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import math

import os

from copy import copy

import matplotlib.pyplot as plt

import numpy as np

import torch

from torch.nn import functional as F

import pdb

def pad(img, pos):

'''

:param img:

:param bk_image:

:return: pasted img

paste 2d img rasterized from mesh onto background imgs

'''

i_h, i_w = img.shape[2:]

h_pad_len = 416 - i_h

w_pad_len = 416 - i_w

paste_imgs = []

# paste

# h_pos = rd.randint(0, h_pad_len)

h_pos = int(h_pad_len * 0.75)

w_pos = int(w_pad_len * 0.5)

# w_pos = rd.randint(0, w_pad_len)

h_top = h_pad_len

h_bottom = 0

w_top = int(pos)

w_bottom = w_pad_len - w_top

# h_top = int((img_size - i_h) / 2) if (img_size - i_h) % 2 == 0 else int((img_size - i_h) / 2) + 1

# h_bottom = int((img_size - i_h) / 2)

# w_top = int((img_size - i_w) / 2) if (img_size - i_h) % 2 == 0 else int((img_size - i_h) / 2) + 1

# w_bottom = int((img_size - i_w) / 2)

# TODO:padiing img

dim = (w_top, w_bottom, h_top, h_bottom)

img = F.pad(img, dim, 'constant', value=0.)

return img

plt.yticks([])

plt.xticks([])

plt.axis('off')

# texture_aug = torch.zeros()

def augment(img, number = 4):

# pdb.set_trace()

img = img.unsqueeze(0)

i_h, i_w = img.shape[2:]

size = torch.linspace(0.4, 1, 5)[0:]

aug_imgs = []

for scale in size:

rots = torch.linspace(-20, 20, number)

# print(rots)

poses = torch.linspace(416, 0, number + 2)

print(poses)

board = torch.zeros(1, 3, 416, 416)

for pi, (rot, pos) in enumerate(zip(rots, poses[2:])):

print(poses[2:])

# print(rot, pos)

image = img.clone()

pallete = torch.zeros(image.shape)

channel = pi % 3

pallete[:, channel, int(i_h * 0.6):, :] = 1.

color_aug = torch.where(image != 0. , pallete, image)

image = torch.where(color_aug != 0., color_aug, image)

image = F.interpolate(image, (int(scale * i_h), int(scale * i_w)), mode='bilinear')

angle = float(rot) * math.pi / 180

theta = torch.tensor([

[math.cos(angle), math.sin(-angle), 0],

[math.sin(angle), math.cos(angle), 0]

], dtype=torch.float)

# blank = torch.ones(target.shape)

grid = F.affine_grid(theta.unsqueeze(0), image.size())

output = F.grid_sample(image, grid)

output = pad(output, pos)

board = torch.where(output == 0., board, output)

plt.imshow(board[0].numpy().transpose(1, 2, 0))

plt.savefig('/home/zhouge/Documents/aug{}.pdf'.format(scale), bbox_inches='tight')

aug_imgs.append(board)

# def logo_augment(logo):

# logo

cur_dir = os.path.dirname(os.path.realpath(__file__))

image_file = os.path.join(cur_dir, 'image/mtl')

