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Create app.py
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app.py
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import os
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os.system('pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu102/torch1.9/index.html')
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import gradio as gr
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# check pytorch installation:
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import torch, torchvision
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print(torch.__version__, torch.cuda.is_available())
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assert torch.__version__.startswith("1.9") # please manually install torch 1.9 if Colab changes its default version
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# Some basic setup:
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# Setup detectron2 logger
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import detectron2
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from detectron2.utils.logger import setup_logger
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# import some common libraries
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import numpy as np
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import os, json, cv2, random
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# import some common detectron2 utilities
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from detectron2 import model_zoo
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from detectron2.engine import DefaultPredictor
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from detectron2.config import get_cfg
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from detectron2.utils.visualizer import Visualizer
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from detectron2.data import MetadataCatalog, DatasetCatalog
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from PIL import Image
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cfg = get_cfg()
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cfg.MODEL.DEVICE='cpu'
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# add project-specific config (e.g., TensorMask) here if you're not running a model in detectron2's core library
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cfg.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"))
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cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5 # set threshold for this model
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# Find a model from detectron2's model zoo. You can use the https://dl.fbaipublicfiles... url as well
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cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml")
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predictor = DefaultPredictor(cfg)
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def inference(img):
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im = cv2.imread(img.name)
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outputs = predictor(im)
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v = Visualizer(im[:, :, ::-1], MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), scale=1.2)
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out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
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return Image.fromarray(np.uint8(out.get_image())).convert('RGB')
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title = "Detectron 2"
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description = "Gradio demo for Detectron 2: A PyTorch-based modular object detection library. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
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article = "<p style='text-align: center'><a href='https://ai.facebook.com/blog/-detectron2-a-pytorch-based-modular-object-detection-library-/' target='_blank'>Detectron2: A PyTorch-based modular object detection library</a> | <a href='https://github.com/facebookresearch/detectron2' target='_blank'>Github Repo</a></p>"
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examples = [['example.png']]
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gr.Interface(inference, inputs=gr.inputs.Image(type="file"), outputs=gr.outputs.Image(type="pil"),enable_queue=True, title=title,
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description=description,
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article=article,
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examples=examples).launch()
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