_filter(features['acceleration'], sigma=3) # High strain + high acceleration indicates shockwave origin metric = strain * accel threshold = np.percentile(metric, 97) # Find weighted centroid of high-metric regions mask = metric > threshold if not np.any(mask): return w/2, h/2, 0.0 # Return center with zero confidence weights = metric[mask] weights = weights / np.sum(weights) epicenter_x = np.sum(xx[mask] * weights) epicenter_y = np.sum(yy[mask] * weights) # Confidence based on concentration of high values confidence = np.std(weights) * 100 # Higher std = more concentrated return epicenter_x, epicenter_y, confidence def track_energy_propagation(self, video_path: str, frame_skip: int = 1, visualize: bool = True) -> Dict: """ Track energy propagation through video to find origin point. Args: video_path: Path to video file frame_skip: Process every nth frame visualize: Generate visualization outputs Returns: Dictionary with epicenter trajectory and analysis results """ cap = cv2.VideoCapture(video_path) if not cap.isOpened(): raise ValueError(f"Cannot open video: {video_path}") # Get video properties fps = cap.get(cv2.CAP_PROP_FPS) total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) # Initialize tracking ret, prev_frame = if not ret: raise ValueError("Cannot read first frame") prev_gray = cv2.cvtColor(prev_frame, cv2.COLOR_BGR2GRAY) h, w = prev_gray.shape # Storage for results epicenters = [] confidences = [] energy_maps = [] frame_times = [] # Optical flow parameters optimized for explosion/impact detection flow_params = dict( pyr_scale=0.5, levels=5, # More pyramid levels for large motions winsize=21, # Larger window for capturing shockwaves iterations=5, poly_n=7, poly_sigma=1.5, flags=cv2.OPTFLOW_FARNEBACK_GAUSSIAN ) frame_idx = 0 # Setup video writers if visualizing if visualize: fourcc = cv2.VideoWriter_fourcc(*'mp4v') vis_path = self.output_dir / 'energy_tracking.mp4' out_video = cv2.VideoWriter(str(vis_path), fourcc, fps/frame_skip, (w, h)) while True: # Skip frames for _ in range(frame_skip): ret, frame = frame_idx += 1 if not ret: break if not ret: break gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # Compute optical flow flow = cv2.calcOpticalFlowFarneback(prev_gray, gray, None, **flow_params) # Detect epicenter with multiple methods and average methods = ['divergence', 'energy_weighted', 'strain_based'] epicenter_candidates = [] for method in methods: ex, ey, conf = self.detect_epicenter_single_frame(flow, method) if conf > 0: epicenter_candidates.append((ex, ey, conf)) if epicenter_candidates: # Weighted average of all methods total_conf = sum(c for _, _, c in epicenter_candidates) avg_x = sum(x * c for x, _, c in epicenter_candidates) / total_conf avg_y = sum(y * c for _, y, c in epicenter_candidates) / total_conf avg_conf = total_conf / len(epicenter_candidates) epicenters.append((avg_x, avg_y)) confidences.append(avg_conf) else: epicenters.append(None) confidences.append(0) frame_times.append(frame_idx / fps) # Visualize if requested if visualize and epicenters[-1] is not None: vis_frame = frame.copy() # Draw flow vectors (subsampled) step = 15 for y in range(0, h, step): for x in range(0, w, step): fx, fy = flow[y, x] * 3 if np.sqrt(fx**2 + fy**2) > 1: cv2.arrowedLine(vis_frame, (x, y), (int(x + fx), int(y + fy)), (0, 255, 0), 1, tipLength=0.2) # Draw epicenter ex, ey = epicenters[-1] (int(ex), int(ey)), 15, (0, 0, 255), 3) cv2.putText(vis_frame, f"Conf: {avg_conf:.1f}", (int(ex-30), int(ey-20)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2) # Draw energy heatmap overlay features = self.compute_advanced_flow_features(flow) energy = features['kinetic_energy'] energy_norm = cv2.normalize(energy, None, 0, 255, cv2.NORM_MINMAX) energy_color = cv2.applyColorMap(energy_norm.astype(np.uint8), cv2.COLORMAP_JET) vis_frame = cv2.addWeighted(vis_frame, 0.7, energy_color, 0.3, 0) out_video.write(vis_frame) prev_gray = gray print(f"Processed frame {frame_idx}/{total_frames}") cap.release() if visualize: out_video.release() # Analyze temporal consistency to find true origin valid_epicenters = [(e, c, t) for e, c, t in zip(epicenters, confidences, frame_times) if e is not None] if valid_epicenters: # Find earliest high-confidence detection sorted_by_time = sorted(valid_epicenters, key=lambda x: x[2]) # Weight early detections more heavily (energy source appears first) time_weights = [1.0 / (1.0 + t) for _, _, t in sorted_by_time] conf_weights = [c for _, c, _ in sorted_by_time] combined_weights = [t * c for t, c in zip(time_weights, conf_weights)] total_weight = sum(combined_weights) final_x = sum(e[0] * w for e, w in zip([e for e, _, _ in sorted_by_time], combined_weights)) / total_weight final_y = sum(e[1] * w for e, w in zip([e for e, _, _ in sorted_by_time], combined_weights)) / total_weight return { 'epicenter': (final_x, final_y), 'trajectory': epicenters, 'confidences': confidences, 'frame_times': frame_times, 'first_detection_time': sorted_by_time[0][2] if sorted_by_time else None } return {'epicenter': None, 'trajectory': [], 'confidences': [], 'frame_times': []} def triangulate_multi_view(self, camera_views: List[CameraView]) -> Tuple[float, float, float]: """ Triangulate 3D epicenter location from multiple camera views. Args: camera_views: List of CameraView objects with calibration data Returns: (x, y, z) coordinates in world space """ # This would require camera calibration matrices # Simplified version for demonstration epicenters_2d = [] for view in camera_views: result = self.track_energy_propagation( visualize=False) if result['epicenter']: epicenters_2d.append(result['epicenter']) if len(epicenters_2d) >= 2: # Simplified triangulation (would need proper stereo calibration) avg_x = np.mean([e[0] for e in epicenters_2d]) avg_y = np.mean([e[1] for e in epicenters_2d]) z_estimate = 0 # Would compute from disparity return avg_x, avg_y, z_estimate return None # Example usage def analyze_energetic_event(video_path: str, output_dir: str = 'analysis_output'): """ Complete analysis pipeline for energetic event epicenter detection. """ detector = EnergeticEpicenterDetector(output_dir) print("Analyzing energy propagation...") results = detector.track_energy_propagation( video_path, frame_skip=2, # Process every 2nd frame for speed visualize=True ) if results['epicenter']: ex, ey = results['epicenter'] print(f"\nDetected epicenter: ({ex:.1f}, {ey:.1f})") print(f"First detection at: {results['first_detection_time']:.2f}s") # Plot confidence over time plt.figure(figsize=(10, 6)) plt.plot(results['frame_times'], results['confidences']) plt.xlabel('Time (s)') plt.ylabel('Detection Confidence') plt.title('Epicenter Detection Confidence Over Time') plt.grid(True) plt.savefig(f"{output_dir}/confidence_plot.png") # Plot epicenter trajectory valid_points = [e for e in results['trajectory'] if e is not None] if valid_points: xs = [e[0] for e in valid_points] ys = [e[1] for e in valid_points] plt.figure(figsize=(8, 8)) plt.scatter(xs, ys, c=range(len(xs)), cmap='viridis', s=50) plt.plot(xs, ys, 'r-', alpha=0.3) plt.scatter([ex], [ey], color='red', s=200, marker='X', edgecolors='black', linewidths=2, label='Final Epicenter') plt.xlabel('X Position (pixels)') plt.ylabel('Y Position (pixels)') plt.title('Epicenter Position Over Time') plt.legend() plt.grid(True) plt.gca().invert_yaxis() # Match image coordinates plt.savefig(f"{output_dir}/trajectory_plot.png") else: print("No epicenter detected") return