该代码实现了一个Sobel边缘检测算法,用于计算图像梯度。通过定义3x3像素矩阵,分别计算水平和垂直方向的梯度(Gx和Gy)。Gx使用[-1,0,1;-2,0,2;-1,0,1]核,Gy使用[-1,-2,-1;0,0,0;1,2,1]核。最终梯度幅度通过G_data=√(Gx²+Gy²)计算得出。代码在时钟上升沿触发,使用寄存器暂存中间结果,通过符号运算完成梯度计算,最终输出10位的梯度幅值数据。
cpp
wire [7:0] matrix_p11;
wire [7:0] matrix_p12;
wire [7:0] matrix_p13;
wire [7:0] matrix_p21;
wire [7:0] matrix_p22;
wire [7:0] matrix_p23;
wire [7:0] matrix_p31;
wire [7:0] matrix_p32;
wire [7:0] matrix_p33;
//----------------------------------------------------------------------
// [p11,p12,p13] [-1,0,1]
// Gx_data = [p21,p22,p23] * [-2,0,2] = (p13+2*p23+p33) - (p11+2*p21+p31)
// [p31,p32,p33] [-1,0,1]
//
// [p11,p12,p13] [-1,-2,-1]
// Gy_data = [p21,p22,p23] * [ 0, 0, 0] = (p31+2*p32+p33) - (p11+2*p12+p13)
// [p31,p32,p33] [ 1, 2, 1]
//
// G_data = sqrt(Gx_data^2 + Gy_data^2)
reg [ 9:0] Gx_data_tmp1;
reg [ 9:0] Gx_data_tmp2;
reg [ 9:0] Gy_data_tmp1;
reg [ 9:0] Gy_data_tmp2;
reg signed [10:0] Gx_data;
reg signed [10:0] Gy_data;
reg signed [21:0] Gx_square_data;
reg signed [21:0] Gy_square_data;
reg [20:0] G_square_data;
wire [10:0] G_data;
always @(posedge clk)
begin
Gx_data_tmp1 <= matrix_p13 + {matrix_p23,1'b0} + matrix_p33;
Gx_data_tmp2 <= matrix_p11 + {matrix_p21,1'b0} + matrix_p31;
Gy_data_tmp1 <= matrix_p31 + {matrix_p32,1'b0} + matrix_p33;
Gy_data_tmp2 <= matrix_p11 + {matrix_p12,1'b0} + matrix_p13;
Gx_data <= $signed({1'b0,Gx_data_tmp1}) - $signed({1'b0,Gx_data_tmp2});
Gy_data <= $signed({1'b0,Gy_data_tmp1}) - $signed({1'b0,Gy_data_tmp2});
Gx_square_data <= $signed(Gx_data) * $signed(Gx_data);
Gy_square_data <= $signed(Gy_data) * $signed(Gy_data);
G_square_data <= Gx_square_data[20:0] + Gy_square_data[20:0];
end