Capture part of pdf file in python

Question

I have pdf file of two pages and there is a barcode at the top of the first page like that

Is there a way to capture the barcode only from such scanned pdf file in python? I have searched but didn't find such issue. Can you help me with even any link as a clue and I will study the link and try to implement it by myself?

I find such a code but couldn't modify to crop the top part of the first page only

from PyPDF2 import PdfFileWriter, PdfFileReader

with open("Sample.pdf", "rb") as in_f:
    input1 = PdfFileReader(in_f)
    output = PdfFileWriter()
    
    numPages = input1.getNumPages()
    print("Document Has %s pages." % numPages)
    
    for i in range(numPages):
        page = input1.getPage(i)
        print(page.mediaBox.getUpperRight_x(), page.mediaBox.getUpperRight_y())
        page.trimBox.lowerLeft = (25, 25)
        page.trimBox.upperRight = (225, 225)
        page.cropBox.lowerLeft = (50, 50)
        page.cropBox.upperRight = (200, 200)
        output.addPage(page)

    with open("Output.pdf", "wb") as out_f:
        output.write(out_f)

Answer 1

In order to detect and extract the barcode it would be easiest to convert the pdfs to images and subsequently run barcode detection on the images. Conversion to images can be done with pdf2image. A standard way to detect barcodes is using zbar, for example with pyzbar, but I got no detection results for your image, probably due to the quality of the scan. Alternatively barcodes can be extracted with Opencv. For the working example below I repurposed code from the answer to this post:

from pdf2image import convert_from_path, convert_from_bytes
import numpy as np
import imutils
import cv2

from pdf2image.exceptions import (
    PDFInfoNotInstalledError,
    PDFPageCountError,
    PDFSyntaxError
)

images = convert_from_path('Sample.pdf')

#convert PIL images to cv2
images = [np.array(i)[:, :, ::-1] for i in images]

for nr, image in enumerate(images):
  # convert it to grayscale
  gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

  # compute the Scharr gradient magnitude representation of the images
  # in both the x and y direction using OpenCV 2.4
  ddepth = cv2.cv.CV_32F if imutils.is_cv2() else cv2.CV_32F
  gradX = cv2.Sobel(gray, ddepth = ddepth, dx = 1, dy = 0, ksize = -1)
  gradY = cv2.Sobel(gray, ddepth = ddepth, dx = 0, dy = 1, ksize = -1)

  # subtract the y-gradient from the x-gradient
  gradient = cv2.subtract(gradX, gradY)
  gradient = cv2.convertScaleAbs(gradient)

  # blur and threshold the image
  blurred = cv2.blur(gradient, (9, 9))
  (_, thresh) = cv2.threshold(blurred, 225, 255, cv2.THRESH_BINARY)

  # construct a closing kernel and apply it to the thresholded image
  kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (21, 7))
  closed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)

  # perform a series of erosions and dilations
  closed = cv2.erode(closed, None, iterations = 4)
  closed = cv2.dilate(closed, None, iterations = 4)

  # find the contours in the thresholded image, then sort the contours
  # by their area, keeping only the largest one
  cnts = cv2.findContours(closed.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
  cnts = imutils.grab_contours(cnts)
  c = sorted(cnts, key = cv2.contourArea, reverse = True)[0]

  # compute the rotated bounding box of the largest contour
  rect = cv2.minAreaRect(c)
  box = cv2.cv.BoxPoints(rect) if imutils.is_cv2() else cv2.boxPoints(rect)
  box = np.int0(box)

  # draw a bounding box arounded the detected barcode and display the
  min_y = int(np.min(box[:,-1]))
  max_y = int(np.max(box[:,-1]))
  min_x = int(np.min(box[:,0]))
  max_x = int(np.max(box[:,0]))
  image = image[min_y:max_y, min_x:max_x]
  # save cropped image
  cv2.imwrite(f"cropped_{nr}.jpg", image)

Output: