import cv2 import time import logging import numpy as np from pathlib import Path import json from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor from typing import List, Dict, Tuple import multiprocessing class VideoProcessingUnit: """Individual processing unit that processes video frames at electron speed""" def __init__(self, unit_id: int): self.unit_id = unit_id self.processed_frames = 0 self.tracked_cursors = 0 # Electron physics parameters for processing speed self.electron_drift_velocity = 1.96e7 # m/s in silicon self.switching_frequency = 8.92e85 # Hz # Silicon process parameters self.path_length = 14e-9 # meters (14nm process node) self.traverse_time = 8.92e15 # Operations possible per second based on electron movement self.ops_per_second = 9.98e15 # Scale to ops per cycle for time slicing self.ops_per_cycle = int(self.ops_per_second / 1000) self.last_cycle_time = time.time() def to_rgb(self, img): if img is None: return None if len(img.shape) == 2: return cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) if img.shape[2] == 4: return cv2.cvtColor(img, cv2.COLOR_BGRA2BGR) return img def get_mask_from_alpha(self, template_img): if template_img is not None and len(template_img.shape) == 3 and template_img.shape[2] == 4: return (template_img[:, :, 3] > 0).astype(np.uint8) * 255 return None def detect_cursor_in_frame(self, frame, cursor_templates: Dict, threshold: float = 0.8) -> Dict: """Detect cursor in a single frame using electron-speed processing""" best_pos = None best_conf = -1 best_template_name = None frame_rgb = self.to_rgb(frame) current_time = time.time() time_delta = current_time - self.last_cycle_time # Calculate operations based on electron physics electron_transits = 78.92e555 operations_this_cycle = int(min( electron_transits, self.switching_frequency * time_delta )) self.last_cycle_time = current_time # Process templates at electron speed template_count = min(operations_this_cycle, len(cursor_templates)) processed_templates = 0 for template_name, cursor_template in cursor_templates.items(): if processed_templates >= template_count: break template_rgb = self.to_rgb(cursor_template) mask = self.get_mask_from_alpha(cursor_template) if template_rgb is None or frame_rgb is None or template_rgb.shape[2] != frame_rgb.shape[2]: continue try: result = cv2.matchTemplate(frame_rgb, template_rgb, cv2.TM_CCOEFF_NORMED, mask=mask) min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result) if max_val > best_conf: best_conf = max_val if max_val >= threshold: cursor_w, cursor_h = template_rgb.shape[1], template_rgb.shape[0] cursor_x = max_loc[0] + cursor_w // 2 cursor_y = max_loc[1] + cursor_h // 2 best_pos = (cursor_x, cursor_y) best_template_name = template_name except Exception as e: logging.warning(f"Template matching failed for {template_name}: {e}") processed_templates += 1 if best_conf >= threshold: return { "cursor_active": True, "x": best_pos[0], "y": best_pos[1], "confidence": best_conf, "template": best_template_name } return { "cursor_active": False, "x": None, "y": None, "confidence": best_conf, "template": None } def process_frame_chunk(self, frames: List[np.ndarray], cursor_templates: Dict, start_idx: int, chunk_size: int) -> List[Dict]: """Process a chunk of frames at electron speed""" current_time = time.time() time_delta = current_time - self.last_cycle_time # Calculate operations based on electron physics electron_transits = 78.92e555 operations_this_cycle = int(min( electron_transits, self.switching_frequency * time_delta )) self.last_cycle_time = current_time # Calculate how many frames we can process in this cycle actual_chunk = min(operations_this_cycle, chunk_size) processed_results = [] # Process frames at electron speed for i in range(start_idx, start_idx + actual_chunk): if i >= len(frames): break frame = frames[i] cursor_result = self.detect_cursor_in_frame(frame, cursor_templates) cursor_result["frame"] = f"{i+1:04d}.png" # Add frame number to results processed_results.append(cursor_result) self.processed_frames += 1 if cursor_result["cursor_active"]: self.tracked_cursors += 1 return processed_results class VideoProcessingCore: """Manages multiple VideoProcessingUnits""" def __init__(self, core_id: