app.py

try:
    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

model_path = 'model_final.pth'

cfg = get_cfg()
cfg.merge_from_file("./configs/faster_rcnn_R_50_FPN_3x.yaml")
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 1
cfg.MODEL.WEIGHTS = model_path

if not torch.cuda.is_available():
    cfg.MODEL.DEVICE='cpu'

predictor = DefaultPredictor(cfg)

def inference(image):
    print(image.height)

    height = image.height

    img = np.array(image.resize((500, height)))
    outputs = predictor(img)
    v = Visualizer(img[:, :, ::-1],
                   #metadata=val_metadata_dicts, 
                   scale=0.5, 
                   instance_mode=ColorMode.SEGMENTATION   # remove the colors of unsegmented pixels. This option is only available for segmentation models
    )
    # v = Visualizer(img,scale=1.2)
    out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
    
    return out.get_image()
    
title = "Detectron2 Car damage Detection"
description = "This demo introduces an interactive playground for our trained Detectron2 model."

gr.Interface(
    inference, 
    [gr.inputs.Image(type="pil", label="Input")], 
    gr.outputs.Image(type="numpy", label="Output"),
    title=title,
    description=description,
    examples=[]).launch()