app.py

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

# 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"
os.makedirs(CAPTURED_FRAMES_DIR, exist_ok=True)
os.makedirs(OUTPUT_DIR, exist_ok=True)
os.chmod(CAPTURED_FRAMES_DIR, 0o777)
os.chmod(OUTPUT_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

# 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}")

# Mock service functions
def generate_map(gps_coords: List[List[float]], items: List[Dict[str, Any]]) -> str:
    map_path = "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 send_to_salesforce(data: Dict[str, Any]) -> None:
    pass  # Minimal mock

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 = "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
    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 = "processed_output.mp4"
    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
    out = cv2.VideoWriter(output_path, fourcc, fps, (out_width, out_height))

    processed_frames = 0
    all_detections = []
    frame_times = []
    detection_frame_count = 0

    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))
        results = model(frame, verbose=False, conf=0.5, iou=0.7)  # Lower thresholds
        annotated_frame = results[0].plot()

        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]
            frame_detections.append({"label": label, "box": box, "conf": conf})
            log_entries.append(f"Frame {frame_count}: Detected {label} with confidence {conf:.2f}")
            logging.info(f"Frame {frame_count}: Detected {label} with confidence {conf:.2f}")

        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}.jpg")
                if not cv2.imwrite(captured_frame_path, annotated_frame):
                    log_entries.append(f"Error: Failed to save {captured_frame_path}")
                    logging.error(f"Failed to save {captured_frame_path}")
                else:
                    detected_issues.append(captured_frame_path)
                    if len(detected_issues) > 100:
                        detected_issues.pop(0)

        out.write(annotated_frame)
        if frame_skip > 1:
            for _ in range(frame_skip - 1):
                if frame_count + 1 <= total_frames:
                    out.write(annotated_frame)
                    frame_count += 1

        detected_counts.append(len(frame_detections))
        gps_coord = [17.385044 + (frame_count * 0.0001), 78.486671 + (frame_count * 0.0001)]
        gps_coordinates.append(gps_coord)
        for det in frame_detections:
            det["gps"] = gps_coord
        all_detections.extend(frame_detections)

        frame_time = (time.time() - frame_start) * 1000
        frame_times.append(frame_time)
        detection_summary = {
            "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
            "frame": frame_count,
            "cracks": sum(1 for det in frame_detections if det["label"] == "crack"),
            "potholes": sum(1 for det in frame_detections if det["label"] == "pothole"),
            "gps": gps_coord,
            "processing_time_ms": frame_time
        }
        log_entries.append(json.dumps(detection_summary, indent=2))
        if len(log_entries) > 50:
            log_entries.pop(0)

    last_metrics = update_metrics(all_detections)
    send_to_salesforce({
        "detections": all_detections,
        "metrics": last_metrics,
        "timestamp": detection_summary["timestamp"] if all_detections else datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
        "frame_count": frame_count,
        "gps_coordinates": gps_coordinates[-1] if gps_coordinates else [0, 0]
    })

    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
    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}")
    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}")
    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}")

    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("# Crack and Pothole 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()