electron_processing.py

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")