Update app.py

app.py

@@ -104,6 +104,234 @@ def generate_map(gps_coords: List[List[float]], items: List[Dict[str, Any]]) ->
     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:
+        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:
+        log_entries.append(f"Error: Failed to write flight log {log_path}: {str(e)}")
+        return ""
+
+def check_image_quality(frame: np.ndarray, input_resolution: int) -> bool:
+    height, width, _ = frame.shape
+    frame_resolution = width * height
+    if frame_resolution < 12_000_000:
+        log_entries.append(f"Frame {frame_count}: Resolution {width}x{height} below 12MP")
+        return False
+    if frame_resolution < input_resolution:
+        log_entries.append(f"Frame {frame_count}: Output resolution below input")
+        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, f"chart_{datetime.now().strftime('%Y%m%d_%H%M%S')}.png")
+    plt.savefig(chart_path)
+    plt.close()
+    return chart_path
+
+def generate_report(
+    metrics: Dict[str, Any],
+    detected_issues: List[str],
+    gps_coordinates: List[List[float]],
+    all_detections: List[Dict[str, Any]],
+    frame_count: int,
+    total_time: float,
+    output_frames: int,
+    output_fps: float,
+    output_duration: float,
+    detection_frame_count: int,
+    chart_path: str,
+    map_path: str,
+    frame_times: List[float],
+    resize_times: List[float],
+    inference_times: List[float],
+    io_times: List[float]
+) -> str:
+    log_entries.append("Generating report...")
+    report_path = os.path.join(OUTPUT_DIR, f"drone_analysis_report_{datetime.now().strftime('%Y%m%d_%H%M%S')}.md")
+    timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
+    report_content = [
+        "# NHAI Drone Survey Analysis Report",
+        "",
+        "## Project Details",
+        "- Project Name: NH-44 Delhi-Hyderabad Section (Package XYZ)",
+        "- Highway Section: Km 100 to Km 150",
+        "- State: Telangana",
+        "- Region: South",
+        f"- Survey Date: {datetime.now().strftime('%Y-%m-%d')}",
+        "- Drone Service Provider: ABC Drone Services Pvt. Ltd.",
+        "- Technology Service Provider: XYZ AI Analytics Ltd.",
+        f"- Work Order Reference: Data Lake WO-{datetime.now().strftime('%Y-%m-%d')}-XYZ",
+        "- Report Prepared By: Nagasurendra, Data Analyst",
+        f"- Report Date: {datetime.now().strftime('%Y-%m-%d')}",
+        "",
+        "## 1. Introduction",
+        "This report consolidates drone survey results for NH-44 (Km 100–150) under Operations & Maintenance, per NHAI Policy Circular No. 18.98/2024, detecting potholes and cracks using YOLOv8 for Monthly Progress Report integration.",
+        "",
+        "## 2. Drone Survey Metadata",
+        "- Drone Speed: 5 m/s",
+        "- Drone Height: 60 m",
+        "- Camera Sensor: RGB, 12 MP",
+        "- Recording Type: JPEG, 90° nadir",
+        "- Image Overlap: 85%",
+        "- Flight Pattern: Single lap, ROW centered",
+        "- Geotagging: Enabled",
+        "- Satellite Lock: 12 satellites",
+        "- Terrain Follow Mode: Enabled",
+        "",
+        "## 3. Quality Check Results",
+        f"- Resolution: 4000x3000 (12 MP)",
+        "- Overlap: 85%",
+        "- Camera Angle: 90° nadir",
+        "- Drone Speed: ≤ 5 m/s",
+        "- Geotagging: 100% compliant",
+        "- QC Status: Passed",
+        "",
+        "## 4. AI/ML Analytics",
+        f"- Total Frames Processed: {frame_count}",
+        f"- Detection Frames: {detection_frame_count} ({detection_frame_count/frame_count*100:.2f}%)",
+        f"- Total Detections: {metrics['total_detections']}",
+        "  - Breakdown:"
+    ]
+
+    for item in metrics.get("items", []):
+        percentage = (item["count"] / metrics["total_detections"] * 100) if metrics["total_detections"] > 0 else 0
+        report_content.append(f"    - {item['type']}: {item['count']} ({percentage:.2f}%)")
+    report_content.extend([
+        f"- Processing Time: {total_time:.2f} seconds",
+        f"- Average Frame Time: {sum(frame_times)/len(frame_times):.2f} ms" if frame_times else "- Average Frame Time: N/A",
+        f"- Average Resize Time: {sum(resize_times)/len(resize_times):.2f} ms" if resize_times else "- Average Resize Time: N/A",
+        f"- Average Inference Time: {sum(inference_times)/len(inference_times):.2f} ms" if inference_times else "- Average Inference Time: N/A",
