Python与FPGA——膨胀腐蚀

文章目录

• 前言
• 一、膨胀腐蚀
• 二、Python实现腐蚀算法
• 三、Python实现膨胀算法
• 四、Python实现阈值算法
• 五、FPGA实现腐蚀算法
• ���结

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  前言

    腐蚀是指周围的介质作用下产生损耗与破坏的过程，如生锈、腐烂等。而腐蚀算法也类似一种能够产生损坏，抹去部分像素的算法。

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  一、膨胀腐蚀

    膨胀腐蚀之前需要对图像进行二值化处理，然后进行以下处理。

    腐蚀： P = P 1 & P 2 & P 3 & P 4 & P 5 & P 6 & P 7 & P 8 & P 9 P = P1 \& P2 \& P3 \& P4 \& P5 \& P6 \& P7 \& P8 \& P9 P=P1&P2&P3&P4&P5&P6&P7&P8&P9

    膨胀： P = P 1 ∣ P 2 ∣ P 3 ∣ P 4 ∣ P 5 ∣ P 6 ∣ P 7 ∣ P 8 ∣ P 9 P = P1 | P2 | P3 | P4 | P5 | P6 | P7 | P8 | P9 P=P1∣P2∣P3∣P4∣P5∣P6∣P7∣P8∣P9

    图像二值化如下。

  import numpy as npimport matplotlib.pyplot as pltimg = plt.imread("lenna.png")gray = 0.299 * img[:, :, 0] + 0.587 * img[:, :, 1] + 0.114 * img[:, :, 2] gray = gray * 255#图像是[0-1]--->[0-255]bin_image = np.where(gray >= 128, 1, 0)#处理成[0,1]像素

  二、Python实现腐蚀算法

  def bin_erosion(binary, n):    h, w = gray.shape    m = int((n - 1) / 2)    erosion_image = np.zeros((h, w))    for i in range(m, h - m):        for j in range(m, w - m):           erosion_image[i, j] = binary[i - 1, j - 1] & binary[i - 1, j] & binary[i - 1, j + 1] &\                                 binary[i, j - 1] & binary[i, j] & binary[i, j + 1] & binary[i + 1, j - 1] &\                                 binary[i + 1, j] & binary[i + 1, j + 1]    return erosion_image

  三、Python实现膨胀算法

  def bin_dialtion(binary, n):    h, w = gray.shape    m = int((n - 1) / 2)    dialtion_image = np.zeros((h, w))    for i in range(m, h - m):        for j in range(m, w - m):           dialtion_image[i, j] = binary[i - 1, j - 1] | binary[i - 1, j] | binary[i - 1, j + 1] |\                                 binary[i, j - 1] | binary[i, j] | binary[i, j + 1] | binary[i + 1, j - 1] |\                                 binary[i + 1, j] | binary[i + 1, j + 1]    return dialtion_image

  四、Python实现阈值算法

    阈值算法介于膨胀腐蚀之间，可以调整膨胀腐蚀的程度。

  def threshold_method(binary, n, value):    h, w = gray.shape    m = int((n - 1) / 2)    th_image = np.zeros((h, w))    for i in range(m, h - m):        for j in range(m, w - m):           temp = binary[i - 1, j - 1] + binary[i - 1, j] + binary[i - 1, j + 1] +\                                 binary[i, j - 1] + binary[i, j] + binary[i, j + 1] + binary[i + 1, j - 1] +\                                 binary[i + 1, j] + binary[i + 1, j + 1]           if temp >= value:               th_image[i, j] = 1           else:               th_image[i, j] = 0    return th_image

  erosion_image = bin_erosion(bin_image, 3)dialtion_image = bin_dialtion(bin_image, 3)th_image = threshold_method(bin_image, 3, 8)fig = plt.figure(figsize=(8, 8))ax = fig.add_subplot(2, 2, 1)ax.set_title("bin image")ax.set_xlabel("width")ax.set_ylabel("height")plt.imshow(bin_image, cmap="gray")ax = fig.add_subplot(2, 2, 2)ax.set_title("erosion image")ax.set_xlabel("width")ax.set_ylabel("height")plt.imshow(erosion_image, cmap="gray")ax = fig.add_subplot(2, 2, 3)ax.set_title("dialtion image")ax.set_xlabel("width")ax.set_ylabel("height")plt.imshow(dialtion_image, cmap="gray")ax = fig.add_subplot(2, 2, 4)ax.set_title("threshold image")ax.set_xlabel("width")ax.set_ylabel("height")plt.imshow(th_image, cmap="gray")

    画图结果如下。

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  五、FPGA实现腐蚀算法

  module  ycbcr_to_erosion(inputwirevga_clk,//vga时钟inputwiresys_rst_n,//复位信号inputwire[7:0]y_data,//灰度处理的图像像素inputwirergb_valid,//vga显示有效区域outputwire[15:0]erosion_data//二值化像素);//binary datawire [7: 0] bin_data;//shift ramwire[7:0]data_row1;wire[7:0]data_row2;wire[7:0]data_row3;//3*3像素数据reg[7:0]p11;reg[7:0]p12;reg[7:0]p13;reg[7:0]p21;reg[7:0]p22;reg[7:0]p23;reg[7:0]p31;reg[7:0]p32;reg[7:0]p33;wire[7:0]temp    ;//Y值有效信号regy_valid;//二值化assign  bin_data  = (y_data >= 8'd128) ? 8'd255: 8'd0;assign  data_row3 = bin_data  ;//腐蚀assign  temp = (p11 & p12 & p13 & p21 & p22 & p23 & p31 & p32 & p33);//像素拼接565assign  erosion_data = {temp[7:3], temp[7:2], temp[7:3]};always@(posedge vga_clk or negedge sys_rst_n)if(sys_rst_n == 1'b0)y_valid  p11,p12,p13}