import cv2 import torch import gradio as gr import numpy as np import os import json import logging import matplotlib.pyplot as plt from datetime import datetime from collections import Counter from typing import List, Dict, Any, Optional from ultralytics import YOLO import ultralytics import time import piexif # Set YOLO config directory os.environ["YOLO_CONFIG_DIR"] = "/tmp/Ultralytics" # Set up logging logging.basicConfig( filename="app.log", level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s" ) # Directories CAPTURED_FRAMES_DIR = "captured_frames" OUTPUT_DIR = "outputs" FLIGHT_LOG_DIR = "flight_logs" os.makedirs(CAPTURED_FRAMES_DIR, exist_ok=True) os.makedirs(OUTPUT_DIR, exist_ok=True) os.makedirs(FLIGHT_LOG_DIR, exist_ok=True) os.chmod(CAPTURED_FRAMES_DIR, 0o777) os.chmod(OUTPUT_DIR, 0o777) os.chmod(FLIGHT_LOG_DIR, 0o777) # Global variables log_entries: List[str] = [] detected_counts: List[int] = [] detected_issues: List[str] = [] gps_coordinates: List[List[float]] = [] last_metrics: Dict[str, Any] = {} frame_count: int = 0 SAVE_IMAGE_INTERVAL = 1 # Save every frame with detections # Detection classes (as per original code) DETECTION_CLASSES = ["Longitudinal", "Pothole", "Transverse", "Crack"] # Debug: Check environment print(f"Torch version: {torch.__version__}") print(f"Gradio version: {gr.__version__}") print(f"Ultralytics version: {ultralytics.__version__}") print(f"CUDA available: {torch.cuda.is_available()}") # Load custom YOLO model device = "cuda" if torch.cuda.is_available() else "cpu" print(f"Using device: {device}") model = YOLO('./data/best.pt').to(device) if device == "cuda": model.half() # Use half-precision (FP16) print(f"Model classes: {model.names}") def generate_map(gps_coords: List[List[float]], items: List[Dict[str, Any]]) -> str: map_path = os.path.join(OUTPUT_DIR, "map_temp.png") plt.figure(figsize=(4, 4)) plt.scatter([x[1] for x in gps_coords], [x[0] for x in gps_coords], c='blue', label='GPS Points') plt.title("Issue Locations Map") plt.xlabel("Longitude") plt.ylabel("Latitude") plt.legend() plt.savefig(map_path) plt.close() return map_path def write_geotag(image_path: str, gps_coord: List[float]) -> bool: try: lat = abs(gps_coord[0]) lon = abs(gps_coord[1]) lat_ref = "N" if gps_coord[0] >= 0 else "S" lon_ref = "E" if gps_coord[1] >= 0 else "W" exif_dict = piexif.load(image_path) if os.path.exists(image_path) else {"GPS": {}} exif_dict["GPS"] = { piexif.GPSIFD.GPSLatitudeRef: lat_ref, piexif.GPSIFD.GPSLatitude: ((int(lat), 1), (0, 1), (0, 1)), piexif.GPSIFD.GPSLongitudeRef: lon_ref, piexif.GPSIFD.GPSLongitude: ((int(lon), 1), (0, 1), (0, 1)) } piexif.insert(piexif.dump(exif_dict), image_path) return True except Exception as e: logging.error(f"Failed to geotag {image_path}: {str(e)}") log_entries.append(f"Error: Failed to geotag {image_path}: {str(e)}") return False def write_flight_log(frame_count: int, gps_coord: List[float], timestamp: str) -> str: log_path = os.path.join(FLIGHT_LOG_DIR, f"flight_log_{frame_count:06d}.csv") try: with open(log_path, 'w', newline='') as csvfile: writer = csv.writer(csvfile) writer.writerow(["Frame", "Timestamp", "Latitude", "Longitude", "Speed_ms", "Satellites", "Altitude_m"]) writer.writerow([frame_count, timestamp, gps_coord[0], gps_coord[1], 5.0, 12, 60]) return log_path except Exception as e: logging.error(f"Failed to write flight log {log_path}: {str(e)}") log_entries.append(f"Error: Failed to write flight log {log_path}: {str(e)}") return "" def check_image_quality(frame: np.ndarray) -> bool: height, width, _ = frame.shape if width * height < 12_000_000: # 12 MP requirement log_entries.append(f"Frame {frame_count}: Resolution below 12MP") return False return True def update_metrics(detections: List[Dict[str, Any]]) -> Dict[str, Any]: counts = Counter([det["label"] for det in detections]) return { "items": [{"type": k, "count": v} for k, v in counts.items()], "total_detections": len(detections), "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S") } def generate_line_chart() -> Optional[str]: if not detected_counts: return None plt.figure(figsize=(4, 2)) plt.plot(detected_counts[-50:], marker='o', color='#FF8C00') plt.title("Detections Over Time") plt.xlabel("Frame") plt.ylabel("Count") plt.grid(True) plt.tight_layout() chart_path = os.path.join(OUTPUT_DIR, "chart_temp.png") plt.savefig(chart_path) plt.close() return chart_path def process_video(video, resize_width=320, resize_height=240, frame_skip=5): global