Spaces:
Runtime error
Runtime error
import os | |
os.system('pip install pyyaml==5.1') | |
# workaround: install old version of pytorch since detectron2 hasn't released packages for pytorch 1.9 (issue: https://github.com/facebookresearch/detectron2/issues/3158) | |
os.system('pip install torch==1.8.0+cu101 torchvision==0.9.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html') | |
# install detectron2 that matches pytorch 1.8 | |
# See https://detectron2.readthedocs.io/tutorials/install.html for instructions | |
os.system('pip install -q detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/torch1.8/index.html') | |
## install PyTesseract | |
os.system('pip install -q pytesseract') | |
import gradio as gr | |
import numpy as np | |
from transformers import LayoutLMv3Processor, LayoutLMv3ForTokenClassification | |
from datasets import load_dataset | |
from PIL import Image, ImageDraw, ImageFont, ImageColor | |
processor = LayoutLMv3Processor.from_pretrained("microsoft/layoutlmv3-base") | |
model = LayoutLMv3ForTokenClassification.from_pretrained("nielsr/layoutlmv3-finetuned-cord") | |
# load image example | |
dataset = load_dataset("nielsr/cord-layoutlmv3", split="test") | |
#image = Image.open(dataset[0]["image_path"]).convert("RGB") | |
image = Image.open("./test0.jpeg") | |
# define id2label, label2color | |
labels = dataset.features['ner_tags'].feature.names | |
id2label = {v: k for v, k in enumerate(labels)} | |
label_ints = np.random.randint(0, len(ImageColor.colormap.items()), 61) | |
label_color_pil = [k for k,_ in ImageColor.colormap.items()] | |
label_color = [label_color_pil[i] for i in label_ints] | |
label2color = {} | |
for k,v in id2label.items(): | |
label2color[v[2:]]=label_color[k] | |
def unnormalize_box(bbox, width, height): | |
return [ | |
width * (bbox[0] / 1000), | |
height * (bbox[1] / 1000), | |
width * (bbox[2] / 1000), | |
height * (bbox[3] / 1000), | |
] | |
def iob_to_label(label): | |
label = label[2:] | |
if not label: | |
return 'other' | |
return label | |
def process_image(image): | |
width, height = image.size | |
# encode | |
encoding = processor(image, truncation=True, return_offsets_mapping=True, return_tensors="pt") | |
offset_mapping = encoding.pop('offset_mapping') | |
# forward pass | |
outputs = model(**encoding) | |
# get predictions | |
predictions = outputs.logits.argmax(-1).squeeze().tolist() | |
token_boxes = encoding.bbox.squeeze().tolist() | |
# only keep non-subword predictions | |
is_subword = np.array(offset_mapping.squeeze().tolist())[:,0] != 0 | |
true_predictions = [id2label[pred] for idx, pred in enumerate(predictions) if not is_subword[idx]] | |
true_boxes = [unnormalize_box(box, width, height) for idx, box in enumerate(token_boxes) if not is_subword[idx]] | |
# draw predictions over the image | |
draw = ImageDraw.Draw(image) | |
font = ImageFont.load_default() | |
for prediction, box in zip(true_predictions, true_boxes): | |
predicted_label = iob_to_label(prediction) #.lower() | |
draw.rectangle(box, outline=label2color[predicted_label]) | |
draw.text((box[0]+10, box[1]-10), text=predicted_label, fill=label2color[predicted_label], font=font) | |
return image | |
title = "Interactive demo: LayoutLMv3" | |
description = "Demo for Microsoft's LayoutLMv3, a Transformer for state-of-the-art document image understanding tasks. This particular model is fine-tuned on CORD, a dataset of receipts. It annotates the words appearing in the image as ***. 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/2204.08387' target='_blank'>LayoutLMv3: Multi-modal Pre-training for Visually-Rich Document Understanding</a> | <a href='https://github.com/microsoft/unilm' target='_blank'>Github Repo</a></p>" | |
examples =[['test0.jpeg'],['./test1.jpeg'],['test2.jpeg']] | |
#css = ".output-image, .input-image" | |
#css = ".output-image, .input-image {height: 40rem !important; width: 100% !important;}" | |
#css = "@media screen and (max-width: 600px) { .output_image, .input_image {height:20rem !important; width: 100% !important;} }" | |
#css = ".output_image {height: 600px !important}, .input_image {height: 300px !important}" | |
css = ".image-preview {height: auto !important;}" | |
iface = gr.Interface(fn=process_image, | |
inputs=gr.inputs.Image(type="pil", tool="editor"), | |
outputs=gr.outputs.Image(type="pil", label="annotated image"), | |
title=title, | |
description=description, | |
article=article, | |
examples=examples, | |
css=css) | |
iface.launch(debug=True) |