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import os
os.system('pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu102/torch1.9/index.html')
os.system("git clone https://github.com/microsoft/unilm.git")
import sys
sys.path.append("unilm")
import cv2
from unilm.dit.object_detection.ditod import add_vit_config
import torch
import numpy as np
from detectron2.config import CfgNode as CN
from detectron2.config import get_cfg
from detectron2.utils.visualizer import ColorMode, Visualizer
from detectron2.data import MetadataCatalog
from detectron2.engine import DefaultPredictor
import gradio as gr
# Step 1: instantiate config
cfg = get_cfg()
add_vit_config(cfg)
cfg.merge_from_file("cascade_dit_base.yml")
# Step 2: add model weights URL to config
cfg.MODEL.WEIGHTS = "https://layoutlm.blob.core.windows.net/dit/dit-fts/publaynet_dit-b_cascade.pth"
# Step 3: set device
cfg.MODEL.DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
# Step 4: define model
predictor = DefaultPredictor(cfg)
def analyze_image(img):
md = MetadataCatalog.get(cfg.DATASETS.TEST[0])
if cfg.DATASETS.TEST[0]=='icdar2019_test':
md.set(thing_classes=["table"])
else:
md.set(thing_classes=["text","title","list","table","figure"])
output = predictor(img)["instances"]
v = Visualizer(img[:, :, ::-1],
md,
scale=1.0,
instance_mode=ColorMode.SEGMENTATION)
result = v.draw_instance_predictions(output.to("cpu"))
result_image = result.get_image()[:, :, ::-1]
num_instances = len(output)
image_size = output._image_size
fields = list(output.get_fields().keys())
for field in fields:
if field == 'pred_boxes':
boxes = output.get_fields()[field]
boxes_numpy = boxes.tensor.cpu().numpy()
boxes_bytes = str(boxes_numpy.tobytes())
boxes_numpy_shape = str(boxes_numpy.shape)
boxes_numpy_dtype = str(boxes_numpy.dtype)
# boxes_recover = torch.from_numpy(np.frombuffer(boxes_bytes, dtype=boxes_numpy_dtype).reshape(boxes_numpy_shape))
elif field == 'scores':
scores = output.get_fields()[field]
scores_numpy = scores.cpu().numpy()
scores_bytes = str(scores_numpy.tobytes())
scores_numpy_shape = str(scores_numpy.shape)
scores_numpy_dtype = str(scores_numpy.dtype)
# scores_recover = torch.from_numpy(np.frombuffer(scores_bytes, dtype=scores_numpy_dtype).reshape(scores_numpy_shape))
return result_image, num_instances, image_size, boxes_bytes, boxes_numpy_shape, boxes_numpy_dtype, scores_bytes, scores_numpy_shape, scores_numpy_dtype
title = "Interactive demo: Document Layout Analysis with DiT"
description = "Demo for Microsoft's DiT, the Document Image Transformer for state-of-the-art document understanding tasks. This particular model is fine-tuned on PubLayNet, a large dataset for document layout analysis (read more at the links below). To use it, simply upload an image or use the example image below and click 'Submit'. Results will show up in a few seconds. If you want to make the output bigger, right-click on it and select 'Open image in new tab'."
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2203.02378' target='_blank'>Paper</a> | <a href='https://github.com/microsoft/unilm/tree/master/dit' target='_blank'>Github Repo</a></p> | <a href='https://huggingface.co/docs/transformers/master/en/model_doc/dit' target='_blank'>HuggingFace doc</a></p>"
examples =[['publaynet_example.jpeg']]
css = ".output-image, .input-image, .image-preview {height: 600px !important}"
iface = gr.Interface(fn=analyze_image,
inputs=gr.inputs.Image(type="numpy", label="document image"),
outputs=[
gr.outputs.Image(type="numpy", label="annotated document"),
gr.outputs.Textbox(label="num instances"),
gr.outputs.Textbox(label="image size (h,w in pixels)"),
gr.outputs.Textbox(label="boxes bytes"),
gr.outputs.Textbox(label="boxes numpy shape"),
gr.outputs.Textbox(label="boxes numpy dtype"),
gr.outputs.Textbox(label="scores bytes"),
gr.outputs.Textbox(label="scores numpy shape"),
gr.outputs.Textbox(label="scores numpy dtype")
],
title=title,
description=description,
examples=examples,
article=article,
css=css,
enable_queue=True)
iface.launch(debug=True, cache_examples=True) |