| | import time |
| | from options.test_options import TestOptions |
| | from data.data_loader_test import CreateDataLoader |
| | from models.networks import ResUnetGenerator, load_checkpoint |
| | from models.afwm import AFWM |
| | import torch.nn as nn |
| | import os |
| | import numpy as np |
| | import torch |
| | import cv2 |
| | import torch.nn.functional as F |
| | from tqdm.auto import tqdm |
| |
|
| | opt = TestOptions().parse() |
| |
|
| | |
| | with open('demo.txt', 'w') as file: |
| | lines = [f'input.png {cloth_img_fn}\n' for cloth_img_fn in os.listdir('dataset/test_clothes')] |
| | file.writelines(lines) |
| |
|
| | data_loader = CreateDataLoader(opt) |
| | dataset = data_loader.load_data() |
| | dataset_size = len(data_loader) |
| | print('[INFO] Data Loaded') |
| |
|
| | warp_model = AFWM(opt, 3) |
| | warp_model.eval() |
| |
|
| | load_checkpoint(warp_model, opt.warp_checkpoint) |
| | print('[INFO] Warp Model Loaded') |
| |
|
| | gen_model = ResUnetGenerator(7, 4, 5, ngf=64, norm_layer=nn.BatchNorm2d) |
| | gen_model.eval() |
| |
|
| | load_checkpoint(gen_model, opt.gen_checkpoint) |
| | print('[INFO] Gen Model Loaded') |
| |
|
| | def get_result_images(): |
| |
|
| | result_images = [] |
| | for i, data in tqdm(enumerate(dataset)): |
| |
|
| | real_image = data['image'] |
| | clothes = data['clothes'] |
| | |
| | edge = data['edge'] |
| | edge = torch.FloatTensor((edge.detach().numpy() > 0.5).astype(np.int)) |
| | clothes = clothes * edge |
| | print(clothes.device, edge.device) |
| |
|
| | flow_out = warp_model(real_image, clothes) |
| | warped_cloth, last_flow, = flow_out |
| | warped_edge = F.grid_sample(edge.cuda(), last_flow.permute(0, 2, 3, 1), |
| | mode='bilinear', padding_mode='zeros') |
| |
|
| |
|
| | gen_inputs = torch.cat([real_image.cuda(), warped_cloth, warped_edge], 1) |
| | gen_outputs = gen_model(gen_inputs) |
| | p_rendered, m_composite = torch.split(gen_outputs, [3, 1], 1) |
| | p_rendered = torch.tanh(p_rendered) |
| | m_composite = torch.sigmoid(m_composite) |
| | m_composite = m_composite * warped_edge |
| | p_tryon = warped_cloth * m_composite + p_rendered * (1 - m_composite) |
| |
|
| | a = real_image.float().cuda() |
| | b= clothes.cuda() |
| | c = p_tryon |
| |
|
| | combine = torch.cat([b[0], c[0]], 2).squeeze() |
| | cv_img = (combine.permute(1, 2, 0).detach().cpu().numpy() + 1) / 2 |
| | rgb = (cv_img * 255).astype(np.uint8) |
| |
|
| | result_images.append(rgb) |
| |
|
| |
|
| | return result_images |
| |
|
| | get_result_images() |
