数字图像预处理

编程入门行业动态更新时间:2024-10-17 23:35:01

数字图像预处理

1.背景

在医学图像处理中，我们一般会进行一些非刚性变换做data augment，这么做可以使得训练数据的分布更加复杂，提高模型的泛化能力。

# coding:utf-8
# Import stuff
import os
import numpy as np
#import pandas as pd
import cv2
from scipy.ndimage.interpolation import map_coordinates
from scipy.ndimage.filters import gaussian_filter
import matplotlib.pyplot as plt# Function to distort image
def elastic_transform(image, alpha, sigma, alpha_affine, random_state=None):# If size is None, then a single value is generated and returned.if random_state is None:random_state = np.random.RandomState(None)# The form of shape is as follows (weight, heght, channels)shape = image.shapeshape_size = shape[:2]# Random affine# 对于仿射变换，我们只需要知道变换前的三个点与其对应的变换后的点，就可以通过cv2.getAffineTransform求得变换矩阵.center_square = np.float32(shape_size) // 2square_size = min(shape_size) // 3# pts1 是变换前的三个点，pts2 是变换后的三个点pts1 = np.float32([center_square + square_size, [center_square[0] + square_size, center_square[1] - square_size],center_square - square_size])pts2 = pts1 + random_state.uniform(-alpha_affine, alpha_affine, size=pts1.shape).astype(np.float32)# 进行放射变换M = cv2.getAffineTransform(pts1, pts2)# 默认使用 双线性插值，这里使用 三次样条插值。处理速度会变慢，但是可以最大程度的保留细节，适用于医学图像image = cv2.warpAffine(image, M, shape_size[::-1], borderMode=cv2.BORDER_REFLECT_101, flags=cv2.INTER_CUBIC)# random_state.rand(*shape) 会产生一个和 shape 一样打的服从[0,1]均匀分布的矩阵# * 2 - 1 是为了将分布平移到 [-1, 1] 的区间# gaussian_filter：将高斯滤波器作用于刚刚的卷积上，其中卷积核的大小由参数 sigma 决定# 这个卷积核具体是如何计算的可以参考如下的链接# .15.1/scipy/ndimage/filters.py#L180# 实际上 dx 和 dy 就是在计算论文中弹性变换的那三步：产生一个随机的位移，将卷积核作用在上面，用 alpha 决定尺度的大小dx = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma) * alphady = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma) * alphadz = np.zeros_like(dx)# np.meshgrid 生成网格点坐标矩阵，并在生成的网格点坐标矩阵上加上刚刚的到的dx dyx, y, z = np.meshgrid(np.arange(shape[1]), np.arange(shape[0]), np.arange(shape[2]))indices = np.reshape(y + dy, (-1, 1)), np.reshape(x + dx, (-1, 1)), np.reshape(z, (-1, 1))# indices = np.reshape(y+dy, (-1, 1)), np.reshape(x+dx, (-1, 1)), np.reshape(z, (-1, 1))return map_coordinates(image, indices, order=3, mode='reflect').reshape(shape)# Define function to draw a grid
def draw_grid(im, grid_size):# Draw grid linesfor i in range(0, im.shape[1], grid_size):cv2.line(im, (i, 0), (i, im.shape[0]), color=(255,))for j in range(0, im.shape[0], grid_size):cv2.line(im, (0, j), (im.shape[1], j), color=(255,))if __name__ == '__main__':img_path = 'data'mask_path = 'mask'img_list = sorted(os.listdir(img_path))mask_list = sorted(os.listdir(mask_path))img_num = len(img_list)mask_num = len(mask_list)assert img_num == mask_num, 'img nuimber is not equal to mask num.'count_total = 0for i in range(img_num):im = cv2.imread(os.path.join(img_path, img_list[i]), -1)im_mask = cv2.imread(os.path.join(mask_path, mask_list[i]), -1)# # Draw grid lines# draw_grid(im, 50)# draw_grid(im_mask, 50)# Merge images into separete channels (shape will be (cols, rols, 2))im_merge = np.concatenate((im[..., None], im_mask[..., None]), axis=2)# get img and mask shortname(img_shotname, img_extension) = os.path.splitext(img_list[i])(mask_shotname, mask_extension) = os.path.splitext(mask_list[i])# Elastic deformation 10 timescount = 0while count < 15:# Apply transformation on imageim_merge_t = elastic_transform(im_merge, im_merge.shape[1] * 2, im_merge.shape[1] * 0.08,im_merge.shape[1] * 0.08)# Split image and maskim_t = im_merge_t[..., 0]im_mask_t = im_merge_t[..., 1]# save the new imgs and maskscv2.imwrite(os.path.join(img_path, img_shotname + '-' + str(count) + img_extension), im_t)cv2.imwrite(os.path.join(mask_path, mask_shotname + '-' + str(count) + mask_extension), im_mask_t)count += 1count_total += 1if count_total % 100 == 0:print('Elastic deformation generated {} imgs', format(count_total))# # Display result# print 'Display result'# plt.figure(figsize = (16,14))# plt.imshow(np.c_[np.r_[im, im_mask], np.r_[im_t, im_mask_t]], cmap='gray')# plt.show()