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README.md

@@ -1,12 +1,13 @@
 ---
-title: Layoutlmv3 Finetuned Cord Dataset
-emoji: 👀
-colorFrom: gray
-colorTo: red
+title: Receipt Extractor
+emoji: 📊
+colorFrom: pink
+colorTo: purple
 sdk: gradio
 sdk_version: 5.5.0
 app_file: app.py
 pinned: false
+license: apache-2.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference

app.py

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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")
+
+## 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")
+
+dataset = load_dataset("ivan-wald/cord-layoutlmv3", split="test", trust_remote_code=True)
+image = Image.open("./test0.jpeg")
+labels = dataset.features['ner_tags'].feature.names
+id2label = {v: k for v, k in enumerate(labels)}
+
+#Need to get discrete colors for each 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 = "LayoutLMv3 - CORD"
+description = "description"
+article = "article"
+examples =[['test0.jpeg'],['test1.jpeg'],['test2.jpeg']]
+css = ".output-image, .input-image, .image-preview {height: 600px !important}"
+
+iface = gr.Interface(fn=process_image, 
+                     inputs=gr.Image(type="pil"), 
+                     outputs=gr.Image(type="pil", label="annotated image"),
+                     title=title,
+                     examples=examples,
+                     css=css)
+
+iface.launch(debug=True)

packages.txt

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+tesseract-ocr

requirements.txt

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+gradio
+Pillow
+datasets
+https://github.com/huggingface/transformers/archive/main.zip
+pyyaml==5.1
+torch==1.11.0
+torchvision==0.12.0
+
+numpy<2
+scipy
+shapely
+timm
+opencv-python