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import sys
sys.path.append('core')
import argparse
import os
import cv2
import glob
import numpy as np
import torch
from PIL import Image
from raft import RAFT
from utils import flow_viz
from utils.utils import InputPadder
DEVICE = 'cuda'
def load_image(imfile):
img = np.array(Image.open(imfile)).astype(np.uint8)
img = torch.from_numpy(img).permute(2, 0, 1).float()
return img[None].to(DEVICE)
def viz(img, flo):
img = img[0].permute(1,2,0).cpu().numpy()
flo = flo[0].permute(1,2,0).cpu().numpy()
# map flow to rgb image
flo = flow_viz.flow_to_image(flo)
img_flo = np.concatenate([img, flo], axis=0)
# import matplotlib.pyplot as plt
# plt.imshow(img_flo / 255.0)
# plt.show()
cv2.imshow('image', img_flo[:, :, [2,1,0]]/255.0)
cv2.waitKey()
def demo(args):
model = torch.nn.DataParallel(RAFT(args))
model.load_state_dict(torch.load(args.model))
model = model.module
model.to(DEVICE)
model.eval()
with torch.no_grad():
images = glob.glob(os.path.join(args.path, '*.png')) + \
glob.glob(os.path.join(args.path, '*.jpg'))
images = sorted(images)
for imfile1, imfile2 in zip(images[:-1], images[1:]):
image1 = load_image(imfile1)
image2 = load_image(imfile2)
padder = InputPadder(image1.shape)
image1, image2 = padder.pad(image1, image2)
flow_low, flow_up = model(image1, image2, iters=20, test_mode=True)
viz(image1, flow_up)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--model', help="restore checkpoint")
parser.add_argument('--path', help="dataset for evaluation")
parser.add_argument('--small', action='store_true', help='use small model')
parser.add_argument('--mixed_precision', action='store_true', help='use mixed precision')
parser.add_argument('--alternate_corr', action='store_true', help='use efficent correlation implementation')
args = parser.parse_args()
demo(args) |