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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()
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