【Python】用Python将图片转换成Vivado Rom初始化coe文件
1. 介绍
使用XIlinx开发平台Vivado玩FPGA时，需要将一种图片转换成.coe文件用于初始化Rom IP。本文用Python编写一个脚本实现图片转换成Vivado Rom初始化coe文件。如下图分别生成RGB三个coe文件。
2. 程序介绍
2.1 读取图片并调整图片大小
有时图片大小不合适需要调整到指定像素比。通过设置set_size、img_w、img_h进行改变。
import numpy as np
from PIL import Image
# 图片文件路径
img_path = 'Dataset/Lena.png'
# 调整原图像大小可设置set_size=1,反之为=0
set_size = 0
img_w = 300
img_h = 200
# 读取图片并做大小调整
def gen_img():
img = Image.open(img_path)
if set_size == 1:
img = img.resize((img_w, img_h), Image.ANTIALIAS)
img.save('re_picture.jpg')
img = np.array(img)
return img
2.2 生成coe文件
# 分别生成R G B的coe文件
def gen_coe(img):
img_R = []
img_G = []
img_B = []
for i in range(img.shape[0]):
for j in range(img.shape[1]):
img_R.append(img[i][j][0])
img_G.append(img[i][j][1])
img_B.append(img[i][j][2])
with open('Picture_R_Rom.coe', 'w') as f:
f.writelines('memory_initialization_radix = 10;\nmemory_initialization_vector = ')
for i in range(len(img_R)):
if i % img.shape[1] == 0:
f.write('\n')
f.write(str(img_R[i]).rjust(4) + ',')
f.close()
with open('Picture_G_Rom.coe', 'w') as f:
f.writelines('memory_initialization_radix = 10;\nmemory_initialization_vector = ')
for i in range(len(img_G)):
if i % img.shape[1] == 0:
f.write('\n')
f.write(str(img_G[i]).rjust(4) + ',')
f.close()
with open('Picture_B_Rom.coe', 'w') as f:
f.writelines('memory_initialization_radix = 10;\nmemory_initialization_vector = ')
for i in range(len(img_B)):
if i % img.shape[1] == 0:
f.write('\n')
f.write(str(img_B[i]).rjust(4) + ',')
f.close()
2.3 coe文件数据转换成图片
将coe文件反向转换成图片
# coe文件转换成图片
def coe2picture():
infos_R = []
with open('Picture_R_Rom.coe', 'r') as f:
info = f.read().splitlines()
for i in range(2, len(info)):
info1 = [int(a) for a in info[i].split(',')[:-1]]
infos_R.append(info1)
f.close()
infos_G = []
with open('Picture_G_Rom.coe', 'r') as f:
info = f.read().splitlines()
for i in range(2, len(info)):
info1 = [int(a) for a in info[i].split(',')[:-1]]
infos_G.append(info1)
f.close()
infos_B = []
with open('Picture_B_Rom.coe', 'r') as f:
info = f.read().splitlines()
for i in range(2, len(info)):
info1 = [int(a) for a in info[i].split(',')[:-1]]
infos_B.append(info1)
f.close()
img_array = np.empty((np.array(infos_B).shape[0],np.array(infos_B).shape[1],3), dtype=int)
if set_size == 1:
img_array = np.empty((img_h,img_w,3), dtype=int)
for i in range(np.array(infos_B).shape[0]):
for j in range(np.array(infos_B).shape[1]):
img_array[i][j][0] = infos_R[i][j]
img_array[i][j][1] = infos_G[i][j]
img_array[i][j][2] = infos_B[i][j]
img_pil = Image.fromarray(np.uint8(img_array))
img_pil.save('coe2picture.jpg')
3. 案例实践
完整程序：
import numpy as np
from PIL import Image
# 图片文件路径
img_path = 'Dataset/Lena.png'
# 调整原图像大小可设置set_size=1,反之为=0
set_size = 0
img_w = 300
img_h = 200
# 读取图片并做大小调整
def gen_img():
img = Image.open(img_path)
if set_size == 1:
img = img.resize((img_w, img_h), Image.ANTIALIAS)
img.save('re_picture.jpg')
img = np.array(img)
return img
# 分别生成R G B的coe文件
def gen_coe(img):
img_R = []
img_G = []
img_B = []
for i in range(img.shape[0]):
for j in range(img.shape[1]):
img_R.append(img[i][j][0])
img_G.append(img[i][j][1])
img_B.append(img[i][j][2])
with open('Picture_R_Rom.coe', 'w') as f:
f.writelines('memory_initialization_radix = 10;\nmemory_initialization_vector = ')
for i in range(len(img_R)):
if i % img.shape[1] == 0:
f.write('\n')
f.write(str(img_R[i]).rjust(4) + ',')
f.close()
with open('Picture_G_Rom.coe', 'w') as f:
f.writelines('memory_initialization_radix = 10;\nmemory_initialization_vector = ')
for i in range(len(img_G)):
if i % img.shape[1] == 0:
f.write('\n')
f.write(str(img_G[i]).rjust(4) + ',')
f.close()
with open('Picture_B_Rom.coe', 'w') as f:
f.writelines('memory_initialization_radix = 10;\nmemory_initialization_vector = ')
for i in range(len(img_B)):
if i % img.shape[1] == 0:
f.write('\n')
f.write(str(img_B[i]).rjust(4) + ',')
f.close()
# coe文件转换成图片
def coe2picture():
infos_R = []
with open('Picture_R_Rom.coe', 'r') as f:
info = f.read().splitlines()
for i in range(2, len(info)):
info1 = [int(a) for a in info[i].split(',')[:-1]]
infos_R.append(info1)
f.close()
infos_G = []
with open('Picture_G_Rom.coe', 'r') as f:
info = f.read().splitlines()
for i in range(2, len(info)):
info1 = [int(a) for a in info[i].split(',')[:-1]]
infos_G.append(info1)
f.close()
infos_B = []
with open('Picture_B_Rom.coe', 'r') as f:
info = f.read().splitlines()
for i in range(2, len(info)):
info1 = [int(a) for a in info[i].split(',')[:-1]]
infos_B.append(info1)
f.close()
img_array = np.empty((np.array(infos_B).shape[0],np.array(infos_B).shape[1],3), dtype=int)
if set_size == 1:
img_array = np.empty((img_h,img_w,3), dtype=int)
for i in range(np.array(infos_B).shape[0]):
for j in range(np.array(infos_B).shape[1]):
img_array[i][j][0] = infos_R[i][j]
img_array[i][j][1] = infos_G[i][j]
img_array[i][j][2] = infos_B[i][j]
img_pil = Image.fromarray(np.uint8(img_array))
img_pil.save('coe2picture.jpg')
if __name__ == '__main__':
img = gen_img()
gen_coe(img)
coe2picture()
运行之后可分别得到RGB三个coe文件和一张coe转图片反向生成的新图片。如果设置了调整图片大小还会生成一个调整图片大小后的图片。
