80 lines
2.0 KiB
Python
80 lines
2.0 KiB
Python
import sys
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sys.path.append('core')
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import argparse
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import os
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import cv2
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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 PIL import Image
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from raft import RAFT
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from utils import flow_viz
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from utils.utils import InputPadder
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DEVICE = 'cuda'
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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
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def load_image_list(image_files):
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images = []
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for imfile in sorted(image_files):
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images.append(load_image(imfile))
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images = torch.stack(images, dim=0)
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images = images.to(DEVICE)
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padder = InputPadder(images.shape)
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return padder.pad(images)[0]
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def viz(img, flo):
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img = img[0].permute(1,2,0).cpu().numpy()
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flo = flo[0].permute(1,2,0).cpu().numpy()
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# map flow to rgb image
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flo = flow_viz.flow_to_image(flo)
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img_flo = np.concatenate([img, flo], axis=0)
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cv2.imshow('image', img_flo[:, :, [2,1,0]]/255.0)
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cv2.waitKey()
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def demo(args):
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model = torch.nn.DataParallel(RAFT(args))
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model.load_state_dict(torch.load(args.model))
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model = model.module
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model.to(DEVICE)
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model.eval()
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with torch.no_grad():
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images = glob.glob(os.path.join(args.path, '*.png')) + \
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glob.glob(os.path.join(args.path, '*.jpg'))
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images = load_image_list(images)
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for i in range(images.shape[0]-1):
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image1 = images[i,None]
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image2 = images[i+1,None]
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flow_low, flow_up = model(image1, image2, iters=20, test_mode=True)
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viz(image1, flow_up)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--model', help="restore checkpoint")
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parser.add_argument('--path', help="dataset for evaluation")
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parser.add_argument('--small', action='store_true', help='use small model')
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parser.add_argument('--mixed_precision', action='store_true', help='use mixed precision')
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args = parser.parse_args()
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demo(args)
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