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import gradio as gr
import torch
from ultralytics import YOLO
import cv2
import numpy as np
from PIL import Image
import json
# Load model
model = YOLO('models/best.pt')
# Class mapping sesuai dengan mobile app
CLASS_NAMES = {
0: 'amblas',
1: 'bergelombang',
2: 'berlubang',
3: 'retak_buaya'
}
def detect_road_damage(image, confidence_threshold=0.5):
"""
Deteksi kerusakan jalan dari gambar
"""
try:
# Convert PIL to numpy array
if isinstance(image, Image.Image):
image = np.array(image)
# Run inference
results = model(image, conf=confidence_threshold)
detections = []
annotated_image = image.copy()
for result in results:
boxes = result.boxes
if boxes is not None:
for box in boxes:
# Extract detection info
x1, y1, x2, y2 = box.xyxy[0].cpu().numpy()
confidence = float(box.conf[0])
class_id = int(box.cls[0])
class_name = CLASS_NAMES.get(class_id, 'unknown')
# Calculate dimensions (estimasi)
width_pixels = x2 - x1
height_pixels = y2 - y1
# Estimasi ukuran dalam cm (asumsi 1 pixel = 0.1 cm)
width_cm = width_pixels * 0.1
depth_cm = height_pixels * 0.1
detection = {
'class': class_name,
'confidence': confidence,
'bbox': [float(x1), float(y1), float(x2), float(y2)],
'width_cm': width_cm,
'depth_cm': depth_cm
}
detections.append(detection)
# Draw bounding box
cv2.rectangle(annotated_image,
(int(x1), int(y1)), (int(x2), int(y2)),
(0, 255, 0), 2)
# Add label
label = f"{class_name}: {confidence:.2f}"
cv2.putText(annotated_image, label,
(int(x1), int(y1-10)),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
# Prepare response
response = {
'detections': detections,
'count': len(detections),
'status': 'success'
}
return annotated_image, json.dumps(response, indent=2)
except Exception as e:
error_response = {
'error': str(e),
'status': 'error'
}
return image, json.dumps(error_response, indent=2)
# Gradio interface
with gr.Blocks(title="VGTec Road Damage Detector") as demo:
gr.Markdown("# 🛣️ VGTec Road Damage Detection API")
gr.Markdown("Upload gambar jalan untuk mendeteksi kerusakan (amblas, bergelombang, berlubang, retak_buaya)")
with gr.Row():
with gr.Column():
input_image = gr.Image(type="pil", label="Upload Gambar Jalan")
confidence_slider = gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.5,
step=0.1,
label="Confidence Threshold"
)
detect_btn = gr.Button("🔍 Deteksi Kerusakan", variant="primary")
with gr.Column():
output_image = gr.Image(label="Hasil Deteksi")
output_json = gr.Code(label="JSON Response", language="json")
# Event handler
detect_btn.click(
fn=detect_road_damage,
inputs=[input_image, confidence_slider],
outputs=[output_image, output_json]
)
# Examples
gr.Examples(
examples=[
["examples/berlubang.jpg", 0.5],
["examples/retak_buaya.jpg", 0.6],
],
inputs=[input_image, confidence_slider],
outputs=[output_image, output_json],
fn=detect_road_damage,
cache_examples=True
)
if __name__ == "__main__":
demo.launch() |