self.stopped = True while not self.queue.empty(): self.queue.get() if __name__ == '__main__': sys.path.append('../preprocessing/') import create_file_lst as create data = Pt_sdf_img( res=256, expr=1.5, listinfo=[["03001627", "ff3581996365bdddc3bd24f986301745"], ["03001627", "ff3581996365bdddc3bd24f986301745"]], info=create.get_all_info(), maxnverts=6000, maxntris=50000, minsurbinvox=4096, num_points=2048, batch_size=2, normalize=False, norm_color=True) batch1 = data.get_batch(0) print(batch1.keys()) print(batch1["verts"].shape) print(batch1["nverts"]) print(batch1["tris"].shape) print(batch1["ntris"]) print(batch1["surfacebinvoxpc"].shape) print(batch1["sdf"].shape)
import h5py import struct BASE_DIR = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) sys.path.append(BASE_DIR) # model sys.path.append(os.path.join(BASE_DIR, 'models')) sys.path.append(os.path.join(BASE_DIR, 'cam_est')) sys.path.append(os.path.join(BASE_DIR, 'data')) sys.path.append(os.path.join(BASE_DIR, 'utils')) sys.path.append(os.path.join(BASE_DIR, 'preprocessing')) import model_normalization as model import model_cam as model_cam from concurrent.futures import ThreadPoolExecutor import create_file_lst import cv2 slim = tf.contrib.slim lst_dir, cats, all_cats, raw_dirs = create_file_lst.get_all_info() parser = argparse.ArgumentParser() parser.add_argument('--gpu', type=str, default='0', help='GPU to use [default: GPU 0]') parser.add_argument('--max_epoch', type=int, default=1, help='Epoch to run [default: 201]') parser.add_argument('--img_h', type=int, default=137, help='Image Height') parser.add_argument('--img_w', type=int, default=137, help='Image Width') parser.add_argument('--batch_size', type=int, default=1, help='Batch Size during training [default: 32]') parser.add_argument('--learning_rate', type=float, default=1e-4, help='Initial learning rate [default: 0.001]') parser.add_argument('--decay_step', type=int, default=200000, help='Decay step for lr decay [default: 200000]') parser.add_argument('--decay_rate', type=float, default=0.9, help='Decay rate for lr decay [default: 0.7]') parser.add_argument('--num_classes', type=int, default=1024, help='vgg dim') parser.add_argument('--num_points', type=int, default=1, help='Point Number [default: 2048]') parser.add_argument('--sdf_res', type=int, default=64, help='sdf grid') parser.add_argument('--alpha', action='store_true') parser.add_argument('--rot', action='store_true')
return self.queue.get() def shutdown(self): self.stopped = True while not self.queue.empty(): self.queue.get() if __name__ == '__main__': sys.path.append('../preprocessing/') import create_file_lst as create data = Pt_sdf_img(res=256, expr=1.5, listinfo=[["03001627", "ff3581996365bdddc3bd24f986301745"], ["03001627", "ff3581996365bdddc3bd24f986301745"]], info=create.get_all_info(), maxnverts=6000, maxntris=50000, minsurbinvox=4096, num_points=2048, batch_size=2, normalize=False, norm_color=True) batch1 = data.get_batch(0) print(batch1.keys()) print(batch1["verts"].shape) print(batch1["nverts"]) print(batch1["tris"].shape) print(batch1["ntris"]) print(batch1["surfacebinvoxpc"].shape) print(batch1["sdf"].shape) print(batch1["sdf_params"]) print(batch1["img"].shape, batch1["img"][0, 64, 64, :]) print(batch1["img_cam"]) # (2048, 3) cloud1 = batch1["surfacebinvoxpc"][0, ...]