app (2).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
import detectron2
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(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"))
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.75
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 19
cfg.MODEL.WEIGHTS = model_path


if not torch.cuda.is_available():
    cfg.MODEL.DEVICE='cpu'
    
predictor = DefaultPredictor(cfg)
my_metadata = MetadataCatalog.get("car_part_merged_dataset_val")
my_metadata.thing_classes = ['_background_',
 'back_bumper',
 'back_glass',
 'back_left_door',
 'back_left_light',
 'back_right_door',
 'back_right_light',
 'front_bumper',
 'front_glass',
 'front_left_door',
 'front_left_light',
 'front_right_door',
 'front_right_light',
 'hood',
 'left_mirror',
 'right_mirror',
 'tailgate',
 'trunk',
 'wheel']

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

    height = image.height

    # img = np.array(image.resize((500, height)))
    img = np.array(image)
    outputs = predictor(img)
    v = Visualizer(img[:, :, ::-1],
                   metadata=my_metadata, 
                   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)
    #print(outputs["instances"].to('cpu'))
    out = v.draw_instance_predictions(outputs["instances"])
    
    return out.get_image()[:, :, ::-1]
    
title = "Detectron2 Car Parts 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()