frame_count, last_metrics, detected_counts, detected_issues, gps_coordinates, log_entries frame_count = 0 detected_counts.clear() detected_issues.clear() gps_coordinates.clear() log_entries.clear() last_metrics = {} if video is None: log_entries.append("Error: No video uploaded") logging.error("No video uploaded") return "processed_output.mp4", json.dumps({"error": "No video uploaded"}, indent=2), "\n".join(log_entries), [], None, None start_time = time.time() cap = cv2.VideoCapture(video) if not cap.isOpened(): log_entries.append("Error: Could not open video file") logging.error("Could not open video file") return "processed_output.mp4", json.dumps({"error": "Could not open video file"}, indent=2), "\n".join(log_entries), [], None, None frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) fps = cap.get(cv2.CAP_PROP_FPS) total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) expected_duration = total_frames / fps if fps > 0 else 0 log_entries.append(f"Input video: {frame_width}x{frame_height}, {fps} FPS, {total_frames} frames, {expected_duration:.2f} seconds") logging.info(f"Input video: {frame_width}x{frame_height}, {fps} FPS, {total_frames} frames, {expected_duration:.2f} seconds") print(f"Input video: {frame_width}x{frame_height}, {fps} FPS, {total_frames} frames, {expected_duration:.2f} seconds") out_width, out_height = resize_width, resize_height output_path = os.path.join(OUTPUT_DIR, "processed_output.mp4") codecs = [('mp4v', '.mp4'), ('MJPG', '.avi'), ('XVID', '.avi')] out = None for codec, ext in codecs: fourcc = cv2.VideoWriter_fourcc(*codec) temp_output_path = os.path.join(OUTPUT_DIR, f"processed_output{ext}") out = cv2.VideoWriter(temp_output_path, fourcc, fps, (out_width, out_height)) if out.isOpened(): output_path = temp_output_path log_entries.append(f"Using codec: {codec}, output: {output_path}") logging.info(f"Using codec: {codec}, output: {output_path}") break else: log_entries.append(f"Failed to initialize codec: {codec}") logging.warning(f"Failed to initialize codec: {codec}") if not out or not out.isOpened(): log_entries.append("Error: All codecs failed to initialize video writer") logging.error("All codecs failed to initialize video writer") cap.release() return "processed_output.mp4", json.dumps({"error": "All codecs failed"}, indent=2), "\n".join(log_entries), [], None, None processed_frames = 0 all_detections = [] frame_times = [] detection_frame_count = 0 output_frame_count = 0 last_annotated_frame = None data_lake_submission = { "images": [], "flight_logs": [], "analytics": [], "metrics": {} } while True: ret, frame = cap.read() if not ret: break frame_count += 1 if frame_count % frame_skip != 0: continue processed_frames += 1 frame_start = time.time() frame = cv2.resize(frame, (out_width, out_height)) if not check_image_quality(frame): log_entries.append(f"Frame {frame_count}: Skipped due to low resolution") continue results = model(frame, verbose=False, conf=0.5, iou=0.7) annotated_frame = results[0].plot() frame_timestamp = frame_count / fps if fps > 0 else 0 timestamp_str = f"{int(frame_timestamp // 60)}:{int(frame_timestamp % 60):02d}" gps_coord = [17.385044 + (frame_count * 0.0001), 78.486671 + (frame_count * 0.0001)] gps_coordinates.append(gps_coord) frame_detections = [] for detection in results[0].boxes: cls = int(detection.cls) conf = float(detection.conf) box = detection.xyxy[0].cpu().numpy().astype(int).tolist() label = model.names[cls] if label in DETECTION_CLASSES: # Only process relevant classes frame_detections.append({ "label": label, "box": box, "conf": conf, "gps": gps_coord, "timestamp": timestamp_str }) log_message = f"Frame {frame_count} at {timestamp_str}: Detected {label} with confidence {conf:.2f}" log_entries.append(log_message) logging.info(log_message) if frame_detections: detection_frame_count += 1 if detection_frame_count % SAVE_IMAGE_INTERVAL == 0: captured_frame_path = os.path.join(CAPTURED_FRAMES_DIR, f"detected_{frame_count:06d}.jpg") if cv2.imwrite(captured_frame_path, annotated_frame): if write_geotag(captured_frame_path, gps_coord): detected_issues.append(captured_frame_path) data_lake_submission["images"].append({ "path": captured_frame_path, "frame": frame_count, "gps": gps_coord, "timestamp": timestamp_str }) if len(detected_issues) > 100: detected_issues.pop(0) else: log_entries.append(f"Frame {frame_count}: Geotagging failed") else: log_entries.append(f"Error: Failed to save {captured_frame_path}") logging.error(f"Failed to