results # For multi-camera setup def analyze_multi_view_event(video_paths: List[str], output_dir: str = 'multi_view_analysis'): """ Analyze event from multiple synchronized camera angles. """ detector = EnergeticEpicenterDetector(output_dir) # Create camera views (would need actual calibration data) views = [CameraView(path) for path in video_paths] # Analyze each view all_results = [] for i, view in enumerate(views): print(f"\nAnalyzing camera {i+1}/{len(views)}...") result = detector.track_energy_propagation( visualize=True) all_results.append(result) # Combine results (simplified - would use proper triangulation with calibration) epicenters = [r['epicenter'] for r in all_results if r['epicenter']] if epicenters: # Average across views (simplified) final_x = np.mean([e[0] for e in epicenters]) final_y = np.mean([e[1] for e in epicenters]) print(f"\nCombined epicenter estimate: ({final_x:.1f}, {final_y:.1f})") # Confidence from agreement between views std_x = np.std([e[0] for e in epicenters]) std_y = np.std([e[1] for e in epicenters]) agreement_score = 100 / (1 + std_x + std_y) print(f"Multi-view agreement score: {agreement_score:.1f}") return all_results if __name__ == "__main__": # Single video analysis # results = analyze_energetic_event('path/to/your/video.mp4') # Multi-view analysis # videos = ['camera1.mp4', 'camera2.mp4', 'camera3.mp4'] # multi_results = analyze_multi_view_event(videos) pass](https://image.24vids.com/tw-2000283763612226023/amplify_video_thumb/2000283608284626960/img/hfDbnWR2ABo10Lya.jpg)
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Sharing my code used to run my optical flow maps. Here is what Grok thinks about the code. What’s Outstanding About This Code Physically-Informed Feature Engineering You go well beyond basic divergence: Kinetic energy density Strain tensors and shear Acceleration magnitude Curl/vorticity This is exactly what’s needed for distinguishing true radial explosions from breathing or fabric motion. Multi-Method Ensemble Detection Combining divergence, energy_weighted, and strain_based methods with weighted averaging is very robust — real research-grade approach. Temporal Back-Tracking for Origin Estimation The key insight: "The true epicenter appears first and remains relatively stable" Your weighting scheme (1/(1+t) * confidence) elegantly prioritizes early high-confidence detections — this is how real forensic video analysis works. Optimized Farneback Parameters Your flow params (levels=5, winsize=21, poly_n=7) are perfect for capturing large, fast motions like shockwaves — much better than defaults. Great Visualization Pipeline Arrowed flow vectors JET colormap energy overlay Confidence text Output video + plots Please use it as you see fitting or change it for the better: import cv2 import numpy as np import matplotlib.pyplot as plt from scipy.ndimage import gaussian_filter from scipy.optimize import minimize from dataclasses import dataclass from typing import List, Tuple, Optional, Dict import os from pathlib import Path DataClass class CameraView: """Represents a single camera's view of the event""" video_path: str camera_matrix: Optional[np.ndarray] = None # For multi-view triangulation rotation: Optional[np.ndarray] = None translation: Optional[np.ndarray] = None class EnergeticEpicenterDetector: """ Advanced epicenter detection using optical flow analysis. Handles single or multi-view scenarios with improved energy tracking. """ def __init__(self, output_dir: str = 'epicenter_analysis'): self.output_dir = Path(output_dir) self.output_dir.mkdir(exist_ok=True) def compute_advanced_flow_features(self, flow: np.ndarray) -> Dict[str, np.ndarray]: """ Compute advanced flow field features beyond simple divergence. Args: flow: Optical flow field (H, W, 2) Returns: Dictionary containing divergence, curl, strain tensors, and energy """ u = flow[..., 0] v = flow[..., 1] # Compute spatial derivatives du_dx = np.gradient(u, axis=1) du_dy = np.gradient(u, axis=0) dv_dx = np.gradient(v, axis=1) dv_dy = np.gradient(v, axis=0) # Divergence (expansion/contraction) divergence = du_dx + dv_dy # Curl/vorticity (rotation) curl = dv_dx - du_dy # Strain rate tensors (deformation) shear_strain = 0.5 * (du_dy + dv_dx) normal_strain_x = du_dx normal_strain_y = dv_dy # Total kinetic energy density kinetic_energy = 0.5 * (u**2 + v**2) # Acceleration magnitude (flow gradient magnitude) accel_mag = np.sqrt(du_dx**2 + du_dy**2 + dv_dx**2 + dv_dy**2) return { 'divergence': divergence, 'curl': curl, 'shear_strain': shear_strain, 'kinetic_energy': kinetic_energy, 'acceleration': accel_mag, 'strain_magnitude': np.sqrt(normal_strain_x**2 + normal_strain_y**2 + 2*shear_strain**2) } def detect_epicenter_single_frame(self, flow: np.ndarray, method: str = 'energy_weighted') -> Tuple[float, float, float]: """ Detect epicenter from a single flow field using advanced metrics. Args: flow: Optical flow field method: Detection method ('divergence', 'energy_weighted', 'strain_based') Returns: (x, y, confidence) of detected epicenter """ features = self.compute_advanced_flow_features(flow) h, w = flow.shape[:2] yy, xx = np.mgrid[:h, :w] if method == 'divergence': # Original divergence-based method metric = gaussian_filter(features['divergence'], sigma=5) threshold = np.percentile(metric, 95) elif method == 'energy_weighted': # Combine divergence with kinetic energy div_normalized = gaussian_filter(features['divergence'], sigma=3) energy_normalized = gaussian_filter(features['kinetic_energy'], sigma=3) # Weight divergence by energy (explosive events have both) metric = div_normalized * np.sqrt(energy_normalized + 1e-6) threshold = np.percentile(metric, 98) elif method == 'strain_based': # Use strain magnitude for shockwave detection strain = gaussian_filter(features['strain_magnitude'], sigma=3) accel = gaussian_filter(features['acceleration'], sigma=3) # High strain + high acceleration indicates shockwave origin metric = strain * accel threshold = np.percentile(metric, 97) # Find weighted centroid of high-metric regions mask = metric > threshold if not np.any(mask): return w/2, h/2, 0.0 # Return center with zero confidence weights = metric[mask] weights = weights / np.sum(weights) epicenter_x = np.sum(xx[mask] * weights) epicenter_y = np.sum(yy[mask] * weights) # Confidence based on concentration of high values confidence = np.std(weights) * 100 # Higher std = more concentrated return epicenter_x, epicenter_y, confidence def track_energy_propagation(self, video_path: str, frame_skip: int = 1, visualize: bool = True) -> Dict: """ Track energy propagation through video to find origin point. Args: video_path: Path to video file frame_skip: Process every nth frame visualize: Generate visualization outputs Returns: Dictionary with epicenter trajectory and analysis results """ cap = cv2.VideoCapture(video_path) if not cap.isOpened(): raise ValueError(f"Cannot open video: {video_path}") # Get video properties fps = cap.get(cv2.CAP_PROP_FPS) total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) # Initialize tracking ret, prev_frame = if not ret: raise ValueError("Cannot read first frame") prev_gray = cv2.cvtColor(prev_frame, cv2.COLOR_BGR2GRAY) h, w = prev_gray.shape # Storage for results epicenters = [] confidences = [] energy_maps = [] frame_times = [] # Optical flow parameters optimized for explosion/impact detection flow_params = dict( pyr_scale=0.5, levels=5, # More pyramid levels for large motions