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| import sys | |
| import os | |
| sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) | |
| ROOT_PATH = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) | |
| import time | |
| import json | |
| import numpy as np | |
| import torch | |
| from torch.utils.data import DataLoader | |
| from lib.options import BaseOptions | |
| from lib.mesh_util import * | |
| from lib.sample_util import * | |
| from lib.train_util import * | |
| from lib.model import * | |
| from PIL import Image | |
| import torchvision.transforms as transforms | |
| import trimesh | |
| from datetime import datetime | |
| # get options | |
| opt = BaseOptions().parse() | |
| class Evaluator: | |
| def __init__(self, opt, projection_mode='orthogonal'): | |
| self.opt = opt | |
| self.load_size = self.opt.loadSize | |
| self.to_tensor = transforms.Compose([ | |
| transforms.Resize(self.load_size), | |
| transforms.ToTensor(), | |
| transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) | |
| ]) | |
| # set cuda | |
| cuda = torch.device('cuda:%d' % opt.gpu_id) if torch.cuda.is_available() else torch.device('cpu') | |
| print("CUDDAAAAA ???", torch.cuda.get_device_name(0) if torch.cuda.is_available() else "NO ONLY CPU") | |
| # create net | |
| netG = HGPIFuNet(opt, projection_mode).to(device=cuda) | |
| print('Using Network: ', netG.name) | |
| if opt.load_netG_checkpoint_path: | |
| netG.load_state_dict(torch.load(opt.load_netG_checkpoint_path, map_location=cuda)) | |
| if opt.load_netC_checkpoint_path is not None: | |
| print('loading for net C ...', opt.load_netC_checkpoint_path) | |
| netC = ResBlkPIFuNet(opt).to(device=cuda) | |
| netC.load_state_dict(torch.load(opt.load_netC_checkpoint_path, map_location=cuda)) | |
| else: | |
| netC = None | |
| os.makedirs(opt.results_path, exist_ok=True) | |
| os.makedirs('%s/%s' % (opt.results_path, opt.name), exist_ok=True) | |
| opt_log = os.path.join(opt.results_path, opt.name, 'opt.txt') | |
| with open(opt_log, 'w') as outfile: | |
| outfile.write(json.dumps(vars(opt), indent=2)) | |
| self.cuda = cuda | |
| self.netG = netG | |
| self.netC = netC | |
| def load_image(self, image_path, mask_path): | |
| # Name | |
| img_name = os.path.splitext(os.path.basename(image_path))[0] | |
| # Calib | |
| B_MIN = np.array([-1, -1, -1]) | |
| B_MAX = np.array([1, 1, 1]) | |
| projection_matrix = np.identity(4) | |
| projection_matrix[1, 1] = -1 | |
| calib = torch.Tensor(projection_matrix).float() | |
| # Mask | |
| mask = Image.open(mask_path).convert('L') | |
| mask = transforms.Resize(self.load_size)(mask) | |
| mask = transforms.ToTensor()(mask).float() | |
| # image | |
| image = Image.open(image_path).convert('RGB') | |
| image = self.to_tensor(image) | |
| image = mask.expand_as(image) * image | |
| return { | |
| 'name': img_name, | |
| 'img': image.unsqueeze(0), | |
| 'calib': calib.unsqueeze(0), | |
| 'mask': mask.unsqueeze(0), | |
| 'b_min': B_MIN, | |
| 'b_max': B_MAX, | |
| } | |
| def eval(self, data, use_octree=False): | |
| ''' | |
| Evaluate a data point | |
| :param data: a dict containing at least ['name'], ['image'], ['calib'], ['b_min'] and ['b_max'] tensors. | |
| :return: | |
| ''' | |
| opt = self.opt | |
| with torch.no_grad(): | |
| self.netG.eval() | |
| if self.netC: | |
| self.netC.eval() | |
| save_path = '%s/%s/result_%s.obj' % (opt.results_path, opt.name, data['name']) | |
| if self.netC: | |
| gen_mesh_color(opt, self.netG, self.netC, self.cuda, data, save_path, use_octree=use_octree) | |
| else: | |
| gen_mesh(opt, self.netG, self.cuda, data, save_path, use_octree=use_octree) | |
| if __name__ == '__main__': | |
| evaluator = Evaluator(opt) | |
| results_path = opt.results_path | |
| name = opt.name | |
| test_image_path = opt.img_path | |
| test_mask_path = test_image_path[:-4] +'_mask.png' | |
| test_img_name = os.path.splitext(os.path.basename(test_image_path))[0] | |
| print("test_image: ", test_image_path) | |
| print("test_mask: ", test_mask_path) | |
| try: | |
| time = datetime.now() | |
| print("evaluating" , time) | |
| data = evaluator.load_image(test_image_path, test_mask_path) | |
| evaluator.eval(data, False) | |
| print("done evaluating" , datetime.now() - time) | |
| except Exception as e: | |
| print("error:", e.args) | |
| try: | |
| mesh = trimesh.load(f'{results_path}/{name}/result_{test_img_name}.obj') | |
| mesh.apply_transform([[1, 0, 0, 0], | |
| [0, 1, 0, 0], | |
| [0, 0, -1, 0], | |
| [0, 0, 0, 1]]) | |
| mesh.export(file_obj=f'{results_path}/{name}/result_{test_img_name}.glb') | |
| except Exception as e: | |
| print("error generating MESH", e) | |