Table of contents
1. Preview of algorithm operation renderings
2.Algorithm running software version
4. Overview of algorithm theory
5. Algorithm complete program engineering
1. Preview of algorithm operation renderings
2.Algorithm running software version
vivado2019.2
matlab2022a
3. Some core programs
`timescale 1ns / 1ps
//
// Company:
// Engineer:
//
// Create Date: 2023/08/01
// Design Name:
// Module Name: RGB2gray
// Project Name:
// Target Devices:
// Tool Versions:
// Description:
//
// Dependencies:
//
// Revision:
// Revision 0.01 - File Created
// Additional Comments:
//
//
module test_image;
reg i_clk;
reg i_rst;
reg [7:0] Rbuff [0:100000];
reg [7:0] Gbuff [0:100000];
reg [7:0] Bbuff [0:100000];
reg [7:0] i_Ir,i_Ig,i_Ib;
wire [7:0] o_gray;
integer fids1,dat1,fids2,dat2,fids3,dat3,jj=0;
//D:\FPGA_Proj\FPGAtest\code
initial
begin
fids1 = $fopen("D:\\FPGA_Proj\\FPGAtest\\code\\R.bmp","rb");
dat1 = $fread(Rbuff,fids1);
$fclose(fids1);
end
initial
begin
fids2 = $fopen("D:\\FPGA_Proj\\FPGAtest\\code\\G.bmp","rb");
dat2 = $fread(Gbuff,fids2);
$fclose(fids2);
end
initial
begin
fids3 = $fopen("D:\\FPGA_Proj\\FPGAtest\\code\\b.bmp","rb");
dat3 = $fread(Bbuff,fids3);
$fclose(fids3);
end
initial
begin
i_clk=1;
i_rst=1;
#1200;
i_rst=0;
end
always #5 i_clk=~i_clk;
always@(posedge i_clk)
begin
i_Ir<=Rbuff[jj];
i_Ig<=Gbuff[jj];
i_Ib<=Bbuff[jj];
jj<=jj+1;
end
main_gray main_gray_u(
.i_clk (i_clk),
.i_rst (i_rst),
.i_image_R (i_Ir),
.i_image_G (i_Ig),
.i_image_B (i_Ib),
.o_gray (o_gray)
);
integer fout1;
initial begin
fout1 = $fopen("rgb2gray.txt","w");
end
always @ (posedge i_clk)
begin
if(jj<=66616)
$fwrite(fout1,"%d\n",o_gray);
else
$fwrite(fout1,"%d\n",0);
end
endmodule
0X_003m4. Overview of algorithm theory
FPGA-based conversion of RGB images into grayscale images is a common operation in the field of image processing. This operation makes image processing simpler and more efficient by converting the three RGB channels of a color image into a single grayscale value.
RGB images are the most common representation of color images, consisting of three channels: red (R), green (G), and blue (B). The intensity of each channel ranges from 0 to 255, and together they determine the color of the pixel.
A grayscale image is a form of image that contains only brightness information and not color information. Each pixel value of a grayscale image ranges from 0 to 255, indicating the brightness of the pixel.
The basic principle of converting an RGB image into a grayscale image is to combine the intensity values of the three RGB channels into a single grayscale value through a weighted average method. In this way, the color information of each pixel is reduced to a brightness information, allowing for simpler image processing and analysis.
The standard formula for converting an RGB image to grayscale is as follows:
Gray = 0.2989 * R + 0.5870 * G + 0.1140 * B
The meaning of this formula is that the gray value is determined by the weighted average of the three channels R, G, and B. The weight of each channel is 0.2989, 0.5870, and 0.1140 respectively. These weights are set based on the human eye's sensitivity to different colors.
In FPGA-based implementations, this formula is usually converted into a fixed-point arithmetic form for efficient implementation in hardware. For example, you can use the following formula:
Gray = (37 * R + 77 * G + 13 * B) / 255
The weights of this formula are the same as the previous formula, but are integerized for more efficient implementation on the FPGA.
In FPGA-based implementation, a hardware description language (such as VHDL or Verilog) is usually used to describe and implement the image conversion logic. The specific implementation steps are as follows:
- Read RGB image data from external memory.
- Convert RGB data into parallel 8-bit integers (or 16-bit integers, depending on the number of FPGA bits).
- Calculate the gray value of each pixel using the above formula.
- Write the calculated grayscale value to external memory.
During the implementation process, you need to pay attention to the following points:
- Since FPGA resources are limited, algorithms and codes need to be optimized to reduce resource usage.
- The width and height of the image need to be considered to determine what data structures and algorithms to use.
- The format and color space of the image data need to be considered to correctly handle RGB data.
- Testing and verification are required to ensure the correctness and stability of the conversion results.
FPGA-based conversion of RGB images into grayscale images is a common image processing operation. By using hardware description languages and optimization algorithms, an efficient conversion process can be achieved and can be widely used in fields such as image processing, computer vision, and machine learning.
5. Algorithm complete program engineering
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