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import streamlit as st
from paddleocr import PaddleOCR
from PIL import ImageDraw, ImageFont
import torch
from transformers import AutoProcessor,LayoutLMv3ForTokenClassification
import numpy as np
model_Hugging_path = "Noureddinesa/Output_LayoutLMv3_v6"
#############################################################################
#############################################################################
def Labels():
labels = ['InvNum', 'InvDate', 'Fourni', 'TTC', 'TVA', 'TT', 'Autre']
id2label = {v: k for v, k in enumerate(labels)}
label2id = {k: v for v, k in enumerate(labels)}
return id2label, label2id
#############################################################################
#############################################################################
def Paddle():
ocr = PaddleOCR(use_angle_cls=False,lang='fr',rec=False)
return ocr
def processbbox(BBOX, width, height):
bbox = []
bbox.append(BBOX[0][0])
bbox.append(BBOX[0][1])
bbox.append(BBOX[2][0])
bbox.append(BBOX[2][1])
#Scaling
bbox[0]= 1000*bbox[0]/width # X1
bbox[1]= 1000*bbox[1]/height # Y1
bbox[2]= 1000*bbox[2]/width # X2
bbox[3]= 1000*bbox[3]/height # Y2
for i in range(4):
bbox[i] = int(bbox[i])
return bbox
def Preprocess(image):
image_array = np.array(image)
ocr = Paddle()
width, height = image.size
results = ocr.ocr(image_array, cls=True)
results = results[0]
test_dict = {'image': image ,'tokens':[], "bboxes":[]}
for item in results :
bbox = processbbox(item[0], width, height)
test_dict['tokens'].append(item[1][0])
test_dict['bboxes'].append(bbox)
print(test_dict['bboxes'])
print(test_dict['tokens'])
return test_dict
#############################################################################
#############################################################################
def Encode(image):
example = Preprocess(image)
image = example["image"]
words = example["tokens"]
boxes = example["bboxes"]
processor = AutoProcessor.from_pretrained(model_Hugging_path, apply_ocr=False)
encoding = processor(image, words, boxes=boxes,return_offsets_mapping=True,truncation=True, max_length=512, padding="max_length", return_tensors="pt")
offset_mapping = encoding.pop('offset_mapping')
return encoding, offset_mapping,words
#############################################################################
#############################################################################
def unnormalize_box(bbox, width, height):
return [
width * (bbox[0] / 1000),
height * (bbox[1] / 1000),
width * (bbox[2] / 1000),
height * (bbox[3] / 1000),
]
#############################################################################
#############################################################################
def Run_model(image):
encoding,offset_mapping,words = Encode(image)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# load the fine-tuned model from the hub
model = LayoutLMv3ForTokenClassification.from_pretrained(model_Hugging_path)
model.to(device)
# forward pass
outputs = model(**encoding)
predictions = outputs.logits.argmax(-1).squeeze().tolist()
token_boxes = encoding.bbox.squeeze().tolist()
width, height = image.size
id2label, _ = Labels()
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]]
return true_predictions,true_boxes,words
#############################################################################
#############################################################################
def Get_Json(true_predictions,words):
Results = {}
i = 0
for prd in true_predictions:
if prd in ['InvNum','Fourni', 'InvDate','TT','TTC','TVA']:
#print(i,prd,words[i-1])
Results[prd] = words[i-1]
i+=1
key_mapping = {'InvNum':'Numéro de facture','Fourni':'Fournisseur', 'InvDate':'Date Facture','TT':'Total HT','TTC':'Total TTC','TVA':'TVA'}
Results = {key_mapping.get(key, key): value for key, value in Results.items()}
return Results
#############################################################################
#############################################################################
def Draw(image):
true_predictions, true_boxes,words = Run_model(image)
draw = ImageDraw.Draw(image)
label2color = {
'InvNum': 'blue',
'InvDate': 'green',
'Fourni': 'orange',
'TTC':'purple',
'TVA': 'magenta',
'TT': 'red',
'Autre': 'black'
}
# Adjust the thickness of the rectangle outline and label text position
rectangle_thickness = 4
label_x_offset = 20
label_y_offset = -30
# Custom font size
custom_font_size = 25
# Load a font with the custom size
font_path = "arial.ttf" # Specify the path to your font file
custom_font = ImageFont.truetype(font_path, custom_font_size)
for prediction, box in zip(true_predictions, true_boxes):
predicted_label = prediction
# Check if the predicted label exists in the label2color dictionary
if predicted_label in label2color:
color = label2color[predicted_label]
else:
color = 'black' # Default color if label is not found
if predicted_label != "Autre":
draw.rectangle(box, outline=color, width=rectangle_thickness)
# Draw text using the custom font and size
draw.rectangle((box[0], box[1]+ label_y_offset,box[2],box[3]+ label_y_offset), fill=color)
draw.text((box[0] + label_x_offset, box[1] + label_y_offset), text=predicted_label, fill='white', font=custom_font)
# Get the Results Json File
Results = Get_Json(true_predictions,words)
return image,Results
#############################################################################
#############################################################################
def Add_Results(data):
# Render the table
for key, value in data.items():
data[key] = st.sidebar.text_input(key, value)
#############################################################################
#############################################################################
def check_if_changed(original_values, updated_values):
for key, value in original_values.items():
if updated_values[key] != value:
return True
return False
#############################################################################
#############################################################################
def Update(Results):
New_results = {}
if "Fournisseur" in Results.keys():
text_fourni = st.sidebar.text_input("Fournisseur", value=Results["Fournisseur"])
New_results["Fournisseur"] = text_fourni
if "Date Facture" in Results.keys():
text_InvDate = st.sidebar.text_input("Date Facture", value=Results["Date Facture"])
New_results["Date Facture"] = text_InvDate
if "Numéro de facture" in Results.keys():
text_InvNum = st.sidebar.text_input("Numéro de facture", value=Results["Numéro de facture"])
New_results["Numéro de facture"] = text_InvNum
if "Total HT" in Results.keys():
text_TT = st.sidebar.text_input("Total HT", value=Results["Total HT"])
New_results["Total HT"] = text_TT
if "TVA" in Results.keys():
text_TVA = st.sidebar.text_input("TVA", value=Results["TVA"])
New_results["TVA"] = text_TVA
if "Total TTC" in Results.keys():
text_TTC = st.sidebar.text_input("TTC", value=Results["Total TTC"])
New_results["Total TTC"] = text_TTC
return New_results
#############################################################################
#############################################################################
def Change_Image(image1,image2):
# Initialize session state
if 'current_image' not in st.session_state:
st.session_state.current_image = 'image1'
# Button to switch between images
if st.sidebar.button('Remove'):
if st.session_state.current_image == 'image1':
st.session_state.current_image = 'image2'
else:
st.session_state.current_image = 'image1'
# Display the selected image
if st.session_state.current_image == 'image1':
st.image(image1, caption='Output', use_column_width=True)
else:
st.image(image2, caption='Image initiale', use_column_width=True) |