save {captured_frame_path}") flight_log_path = write_flight_log(frame_count, gps_coord, timestamp_str) if flight_log_path: data_lake_submission["flight_logs"].append({ "path": flight_log_path, "frame": frame_count }) out.write(annotated_frame) output_frame_count += 1 last_annotated_frame = annotated_frame if frame_skip > 1: for _ in range(frame_skip - 1): out.write(annotated_frame) output_frame_count += 1 detected_counts.append(len(frame_detections)) all_detections.extend(frame_detections) detection_summary = { "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"), "video_timestamp": timestamp_str, "frame": frame_count, "gps": gps_coord, "processing_time_ms": (time.time() - frame_start) * 1000, "detections": {label: sum(1 for det in frame_detections if det["label"] == label) for label in DETECTION_CLASSES} } data_lake_submission["analytics"].append(detection_summary) log_entries.append(json.dumps(detection_summary, indent=2)) if len(log_entries) > 50: log_entries.pop(0) while output_frame_count < total_frames and last_annotated_frame is not None: out.write(last_annotated_frame) output_frame_count += 1 last_metrics = update_metrics(all_detections) data_lake_submission["metrics"] = last_metrics data_lake_submission["frame_count"] = frame_count data_lake_submission["gps_coordinates"] = gps_coordinates[-1] if gps_coordinates else [0, 0] submission_json_path = os.path.join(OUTPUT_DIR, "data_lake_submission.json") try: with open(submission_json_path, 'w') as f: json.dump(data_lake_submission, f, indent=2) log_entries.append(f"Submission JSON saved: {submission_json_path}") logging.info(f"Submission JSON saved: {submission_json_path}") except Exception as e: log_entries.append(f"Error: Failed to save submission JSON: {str(e)}") logging.error(f"Failed to save submission JSON: {str(e)}") cap.release() out.release() cap = cv2.VideoCapture(output_path) output_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) output_fps = cap.get(cv2.CAP_PROP_FPS) output_duration = output_frames / output_fps if output_fps > 0 else 0 cap.release() total_time = time.time() - start_time avg_frame_time = sum(frame_times) / len(frame_times) if frame_times else 0 log_entries.append(f"Output video: {output_frames} frames, {output_fps} FPS, {output_duration:.2f} seconds") log_entries.append(f"Total processing time: {total_time:.2f} seconds, Avg frame time: {avg_frame_time:.2f} ms, Detection frames: {detection_frame_count}, Output frames: {output_frame_count}") logging.info(f"Output video: {output_frames} frames, {output_fps} FPS, {output_duration:.2f} seconds") logging.info(f"Total processing time: {total_time:.2f} seconds, Avg frame time: {avg_frame_time:.2f} ms, Detection frames: {detection_frame_count}, Output frames: {output_frame_count}") print(f"Output video: {output_frames} frames, {output_fps} FPS, {output_duration:.2f} seconds") print(f"Total processing time: {total_time:.2f} seconds, Avg frame time: {avg_frame_time:.2f} ms, Detection frames: {detection_frame_count}, Output frames: {output_frame_count}") chart_path = generate_line_chart() map_path = generate_map(gps_coordinates[-5:], all_detections) return ( output_path, json.dumps(last_metrics, indent=2), "\n".join(log_entries[-10:]), detected_issues, chart_path, map_path ) # Gradio interface with gr.Blocks(theme=gr.themes.Soft(primary_hue="orange")) as iface: gr.Markdown("# NHAI Road Defect Detection Dashboard") with gr.Row(): with gr.Column(scale=3): video_input = gr.Video(label="Upload Video") width_slider = gr.Slider(320, 640, value=320, label="Output Width", step=1) height_slider = gr.Slider(240, 480, value=240, label="Output Height", step=1) skip_slider = gr.Slider(1, 10, value=5, label="Frame Skip", step=1) process_btn = gr.Button("Process Video", variant="primary") with gr.Column(scale=1): metrics_output = gr.Textbox(label="Detection Metrics", lines=5, interactive=False) with gr.Row(): video_output = gr.Video(label="Processed Video") issue_gallery = gr.Gallery(label="Detected Issues", columns=4, height="auto", object_fit="contain") with gr.Row(): chart_output = gr.Image(label="Detection Trend") map_output = gr.Image(label="Issue Locations Map") with gr.Row(): logs_output = gr.Textbox(label="Logs", lines=5, interactive=False) process_btn.click( process_video, inputs=[video_input, width_slider, height_slider, skip_slider], outputs=[video_output, metrics_output, logs_output, issue_gallery, chart_output, map_output] ) if __name__ == "__main__": iface.launch()