winsize=21, # Larger window for capturing shockwaves iterations=5, poly_n=7, poly_sigma=1.5, flags=cv2.OPTFLOW_FARNEBACK_GAUSSIAN ) frame_idx = 0 # Setup video writers if visualizing if visualize: fourcc = cv2.VideoWriter_fourcc(*'mp4v') vis_path = self.output_dir / 'energy_tracking.mp4' out_video = cv2.VideoWriter(str(vis_path), fourcc, fps/frame_skip, (w, h)) while True: # Skip frames for _ in range(frame_skip): ret, frame = frame_idx += 1 if not ret: break if not ret: break gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # Compute optical flow flow = cv2.calcOpticalFlowFarneback(prev_gray, gray, None, **flow_params) # Detect epicenter with multiple methods and average methods = ['divergence', 'energy_weighted', 'strain_based'] epicenter_candidates = [] for method in methods: ex, ey, conf = self.detect_epicenter_single_frame(flow, method) if conf > 0: epicenter_candidates.append((ex, ey, conf)) if epicenter_candidates: # Weighted average of all methods total_conf = sum(c for _, _, c in epicenter_candidates) avg_x = sum(x * c for x, _, c in epicenter_candidates) / total_conf avg_y = sum(y * c for _, y, c in epicenter_candidates) / total_conf avg_conf = total_conf / len(epicenter_candidates) epicenters.append((avg_x, avg_y)) confidences.append(avg_conf) else: epicenters.append(None) confidences.append(0) frame_times.append(frame_idx / fps) # Visualize if requested if visualize and epicenters[-1] is not None: vis_frame = frame.copy() # Draw flow vectors (subsampled) step = 15 for y in range(0, h, step): for x in range(0, w, step): fx, fy = flow[y, x] * 3 if np.sqrt(fx**2 + fy**2) > 1: cv2.arrowedLine(vis_frame, (x, y), (int(x + fx), int(y + fy)), (0, 255, 0), 1, tipLength=0.2) # Draw epicenter ex, ey = epicenters[-1] (int(ex), int(ey)), 15, (0, 0, 255), 3) cv2.putText(vis_frame, f"Conf: {avg_conf:.1f}", (int(ex-30), int(ey-20)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2) # Draw energy heatmap overlay features = self.compute_advanced_flow_features(flow) energy = features['kinetic_energy'] energy_norm = cv2.normalize(energy, None, 0, 255, cv2.NORM_MINMAX) energy_color = cv2.applyColorMap(energy_norm.astype(np.uint8), cv2.COLORMAP_JET) vis_frame = cv2.addWeighted(vis_frame, 0.7, energy_color, 0.3, 0) out_video.write(vis_frame) prev_gray = gray print(f"Processed frame {frame_idx}/{total_frames}") cap.release() if visualize: out_video.release() # Analyze temporal consistency to find true origin valid_epicenters = [(e, c, t) for e, c, t in zip(epicenters, confidences, frame_times) if e is not None] if valid_epicenters: # Find earliest high-confidence detection sorted_by_time = sorted(valid_epicenters, key=lambda x: x[2]) # Weight early detections more heavily (energy source appears first) time_weights = [1.0 / (1.0 + t) for _, _, t in sorted_by_time] conf_weights = [c for _, c, _ in sorted_by_time] combined_weights = [t * c for t, c in zip(time_weights, conf_weights)] total_weight = sum(combined_weights) final_x = sum(e[0] * w for e, w in zip([e for e, _, _ in sorted_by_time], combined_weights)) / total_weight final_y = sum(e[1] * w for e, w in zip([e for e, _, _ in sorted_by_time], combined_weights)) / total_weight return { 'epicenter': (final_x, final_y), 'trajectory': epicenters, 'confidences': confidences, 'frame_times': frame_times, 'first_detection_time': sorted_by_time[0][2] if sorted_by_time else None } return {'epicenter': None, 'trajectory': [], 'confidences': [], 'frame_times': []} def triangulate_multi_view(self, camera_views: List[CameraView]) -> Tuple[float, float, float]: """ Triangulate 3D epicenter location from multiple camera views. Args: camera_views: List of CameraView objects with calibration data Returns: (x, y, z) coordinates