# for file_name in os.listdir(image_file):

# file_path = os.path.join(image_file, file_name)

#

# target, logo, syn = torch.load(file_path)

# np_blank = np.ones(target.shape, dtype=np.float32)

# # target = np.where(target == 0., np_blank, target)

# # plt.imshow(target[0].transpose(1,2,0))

# # plt.savefig('/home/zhouge/Documents/target.pdf', bbox_inches = 'tight')

# # logo = np.where(logo == 0., np_blank, logo)

# # plt.imshow(logo[0].transpose(1,2,0))

# # plt.savefig('/home/zhouge/Documents/logo.pdf', bbox_inches = 'tight')

# # transform = transforms.ToTensor()

# # image = transform(image)

# blank = torch.ones(target.shape)

#

# target = torch.from_numpy(target).squeeze(0)

#

# logo = torch.from_numpy(logo).squeeze(0)

# syn = torch.from_numpy(syn).squeeze(0)

# noise = torch.FloatTensor(logo.size()).uniform_(-1, 1)

# target = torch.where(target == 0., blank, target).squeeze(0)

# syn = torch.where(syn == 0., blank, syn).squeeze(0)

# noise_image = torch.add(noise, logo)

# noise_image = torch.where(logo == 0., logo, noise_image).squeeze(0)

# logo = torch.where(logo == 0., blank, logo).squeeze(0)

# noise_image = torch.where(noise_image == 0., blank, noise_image).squeeze(0)

# # plt.imshow(target.numpy().transpose(1,2,0))

# # plt.savefig('/home/zhouge/Documents/target.pdf', bbox_inches = 'tight')

# # plt.imshow(logo.numpy().transpose(1,2,0))

# # plt.savefig('/home/zhouge/Documents/logo.pdf', bbox_inches = 'tight')

# # image = torch.where(logo == 0., target, logo)

#

# # augment(image)

#

# # aug_img = copy(image)

# # aug_img[2, 150:, :] = 1.

# # plt.imshow(aug_img.numpy().transpose(1, 2, 0))

# # plt.savefig('/home/zhouge/Documents/aug_img.pdf', bbox_inches = 'tight')

# # image = torch.where(image == 0., blank, image.unsqueeze(0)).squeeze(0)

# # plt.imshow(image.numpy().transpose(1,2,0))

# # plt.savefig('/home/zhouge/Documents/merge.pdf', bbox_inches = 'tight')

# angle = -30 * math.pi / 180

# theta = torch.tensor([

# [math.cos(angle), math.sin(-angle), 0],

# [math.sin(angle), math.cos(angle), 0]

# ], dtype=torch.float)

# # blank = torch.ones(target.shape)

# grid = F.affine_grid(theta.unsqueeze(0), syn.unsqueeze(0).size())

# output = F.grid_sample(syn.unsqueeze(0), grid)

#

# output = torch.where(output == 0., blank, output)

# new_img_torch = output[0]

# # print(new_img_torch)

# plt.imshow(new_img_torch.numpy().transpose(1, 2, 0))

# plt.savefig('/home/zhouge/Documents/{}_{}.pdf'.format(file_name, 'aug'), bbox_inches='tight')

# plt.imshow(syn.numpy().transpose(1, 2, 0))

# plt.savefig('/home/zhouge/Documents/{}_{}.png'.format(file_name, 'syn'), bbox_inches='tight')

# plt.imshow(target.numpy().transpose(1, 2, 0))

# plt.savefig('/home/zhouge/Documents/{}_{}.png'.format(file_name, 'target'), bbox_inches='tight')

# plt.imshow(logo.numpy().transpose(1, 2, 0))

# plt.savefig('/home/zhouge/Documents/{}_{}.png'.format(file_name, 'logo'), bbox_inches='tight')

# plt.imshow(noise_image.numpy().transpose(1, 2, 0))

# plt.savefig('/home/zhouge/Documents/{}_{}.png'.format(file_name, 'noise_logo'), bbox_inches='tight')

# # plt.savefig('/home/zhouge/Documents/aug_img.jpg', bbox_inches='tight')

# # plt.show()

img_dir = '/home/zhouge/Downloads/park_adv/selection'

def image_process(imgdir):

import cv2

count = 0

# for i in range(1, 3 + 1):

# for j in range(2, 6 + 1):

# count += 1

# file_name = '{}_{}.png'.format(j, i)

# print(file_name)

# print(i * j - i + j)

# file_path = os.path.join(imgdir, file_name)

# img = cv2.imread(file_path)

# # plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))

# # plt.savefig(img_dir + '/{}_{}'.format(j, i) + '.pdf', bbox_inches='tight')

# plt.subplot(3, 5, count)

# plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))

# plt.axis('off')

# plt.subplots_adjust(left=0.125, right=0.9, bottom=0.1, top=0.9, wspace=0.2, hspace=0.2)

# plt.savefig(img_dir + '/mtl.pdf', bbox_inches='tight')

for file in os.listdir(imgdir):

filename = file.split('.')[0]

file_path = os.path.join(imgdir, file)

# print(file_path)

# file_name = '{}_{}.png'.format(j, i)

# print(file_name)

# print(i * j - i + j)

# file_path = os.path.join(imgdir, file_name)

img = cv2.imread(file_path)

plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))

plt.savefig(imgdir + '/' + filename + '.pdf', bbox_inches='tight')

# image_process(img_dir)

mtl_dir = '/home/zhouge/Documents/3dmesh'

def prepare_mtl(datadir):

import numpy as np

import cv2

mtldir = datadir + '/mtl'

count = 0

# for i in range(1, 3 + 1):

# for j in range(2, 6 + 1):

# count += 1

# file_name = '{}_{}.png'.format(j, i)

# print(file_name)

# print(i * j - i + j)

# file_path = os.path.join(imgdir, file_name)

# img = cv2.imread(file_path)

# # plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))

# # plt.savefig(img_dir + '/{}_{}'.format(j, i) + '.pdf', bbox_inches='tight')

# plt.subplot(3, 5, count)

# plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))

# plt.axis('off')

# plt.subplots_adjust(left=0.125, right=0.9, bottom=0.1, top=0.9, wspace=0.2, hspace=0.2)

# plt.savefig(img_dir + '/mtl.pdf', bbox_inches='tight')

blank = np.ones((256, 256, 3), dtype=np.uint8) * 255

for file in os.listdir(mtldir):

filename = file.split('.')[0]

file_path = os.path.join(mtldir, file)

print(file_path)

# file_name = '{}_{}.png'.format(j, i)

# print(file_name)

# print(i * j - i + j)

# file_path = os.path.join(imgdir, file_name)

img = cv2.imread(file_path)

print(img.dtype)

img = np.where(img == 0, blank, img)

plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))

plt.savefig(datadir + '/pdf/' + filename + '.png', bbox_inches='tight')

prepare_mtl(mtl_dir)