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raft_flow.py
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raft_flow.py
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from tqdm import tqdm as tqdm
import os
import cv2
import torch
import kornia
import numpy as np
import utils_func
from utils_func import warp
from flow_viz import flow_to_image
from raft_wrapper.utils.utils import InputPadder, coords_grid, bilinear_sampler
from raft_wrapper.raft import RAFT
class RAFT_Flow:
def __init__(self, raft_args, device):
self.device = device
self.model = torch.nn.DataParallel(RAFT(raft_args))
self.model.load_state_dict(torch.load(raft_args.model))
self.model = self.model.module
self.model.to(self.device)
self.model.eval()
def __call__(self, start, end, root_dir, output_dir,
imgsize, skip=1, zfill_length=5):
save_dir = os.path.join(output_dir, f"flow_{imgsize}_skip{skip}")
count = 0
video = None
os.makedirs(save_dir, exist_ok=True)
for k in tqdm(range(start, end)):
# device
device = torch.device('cuda')
kernel = torch.ones(1,1).to(device)
if (k - start) > skip - 1:
image0 = utils_func.image_loader(
f"{root_dir}/{str(k-skip).zfill(zfill_length)}.jpg", imgsize, device) * 255.
image1 = utils_func.image_loader(
f"{root_dir}/{str(k).zfill(zfill_length)}.jpg", imgsize, device) * 255.
padder = InputPadder(image1.shape)
image0, image1 = padder.pad(image0, image1)
thresh = 1.0
occ_thresh = 0.1 - 0.05 * skip / 32
with torch.no_grad():
_, flow12 = self.model(
image0, image1, iters=24, test_mode=True)
_, flow21 = self.model(
image1, image0, iters=24, test_mode=True)
coords0 = coords_grid(
1, image1.shape[2], image1.shape[3], device)
coords1 = coords0 + flow21
coords2 = coords1 + \
bilinear_sampler(flow12, coords1.permute(0, 2, 3, 1))
err = (coords0 - coords2).norm(dim=1)
occ_flow21 = (
err[0] > thresh).float().unsqueeze(0).unsqueeze(0)
warped_images_next_21, warped_mask_next = warp(
image0, flow21, device)
new_mask_21 = 1.0 - \
((1.0 - warped_mask_next) + occ_flow21).clamp(0, 1)
occ21 = (
(((new_mask_21 *
warped_images_next_21) -
image1) /
255).norm(
dim=1) > occ_thresh).float().unsqueeze(0)
occ21 = kornia.morphology.opening(occ21, kernel)
occ21 = occ_flow21
save_path = os.path.join(
save_dir, f"occ_flow21_forward_{str(k).zfill(zfill_length)}.npy")
np.save(save_path, occ21.detach().cpu().numpy())
coords0 = coords_grid(
1, image0.shape[2], image0.shape[3], device)
coords1 = coords0 + flow12
coords2 = coords1 + \
bilinear_sampler(flow21, coords1.permute(0, 2, 3, 1))
err = (coords0 - coords2).norm(dim=1)
occ_flow12 = (
err[0] > thresh).float().unsqueeze(0).unsqueeze(0)
warped_images_next_12, warped_mask_next = warp(
image1, flow12, device)
new_mask_12 = 1.0 - \
((1.0 - warped_mask_next) + occ_flow12).clamp(0, 1)
occ12 = (
(((new_mask_12 *
warped_images_next_12) -
image0) /
255).norm(
dim=1) > occ_thresh).float().unsqueeze(0)
occ12 = kornia.morphology.opening(occ12, kernel)
occ12 = occ_flow12
save_path = os.path.join(
save_dir, f"occ_flow12_backward_{str(k).zfill(zfill_length)}.npy")
np.save(save_path, occ12.detach().cpu().numpy())
view_all = np.vstack(
[
np.hstack(
[
image0.detach().cpu().numpy()[
0].transpose(1, 2, 0),
image1.detach().cpu().numpy()[
0].transpose(1, 2, 0),
]
),
np.hstack(
[
flow_to_image(flow21.detach().cpu().numpy()[
0].transpose(1, 2, 0)),
flow_to_image(flow12.detach().cpu().numpy()[
0].transpose(1, 2, 0)),
]
),
np.hstack(
[
cv2.cvtColor(occ21.detach().cpu().numpy()[0,0] * 255,cv2.COLOR_GRAY2BGR),
cv2.cvtColor(occ12.detach().cpu().numpy()[0,0] * 255,cv2.COLOR_GRAY2BGR),
]
),
]
).astype(np.uint8)
view_all = cv2.cvtColor(view_all, cv2.COLOR_RGB2BGR)
save_path = os.path.join(
save_dir, f"view_all_{str(k).zfill(zfill_length)}.jpg")
cv2.imwrite(save_path, view_all)
if count == 0:
fps = 5
fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')
video_path = os.path.join(save_dir, "view_all_video.mp4")
h_all, w_all, _ = view_all.shape
video = cv2.VideoWriter(
video_path, fourcc, fps, (w_all, h_all))
save_path = os.path.join(
save_dir, f"flow12_{str(k).zfill(zfill_length)}.npy")
np.save(save_path, flow12.detach().cpu().numpy())
save_path = os.path.join(
save_dir, f"flow21_{str(k).zfill(zfill_length)}.npy")
np.save(save_path, flow21.detach().cpu().numpy())
video.write(view_all)
count += 1
if video is not None:
video.release()