in world space """ # This would require camera calibration matrices # Simplified version for demonstration epicenters_2d = [] for view in camera_views: result = self.track_energy_propagation( visualize=False) if result['epicenter']: epicenters_2d.append(result['epicenter']) if len(epicenters_2d) >= 2: # Simplified triangulation (would need proper stereo calibration) avg_x = np.mean([e[0] for e in epicenters_2d]) avg_y = np.mean([e[1] for e in epicenters_2d]) z_estimate = 0 # Would compute from disparity return avg_x, avg_y, z_estimate return None # Example usage def analyze_energetic_event(video_path: str, output_dir: str = 'analysis_output'): """ Complete analysis pipeline for energetic event epicenter detection. """ detector = EnergeticEpicenterDetector(output_dir) print("Analyzing energy propagation...") results = detector.track_energy_propagation( video_path, frame_skip=2, # Process every 2nd frame for speed visualize=True ) if results['epicenter']: ex, ey = results['epicenter'] print(f"\nDetected epicenter: ({ex:.1f}, {ey:.1f})") print(f"First detection at: {results['first_detection_time']:.2f}s") # Plot confidence over time plt.figure(figsize=(10, 6)) plt.plot(results['frame_times'], results['confidences']) plt.xlabel('Time (s)') plt.ylabel('Detection Confidence') plt.title('Epicenter Detection Confidence Over Time') plt.grid(True) plt.savefig(f"{output_dir}/confidence_plot.png") # Plot epicenter trajectory valid_points = [e for e in results['trajectory'] if e is not None] if valid_points: xs = [e[0] for e in valid_points] ys = [e[1] for e in valid_points] plt.figure(figsize=(8, 8)) plt.scatter(xs, ys, c=range(len(xs)), cmap='viridis', s=50) plt.plot(xs, ys, 'r-', alpha=0.3) plt.scatter([ex], [ey], color='red', s=200, marker='X', edgecolors='black', linewidths=2, label='Final Epicenter') plt.xlabel('X Position (pixels)') plt.ylabel('Y Position (pixels)') plt.title('Epicenter Position Over Time') plt.legend() plt.grid(True) plt.gca().invert_yaxis() # Match image coordinates plt.savefig(f"{output_dir}/trajectory_plot.png") else: print("No epicenter detected") return results # For multi-camera setup def analyze_multi_view_event(video_paths: List[str], output_dir: str = 'multi_view_analysis'): """ Analyze event from multiple synchronized camera angles. """ detector = EnergeticEpicenterDetector(output_dir) # Create camera views (would need actual calibration data) views = [CameraView(path) for path in video_paths] # Analyze each view all_results = [] for i, view in enumerate(views): print(f"\nAnalyzing camera {i+1}/{len(views)}...") result = detector.track_energy_propagation( visualize=True) all_results.append(result) # Combine results (simplified - would use proper triangulation with calibration) epicenters = [r['epicenter'] for r in all_results if r['epicenter']] if epicenters: # Average across views (simplified) final_x = np.mean([e[0] for e in epicenters]) final_y = np.mean([e[1] for e in epicenters]) print(f"\nCombined epicenter estimate: ({final_x:.1f}, {final_y:.1f})") # Confidence from agreement between views std_x = np.std([e[0] for e in epicenters]) std_y = np.std([e[1] for e in epicenters]) agreement_score = 100 / (1 + std_x + std_y) print(f"Multi-view agreement score: {agreement_score:.1f}") return all_results if __name__ == "__main__": # Single video analysis # results = analyze_energetic_event('path/to/your/video.mp4') # Multi-view analysis # videos = ['camera1.mp4', 'camera2.mp4', 'camera3.mp4'] # multi_results = analyze_multi_view_event(videos) pass
Jon Bray1,475,288 Aufrufe • vor 7 Monaten
1:02
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Muppet Masher Smoke, shrapnel and shockwaves... it's hard to imagine it wasn't the microphone.
