How to detect 2d datamatrix in image with Python

Here are the steps to detect the 2D DataMatrix:

1. Install dynamsoft_capture_vision_bundle and opencv python

   pip install dynamsoft-capture-vision-bundle opencv-python
   

2. Create a Python script as follows. You need to replace the test.png and LICENSE-KEY with your own. The license key can be obtained from here.

   import sys
   from dynamsoft_capture_vision_bundle import *
   import os
   import cv2
   import numpy as np
   from utils import *
   
   image_path = 'test.png'
   
   error_code, error_message = LicenseManager.init_license(
    "LICENSE-KEY")
   if error_code != EnumErrorCode.EC_OK and error_code != 
   EnumErrorCode.EC_LICENSE_CACHE_USED:
       print("License initialization failed: ErrorCode:",
          error_code, ", ErrorString:", error_message)
   
       sys.exit()
   
   cvr_instance = CaptureVisionRouter()
   result = cvr_instance.capture(
    'test.png', EnumPresetTemplate.PT_READ_BARCODES.value)
   if result.get_error_code() != EnumErrorCode.EC_OK:
       print("Error:", result.get_error_code(),
          result.get_error_string())
   
       sys.exit()
   
   cv_image = cv2.imread(image_path)
   
   items = result.get_items()
   print('Found {} barcodes.'.format(len(items)))
   for item in items:
       format_type = item.get_format()
       text = item.get_text()
       print("Barcode Format:", format_type)
       print("Barcode Text:", text)
   
       location = item.get_location()
       x1 = location.points[0].x
       y1 = location.points[0].y
       x2 = location.points[1].x
       y2 = location.points[1].y
       x3 = location.points[2].x
       y3 = location.points[2].y
       x4 = location.points[3].x
       y4 = location.points[3].y
       del location
   
       cv2.drawContours(
        cv_image, [np.intp([(x1, y1), (x2, y2), (x3, y3), (x4, y4)])], 0, (0, 255, 0), 2)
   
       cv2.putText(cv_image, text, (x1, y1 - 10),
                cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
   
   max_width = 1600
   max_height = 1000
   
   original_height, original_width = cv_image.shape[:2]
   
   scaling_factor = min(
    max_width / original_width, max_height / original_height)
   
   new_width = int(original_width * scaling_factor)
   new_height = int(original_height * scaling_factor)
   
   resized_image = cv2.resize(
    cv_image, (new_width, new_height), interpolation=cv2.INTER_AREA)
   
   cv2.imshow(
    "Original Image with Detected Barcodes", resized_image)
   
   cv2.waitKey(0)
   cv2.destroyAllWindows()
   
   

3. Run the script:

   Found 1 barcodes.
   Barcode Format: 134217728
   Barcode Text: Edu-barcode-45
   

   

More recent copy of those forms when issued generally DO NOT include a matrix however it may be detected even when not clear what the data is as in the sample.

Normally PDF does not use "DPI" and each pixel would be as sharp as a vector image. However when reprinted as an image then a DPI is required and that is when anti-aliasing causes issues.

The code should be roughly the equivalence of 4 or more pixels per square although one will do.

Python has PyMuPDF and I don't know how well it functions but taking the example page we get verification there is a datamatrix and its decoded content seen as

Call function via shell

mutool barcode -d MedicalForm.png

Response

datamatrix: Edu-barcode-45

Thus verified easily in a single command.

If you need to regenerate as a crisp clean image use

mutool barcode -c -o Sample.png -F datamatrix -t -q "Edu-barcode-45"

However do not be surprised if a different compact format is used it is the same type and data.

Both source and replacement are identical type and data. Just based on a different Modular Value.