How to Invoke HTTP Web Service of Dynamsoft SDKs with Power Automate for Desktop

May 22, 2023

Microsoft Power Automate is a powerful service designed to automate workflows between various apps and services. Its Desktop version provides the added capability of automating desktop-centric operations, with or without user interface interactions. In this article, we will explore how to integrate Dynamsoft Barcode Reader, Dynamsoft Document Normalizer, and Dynamsoft Label Recognizer into a web service using Flask. By leveraging this web API, we can seamlessly invoke these Dynamsoft tools within Power Automate for Desktop. This enables you to transform the created flow into a convenient desktop application that significantly enhances your daily work efficiency.

This article is Part 1 in a 2-Part Series.

Installing Python Packages

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

dynamsoft-capture-vision-bundle: Dynamsoft Capture Vision Bundle (includes Barcode Reader, Document Normalizer, and Label Recognizer)
opencv-python: OpenCV for Python
flask: Flask web framework
numpy: NumPy for array operations

To use Dynamsoft SDKs, you need to apply for a trial license.

Does Power Automate Support External Python Libraries?

If you have tried to use Python script in Power Automate for Desktop, you may have noticed that it does not support external libraries installed via pip. This is because Power Automate Desktop uses IronPython, which is a .NET implementation of Python. IronPython is a subset of the Python language, and it does not support external libraries. To address this issue, we can create a web service using Flask, and then invoke the web API using Power Automate HTTP action.

Setting Up an HTTP Web Service Using Flask

Import the dependent packages:

 from flask import Flask, request, jsonify
 import base64
 import urllib.parse

 from dynamsoft_capture_vision_bundle import (
     LicenseManager, CaptureVisionRouter, EnumPresetTemplate, EnumErrorCode,
     EnumImagePixelFormat
 )
 import cv2
 import numpy as np
 import time
 import os

Set the Dynamsoft license key and initialize the CaptureVisionRouter instances. You need to replace the license key with your own.

 license_key = "LICENSE-KEY"

 # Initialize license
 LicenseManager.init_license(license_key)

 # Create CaptureVisionRouter instance for barcode detection
 cvr_barcode = CaptureVisionRouter()

 # Create CaptureVisionRouter instance for MRZ detection
 cvr_mrz = CaptureVisionRouter()

 # Create CaptureVisionRouter instance for document detection
 cvr_document = CaptureVisionRouter()

Create a Flask app and define the web API endpoints:

 app = Flask(__name__)

 @app.route('/api/dbr/decode', methods=['POST'])
 def dbr_decode():
     return handle_request(request, 'dbr')

 @app.route('/api/mrz/scan', methods=['POST'])
 def mrz_scan():
     return handle_request(request, 'mrz')

 @app.route('/api/document/rectify', methods=['POST'])
 def document_rectify():
     return handle_request(request, 'document')

 if __name__ == '__main__':
     # Use --host-all flag to allow external connections (requires admin on Windows)
     import sys
     host = '0.0.0.0' if '--host-all' in sys.argv else '127.0.0.1'
     app.run(host=host, port=5000)

Handle the HTTP request to get the image data from request.files or request body. The image data from the request body is encoded in base64 format, so we need to decode it first.

 def handle_request(request, sdk):
     output = []
        
     request_body = request.data.decode('utf-8')
     if request_body != '':
         try:
                
             base64_content = urllib.parse.unquote(request_body)
             file_content = base64.b64decode(base64_content)
         except:
             return 'Invalid base64 string', 400
            
         output = process_file(file_content, sdk)
            
     else:
         if 'file' not in request.files:
             return 'No file uploaded', 400
            
         file = request.files['file']
            
         if file.filename == '':
             return 'Empty file', 400
            
         file_content = file.read()
            
         output = process_file(file_content, sdk)
            
     return jsonify(results=output)

Invoke the corresponding SDK methods to process the image data for barcode detection, document rectification and MRZ recognition respectively.

 def decode_file_stream(file_content):
     output = []
     try:
         results = cvr_barcode.capture_multi_pages(file_content, EnumPresetTemplate.PT_READ_BARCODES)
         result_list = results.get_results()
         for result in result_list:
             if result.get_error_code() == EnumErrorCode.EC_OK:
                 items = result.get_items()
                 for item in items:
                     text = item.get_text()
                     format_str = item.get_format_string()
                     output.append({'format': format_str, 'text': text})
     except Exception as error:
         output = str(error)
            
     return output

 def mrz_decode_file_stream(file_content):
     output = []
     try:
         results = cvr_mrz.capture_multi_pages(file_content, "ReadPassportAndId")
         result_list = results.get_results()
         for result in result_list:
             if result.get_error_code() == EnumErrorCode.EC_OK:
                 # Get recognized text lines (raw MRZ text)
                 line_result = result.get_recognized_text_lines_result()
                 if line_result:
                     items = line_result.get_items()
                     for item in items:
                         text = item.get_text()
                         output.append(text)
                    
                 # Get parsed results (structured MRZ data)
                 parsed_result = result.get_parsed_result()
                 if parsed_result:
                     parsed_items = parsed_result.get_items()
                     for item in parsed_items:
                         code_type = item.get_code_type()
                         # Extract key fields from parsed MRZ
                         doc_id = item.get_field_value("passportNumber") or item.get_field_value("documentNumber")
                         nationality = item.get_field_value("nationality")
                         issuer = item.get_field_value("issuingState")
                         dob = item.get_field_value("dateOfBirth")
                         expiry = item.get_field_value("dateOfExpiry")
                         gender = item.get_field_value("sex")
                         surname = item.get_field_value("primaryIdentifier")
                         given_name = item.get_field_value("secondaryIdentifier")
                            
