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Runtime error
Runtime error
| 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 = 0.8 | |
| 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) | |
| my_metadata = MetadataCatalog.get("car_dataset_val") | |
| my_metadata.thing_classes = ["damage"] | |
| 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 Scratch 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() |