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import detectron2
except:
import os
os.system('pip install git+https://github.com/facebookresearch/detectron2.git')
from matplotlib.pyplot import axis
import gradio as gr
import requests
import numpy as np
from torch import nn
import requests
import torch
from detectron2 import model_zoo
from detectron2.engine import DefaultPredictor
from detectron2.config import get_cfg
from detectron2.utils.visualizer import Visualizer
from detectron2.data import MetadataCatalog
from detectron2.utils.visualizer import ColorMode
import os
model_path = "model_final.pth"
cfg = get_cfg()
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.30
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 6
cfg.MODEL.WEIGHTS = model_path
car_metadata = MetadataCatalog.get("test11")
car_metadata.thing_classes = ['Damged-or-undamaged',' light damaged ','dent ','scratch','glass damaged','undamged']
if not torch.cuda.is_available():
cfg.MODEL.DEVICE='cpu'
predictor = DefaultPredictor(cfg)
def inference(image):
#im = cv2.imread(image)
#height = image.height
img = np.array(image)
outputs = predictor(img)
v = Visualizer(img[:, :, ::-1],
metadata=car_metadata,
scale=0.5,
instance_mode=ColorMode.IMAGE_BW
)
#v = Visualizer(img, my_metadata, scale=1.2)
out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
return out.get_image()
title = "Car Damage Detection Model(Detectron2)"
gr.Interface(inference,[gr.inputs.Image(type="pil", label="Input")],gr.outputs.Image(type="numpy", label="Output"),title=title,examples=[]).launch(debug=True)
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