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from PIL import Image
from tqdm import tqdm
import numpy as np
import os
class PreDigest():
def __init__(self, source, dirpath, K, block, number):
'''
预处理初始化类
:param source: 图片原路径
:param dirpath: 保存目录路径
:param K: 保留的特征值数量
:param block: 二值化操作梯度的大小
:param number: 进行二值化操作的次数
'''
self.source = source
self.dirpath = dirpath
self.K = K
self.block = block
self.number = number
def _get_bin_table(self, threshold=200):
'''
获取灰度转二值的映射table
0表示黑色,1表示白色
:param threshold: 二值化的边界阈值
:return:
'''
table = []
for i in range(256):
if i < threshold:
table.append(0)
else:
table.append(1)
return table
def _sum_9_region_new(self, img, x, y):
'''
确认噪声点
:param img:图片
:param x:
:param y:
:return:
'''
cur_pixel = img.getpixel((x, y))
width = img.width
height = img.height
if cur_pixel == 1:
return 0
if y < 3:
return 1
elif y > height - 3:
return 1
else:
if x < 3:
return 1
elif x == width - 1:
return 1
else:
sum = img.getpixel((x - 1, y - 1)) \
+ img.getpixel((x - 1, y)) \
+ img.getpixel((x - 1, y + 1)) \
+ img.getpixel((x, y - 1)) \
+ cur_pixel \
+ img.getpixel((x, y + 1)) \
+ img.getpixel((x + 1, y - 1)) \
+ img.getpixel((x + 1, y)) \
+ img.getpixel((x + 1, y + 1))
return 9 - sum
def _collect_noise_point(self, img):
'''
搜索到所有的噪声点
:param img: 图片数组
:return:
'''
noise_point_list = []
for x in range(img.width):
for y in range(img.height):
res_9 = self._sum_9_region_new(img, x, y)
if (0 < res_9 < 3) and img.getpixel((x, y)) == 0:
pos = (x, y)
noise_point_list.append(pos)
return noise_point_list
def _remove_noise_pixel(self, img, noise_point_list):
'''
根据噪点的位置信息，消除二值图片的黑点噪声
:param img: 图片数组
:param noise_point_list: 统计到的噪声点的情况
:return:
'''
for item in noise_point_list:
img.putpixel((item[0], item[1]), 1)
def _restore(self, u, sigma, v, k):
'''
转换成图片数组
:param u:
:param sigma:
:param v:
:param k:
:return:
'''
a = np.dot(u[:, :k], np.diag(sigma[:k])).dot(v[:k, :])
a[a < 0] = 0
a[a > 255] = 255
return np.rint(a).astype('uint8')
def _SVD(self, frame, K = 10):
'''
进行SVD操作
:param frame: 图片数组
:param K: 保留特征值数量
:return:
'''
a = np.array(frame)
u_r, sigma_r, v_r = np.linalg.svd(a[:, :, 0])
u_g, sigma_g, v_g = np.linalg.svd(a[:, :, 1])
u_b, sigma_b, v_b = np.linalg.svd(a[:, :, 2])
R = self._restore(u_r, sigma_r, v_r, K)
G = self._restore(u_g, sigma_g, v_g, K)
B = self._restore(u_b, sigma_b, v_b, K)
I = np.stack((R, G, B), axis=2)
return I
def _pre_digest(self, gray, threshold):
table = self._get_bin_table(threshold)
binary = gray.point(table, '1')
noise_point_list = self._collect_noise_point(binary)
self._remove_noise_pixel(binary, noise_point_list)
binary.save(os.path.join(self.dirpath, "T matrix_" + str(threshold) + ".png"))
def _svd_img(self, filepath):
'''
开始执行svd操作
:param filepath:
:return:
'''
img = Image.open(filepath)
svd_array = self._SVD(img, self.K)
svd_img = Image.fromarray(svd_array)
new_path = os.path.join(self.dirpath, "T matrix_svd.png")
svd_img.save(new_path)
return new_path
def run(self):
print("开始SVD处理!")
new_path = self._svd_img(self.source)
print("结束SVD处理!")
img = Image.open(new_path)
gray = img.convert('L')
gray.save(os.path.join(self.dirpath, "T matrix_grey.png"))
for i in tqdm(range(1, self.number)):
self._pre_digest(gray, self.block * i)
if __name__ == '__main__':
K = 400
block = 25
number = int(256 / block)
dir_path = "./after_img_" + str(K) + "_" + str(block)
if(not os.path.exists(dir_path)):
os.mkdir(dir_path)
tmp = PreDigest("./img/T matrix.png", dir_path, K, block, number)
tmp.run()
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