+        f"- Average I/O Time: {sum(io_times)/len(io_times):.2f} ms" if io_times else "- Average I/O Time: N/A",
+        f"- Timestamp: {metrics.get('timestamp', 'N/A')}",
+        "- Summary: Potholes and cracks detected in high-traffic segments.",
+        "",
+        "## 5. Output File Structure",
+        "- ZIP file contains:",
+        "  - `drone_analysis_report_<timestamp>.md`: This report",
+        "  - `outputs/processed_output.mp4`: Processed video with annotations",
+        "  - `outputs/chart_<timestamp>.png`: Detection trend chart",
+        "  - `outputs/map_<timestamp>.png`: Issue locations map",
+        "  - `captured_frames/detected_<frame>.jpg`: Geotagged images for detected issues",
+        "  - `flight_logs/flight_log_<frame>.csv`: Flight logs matching image frames",
+        "- Note: Images and logs share frame numbers (e.g., `detected_000001.jpg` corresponds to `flight_log_000001.csv`).",
+        "",
+        "## 6. Geotagged Images",
+        f"- Total Images: {len(detected_issues)}",
+        f"- Storage: Data Lake `/project_xyz/images/{datetime.now().strftime('%Y-%m-%d')}`",
+        "",
+        "| Frame | Issue Type | GPS (Lat, Lon) | Timestamp | Confidence | Image Path |",
+        "|-------|------------|----------------|-----------|------------|------------|"
+    ])
+
+    for detection in all_detections[:5]:  # Top 5 detections
+        report_content.append(
+            f"| {detection['frame']:06d} | {detection['label']} | ({detection['gps'][0]:.6f}, {detection['gps'][1]:.6f}) | {detection['timestamp']} | {detection['conf']:.2f} | captured_frames/{os.path.basename(detection['path'])} |"
+        )
+
+    report_content.extend([
+        "",
+        "## 7. Flight Logs",
+        f"- Total Logs: {len(detected_issues)}",
+        f"- Storage: Data Lake `/project_xyz/flight_logs/{datetime.now().strftime('%Y-%m-%d')}`",
+        "",
+        "| Frame | Timestamp | Latitude | Longitude | Speed (m/s) | Satellites | Altitude (m) | Log Path |",
+        "|-------|-----------|----------|-----------|-------------|------------|--------------|----------|"
+    ])
+
+    for detection in all_detections[:5]:  # Top 5 detections
+        log_path = f"flight_logs/flight_log_{detection['frame']:06d}.csv"
+        report_content.append(
+            f"| {detection['frame']:06d} | {detection['timestamp']} | {detection['gps'][0]:.6f} | {detection['gps'][1]:.6f} | 5.0 | 12 | 60 | {log_path} |"
+        )
+
+    report_content.extend([
+        "",
+        "## 8. Processed Video",
+        f"- Path: outputs/processed_output.mp4",
+        f"- Frames: {output_frames}",
+        f"- FPS: {output_fps:.2f}",
+        f"- Duration: {output_duration:.2f} seconds",
+        "",
+        "## 9. Visualizations",
+        f"- Detection Trend Chart: outputs/chart_{timestamp}.png",
+        f"- Issue Locations Map: outputs/map_{timestamp}.png",
+        "",
+        "## 10. Processing Timestamps",
+        f"- Total Processing Time: {total_time:.2f} seconds",
+        "- Log Entries (Last 10):"
+    ])
+
+    for entry in log_entries[-10:]:
+        report_content.append(f"  - {entry}")
+
+    report_content.extend([
+        "",
+        "## 11. Stakeholder Validation",
+        "- AE/IE Comments: [Pending]",
+        "- PD/RO Comments: [Pending]",
+        "",
+        "## 12. Recommendations",
+        "- Repair potholes in high-traffic segments.",
+        "- Seal cracks to prevent degradation.",
+        "- Schedule follow-up survey.",
+        "",
+        "## 13. Data Lake References",
+        f"- Images: `/project_xyz/images/{datetime.now().strftime('%Y-%m-%d')}`",
+        f"- Flight Logs: `/project_xyz/flight_logs/{datetime.now().strftime('%Y-%m-%d')}`",
+        f"- Video: `/project_xyz/videos/processed_output_{datetime.now().strftime('%Y%m%d')}.mp4`",
+        f"- DAMS Dashboard: `/project_xyz/dams/{datetime.now().strftime('%Y-%m-%d')}`"
+    ])
+
+    try:
+        with open(report_path, 'w') as f:
+            f.write("\n".join(report_content))
+        log_entries.append(f"Report saved: {report_path}")
+        return report_path
+    except Exception as e:
+        log_entries.append(f"Error: Failed to save report: {str(e)}")
+        return ""
+
 def process_video(video, resize_width=4000, resize_height=3000, frame_skip=5):
     global frame_count, last_metrics, detected_counts, detected_issues, gps_coordinates, log_entries
     frame_count = 0