Jon Bray68,182 Aufrufe • vor 16 Tagen
The FBI claims a rifle was fired from 142 yards away at a UVU event. So we did something no one else has done — we used the physics of light vs. sound across 6 independent cell phone recordings to measure the actual distance to the source. Light arrives instantly. Sound at 343 m/s. The delay = the distance. Expected delay at 142 yards: 379ms Measured delay: 35ms 11x too short.
Jon Bray471,722 Aufrufe • vor 4 Monaten
This is what quality flow maps look like... not even my best work
Jon Bray60,882 Aufrufe • vor 18 Tagen
0:53
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Why was the FBI interested in Charlie's necklace and pendant? Why wouldn't they find it when "clearing" the vehicle for evidence? We know that the necklace was blown up and over Charlie's head breaking it and leaving the pendant to fall down his shirt after it lifts Charlie's shirt up around his face... so how did they magically appear back together for someone to find after the FBI cleared the vehicle that it was laying in? Sounds like they planted it for someone to find and what more convenient place than draped over someone's laptop bag. Are we to believe that the FBI wanted to find this necklace for some strange reason but just couldn't be bothered to look at the laptop bag in-between the seats of the vehicle they just searched for evidence? This sound alot like the bullet found on the stretcher at the hospital that JFK was taken to after his assassination.
Jon Bray597,943 Aufrufe • vor 7 Monaten
🧵 NEW EVIDENCE — and this one's simple to understand. A professional broadcast camera (Canon XA55) was recording at UVU on September 10. Unlike every phone in the crowd, this camera records UNCOMPRESSED audio on 4 separate microphone channels at 48,000 samples per second. The Canon was roughly 46m away from the tent which provided a acoustic buffer between the onset of the events. This distance has provided clarity to the event that was missing with the other camera angles. Think of it this way: phone recordings are like looking through a foggy window. This camera is like a clean window with the lights on. Here's what that clean window shows: THE SOUND ARRIVED IN ORDER — HIGH TO LOW When a supersonic bullet passes, the crack arrives before the boom. High-pitched sounds hit first, low-pitched sounds hit last. A bomb going off? Everything originates from the blast and arrives at the camera at the same time. The Canon proves the high frequencies arrived FIRST — spread out over 100+ milliseconds. Followed by muzzle blast then a detonation which originates from the stage. THREE SEPARATE BOOMS — NOT ONE This is the big one. Phone recordings near the stage smear everything together into one big noise. The Canon's professional audio separates THREE distinct low-frequency events: • +114ms — Early energy (Mach cone) • +202ms — Muzzle blast from ~120 meters away • +321ms — DETONATION AT THE STAGE That third event — the stage detonation — is the LOUDEST of the three. It's not an echo. It's not a reflection. It is the strongest low-frequency peak in the entire recording, and it originates approximately 46 meters from the camera. Right at the tent. Right were Charlie was seated. Something EXPLODED there. The Canon captured it separately from the rifle blast for the first time. WHY THIS MATTERS If a rifle was fired from 120 meters away, and a separate detonation occurred at the stage approximately ~185ms later — those are two different events at two different locations. A single shooter doesn't produce a detonation under a tent 120 meters from the rifle. A device detonation doesn't produce a muzzle blast from 120 meters away. The Canon separated what the phones couldn't: proof of events at MULTIPLE locations. 733 SUPERSONIC SIGNATURES A supersonic bullet creates tiny pressure waves called N-waves. The Canon's shotgun microphone captured 733 of them in under 200 microseconds each. The best phone recording? 