import os
import subprocess
from datetime import datetime
import gradio as gr
from Plan.AiLLM import llm_recognition
from Plan.pytesseractJsOCR import pytesseractJs_recognition
from Plan.pytesseractOCR import ocr_recognition
from Preprocess.preprocessImg import PreprocessImg
# 切換到 node_app 目錄
os.chdir("node_app")
# 如果 node_modules 目錄不存在，則執行 npm install
if not os.path.exists("node_modules"):
print(" ######################## NPM INSTALL ########################")
subprocess.run(["npm", "install"])
# 切換回上級目錄
os.chdir("..")
# 取得所有語言清單
languages = os.popen('tesseract --list-langs').read().split('\n')[1:-1]
# 預處理圖片
def preprocess_image(image):
if image is None:
gr.Warning("尚未上傳圖片！")
raise ValueError("尚未上傳圖片！")
preprocessed_images = PreprocessImg(image)
return (
preprocessed_images,
True,
preprocessed_images[0],
preprocessed_images[1],
preprocessed_images[2],
preprocessed_images[3],
preprocessed_images[4]
)
# pytesseract OCR
def Basic_ocr(valid_type, language, preprocessed_images, finish_pre_img):
if not finish_pre_img:
gr.Warning("請先執行圖像預處理，再進行分析！")
raise ValueError("請先執行圖像預處理，再進行分析！")
# 方案一
ocr_result_001 = ocr_recognition(preprocessed_images[0], valid_type, language)
# 方案二
ocr_result_002 = ocr_recognition(preprocessed_images[1], valid_type, language)
# 方案三
ocr_result_003 = ocr_recognition(preprocessed_images[2], valid_type, language)
# 方案四
ocr_result_004 = ocr_recognition(preprocessed_images[3], valid_type, language)
# 方案五
ocr_result_005 = ocr_recognition(preprocessed_images[4], valid_type, language)
return ocr_result_001, ocr_result_002, ocr_result_003, ocr_result_004, ocr_result_005
# AI LLM OCR
def AiLLM_ocr(valid_type, language, preprocessed_images, finish_pre_img):
if not finish_pre_img:
gr.Warning("請先執行圖像預處理，再進行分析！")
raise ValueError("請先執行圖像預處理，再進行分析！")
# 方案一
llm_result_001 = llm_recognition(preprocessed_images[0], valid_type, language)
# 方案二
llm_result_002 = llm_recognition(preprocessed_images[1], valid_type, language)
# 方案三
llm_result_003 = llm_recognition(preprocessed_images[2], valid_type, language)
# 方案四
llm_result_004 = llm_recognition(preprocessed_images[3], valid_type, language)
# 方案五
llm_result_005 = llm_recognition(preprocessed_images[4], valid_type, language)
return llm_result_001, llm_result_002, llm_result_003, llm_result_004, llm_result_005
def pytesseractJs_ocr(valid_type, language, preprocessed_images, finish_pre_img):
if not finish_pre_img:
gr.Warning("請先執行圖像預處理，再進行分析！")
raise ValueError("請先執行圖像預處理，再進行分析！")
temp_path = 'node_app/TempFile/' + datetime.now().strftime('%Y%m%d_%H%M%S') + '/'
# 檢查目錄是否存在，如果不存在則建立
if not os.path.exists(temp_path):
os.makedirs(temp_path)
image_files = []
for i, image in enumerate(preprocessed_images):
filename = temp_path + f'preprocessed_image_{i}.png'
image.save(filename)
image_files.append(filename)
# 方案一
file_name = 'out_pytesseractJs_result_1.txt'
out_ocr_text_001 = pytesseractJs_recognition(valid_type, image_files[0], temp_path, file_name, language)
# 方案二
file_name = 'out_pytesseractJs_result_2.txt'
out_ocr_text_002 = pytesseractJs_recognition(valid_type, image_files[1], temp_path, file_name, language)
# file_name = 'out_pytesseractJs_result_2.txt'
# 使用 subprocess 執行 JavaScript 代碼，傳遞語言參數
# subprocess.run(['node', 'pytesseractJsOCR.js', image_files[1], language, temp_path + file_name], capture_output=True,
# text=True)
# with open(temp_path + file_name, 'r') as file:
# out_ocr_text_002 = file.read()
# 方案三
file_name = 'out_pytesseractJs_result_3.txt'
out_ocr_text_003 = pytesseractJs_recognition(valid_type, image_files[2], temp_path, file_name, language)
# 方案四
file_name = 'out_pytesseractJs_result_4.txt'
out_ocr_text_004 = pytesseractJs_recognition(valid_type, image_files[3], temp_path, file_name, language)
# 方案五
file_name = 'out_pytesseractJs_result_5.txt'
out_ocr_text_005 = pytesseractJs_recognition(valid_type, image_files[4], temp_path, file_name, language)
