When I was using CPython to create the Python barcode extension with Dynamsoft Barcode Reader, I had to take concern for the compatibility of Python versions. However, I’m reluctant to install all Python versions in my operating system. To test the compatibility of my Python barcode app in Windows, I picked Docker container as the workaround.Read more
When scanning barcodes, the recognition rate is affected by image quality. If a barcode image is severely damaged, the barcode algorithm may fail to work. Fortunately, most of the linear barcodes (1D barcode) are printed with corresponding texts. OCR (optical character recognition) algorithm could be a complement to the barcode algorithm in such a scenario. In this article, I will share how to use Tesseract OCR to boost the barcode scan.Read more
AppVeyor is a continuous integration (CI) service used to automatically build code projects and deploy relevant artifacts. It provides build environments for Windows, Linux, and macOS. In this article, I will share how to use AppVeyor to build and deploy Python Wheels (Windows edition) from C/C++ code.Read more
Direct Part Marking (DPM) is a process to mark equipment with some information, such as barcodes, permanently. The typical DPM barcode symbologies include DataMatrix and QR code. Since version 7.2, Dynamsoft Barcode Reader SDK has been capable of decoding DPM barcodes. In this article, I will share how to create a simple python barcode reader to read the DPM DataMatrix code.
When evaluating an image processing and recognition SDK, image dataset is vital for benchmarking the performance. Google is absolutely the best place for finding and downloading required images. An automation tool would be handy for getting amounts of image files rapidly. In this article, I will share how to use Python to download barcode images from Google, as well as how to test Dynamsoft Barcode Reader SDK with the image set.
Three years ago, I created a Python extension module for Dynamsoft Barcode Reader C/C++ SDK. The code skeleton has never been changed until recently the SDK updated to v7.0. In the latest barcode SDK, besides the values of barcode symbologies, there are more constant variables needed to be predefined. The original Python barcode extension is initialized only with some methods, and now I have to add some object members. The article shares the code I’ve refactored in order to add Python object members.
Dynamsoft Barcode Reader 7.0 brings a set of thread-based APIs for continuous frame management and corresponding barcode decoding. It extremely simplifies the programming complexity, especially for Python. It is known that Python’s GIL (Global Interpreter Lock) affects the performance in a multi-threaded scenario. Running computation intensive tasks in Python thread cannot improve the Python app performance. If you create a Python barcode scanner app with OpenCV and Dynamsoft Barcode Reader 6.x, Python multiprocessing is the only way for getting a high camera frame rate. With the thread-based APIs of Dynamsoft Barcode Reader 7.x, your Python apps will not be limited by GIL anymore. This tutorial shares how to integrate the thread-based C/C++ APIs into Python barcode extension.
If you want to quickly create a walking robot, you can use the Lego Boost. In this post, I will share how to use a webcam, Lego Boost, and Dynamsoft Barcode Reader SDK to make a robot for scanning barcodes. The programming language used in this article is Python.
LEGO Wedo 2.0 is a good start for learning robotics. I created a simple GUI app controlling the LEGO motor using Python. In this article, I will share my experience in how to select the development environment and how to build the Python app.
If you want to use Raspberry Pi as an economical way of detecting barcodes, you can take account for Dynamsoft Barcode Reader SDK. As a business software, Dynamsoft Barcode Reader SDK is designed for overcoming a variety of complicated scenarios with sophisticated algorithms and heavy computations. Although the SDK is flexible for customizing algorithm parameters, subject to the low-frequency CPU of Raspberry Pi, the tradeoff between recognition accuracy and detection speed is still a big challenge. In this article, I will use Socket client-server model as a substitute solution. Thanks to Sabjorn’s NumpySocket module.