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import torch
import cv2
import numpy as np
from PIL import Image, ImageDraw, ImageFont
from transformers import LayoutLMv3TokenizerFast, LayoutLMv3Processor, LayoutLMv3ForTokenClassification
from utils import OCR, unnormalize_box
labels = ["O", "B-COMPANY", "I-COMPANY", "B-DATE", "I-DATE", "B-ADDRESS", "I-ADDRESS", "B-TOTAL", "I-TOTAL"]
id2label = {v: k for v, k in enumerate(labels)}
label2id = {k: v for v, k in enumerate(labels)}
tokenizer = LayoutLMv3TokenizerFast.from_pretrained("Theivaprakasham/layoutlmv3-finetuned-sroie", apply_ocr=False)
processor = LayoutLMv3Processor.from_pretrained("Theivaprakasham/layoutlmv3-finetuned-sroie", apply_ocr=False)
model = LayoutLMv3ForTokenClassification.from_pretrained("Theivaprakasham/layoutlmv3-finetuned-sroie")
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
def blur(image, boxes):
image = np.array(image)
for box in boxes:
blur_x = int(box[0])
blur_y = int(box[1])
blur_width = int(box[2]-box[0])
blur_height = int(box[3]-box[1])
roi = image[blur_y:blur_y + blur_height, blur_x:blur_x + blur_width]
blur_image = cv2.GaussianBlur(roi, (201, 201), 0)
image[blur_y:blur_y + blur_height, blur_x:blur_x + blur_width] = blur_image
return Image.fromarray(image, 'RGB')
def prediction(image):
boxes, words = OCR(image)
encoding = processor(image, words, boxes=boxes, return_offsets_mapping=True, return_tensors="pt", truncation=True)
offset_mapping = encoding.pop('offset_mapping')
for k, v in encoding.items():
encoding[k] = v.to(device)
outputs = model(**encoding)
predictions = outputs.logits.argmax(-1).squeeze().tolist()
token_boxes = encoding.bbox.squeeze().tolist()
probabilities = torch.softmax(outputs.logits, dim=-1)
confidence_scores = probabilities.max(-1).values.squeeze().tolist()
inp_ids = encoding.input_ids.squeeze().tolist()
inp_words = [tokenizer.decode(i) for i in inp_ids]
width, height = image.size
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]]
true_confidence_scores = [confidence_scores[idx] for idx, conf in enumerate(confidence_scores) if not is_subword[idx]]
true_words = []
for id, i in enumerate(inp_words):
if not is_subword[id]:
true_words.append(i)
else:
true_words[-1] = true_words[-1]+i
true_predictions = true_predictions[1:-1]
true_boxes = true_boxes[1:-1]
true_words = true_words[1:-1]
true_confidence_scores = true_confidence_scores[1:-1]
preds = []
l_words = []
bboxes = []
for i, j in enumerate(true_predictions):
if true_confidence_scores[i] < 0.9: #####################################àà
true_predictions[i] = "O"
preds.append(true_predictions[i])
l_words.append(true_words[i])
bboxes.append(true_boxes[i])
d = {}
for id, i in enumerate(preds):
if i not in d.keys():
d[i] = l_words[id]
else:
d[i] = d[i] + ", " + l_words[id]
d = {k: v.strip() for (k, v) in d.items()}
keys_to_pop = []
for k, v in d.items():
if k[:2] == "I-":
d["B-" + k[2:]] = d["B-" + k[2:]] + ", " + v
keys_to_pop.append(k)
if "O" in d: d.pop("O")
if "B-TOTAL" in d: d.pop("B-TOTAL")
for k in keys_to_pop: d.pop(k)
blur_boxes = []
for prediction, box in zip(preds, bboxes):
if prediction != 'O' and prediction[2:] != 'TOTAL':
blur_boxes.append(box)
image = (blur(image, blur_boxes))
#draw = ImageDraw.Draw(image, "RGBA")
#font = ImageFont.load_default()
#for prediction, box in zip(preds, bboxes):
# draw.rectangle(box)
# draw.text((box[0]+10, box[1]-10), text=prediction, font=font, fill="black", font_size="8")
return d, image
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