return out_ocr_text_001, out_ocr_text_002, out_ocr_text_003, out_ocr_text_004, out_ocr_text_005
# VIEW
with gr.Blocks() as demo:
with gr.Row():
image_input = gr.Image(type="pil", label="上傳圖片")
with gr.Column():
validation_type = gr.Dropdown(choices=["全文分析", "身分證正面", "身分證反面"], value='全文分析',
label="驗證類別")
language_dropdown = gr.Dropdown(choices=languages, value="chi_tra", label="語言")
with gr.Row():
with gr.Column():
preImg_button = gr.Button("圖片預先處理")
gr.Markdown(
"<div style='display: flex;justify-content: center;align-items: center;background-color: #ffdf00;font-weight: bold;text-decoration: underline;font-size: 20px;'>多模態預處理圖像</div>")
with gr.Row():
with gr.Column():
ocr_button = gr.Button("使用 Pytesseract OCR 辨識")
gr.Markdown(
"<div style='display: flex;justify-content: center;align-items: center;background-color: #ffdf00;font-weight: bold;text-decoration: underline;font-size: 20px;'>Package: Google Pytesseract</div>")
with gr.Column():
llm_button = gr.Button("使用 AI LLM 模型辨識")
gr.Markdown(
"<div style='display: flex;justify-content: center;align-items: center;background-color: #ffdf00;font-weight: bold;text-decoration: underline;font-size: 20px;'>Package：Bert-base-chinese</div>")
with gr.Column():
pytesseractJS_button = gr.Button("使用 PytesseractJS 模型辨識")
gr.Markdown(
"<div style='display: flex;justify-content: center;align-items: center;background-color: #ffdf00;font-weight: bold;text-decoration: underline;font-size: 20px;'>Package：PytesseractJS</div>")
with gr.Row():
preprocess_output_001 = gr.Image(type="pil", label="預處理後的圖片-方案一")
ocr_output_001 = gr.JSON(label="OCR-001-解析結果")
llm_output_001 = gr.JSON(label="AiLLM-001-解析結果")
pytesseractJS_output_001 = gr.JSON(label="PytesseractJS-001-解析結果")
with gr.Row():
preprocess_output_002 = gr.Image(type="pil", label="預處理後的圖片-方案二")
ocr_output_002 = gr.JSON(label="OCR-002-解析結果")
llm_output_002 = gr.JSON(label="AiLLM-002-解析結果")
pytesseractJS_output_002 = gr.JSON(label="PytesseractJS-002-解析結果")
with gr.Row():
preprocess_output_003 = gr.Image(type="pil", label="預處理後的圖片-方案三")
ocr_output_003 = gr.JSON(label="OCR-003-解析結果")
llm_output_003 = gr.JSON(label="AiLLM-003-解析結果")
pytesseractJS_output_003 = gr.JSON(label="PytesseractJS-003-解析結果")
with gr.Row():
preprocess_output_004 = gr.Image(type="pil", label="預處理後的圖片-方案四")
ocr_output_004 = gr.JSON(label="OCR-004-解析結果")
llm_output_004 = gr.JSON(label="AiLLM-004-解析結果")
pytesseractJS_output_004 = gr.JSON(label="PytesseractJS-004-解析結果")
with gr.Row():
preprocess_output_005 = gr.Image(type="pil", label="預處理後的圖片-方案五")
ocr_output_005 = gr.JSON(label="OCR-005-解析結果")
llm_output_005 = gr.JSON(label="AiLLM-005-解析結果")
pytesseractJS_output_005 = gr.JSON(label="PytesseractJS-005-解析結果")
# 定義狀態
finish_pre_img_state = gr.State(False)
preprocessed_images_state = gr.State([])
# 預先處理圖片 按鈕
preImg_button.click(preprocess_image, inputs=[image_input],
outputs=[preprocessed_images_state, finish_pre_img_state,
preprocess_output_001, preprocess_output_002,
preprocess_output_003, preprocess_output_004,
preprocess_output_005])
# pytesseract 按鈕
ocr_button.click(Basic_ocr, inputs=[validation_type, language_dropdown,
preprocessed_images_state, finish_pre_img_state],
outputs=[ocr_output_001, ocr_output_002, ocr_output_003, ocr_output_004, ocr_output_005])
# AI LLM 按鈕
llm_button.click(AiLLM_ocr, inputs=[validation_type, language_dropdown,
preprocessed_images_state, finish_pre_img_state],
outputs=[llm_output_001, llm_output_002, llm_output_003, llm_output_004, llm_output_005])
# pytesseract 按鈕
pytesseractJS_button.click(pytesseractJs_ocr, inputs=[validation_type, language_dropdown,
preprocessed_images_state, finish_pre_img_state],
outputs=[pytesseractJS_output_001, pytesseractJS_output_002, pytesseractJS_output_003,
pytesseractJS_output_004, pytesseractJS_output_005])
demo.launch(share=False)