# -*-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()

if __name__ == '__main__':

    file_list = glob(file_path + '/*.JPEG')  # 将文件夹下所有的JPEG文件列成一个list

    m = 32  # 假设CPU有32个核心

    n = int(math.ceil(len(file_list) / float(m)))  # 每一个核心需要处理的list的数目

    result = []

    pool = multiprocessing.Pool(processes=m)  # 开32线程的线程池

    for i in range(0, len(file_list), n):

        result.append(pool.apply_async(generate_flow_field, (file_list[i: i+n],)))  # 对每一个list都用上面我们定义的函数进行处理

    pool.close()  # 处理结束之后，关闭线程池

    pool.join()

pool = multiprocessing.Pool(processes=m)  # 开32线程的线程池

result.append(pool.apply_async(generate_flow_field, (file_list[i: i+n],)))  # 对每一个list都用上面我们定义的函数进行处理