Embedded Vision Kit

The evolution of the classical machine vision towards embedded vision is rapidly evolving. These compact systems, in which the cost factor plays a major role, consume less energy by increasing performance. But developing an embedded vision device can be very time consuming and cost-intensive. The limitations of these highly specialized devices regarding data interfaces, performance, storage space and user interfaces make hardware handling and software development very difficult compared to a desktop workstation with standard components. Especially with proprietary developments (hardware platform, firmware and software) you may lose a lot of time until the first results are available.

But especially for the pre-development phase there are now a number of suitable embedded standard components that allow out-of-the-box testing. In combination with qualified software solutions, the first insights for vision applications can be derived very quickly.

Our TechTip shows in a few simple steps how to implement a simple embedded vision application with a uEye camera and a Raspberry Pi 3.

Step 1: Prepare hardware

Set up a Raspberry Pi 3 with the Raspbian OS and update the system to the latest software version.

pi@raspberrypi:~ $ sudo apt-get update && apt-get upgrade

Instructions on setting up a Raspberry Pi can be found on the Internet. Basically, you can use any other ARMv7 compatible embedded board (e. g. Odroid XU4) for the demo. However, the Raspberry Pi3 with its quad-core CPU has enough power for simple image processing tasks and the Raspbian OS comes with many pre-installed components. Everything else can be easily installed via the package sources.

Connect a uEye USB camera to an USB port of the Raspberry Pi.

pi@raspberrypi:~ $ sudo pip install pyueye

This installs the uEye Python interface to be used with Python 2.7. Necessary module dependencies are installed automatically by PIP. To check the proper installation, use the Python interpreter and import the uEye module.

pi@raspberrypi:~ $ python
Python 2.7.9 (default, Sep 17 2016, 20:26:04)
[GCC 4.9.2] on linux2
>>> from pyueye import ueye

If no error message is returned, the installation was successful.

Step 3: Install OpenCV

The OpenCV development libraries can be easily installed from the Raspbian package sources. Although they have an older version (, they are sufficient for our demo. The Python 2.7 bindings for the OpenCV libraries can also be installed from the package sources. If you want to use Python 3, you have to compile them yourself from the code sources for the embedded platform. You can also find simple instructions online.

pi@raspberrypi:~ $ sudo apt-get install libopencv-dev python-opencv

You can also check this installation with the Python interpreter by importing the OpenCV module "cv2".

Step 4: Download and run the PyuEye sample application

Start your own image processing applications with uEye and the Python interface by downloading the source code example, which is linked within the TechTipp website and extract it into a directory on your Raspberry Pi.

The source code example is completely made with Python. Therefore, you do not need to cross-compile it for the system architecture (ARMv7 A) of the Raspberry Pi. This makes it platform-independent and you can execute it directly. In other words, you can also run this source code example on a Windows or Linux desktop system if the requirements (uEye drivers, PyuEye interface, Python 2.7) are installed on these systems. The PyuEye source code example consists of four Python files that provide classes and functionality for different parts of the example program:

1) pyueye_example_camera.py

Provides a "camera" class with frequently used camera functions.

2) pyueye_example_gui.py

With the classes "PyuEyeQtView" and "PyuEyeQtApp" you can create a simple Qt widget GUI application. This module is based on Qt4 and uses the Python Qt4 bindings (PyGt4). Qt4 is already integrated in Raspbian Jessie. So you can install the Python bindings from the package sources:

pi@raspberrypi:~ $ sudo apt-get install python-qt4 python-qt4-doc

3) pyueye_example_utils.py

This module provides important convenience functions and classes that are very helpful when developing a camera application. From exception handling to the management of camera data and image storage, there are a number of useful options.

4) pyueye_example_main.py

The main module creates a simple Qt application framework, opens and initializes the connected camera and provides you with an image processing callback function, in which a simple image processing with OpenCV is implemented. The running demo shows a live image of the connected camera and overlays the processing results.

pi@raspberrypi:~/example $ python pyueye_example_main.py

OpenCV image processing

Our simple image processing task with OpenCV searches for circles in the image and highlights them.

A few lines of code in the main module are sufficient to do this, since OpenCV contains a complete implementation with the cv2.HoughCircles() function for this task. To work with OpenCV, "cv2" and "numpy" have been imported:

from pyueye_example_camera import Camera
from pyueye_example_utils import FrameThread
from pyueye_example_gui import PyuEyeQtApp, PyuEyeQtView
from PyQt4 import QtGui

from pyueye import ueye

import cv2
import numpy as np
def process_image(self, image_data):

    # reshape the image data as 1dimensional array
    image = image_data.as_1d_image()    
    # make a gray image
    image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    #image = cv2.medianBlur(image,5)
    # find circles in the image
    circles = cv2.HoughCircles(image, cv2.cv.CV_HOUGH_GRADIENT, 1.2, 100)
    # make a color image again to mark the circles in green
    image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
    if circles is not None:
	   # convert the (x, y) coordinates and radius of the circles to integers
	   circles = np.round(circles[0, :]).astype("int") 
	   # loop over the (x, y) coordinates and radius of the circles
	   for (x, y, r) in circles:
		  # draw the circle in the output image, then draw a rectangle
		  # corresponding to the center of the circle
		  cv2.circle(image, (x, y), r, (0, 255, 0), 6)
    # show the image with Qt
    return QtGui.QImage(image.data,

You can easily modify the application and test other processing tasks with OpenCV. Typical for Python, you can directly execute the modified application.