89 lines
4.5 KiB
Python
89 lines
4.5 KiB
Python
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import sys
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sys.path.append('core')
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DEVICE = 'cuda'
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import os
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os.environ['CUDA_VISIBLE_DEVICES'] = '0'
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import argparse
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import glob
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import numpy as np
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import torch
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from tqdm import tqdm
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from pathlib import Path
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from igev_stereo import IGEVStereo
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from utils.utils import InputPadder
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from PIL import Image
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from matplotlib import pyplot as plt
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import os
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import cv2
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def load_image(imfile):
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img = np.array(Image.open(imfile)).astype(np.uint8)
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img = torch.from_numpy(img).permute(2, 0, 1).float()
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return img[None].to(DEVICE)
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def demo(args):
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model = torch.nn.DataParallel(IGEVStereo(args), device_ids=[0])
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model.load_state_dict(torch.load(args.restore_ckpt))
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model = model.module
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model.to(DEVICE)
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model.eval()
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output_directory = Path(args.output_directory)
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output_directory.mkdir(exist_ok=True)
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with torch.no_grad():
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left_images = sorted(glob.glob(args.left_imgs, recursive=True))
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right_images = sorted(glob.glob(args.right_imgs, recursive=True))
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print(f"Found {len(left_images)} images. Saving files to {output_directory}/")
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for (imfile1, imfile2) in tqdm(list(zip(left_images, right_images))):
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image1 = load_image(imfile1)
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image2 = load_image(imfile2)
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padder = InputPadder(image1.shape, divis_by=32)
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image1, image2 = padder.pad(image1, image2)
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disp = model(image1, image2, iters=args.valid_iters, test_mode=True)
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disp = disp.cpu().numpy()
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disp = padder.unpad(disp)
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file_stem = imfile1.split('/')[-2]
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filename = os.path.join(output_directory, f"{file_stem}.png")
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plt.imsave(output_directory / f"{file_stem}.png", disp.squeeze(), cmap='jet')
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# disp = np.round(disp * 256).astype(np.uint16)
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# cv2.imwrite(filename, cv2.applyColorMap(cv2.convertScaleAbs(disp.squeeze(), alpha=0.01),cv2.COLORMAP_JET), [int(cv2.IMWRITE_PNG_COMPRESSION), 0])
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--restore_ckpt', help="restore checkpoint", default='./pretrained_models/sceneflow/sceneflow.pth')
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parser.add_argument('--save_numpy', action='store_true', help='save output as numpy arrays')
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parser.add_argument('-l', '--left_imgs', help="path to all first (left) frames", default="./demo-imgs/*/im0.png")
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parser.add_argument('-r', '--right_imgs', help="path to all second (right) frames", default="./demo-imgs/*/im1.png")
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# parser.add_argument('-l', '--left_imgs', help="path to all first (left) frames", default="/data/Middlebury/trainingH/*/im0.png")
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# parser.add_argument('-r', '--right_imgs', help="path to all second (right) frames", default="/data/Middlebury/trainingH/*/im1.png")
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# parser.add_argument('-l', '--left_imgs', help="path to all first (left) frames", default="/data/ETH3D/two_view_training/*/im0.png")
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# parser.add_argument('-r', '--right_imgs', help="path to all second (right) frames", default="/data/ETH3D/two_view_training/*/im1.png")
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parser.add_argument('--output_directory', help="directory to save output", default="./demo-output/")
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parser.add_argument('--mixed_precision', action='store_true', help='use mixed precision')
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parser.add_argument('--valid_iters', type=int, default=32, help='number of flow-field updates during forward pass')
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# Architecture choices
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parser.add_argument('--hidden_dims', nargs='+', type=int, default=[128]*3, help="hidden state and context dimensions")
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parser.add_argument('--corr_implementation', choices=["reg", "alt", "reg_cuda", "alt_cuda"], default="reg", help="correlation volume implementation")
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parser.add_argument('--shared_backbone', action='store_true', help="use a single backbone for the context and feature encoders")
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parser.add_argument('--corr_levels', type=int, default=2, help="number of levels in the correlation pyramid")
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parser.add_argument('--corr_radius', type=int, default=4, help="width of the correlation pyramid")
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parser.add_argument('--n_downsample', type=int, default=2, help="resolution of the disparity field (1/2^K)")
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parser.add_argument('--slow_fast_gru', action='store_true', help="iterate the low-res GRUs more frequently")
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parser.add_argument('--n_gru_layers', type=int, default=3, help="number of hidden GRU levels")
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parser.add_argument('--max_disp', type=int, default=192, help="max disp of geometry encoding volume")
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args = parser.parse_args()
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Path(args.output_directory).mkdir(exist_ok=True, parents=True)
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demo(args)
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