# -*-coding:utf-8-*-
import numpy as np
from glob import glob
import math
import os
import torch
from tqdm import tqdm
import multiprocessing
label_path = '/home/ying/data/shiyongjie/distortion_datasets/new_distortion_dataset/train/label.txt'
file_path = '/home/ying/data/shiyongjie/distortion_datasets/new_distortion_dataset/train/distortion_image'
save_path = '/home/ying/data/shiyongjie/distortion_datasets/new_distortion_dataset/train/flow_field'
r_d_max = 128
image_index = 0
txt_file = open(label_path)
file_list = txt_file.readlines()
txt_file.close()
file_label = {}
for i in file_list:
i = i.split()
file_label[i[0]] = i[1]
r_d_max = 128
eps = 1e-32
H = 256
W = 256
def generate_flow_field(image_list):
for image_file_path in ((image_list)):
pixel_flow = np.zeros(shape=tuple([256, 256, 2])) # 按照pytorch中的grid来写
image_file_name = os.path.basename(image_file_path)
# print(image_file_name)
k = float(file_label[image_file_name])*(-1)*1e-7
# print(k)
r_u_max = r_d_max/(1+k*r_d_max**2) # 计算出畸变校正之后的对角线的理论长度
scale = r_u_max/128 # 将这个长度压缩到256的尺寸，会有一个scale，实际上这里写128*sqrt(2)可能会更加直观
for i_u in range(256):
for j_u in range(256):
x_u = float(i_u - 128)
y_u = float(128 - j_u)
theta = math.atan2(y_u, x_u)
r = math.sqrt(x_u ** 2 + y_u ** 2)
r = r * scale # 实际上得到的r，即没有resize到256×256的图像尺寸size，并且带入公式中
r_d = (1.0 - math.sqrt(1 - 4.0 * k * r ** 2)) / (2 * k * r + eps) # 对应在原图（畸变图）中的r
x_d = int(round(r_d * math.cos(theta)))
y_d = int(round(r_d * math.sin(theta)))
i_d = int(x_d + W / 2.0)
j_d = int(H / 2.0 - y_d)
if i_d < W and i_d >= 0 and j_d < H and j_d >= 0: # 只有求的的畸变点在原图中的时候才进行赋值
value1 = (i_d - 128.0)/128.0
value2 = (j_d - 128.0)/128.0
pixel_flow[j_u, i_u, 0] = value1 # mesh中存储的是对应的r的比值，在进行畸变校正的时候，给定一张这样的图，进行找像素即可
pixel_flow[j_u, i_u, 1] = value2
# 保存成array格式
saved_image_file_path = os.path.join(save_path, image_file_name.split('.')[0] + '.npy')
pixel_flow = pixel_flow.astype('f2') # 将数据的格式转换成float16类型， 节省空间
# print(saved_image_file_path)
# print(pixel_flow)
np.save(saved_image_file_path, pixel_flow)
return
if __name__ == '__main__':
file_list = glob(file_path + '/*.JPEG')
m = 32
n = int(math.ceil(len(file_list) / float(m))) # 向上取整
result = []
pool = multiprocessing.Pool(processes=m) # 32进程
for i in range(0, len(file_list), n):
result.append(pool.apply_async(generate_flow_field, (file_list[i: i+n],)))
pool.close()
pool.join()
