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 glob import tqdm # 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') # 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) test_images = glob.glob(os.path.join(opt.test_folder_path, '*')) test_images = [f for f in test_images if ('png' in f or 'jpg' in f) and (not 'mask' in f)] test_masks = [f[:-4]+'_mask.png' for f in test_images] print("num; ", len(test_masks)) for image_path, mask_path in tqdm.tqdm(zip(test_images, test_masks)): try: print(image_path, mask_path) data = evaluator.load_image(image_path, mask_path) evaluator.eval(data, True) except Exception as e: print("error:", e.args)