![]() Step 3. To reduce noise, filter the brightness map using Gaussian blur algorithm. Now, based on the brightness maps, we can identify which areas are probably barcodes. Vec4 result21 = texture2D(CameraTexture, textureCoordinate-vec2(2.0*Size,Size))*3.0 Vec4 result12 = texture2D(CameraTexture, textureCoordinate-vec2(Size,2.0*Size))*3.0 Vec4 result11 = texture2D(CameraTexture, textureCoordinate-vec2(Size,Size))*3.0 Vec4 result01 = texture2D(CameraTexture, textureCoordinate-vec2(0,Size))*3.0 Vec4 result10 = texture2D(CameraTexture, textureCoordinate-vec2(Size,0))*3.0 Vec4 result00 = texture2D(CameraTexture, textureCoordinate)*3.0 To save the results, use all four texture channels (R, G, B, A). Employ the filter in these four directions in a single pass. To do this, calculate values of brightness gradient of the image in four directions: 0, 90, 45 and 135 degrees. Step 2. Image filtering can be done using a convolution method. To obtain gray color we use the following terms (hereinafter programming code is in glsl language): float grayscale = dot(texture2D(CameraTexture, textureCoordinate).rgb, vec3(0.299, 0.587, 0.114)) varying vec2 textureCoordinate Step 1. Convert the image captured by the camera into grayscale, since the information about color doesn’t play a role in this algorithm. Therefore, to optimize performance we try to use all 4 channels (RGBA) of each texel for storing data. I must point out that one of the most labor-intensive operations for the GPU is determining the texel value of the texture. Essentially, textures will serve as matrices, using which we will carry out certain operations. To work with the GPU we relied on OpenGL ES 2.0 framework and used its shader model. To obtain acceptable performance, we decided to use the graphics processing unit (GPU) to carry out the necessary calculations. The first stage requires intensive mathematical image processing. The second stage is the analysis and decoding of the barcode. determine which area of the captured frame is the barcode). The first stage is the analysis and processing of input frame captured by the mobile camera in order to find and localize the barcode (i.e. Our approach to solving the challenge can be divided into two main stages. More specifically, the video stream from the mobile camera should not be interrupted, should have a rate of at least 20 frames per second, and all frames from the camera should be recognized until the device receives a signal to stop scanning. According to the requirements, the app should be able to scan barcodes that are not aligned along the horizontal axis and should scan them continuously. The main goal of the project was to develop an iOS application that includes functionality for scanning linear (1D) barcodes. The project is designed as a framework, so it can be easily integrated into existing mobile applications or those currently under development. Here I will describe how we approached the challenge and came up with a fully-functional solution. But first, the real challenge was to come up with an algorithm to ensure we meet all requirements without sacrificing the application’s performance and quality of recognition. One of the projects we completed here in R&D department was to develop a barcode scanner application for iOS that could recognize even blurry barcodes, aligned in an arbitrary direction.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |