app.py

import gradio as gr
import cv2

from ultralytics import YOLO

model = YOLO('best.pt')
path  = [['pothole1.jpg'], ['pothole2.jpg'], ['pothole3.jpg'],['pothole4.jpg']]

import cv2

def resize_image(image_path):
    # Read the image using OpenCV
    img = cv2.imread(image_path)

    # Resize the image to 512x512
    resized_img = cv2.resize(img, (512, 512), interpolation = cv2.INTER_LINEAR)

    return resized_img
def prediction1(image_path):
    # Read the image using OpenCV
    image = cv2.imread(image_path)
    outputs = model.predict(image_path)
    results = outputs[0].cpu().numpy()

    # Initialize maximum area and index
    max_area = 0
    max_index = -1

    # Calculate areas and find the box with the maximum area
    for i, det in enumerate(results.boxes.xyxy):
        width = det[2] - det[0]
        height = det[3] - det[1]
        area = width * height
        if area > max_area:
            max_area = area
            max_index = i

        # Draw bounding box for each detected pothole
        cv2.rectangle(
            image,
            (int(det[0]), int(det[1])),
            (int(det[2]), int(det[3])),
            color=(0, 255, 0),
            thickness=1,
            lineType=cv2.LINE_AA,
        )

    # Add label to the bounding box with the maximum area
    if max_index != -1:
        det = results.boxes.xyxy[max_index]
        # Compute relative width and height
        relative_width = (det[2] - det[0]) / image.shape[1]
        relative_height = (det[3] - det[1]) / image.shape[0]

        # Draw relative width and height on the bounding box
        cv2.putText(
            image,
            f'W: {relative_width:.2f}, H: {relative_height:.2f}',
            (int(det[0]), int(det[1]) - 5),
            cv2.FONT_HERSHEY_SIMPLEX,
            0.5,
            (0, 0, 255),
            1,
            cv2.LINE_AA
        )
    return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

inputs_image = [
    gr.components.Image(type="filepath", label="Input Image"),
]
outputs_image = [
    gr.components.Image(type="numpy", label="Output Image"),
]
interface_image = gr.Interface(
    fn=prediction1,
    inputs=inputs_image,
    outputs=outputs_image,
    title="Pothole detection",
    description="Detects potholes in images",
    #cache_examples=True,
    examples=[['pothole1.jpg'], ['pothole2.jpg'], ['pothole3.jpg'],['pothole4.jpg']]
    
)



interface_image.launch()