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Python document detection SDK built with Dynamsoft Document Normalizer for Windows and Linux

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Python Document Scanner SDK

This project provides Python bindings for the Dynamsoft C/C++ Document Scanner SDK v1.x, enabling developers to quickly create document scanner applications for Windows and Linux desktop environments.

About Dynamsoft Document Scanner

Supported Python Versions

  • Python 3.x

Dependencies

Install the required dependencies using pip:

pip install opencv-python

Command-line Usage

  • Scan documents from images:

    scandocument -f <file-name> -l <license-key>
  • Scan documents from a camera video stream:

    scandocument -c 1 -l <license-key>

Quick Start

  • Scan documents from an image file:

    import argparse
    import docscanner
    import sys
    import numpy as np
    import cv2
    import time
    
    def showNormalizedImage(name, normalized_image):
        mat = docscanner.convertNormalizedImage2Mat(normalized_image)
        cv2.imshow(name, mat)
        return mat
    
    def process_file(filename, scanner):
        image = cv2.imread(filename)
        results = scanner.detectMat(image)
        for result in results:
            x1 = result.x1
            y1 = result.y1
            x2 = result.x2
            y2 = result.y2
            x3 = result.x3
            y3 = result.y3
            x4 = result.x4
            y4 = result.y4
            
            normalized_image = scanner.normalizeBuffer(image, x1, y1, x2, y2, x3, y3, x4, y4)
            showNormalizedImage("Normalized Image", normalized_image)
            cv2.drawContours(image, [np.intp([(x1, y1), (x2, y2), (x3, y3), (x4, y4)])], 0, (0, 255, 0), 2)
        
        cv2.imshow('Document Image', image)
        cv2.waitKey(0)
        
        normalized_image.save(str(time.time()) + '.png')
        print('Image saved')
    
    def scandocument():
        """
        Command-line script for scanning documents from a given image
        """
        parser = argparse.ArgumentParser(description='Scan documents from an image file')
        parser.add_argument('-f', '--file', help='Path to the image file')
        parser.add_argument('-l', '--license', default='', type=str, help='Set a valid license key')
        args = parser.parse_args()
        # print(args)
        try:
            filename = args.file
            license = args.license
            
            if filename is None:
                parser.print_help()
                return
            
            # set license
            if  license == '':
                docscanner.initLicense("LICENSE-KEY")
            else:
                docscanner.initLicense(license)
                
            # initialize mrz scanner
            scanner = docscanner.createInstance()
            ret = scanner.setParameters(docscanner.Templates.color)
    
            if filename is not None:
                process_file(filename, scanner)
                
        except Exception as err:
            print(err)
            sys.exit(1)
    
    scandocument()

    python document scanner from file

  • Scan documents from camera video stream:

    import argparse
    import docscanner
    import sys
    import numpy as np
    import cv2
    import time
    
    g_results = None
    g_normalized_images = []
    
    
    def callback(results):
        global g_results
        g_results = results
    
    
    def showNormalizedImage(name, normalized_image):
        mat = docscanner.convertNormalizedImage2Mat(normalized_image)
        cv2.imshow(name, mat)
        return mat
    
    
    def process_video(scanner):
        scanner.addAsyncListener(callback)
    
        cap = cv2.VideoCapture(0)
        while True:
            ret, image = cap.read()
    
            ch = cv2.waitKey(1)
            if ch == 27:
                break
            elif ch == ord('n'):  # normalize image
                if g_results != None:
                    g_normalized_images = []
                    index = 0
                    for result in g_results:
                        x1 = result.x1
                        y1 = result.y1
                        x2 = result.x2
                        y2 = result.y2
                        x3 = result.x3
                        y3 = result.y3
                        x4 = result.x4
                        y4 = result.y4
    
