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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
model_path = "https://huggingface.co/dbmdz/detectron2-model/resolve/main/model_final.pth"
cfg = get_cfg()
cfg.merge_from_file("./configs/detectron2/faster_rcnn_R_50_FPN_3x.yaml")
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.8
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 2
cfg.MODEL.WEIGHTS = model_path
my_metadata = MetadataCatalog.get("dbmdz_coco_all")
my_metadata.thing_classes = ["Illumination", "Illustration"]
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, my_metadata, scale=1.2)
out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
return out.get_image()
title = "DBMDZ Detectron2 Model Demo"
description = "This demo introduces an interactive playground for our trained Detectron2 model. <br>The model was trained on image from digitized books to detect Illustration or Illumination segments on a given page. Classification threshold is set to 0.8."
article = '<p>Detectron model is available from our repository <a href="">here</a> on the Hugging Face Model Hub.</p>'
gr.Interface(
inference,
[gr.inputs.Image(type="pil", label="Input")],
gr.outputs.Image(type="numpy", label="Output"),
title=title,
description=description,
article=article,
examples=[]).launch()
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