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d5cdaaa c137135 d5cdaaa c137135 d5cdaaa c137135 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | import gradio as gr
import requests
from detectron2.config import get_cfg
from detectron2 import model_zoo
from detectron2.engine import DefaultPredictor
from detectron2.utils.visualizer import Visualizer
from detectron2.data import MetadataCatalog
import numpy as np
import cv2
from PIL import Image
import os
# Dropbox model link
MODEL_URL = "https://www.dropbox.com/scl/fi/m8e7tr4vy887rrmedvpok/model_final-1.pth?rlkey=bf5ov8r1m89u9qp88alpuvmse&st=htkj8ux1&dl=1"
MODEL_PATH = "model_final.pth"
# Download model if not exists
if not os.path.exists(MODEL_PATH):
print("Downloading model...")
response = requests.get(MODEL_URL, stream=True)
with open(MODEL_PATH, 'wb') as f:
for chunk in response.iter_content(chunk_size=8192):
f.write(chunk)
print("Model downloaded.")
# Configure Detectron2
cfg = get_cfg()
cfg.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"))
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 8
cfg.MODEL.WEIGHTS = MODEL_PATH
cfg.MODEL.DEVICE = "cpu" # Set to CPU for Hugging Face Spaces
predictor = DefaultPredictor(cfg)
# Metadata
MetadataCatalog.get("car_parts").set(thing_classes=[
"Dent", "Scratch", "Broken part", "Paint chip",
"Missing part", "Flaking", "Corrosion", "Cracked"
])
metadata = MetadataCatalog.get("car_parts")
def detect_damage(input_image):
# Convert PIL image to OpenCV format
image_cv2 = cv2.cvtColor(np.array(input_image), cv2.COLOR_RGB2BGR)
# Run predictions
outputs = predictor(image_cv2)
# Visualize predictions
v = Visualizer(image_cv2[:, :, ::-1], metadata=metadata, scale=1.0)
out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
visualized_image = out.get_image()[:, :, ::-1]
# Convert back to PIL for display in Gradio
return Image.fromarray(visualized_image)
# Gradio Interface
demo = gr.Interface(
fn=detect_damage,
inputs=gr.Image(type="pil"),
outputs="image",
title="Car Parts Damage Detection",
description="Upload an image of a car to detect damage such as dents, scratches, and broken parts."
)
if __name__ == "__main__":
demo.launch()
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