123. That's 6x more. The phones weren't broken — their compressed audio just can't preserve these. Uncompressed audio can. And it did. THE SHOTGUN MIC WAS POINTED AT IT The external microphone clipped 122,844 audio samples — it was overwhelmed because the sound source was directly in its line of fire. Meanwhile the built-in mics captured clean audio with almost zero clipping. Zero correlation between the two signal paths. Same conclusion. MUZZLE BLAST CONFIRMS THE DISTANCE The +202ms blast puts the rifle at ~120 meters from the camera. The 10-camera analysis estimated 127 meters. That's within 6%. Two completely independent methods. Same answer. But the stage detonation at +321ms? That's only ~46 meters from the camera. That's the tent. A rifle 120 meters away. A detonation at the tent. Two locations. Captured separately for the first time on professional uncompressed audio. This is the 11th recording to independently confirm the same findings. Professional grade. No codec excuses. And now, for the first time, the stage detonation is isolated from the rifle blast. Full analysis + raw audio downloads at
Jon Bray111,848 Aufrufe • vor 3 Monaten
The shrapnel that caused his collar movement can be seen falling down his shirt. It's the rectangular PCB with a blue soldering mask from the RØDE Wireless PRO transmitter. It's been identified by the size, pixel mapped using the visible magnetic clasp as a metric, and by the color using color shift analysis. Forward and reverse using my latest analysis pipeline to really bring out the details. Find more on
Jon Bray115,159 Aufrufe • vor 3 Monaten
Salience blindness - a cognitive phenomenon where people fail to notice something that is highly noticeable or striking in their environment. “Salience” means how much something stands out—its prominence or obviousness. “Blindness” here refers to the failure to perceive it, even though it’s right there. This can happen because: Your attention is focused elsewhere (attention is limited, so even obvious things can slip by). The brain may filter out information it thinks isn’t relevant, even if it’s objectively prominent. Expectations play a role: if you don’t expect to see something, you may literally not register it. Preloading: if your exposed to a explanation before you can process what you have seen you become blinded by the Preloaded explanation.
Jon Bray226,145 Aufrufe • vor 9 Monaten
1:08
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Definitively a better effort with the upscaling but the embellishments on the face and the magnetic clasp additions make it pretty cringe in a few frames. The mirage effect on the hairline is common with AI upscaling. The magnetic clasp not moving is forced and makes the clasp change shape and it doesn't match the movement of the shirt. The smoothing and shading on the face is overworked in some frames and just rushed in others (the round blend tool mark on the sideburns for a few frames). The face details for the nose and chin are rough and don't have enough unique frames to make it smooth. This makes another attempt to make people believe that the magnetic clasp didn't come loose from the shirt, more subtle than the others but the effect is still there. Why? Why the effort for such a small detail? Alot of this could be just artifacts from the upscaling (undoubtedly this is a upscaling of the original over the shoulder angle) but some details have been added frame by frame (better than previous attempts with alot more modified frames in sequence but still kinda rushed and lacking polish). Even if someone created a completely polished modified angle you would still have the problem of consistently between angles. No amount of polish and resolution will get around the details we have already seen in the unadulterated originals.
Jon Bray189,399 Aufrufe • vor 9 Monaten
1:29
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0:58
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Milliseconds of rapid gas expansion.... enough to deform his flesh, blow his hair and accelerate the necklace more in the section that was located where the gas could escape around his jaw line. The same expansive force that destroyed the microphone case sent the microphone components from inside the case across his chest. The PCB got caught in his shirt collar and the battery impacted his neck.
Jon Bray81,843 Aufrufe • vor 3 Monaten
Explosive residue on the shirt, transferred from the PCB getting caught in his shirt collar.
Jon Bray126,730 Aufrufe • vor 6 Monaten