for name in os.listdir(args.test_list):
print('processing {}'.format(name))
path = []
right = os.path.join('data/flow_dataset/ceshi/right', name)
path.append(os.path.join(args.test_list, name))
path.append(right)
imgs = [imageio.imread(img).astype(np.float32) for img in path]
h, w = imgs[0].shape[:2]
res_dict = ts.run(imgs)
flow_12 = res_dict['flows_fw'][0]
flow_21 = res_dict['flows_bw'][0]
flow_12 = resize_flow(flow_12, (h, w)) # [1, 2, H, W]
flow_21 = resize_flow(flow_21, (h, w)) # [1, 2, H, W]
occu_mask1 = 1 - get_occu_mask_bidirection(flow_12,
flow_21) # [1, 1, H, W]
occu_mask2 = 1 - get_occu_mask_bidirection(flow_21, flow_12)
back_occu_mask1 = get_occu_mask_backward(flow_21)
back_occu_mask2 = get_occu_mask_backward(flow_21)
warped_image_12 = flow_warp(torch.from_numpy(
np.transpose(imgs[1], [2, 0, 1])).unsqueeze(0).cuda(),
flow_12,
pad='border')
warped_image_21 = flow_warp(torch.from_numpy(
np.transpose(imgs[0], [2, 0, 1])).unsqueeze(0).cuda(),
flow_21,
pad='border')
'test_shape': args.test_shape,
}
t3 = args.img_list[0].split('_')
t0 = t3[2].split('.')
t = args.img_list[1].split('_')
t1 = t[2].split('.')
ts = TestHelper(cfg)
imgs = [imageio.imread(img).astype(np.float32) for img in args.img_list]
h, w = imgs[0].shape[:2]
flow_12 = ts.run(imgs)['flows_fw'][0]
flow_12 = resize_flow(flow_12, (h, w))
np_flow_12 = flow_12[0].detach().cpu().numpy().transpose([1, 2, 0])
vis_flow = flowpy.flow_to_rgb(np_flow_12)
cv2.imwrite(t0[0] + " " + t1[0] + ".png", vis_flow)
#=flow_warp(im2, flow12, pad='border', mode='bilinear'):
first_image = np.asarray(Image.open(args.img_list[0]))
cv2.imwrite(t0[0] + ".png", first_image)
second_image = np.asarray(Image.open(args.img_list[1]))
np_flow_12[np.isnan(np_flow_12)] = 0
warped_first_image = flowpy.backward_warp(second_image, np_flow_12)
cv2.imwrite("warped_oc" + t1[0] + ".png", warped_first_image)
'reduce_dense': True
},
'pretrained_model': args.model,
'test_shape': args.test_shape,
}
ts = TestHelper(cfg)
if args.show:
imgs = [
imageio.imread(img).astype(np.float32) for img in args.img_list
]
h, w = imgs[0].shape[:2]
flow_12 = ts.run(imgs)['flows_fw'][0]
flow_12 = resize_flow(flow_12, (h, w))
np_flow_12 = flow_12[0].detach().cpu().numpy().transpose([1, 2, 0])
vis_flow = flow_to_image(np_flow_12)
fig = plt.figure()
plt.imshow(vis_flow)
plt.show()
else:
root = args.root + '/{}'.format(args.root_split)
classes = [
d for d in os.listdir(root) if os.path.isdir(os.path.join(root, d))
]
classes.sort()
class_to_idx = {classes[i]: i for i in range(len(classes))}