                        normalized_image = scanner.normalizeBuffer(
                            image, x1, y1, x2, y2, x3, y3, x4, y4)
                        g_normalized_images.append(
                            (str(index), normalized_image))
                        mat = showNormalizedImage(str(index), normalized_image)
                        index += 1
            elif ch == ord('s'):  # save image
                for data in g_normalized_images:
                    # cv2.imwrite('images/' + str(time.time()) + '.png', image)
                    cv2.destroyWindow(data[0])
                    data[1].save(str(time.time()) + '.png')
                    print('Image saved')
    
                g_normalized_images = []
    
            if image is not None:
                scanner.detectMatAsync(image)
    
            if g_results != None:
                for result in g_results:
                    x1 = result.x1
                    y1 = result.y1
                    x2 = result.x2
                    y2 = result.y2
                    x3 = result.x3
                    y3 = result.y3
                    x4 = result.x4
                    y4 = result.y4
    
                    cv2.drawContours(
                        image, [np.intp([(x1, y1), (x2, y2), (x3, y3), (x4, y4)])], 0, (0, 255, 0), 2)
    
            cv2.putText(image, 'Press "n" to normalize image',
                        (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2)
            cv2.putText(image, 'Press "s" to save image', (10, 60),
                        cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2)
            cv2.putText(image, 'Press "ESC" to exit', (10, 90),
                        cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2)
            cv2.imshow('Document Scanner', image)
    
    
    docscanner.initLicense(
        "LICENSE-KEY")
    
    scanner = docscanner.createInstance()
    ret = scanner.setParameters(docscanner.Templates.color)
    process_video(scanner)

    python document scanner from camera

API Methods

  • docscanner.initLicense('YOUR-LICENSE-KEY'): Set the license key.

    docscanner.initLicense("LICENSE-KEY")
  • docscanner.createInstance(): Create a Document Scanner instance.

    scanner = docscanner.createInstance()
  • detectFile(filename): Perform edge detection from an image file.

    results = scanner.detectFile(<filename>)
  • detectMat(Mat image): Perform edge detection from an OpenCV Mat.

    image = cv2.imread(<filename>)
    results = scanner.detectMat(image)
    for result in results:
        x1 = result.x1
        y1 = result.y1
        x2 = result.x2
        y2 = result.y2
        x3 = result.x3
        y3 = result.y3
        x4 = result.x4
        y4 = result.y4
  • setParameters(Template): Select color, binary, or grayscale template.

    scanner.setParameters(docscanner.Templates.color)
  • addAsyncListener(callback function): Start a native thread to run document scanning tasks asynchronously.

  • detectMatAsync(<opencv mat data>): Queue a document scanning task into the native thread.

    def callback(results):
        for result in results:
            print(result.x1)
            print(result.y1)
            print(result.x2)
            print(result.y2)
            print(result.x3)
            print(result.y3)
            print(result.x4)
            print(result.y4)
                                                        
    import cv2
    image = cv2.imread(<filename>)
    scanner.addAsyncListener(callback)
    scanner.detectMatAsync(image)
    sleep(5)
  • normalizeBuffer(mat, x1, y1, x2, y2, x3, y3, x4, y4): Perform perspective correction from an OpenCV Mat.

    normalized_image = scanner.normalizeBuffer(image, x1, y1, x2, y2, x3, y3, x4, y4)
  • normalizeFile(filename, x1, y1, x2, y2, x3, y3, x4, y4): Perform perspective correction from an image file.

    normalized_image = scanner.normalizeFile(<filename>, x1, y1, x2, y2, x3, y3, x4, y4)
  • normalized_image.save(filename): Save the normalized image to a file.

    normalized_image.save(<filename>)
  • normalized_image.recycle(): Release the memory of the normalized image.

  • clearAsyncListener(): Stop the native thread and clear the registered Python function.

How to Build the Python Document Scanner Extension

  • Create a source distribution:

    python setup.py sdist
  • setuptools:

    python setup_setuptools.py build
    python setup_setuptools.py develop 
  • Build wheel:

    pip wheel . --verbose
    # Or
    python setup.py bdist_wheel