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import os
os.system('pip install git+https://github.com/facebookresearch/detectron2.git')
import gradio as gr
import logging
from detectron2.engine import DefaultPredictor
import cv2
from detectron2.config import get_cfg
from utils import add_bboxes
config_file="config.yaml"
cfg = get_cfg()
cfg.merge_from_file(config_file)
cfg.MODEL.DEVICE="cpu"
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5
# cfg.MODEL.WEIGHTS = "checkpoints_model_final_imagenet_40k_synthetic.pth"
def predict(
model,
img
):
if model=="40k synthetic":
weights = "checkpoints_model_final_imagenet_40k_synthetic.pth"
elif model == "100k synthetic":
weights = "checkpoints_model_final_imagenet_100k_synthetic.pth"
else:
weights = "checkpoints_model_final_imagenet_5k_synthetic.pth"
cfg.MODEL.WEIGHTS=weights
predictor = DefaultPredictor(cfg)
im = cv2.imread(img.name)
output = predictor(im)
img = add_bboxes(im, output['instances'].pred_boxes, scores=output['instances'].scores)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
return img
title = "Indoor Pet Detection"
description = "This is an application trained with synthetic data from Unity Computer Vision. We trained a single class object detection model to recognize dogs using images of pets randomly posed and placed in our home interior environment. There are multiple pre-trained models trained with 5k, 40k and 100k synthetic data that you can choose for inference. <p> For more information please refer - <a target='_blank' href='https://github.com/Unity-Technologies/Indoor-Pet-Detection'> Github Project </a> </p>"
examples = [
["5k synthetic", 'example.jpg'],
["40k synthetic", 'example.jpg'],
["100k synthetic", 'example.jpg'],
["5k synthetic", 'example-2.jpg'],
["40k synthetic", 'example-2.jpg'],
["100k synthetic", 'example-2.jpg']
]
gr.Interface(predict, [gr.inputs.Dropdown(["5k synthetic", "40k synthetic", "100k synthetic"]), gr.inputs.Image(type="file")], outputs=gr.outputs.Image(type="pil"),enable_queue=True, title=title,
description=description,
# article=article,
examples=examples).launch(debug=True)
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