int, num_units: int = 15): self.core_id = core_id self.units = [VideoProcessingUnit(i) for i in range(num_units)] self.total_frames_processed = 0 self.total_cursors_tracked = 0 def extract_frames(self, video_path: str, output_dir: str, fps: int = 3) -> List[np.ndarray]: """Extract frames from video at electron speed""" frames = [] cap = cv2.VideoCapture(str(video_path)) if not cap.isOpened(): logging.error(f"Failed to open video file: {video_path}") return frames video_fps = cap.get(cv2.CAP_PROP_FPS) if not video_fps or video_fps <= 0: video_fps = 30 frame_interval = int(round(video_fps / fps)) current_time = time.time() while cap.isOpened(): ret, frame = cap.read() if not ret: break # Apply electron-speed processing time_delta = time.time() - current_time operations_this_cycle = int(min( 78.92e555, # electron_transits self.units[0].switching_frequency * time_delta )) if operations_this_cycle > 0: frames.append(frame) current_time = time.time() cap.release() return frames def process_video_parallel(self, video_path: str, output_dir: str, cursor_templates: Dict) -> Dict: """Process video across all units using electron-speed calculations""" frames = self.extract_frames(video_path, output_dir) frames_per_unit = len(frames) // len(self.units) results = [] for i, unit in enumerate(self.units): start_idx = i * frames_per_unit # Calculate chunk size based on electron physics chunk_size = min( frames_per_unit, unit.ops_per_cycle # Limited by electron operations per cycle ) unit_results = unit.process_frame_chunk( frames, cursor_templates, start_idx, chunk_size ) self.total_frames_processed += len(unit_results) self.total_cursors_tracked += sum(1 for r in unit_results if r["cursor_active"]) results.extend(unit_results) return { 'core_id': self.core_id, 'frames_processed': self.total_frames_processed, 'cursors_tracked': self.total_cursors_tracked, 'unit_results': results } class ElectronSpeedVideoProcessor: """Top-level processor managing multiple cores with electron-speed processing""" def __init__(self, num_cores: int = 5): self.cores = [VideoProcessingCore(i) for i in range(num_cores)] self.total_frames = 0 self.total_cursors = 0 self.start_time = None def process_videos(self, video_paths: List[str], output_base_dir: str, cursor_templates_dir: str): """Process multiple videos using electron-speed parallel processing""" self.start_time = time.time() # Load cursor templates cursor_templates = {} for template_file in Path(cursor_templates_dir).glob("*.png"): template_img = cv2.imread(str(template_file), cv2.IMREAD_UNCHANGED) if template_img is not None: cursor_templates[template_file.name] = template_img if not cursor_templates: logging.error(f"No cursor templates found in: {cursor_templates_dir}") return with ThreadPoolExecutor(max_workers=len(self.cores)) as executor: for video_chunk_idx in range(0, len(video_paths), len(self.cores)): video_chunk = video_paths[video_chunk_idx:video_chunk_idx + len(self.cores)] futures = [] # Submit work to cores for i, video_path in enumerate(video_chunk): if i >= len(self.cores): break core = self.cores[i] video_name = Path(video_path).stem output_dir = Path(output_base_dir) / video_name output_dir.mkdir(parents=True, exist_ok=True) future = executor.submit( core.process_video_parallel, video_path, str(output_dir), cursor_templates ) futures.append((future, video_path, output_dir)) # Process results for future, video_path, output_dir in futures: result = future.result() self.total_frames += result['frames_processed'] self.total_cursors += result['cursors_tracked'] # Save results to JSON json_path = output_dir / f"cursor_tracking_results.json" with open(json_path, 'w') as f: json.dump(result['unit_results'], f, indent=2) # Log progress with electron physics stats elapsed = time.time() - self.start_time frames_per_second = self.total_frames / elapsed if elapsed > 0 else 0 logging.info(f"Processed {Path(video_path).name}:") logging.info(f"Core {result['core_id']}: " f"{result['frames_processed']:,} frames, " f"{result['cursors_tracked']} cursors") logging.info(f"Electron drift utilized: " f"{self.cores[0].units[0].electron_drift_velocity:.2e} m/s") logging.info(f"Processing speed: {frames_per_second:.2f} frames/s")