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import gradio as gr
import cv2
import requests
import os
from ultralytics import YOLO
model = YOLO('best_gd.pt')
image_paths = ['pothole_example.jpg', 'pothole_screenshot.png']
def show_preds_image(image):
# Save the uploaded image temporarily
image_path = "uploaded_image.jpg"
cv2.imwrite(image_path, image[:, :, ::-1]) # Convert BGR to RGB and save the image
image = cv2.imread(image_path)
outputs = model.predict(source=image_path)
#print("output>>>>>>>>>>>>>>>>>>>>>>>>", outputs)
results = outputs[0].boxes.xyxy.cpu().numpy()
for det in results:
x1, y1, x2, y2 = det[:4]
label = ""
cv2.rectangle(
image,
(int(x1), int(y1)),
(int(x2), int(y2)),
color=(0, 0, 255),
thickness=2,
lineType=cv2.LINE_AA,
)
cv2.putText(
image,
label,
(int(x1), int(y1) - 10),
cv2.FONT_HERSHEY_SIMPLEX,
0.9,
(0, 0, 255),
2,
cv2.LINE_AA,
)
os.remove(image_path) # Remove the temporary image file
return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
inputs_image = [
gr.inputs.Image(label="Upload Image"),
]
outputs_image = [
gr.outputs.Image(type="numpy"),
]
interface_image = gr.Interface(
fn=show_preds_image,
inputs=inputs_image,
outputs=outputs_image,
title="Animal detector",
examples=[],
cache_examples=False,
)
interface_image.launch()
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