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import fitz
from PIL import Image
import re
import io
import os
import logging
import shutil
from fastapi import FastAPI, UploadFile, File, HTTPException
from google.cloud import vision
from pdf2image import convert_from_path
class doc_processing:
def __init__(self, name, id_type, doc_type, f_path):
self.name = name
self.id_type = id_type
self.doc_type = doc_type
self.f_path = f_path
# self.o_path = o_path
def pdf_to_image_scale(self):
pdf_document = fitz.open(self.f_path)
if self.id_type == "gst":
page_num = 2
else:
page_num = 0
page = pdf_document.load_page(page_num)
pix = page.get_pixmap() # Render page as a pixmap (image)
# Convert pixmap to PIL Image
image = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
original_width, original_height = image.size
print("original_width", original_width)
print("original_height", original_height)
new_width = (1000 / original_width) * original_width
new_height = (1000 / original_height) * original_height
print("new_width", new_width)
print("new_height", new_height)
# new_width =
# new_height =
image.resize((int(new_width), int(new_height)), Image.Resampling.LANCZOS)
output_path = "processed_images/{}/{}.jpeg".format(self.id_type, self.name)
image.save(output_path)
return {"success": 200, "output_p": output_path}
def scale_img(self):
print("path of file", self.f_path)
image = Image.open(self.f_path).convert("RGB")
original_width, original_height = image.size
print("original_width", original_width)
print("original_height", original_height)
new_width = (1000 / original_width) * original_width
new_height = (1000 / original_height) * original_height
print("new_width", new_width)
print("new_height", new_height)
# new_width =
# new_height =
image.resize((int(new_width), int(new_height)), Image.Resampling.LANCZOS)
output_path = "processed_images/{}/{}.jpeg".format(self.id_type, self.name)
image.save(output_path)
return {"success": 200, "output_p": output_path}
def process(self):
if self.doc_type == "pdf" or self.doc_type == "PDF":
response = self.pdf_to_image_scale()
else:
response = self.scale_img()
return response
from google.cloud import vision
vision_client = vision.ImageAnnotatorClient()
def extract_document_number(ocr_text: str, id_type: str) -> str:
"""
Searches the OCR text for a valid document number based on regex patterns.
Checks for CIN, then MSME, and finally LLPIN.
"""
patterns = {
"cin": re.compile(r"([LUu]{1}[0-9]{5}[A-Za-z]{2}[0-9]{4}[A-Za-z]{3}[0-9]{6})"),
"msme": re.compile(r"(UDYAM-[A-Z]{2}-\d{2}-\d{7})"),
"llpin": re.compile(r"([A-Z]{3}-[0-9]{4})"),
"pan": re.compile(r"^[A-Z]{3}[PCHFTBALJGT][A-Z][\d]{4}[A-Z]$"),
"aadhaar": re.compile(r"^\d{12}$"),
}
if id_type == "cin_llpin":
# Try CIN first
match = patterns["cin"].search(ocr_text)
if match:
return match.group(0)
# If CIN not found, try LLPIN
match = patterns["llpin"].search(ocr_text)
if match:
return match.group(0)
elif id_type in patterns:
match = patterns[id_type].search(ocr_text)
if match:
return match.group(0)
return None
def run_google_vision(file_content: bytes) -> str:
"""
Uses Google Vision OCR to extract text from binary file content.
"""
image = vision.Image(content=file_content)
response = vision_client.text_detection(image=image)
texts = response.text_annotations
if texts:
# The first annotation contains the complete detected text
return texts[0].description
return ""
def extract_text_from_file(file_path: str) -> str:
"""
Reads the file from file_path. If it's a PDF, converts only the first page to an image,
then runs OCR using Google Vision.
"""
if file_path.lower().endswith(".pdf"):
try:
# Open the PDF file using PyMuPDF (fitz)
pdf_document = fitz.open(file_path)
page = pdf_document.load_page(0) # Load the first page
pix = page.get_pixmap() # Render page as an image
# Convert pixmap to PIL Image
image = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
# Convert image to bytes for OCR
img_byte_arr = io.BytesIO()
image.save(img_byte_arr, format="JPEG")
file_content = img_byte_arr.getvalue()
except Exception as e:
logging.error(f"Error converting PDF to image: {e}")
return ""
else:
with open(file_path, "rb") as f:
file_content = f.read()
return run_google_vision(file_content)
def extract_document_number_from_file(file_path: str, id_type: str) -> str:
"""
Extracts the document number (CIN, MSME, or LLPIN) from the file at file_path.
"""
ocr_text = extract_text_from_file(file_path)
return extract_document_number(ocr_text, id_type)
# files = {
# "aadhar_file": "/home/javmulla/model_one/test_images_aadhar/test_two.jpg",
# "pan_file": "/home/javmulla/model_one/test_images_pan/6ea33087.jpeg",
# "cheque_file": "/home/javmulla/model_one/test_images_cheque/0f81678a.jpeg",
# "gst_file": "/home/javmulla/model_one/test_images_gst/0a52fbcb_page3_image_0.jpg"
# }
# files = {
# "aadhar_file": "/home/javmulla/model_one/test_images_aadhar/test_two.jpg",
# "pan_file": "/home/javmulla/model_one/test_images_pan/6ea33087.jpeg",
# "cheque_file": "/home/javmulla/model_one/test_images_cheque/0f81678a.jpeg",
# "gst_file": "test_Images_folder/gst/e.pdf"
# }
# for key, value in files.items():
# name = value.split("/")[-1].split(".")[0]
# id_type = key.split("_")[0]
# doc_type = value.split("/")[-1].split(".")[1]
# f_path = value
# preprocessing = doc_processing(name,id_type,doc_type,f_path)
# response = preprocessing.process()
# print("response",response)
# id_type, doc_type, f_path
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