Tutorial: Image Filtering, System Generator Matlab/Simulink
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1 Electrical and Computer Engineering Tutorial: Image Filtering, System Generator Matlab/Simulink By Prawat Nagvajara Targeting the Spartan - 6 Starter Kit 1. Introduction The tutorial describes steps in specifying, simulating, and implementing image filtering using the System Generator FIR (Finite Impulse Response) block, Simulink simulation and FPGA implementation. The tutorial introduces hardware-in-the-loop (co-simulation) - Matlab/Simulink and FPGA board - in verifying the functionality of the design. It uses a design of 2-D image filter for image modification, e.g., blur or sharpen. The image filtering considered is based on convolution of the filter matrix with the image pixel matrix. The design also introduces the shared memory data transfer (interface) to facilitate high speed transfers between the computer (running MATLAB) and FPGA. Ready-made referenced Simulink models and image include a) FIFO image filter model fifo_image_filter.mdl b) FIFO image filter simulation model fifo_image_filter_sim.mdl c) FIFO image filter hardware-in-the-loop model (test bench) fifo_image_filter_tb.mdl d) Shared memories image filter hardware-in-the-loop model shmem_image_filter_tb.mdl e) Image file Martina_Hingis.png
2 2. FIFO Interface Design 2.1 Make Directory for the tutorial and copy the ready-made mdl and pgn files 2.2 Launch System Generator: Located in ISE Design Suite System Generator System Generator as shown below 2.3 MATLAB directory path: Navigate to the drive where the tutorial directory appears on the current folder pane. Double-click on the tutorial folder to add to path (below shows an example).
3 2.3 Run Simulation i) Open fifo_image_filter_sim.mdl (the 4 th file in the current folder pane) ii) Click on the run button to run a simulation (see the screen capture shown below) iii) After a satisfactory simulation result, next Step 2.4 generates the filter model hardware for hardware-in-the-loop verification the hardware prototype and simulation are concurrently running during verification.
4 2.3 Generate Hardware from Filter model i) Open the FIFO filter model, fifo_image_filter.mdl (the last file in the list above) ii) Double click on the System Generator (red icon ) and set the setting as shown below iii) Click Apply and Generate 2.4 Generated Hardware The process may 4-5 minutes and a successful generation brings up a pane showing the generated co-simulation hardware (fifo_image_filter_hwcosim_lib) icon ready for drag-drop into a design, as shown below. (Warning messages regarding licenses may appear).
5 2.5 Run Hardware Co-simulation (Hardware-in-the-Loop) i) Open the test bench model, fifo_image_filter_tb.mdl ii) Drag and drop the fifo_image_filter_hwcosim icon into the test bench model, as shown below iii) Double click on the JTAG Co-sim of fifo_image_filter_hwcosim icon. Set Clock Source: Single Step; Cable: Auto Detect and Local, as shown below
6 iv) Connect the Spartan 6 board: USB/JTAG cable and click the run button. FIFO image data transfer to and from FPGA uses single stepped clocking, this does not provide practical performance. A successful result matches the result from the simulation as shown below.
7 3. Shared Memory Design 3.1 Open shmem_image_filter_tb.mdl. Drag and drop fifo_image_filter_hwcosim icon into the model. The library model of fifo_image_filter_hwcosim is located in the Netlist folder that was generated during the hardware generation. Below is a screen capture of MATLAB current folder. 3.2 Double click on the JTAG Co-sim of fifo_image_filter_hwcosim icon. Set Clock Source: Free Running; Cable: Auto Detect and Local
8 3.3.Connect the Spartan 6 board: USB/JTAG cable and click the run button. The run completed immediately since the transfer of image data and filtered data uses shared memories which transfers data in blocks. 4. Exploration 4.1 Simulink Model Properties: Right click on the model canvas and select model properties. Observe the Callback functions. The script for the initialize function is clear; pic = imread('martina_hingis.png'); input = pic(:,:,1)'; %(1:64,1:64,1); inputbuff = reshape(input,[],1); imread function reads the.png image into pic input is the transposed of pic with the 3rd index = 1. The image is 64x64 grayscale as commented by %(1:64,1:64,1);. InputBuff is a reshape function of input (n x m) into automatically calculated number of rows (denoted by a placeholder []) by 1 column. 4.2 Redo the project with a different type of filter, starting from simulation model. Double click on the filter block to access filter properties. End of Tutorial
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