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| import os | |
| import sys | |
| import glob | |
| import argparse | |
| import numpy as np | |
| import torch | |
| import torch.nn.functional as F | |
| from torchvision import transforms | |
| from PIL import Image | |
| import utils.utils as utils | |
| def test_samples(args, model, intrins=None, device="cpu"): | |
| img_paths = glob.glob("./samples/img/*.png") + glob.glob("./samples/img/*.jpg") | |
| img_paths.sort() | |
| # normalize | |
| normalize = transforms.Normalize( | |
| mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] | |
| ) | |
| with torch.no_grad(): | |
| for img_path in img_paths: | |
| print(img_path) | |
| ext = os.path.splitext(img_path)[1] | |
| img = Image.open(img_path).convert("RGB") | |
| img = np.array(img).astype(np.float32) / 255.0 | |
| img = torch.from_numpy(img).permute(2, 0, 1).unsqueeze(0).to(device) | |
| _, _, orig_H, orig_W = img.shape | |
| # zero-pad the input image so that both the width and height are multiples of 32 | |
| l, r, t, b = utils.pad_input(orig_H, orig_W) | |
| img = F.pad(img, (l, r, t, b), mode="constant", value=0.0) | |
| img = normalize(img) | |
| intrins_path = img_path.replace(ext, ".txt") | |
| if os.path.exists(intrins_path): | |
| # NOTE: camera intrinsics should be given as a txt file | |
| # it should contain the values of fx, fy, cx, cy | |
| intrins = utils.get_intrins_from_txt( | |
| intrins_path, device=device | |
| ).unsqueeze(0) | |
| else: | |
| # NOTE: if intrins is not given, we just assume that the principal point is at the center | |
| # and that the field-of-view is 60 degrees (feel free to modify this assumption) | |
| intrins = utils.get_intrins_from_fov( | |
| new_fov=60.0, H=orig_H, W=orig_W, device=device | |
| ).unsqueeze(0) | |
| intrins[:, 0, 2] += l | |
| intrins[:, 1, 2] += t | |
| pred_norm = model(img, intrins=intrins)[-1] | |
| pred_norm = pred_norm[:, :, t : t + orig_H, l : l + orig_W] | |
| # save to output folder | |
| # NOTE: by saving the prediction as uint8 png format, you lose a lot of precision | |
| # if you want to use the predicted normals for downstream tasks, we recommend saving them as float32 NPY files | |
| pred_norm_np = ( | |
| pred_norm.cpu().detach().numpy()[0, :, :, :].transpose(1, 2, 0) | |
| ) # (H, W, 3) | |
| pred_norm_np = ((pred_norm_np + 1.0) / 2.0 * 255.0).astype(np.uint8) | |
| target_path = img_path.replace("/img/", "/output/").replace(ext, ".png") | |
| im = Image.fromarray(pred_norm_np) | |
| im.save(target_path) | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--ckpt", default="dsine", type=str, help="model checkpoint") | |
| parser.add_argument("--mode", default="samples", type=str, help="{samples}") | |
| args = parser.parse_args() | |
| # define model | |
| device = torch.device("cpu") | |
| from models.dsine import DSINE | |
| model = DSINE().to(device) | |
| model.pixel_coords = model.pixel_coords.to(device) | |
| model = utils.load_checkpoint("./checkpoints/%s.pt" % args.ckpt, model) | |
| model.eval() | |
| if args.mode == "samples": | |
| test_samples(args, model, intrins=None, device=device) | |