                         parsed_info = f"Type: {code_type}"
                         if doc_id:
                             parsed_info += f", ID: {doc_id}"
                         if surname:
                             parsed_info += f", Name: {surname}"
                         if given_name:
                             parsed_info += f" {given_name}"
                         if nationality:
                             parsed_info += f", Nationality: {nationality}"
                         if dob:
                             parsed_info += f", DOB: {dob}"
                         if expiry:
                             parsed_info += f", Expiry: {expiry}"
                         if gender:
                             parsed_info += f", Gender: {gender}"
                         output.append(parsed_info)
     except Exception as error:
         output = str(error)
            
     return output


 def convertImageData2Mat(normalized_image):
     """Convert ImageData to OpenCV Mat"""
     ba = bytearray(normalized_image.get_bytes())
     width = normalized_image.get_width()
     height = normalized_image.get_height()

     channels = 3
     if normalized_image.get_image_pixel_format() == EnumImagePixelFormat.IPF_BINARY:
         channels = 1
         all_pixels = []
         skip = normalized_image.stride * 8 - width

         index = 0
         n = 1
         for byte in ba:
             byteCount = 7
             while byteCount >= 0:
                 b = (byte & (1 << byteCount)) >> byteCount

                 if index < normalized_image.stride * 8 * n - skip:
                     if b == 1:
                         all_pixels.append(255)
                     else:
                         all_pixels.append(0)

                 byteCount -= 1
                 index += 1

             if index == normalized_image.stride * 8 * n:
                 n += 1

         mat = np.array(all_pixels, dtype=np.uint8).reshape(height, width, channels)
         return mat

     elif normalized_image.get_image_pixel_format() == EnumImagePixelFormat.IPF_GRAYSCALED:
         channels = 1

     mat = np.array(ba, dtype=np.uint8).reshape(height, width, channels)
     return mat


 def document_rectify_file_stream(file_content):
     output = []
     try:
         results = cvr_document.capture_multi_pages(file_content, EnumPresetTemplate.PT_DETECT_AND_NORMALIZE_DOCUMENT)
         result_list = results.get_results()
            
         for result in result_list:
             if result.get_error_code() == EnumErrorCode.EC_OK:
                 processed_doc_result = result.get_processed_document_result()
                 if processed_doc_result:
                     deskewed_items = processed_doc_result.get_deskewed_image_result_items()
                     for item in deskewed_items:
                         # Get the normalized image data
                         normalized_image_data = item.get_image_data()
                         if normalized_image_data:
                             # Convert ImageData to OpenCV Mat
                             normalized_image = convertImageData2Mat(normalized_image_data)
                                
                             image_path = os.path.join(os.getcwd(), str(time.time()) + '.png')
                             cv2.imwrite(image_path, normalized_image)
                             output.append(image_path)
                             break
     except Exception as error:
         output = str(error)
            
     return output

 def process_file(file_content, sdk):
     output = []
     if sdk == 'dbr':
         output = decode_file_stream(file_content)
     elif sdk == 'mrz':
         output = mrz_decode_file_stream(file_content)
     elif sdk == 'document':
         output = document_rectify_file_stream(file_content)
     return output

Start the Flask server and test the web API with curl commands (use curl.exe in PowerShell, or curl in CMD/Git Bash):

 # Run server (localhost only)
 python app.py
    
 # Or run with external access (requires admin on Windows)
 python app.py --host-all

 # barcode
 curl.exe -X POST -F 'file=@./barcode.jpg' http://127.0.0.1:5000/api/dbr/decode

 # mrz
 curl.exe -X POST -F 'file=@./mrz.png' http://127.0.0.1:5000/api/mrz/scan

 # document
 curl.exe -X POST -F 'file=@./document.png' http://127.0.0.1:5000/api/document/rectify

Invoking Web Service in Power Automate for Desktop

In the following section, we will show you how to create a flow step by step.

Launch Power Automate for Desktop and create a new flow.
Create a loop. Since we want to keep the flow running as a tool, we can set a big number for the loop count.
In the loop, add a Display custom form action. Open Custom form designer to add Choice set input and Submit. The ID of the Choice set input is Options, which will be used in the next step.

The custom form looks like this:
Create a Switch action to check the option value with %CustomFormData2['Options']%.
Create four cases: Barcode, MRZ, Document, and Quit.
- The Barcode case is used to decode 1D/2D barcodes from an image file or a screenshot.
- The MRZ case is used to scan MRZ from an image file.
- The Document case is used to rectify a document image.
- The default case is used to quit the loop when the selected option is Quit.

Decode Barcodes from an Image File or a Screenshot

Image

Add a Display select file dialog action to let the user select an image file.
Add a Convert file to Base64 action to convert the image file to base64 format.
Add a Invoke web service action. Set the URL to http://127.0.0.1:5000/api/dbr/decode, the Method to POST, the Accept to application/json, the Content type to application/json and the Request body to %Base64Text%.
Add a Display message action to show the decoded results.

Screenshot

Add a Take screenshot action to take a screenshot. Set the Capture to All screens, the Save screenshot to to File, the Image file to any path you want, and the Image format to PNG.
Take the same steps 2 through 4 in the image example we just mentioned.

Scan MRZ from an Image File

The MRZ case is similar to the barcode case. The only difference is that we change URL to http://127.0.0.1:5000/api/mrz/scan instead of http://127.0.0.1:5000/api/dbr/decode.

Rectify a Document Image

Just like the MRZ scenario, we modify the URL to http://127.0.0.1:5000/api/document/rectify. Once the web API is invoked, we acquire the path of the rectified image by using the Convert JSON to custom object action. Subsequently, we use the Write to CMD session action to display the rectified image.

rectify document

Source Code

https://github.com/yushulx/dynamsoft-sdk-webapi-restful-service/tree/main/python