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| import os | |
| os.system('git clone https://github.com/facebookresearch/detectron2.git') | |
| os.system('pip install -e detectron2') | |
| os.system("git clone https://github.com/microsoft/unilm.git") | |
| os.system("sed -i 's/from collections import Iterable/from collections.abc import Iterable/' unilm/dit/object_detection/ditod/table_evaluation/data_structure.py") | |
| os.system("curl -LJ -o publaynet_dit-b_cascade.pth 'https://layoutlm.blob.core.windows.net/dit/dit-fts/publaynet_dit-b_cascade.pth?sv=2022-11-02&ss=b&srt=o&sp=r&se=2033-06-08T16:48:15Z&st=2023-06-08T08:48:15Z&spr=https&sig=a9VXrihTzbWyVfaIDlIT1Z0FoR1073VB0RLQUMuudD4%3D'") | |
| import sys | |
| sys.path.append("unilm") | |
| sys.path.append("detectron2") | |
| import cv2 | |
| import torch | |
| from collections.abc import Iterable as Iterable | |
| 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 | |
| from unilm.dit.object_detection.ditod import add_vit_config | |
| 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 = "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] | |
| return result_image | |
| 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"), | |
| title=title, | |
| description=description, | |
| examples=examples, | |
| article=article, | |
| css=css, | |
| enable_queue=True) | |
| iface.launch(debug=True, cache_examples=True) |