app.py

from PIL import Image, ImageDraw
from ultralytics import YOLO
import streamlit as st
import tempfile
import cv2
import numpy as np
import base64

# Initialize YOLO model
model = YOLO("best.pt")

# Function to perform object detection on an image
def detect_objects_image(image):
    results = model(image)
    result = results[0]
    output = []
    num_potholes_detected = 0
    num_cracks_detected = 0
    num_alligator_cracks_detected = 0
    
    for box in result.boxes:
        x1, y1, x2, y2 = [round(x) for x in box.xyxy[0].tolist()]
        class_id = box.cls[0].item()
        prob = round(box.conf[0].item(), 2)
        class_name = result.names[class_id]
        output.append([x1, y1, x2, y2, class_name, prob])
        
        # Count detections by class
        if class_name == "pothole":
            num_potholes_detected += 1
        elif class_name == "crack":
            num_cracks_detected += 1
        elif class_name == "alligator-crack":
            num_alligator_cracks_detected += 1

    return output, num_potholes_detected, num_cracks_detected, num_alligator_cracks_detected

# Function to process and annotate a video
def process_video(video_path, output_path, frame_interval):
    cap = cv2.VideoCapture(video_path)
    fps = int(cap.get(cv2.CAP_PROP_FPS))
    frame_interval_count = int(fps * frame_interval)
    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
    out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
    frame_count = 0
    detections_summary = {
        'potholes': 0,
        'cracks': 0,
        'alligator_cracks': 0
    }

    while cap.isOpened():
        ret, frame = cap.read()
        if not ret:
            break

        if frame_count % frame_interval_count == 0:
            image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
            detections, num_potholes, num_cracks, num_alligator_cracks = detect_objects_image(image)
            detections_summary['potholes'] += num_potholes
            detections_summary['cracks'] += num_cracks
            detections_summary['alligator_cracks'] += num_alligator_cracks

            draw = ImageDraw.Draw(image)
            for detection in detections:
                x1, y1, x2, y2, class_name, prob = detection
                draw.rectangle([x1, y1, x2, y2], outline="red", width=3)
                text = f"{class_name} {prob:.2f}"
                draw.text((x1, y1), text, fill="red")

            annotated_frame = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
        else:
            annotated_frame = frame

        out.write(annotated_frame)
        frame_count += 1

    cap.release()
    out.release()

    return detections_summary

# Function to generate a download link for a file
def get_download_link(file_path, text, file_type):
    with open(file_path, 'rb') as f:
        file_bytes = f.read()
    file_b64 = base64.b64encode(file_bytes).decode()
    download_link = f'<a href="data:{file_type};base64,{file_b64}" download="{text}">{text}</a>'
    return download_link

# Streamlit app
def main():
    st.title("Road Condition Inspection")
    st.subheader("Upload an image or video to detect objects")

    # File uploader for image and video
    uploaded_file = st.file_uploader("Choose a file...", type=["jpg", "jpeg", "png", "mp4"])

    if uploaded_file is not None:
        file_type = uploaded_file.type
        temp_file = tempfile.NamedTemporaryFile(delete=False, suffix="." + uploaded_file.name.split('.')[-1])
        temp_file.write(uploaded_file.read())
        temp_file.close()

        if file_type.startswith("image"):
            image = Image.open(temp_file.name)
            st.image(image, caption='Uploaded Image', use_column_width=True)

            if st.button('Detect Objects (Image)'):
                detections, num_potholes, num_cracks, num_alligator_cracks = detect_objects_image(image)
                draw = ImageDraw.Draw(image)
                for detection in detections:
                    x1, y1, x2, y2, class_name, prob = detection
                    draw.rectangle([x1, y1, x2, y2], outline="red", width=3)
                    text = f"{class_name} {prob:.2f}"
                    draw.text((x1, y1), text, fill="red")

                st.image(image, caption='Annotated Image', use_column_width=True)
                st.subheader("Detection Summary")
                if num_potholes > 0:
                    st.write(f"Potholes Detected: {num_potholes}")
                if num_cracks > 0:
                    st.write(f"Cracks Detected: {num_cracks}")
                if num_alligator_cracks > 0:
                    st.write(f"Alligator Cracks Detected: {num_alligator_cracks}")

                annotated_image_path = temp_file.name.replace(".", "_annotated.")
                image.save(annotated_image_path)
                st.markdown(get_download_link(annotated_image_path, "Download Annotated Image", "image/png"), unsafe_allow_html=True)

        elif file_type.startswith("video"):
            video_bytes = open(temp_file.name, 'rb').read()
            st.video(video_bytes)

            if st.button('Detect Objects (Video)'):
                annotated_video_path = temp_file.name.replace(".", "_annotated") + ".mp4"
                detections_summary = process_video(temp_file.name, annotated_video_path, frame_interval=1)
                
                st.subheader("Annotated Video Download")
                st.markdown(get_download_link(annotated_video_path, "Download Annotated Video", "video/mp4"), unsafe_allow_html=True)

                st.subheader("Detection Summary")
                if detections_summary['potholes'] > 0:
                    st.write(f"Total Potholes Detected: {detections_summary['potholes']}")
                if detections_summary['cracks'] > 0:
                    st.write(f"Total Cracks Detected: {detections_summary['cracks']}")
                if detections_summary['alligator_cracks'] > 0:
                    st.write(f"Total Alligator Cracks Detected: {detections_summary['alligator_cracks']}")

if __name__ == '__main__':
    main()