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import cv2
import imageio
import numpy as np
import torch
def load_flow(path):
if path.endswith(".png"):
# for KITTI which uses 16bit PNG images
# see 'https://github.com/ClementPinard/FlowNetPytorch/blob/master/datasets/KITTI.py'
# The -1 is here to specify not to change the image depth (16bit), and is compatible
# with both OpenCV2 and OpenCV3
flo_file = cv2.imread(path, -1)
flo_img = flo_file[:, :, 2:0:-1].astype(np.float32)
invalid = flo_file[:, :, 0] == 0 # mask
flo_img = flo_img - 32768
flo_img = flo_img / 64
flo_img[np.abs(flo_img) < 1e-10] = 1e-10
flo_img[invalid, :] = 0
return flo_img, np.expand_dims(flo_file[:, :, 0], 2)
else:
with open(path, "rb") as f:
magic = np.fromfile(f, np.float32, count=1)
assert 202021.25 == magic, "Magic number incorrect. Invalid .flo file"
h = np.fromfile(f, np.int32, count=1)[0]
w = np.fromfile(f, np.int32, count=1)[0]
data = np.fromfile(f, np.float32, count=2 * w * h)
# Reshape data into 3D array (columns, rows, bands)
data2D = np.resize(data, (w, h, 2))
return data2D
def load_mask(path):
# 0~255 HxWx1
mask = imageio.imread(path).astype(np.float32) / 255.0
if len(mask.shape) == 3:
mask = mask[:, :, 0]
return np.expand_dims(mask, -1)
# def flow_to_image(flow, max_flow=256):
# import numpy as np
# from matplotlib.colors import hsv_to_rgb
# if max_flow is not None:
# max_flow = max(max_flow, 1.0)
# else:
# max_flow = np.max(flow)
# n = 8
# u, v = flow[:, :, 0], flow[:, :, 1]
# mag = np.sqrt(np.square(u) + np.square(v))
# angle = np.arctan2(v, u)
# im_h = np.mod(angle / (2 * np.pi) + 1, 1)
# im_s = np.clip(mag * n / max_flow, a_min=0, a_max=1)
# im_v = np.clip(n - im_s, a_min=0, a_max=1)
# im = hsv_to_rgb(np.stack([im_h, im_s, im_v], 2))
# return (im * 255).astype(np.uint8)
def resize_flow(flow, new_shape):
_, _, h, w = flow.shape
new_h, new_w = new_shape
flow = torch.nn.functional.interpolate(
flow, (new_h, new_w), mode="bilinear", align_corners=True
)
scale_h, scale_w = h / float(new_h), w / float(new_w)
flow[:, 0] /= scale_w
flow[:, 1] /= scale_h
return flow
# credit: https://github.com/princeton-vl/RAFT/blob/master/core/utils/frame_utils.py
def writeFlowSintel(filename, uv, v=None):
"""Write optical flow to file.
If v is None, uv is assumed to contain both u and v channels,
stacked in depth.
Original code by Deqing Sun, adapted from Daniel Scharstein.
"""
nBands = 2
TAG_CHAR = np.array([202021.25], np.float32)
if v is None:
assert uv.ndim == 3
assert uv.shape[2] == 2
u = uv[:, :, 0]
v = uv[:, :, 1]
else:
u = uv
assert u.shape == v.shape
height, width = u.shape
os.makedirs(os.path.dirname(filename), exist_ok=True)
with open(filename, "wb") as f:
# write the header
f.write(TAG_CHAR)
np.array(width).astype(np.int32).tofile(f)
np.array(height).astype(np.int32).tofile(f)
# arrange into matrix form
tmp = np.zeros((height, width * nBands))
tmp[:, np.arange(width) * 2] = u
tmp[:, np.arange(width) * 2 + 1] = v
tmp.astype(np.float32).tofile(f)
# credit: https://github.com/princeton-vl/RAFT/blob/master/core/utils/frame_utils.py
def writeFlowKITTI(filename, uv):
uv = 64.0 * uv + 2**15
valid = np.ones([uv.shape[0], uv.shape[1], 1])
uv = np.concatenate([uv, valid], axis=-1).astype(np.uint16)
cv2.imwrite(filename, uv[..., ::-1])
def evaluate_flow(gt_flows, pred_flows, moving_masks=None):
# credit "undepthflow/eval/evaluate_flow.py"
def calculate_error_rate(epe_map, gt_flow, mask):
bad_pixels = np.logical_and(
epe_map * mask > 3,
epe_map * mask > 0.05 * np.sqrt(np.sum(np.square(gt_flow), axis=2)),
)
return bad_pixels.sum() / mask.sum() * 100.0
(
error,
error_noc,
error_occ,
error_move,
error_static,
error_rate,
error_rate_noc,
) = (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0)
error_move_rate, error_static_rate = 0.0, 0.0
B = len(gt_flows)
for gt_flow, pred_flow, i in zip(gt_flows, pred_flows, range(B)):
H, W = gt_flow.shape[:2]
h, w = pred_flow.shape[:2]
# pred_flow = np.copy(pred_flow)
# pred_flow[:, :, 0] = pred_flow[:, :, 0] / w * W
# pred_flow[:, :, 1] = pred_flow[:, :, 1] / h * H
# flo_pred = cv2.resize(pred_flow, (W, H), interpolation=cv2.INTER_LINEAR)
pred_flow = torch.from_numpy(pred_flow)[None].permute(0, 3, 1, 2)
flo_pred = resize_flow(pred_flow, (H, W))
flo_pred = flo_pred[0].numpy().transpose(1, 2, 0)
epe_map = np.sqrt(
np.sum(np.square(flo_pred[:, :, :2] - gt_flow[:, :, :2]), axis=2)
)
if gt_flow.shape[-1] == 2:
error += np.mean(epe_map)
elif gt_flow.shape[-1] == 4: # with occ and noc mask
error += np.sum(epe_map * gt_flow[:, :, 2]) / np.sum(gt_flow[:, :, 2])
noc_mask = gt_flow[:, :, -1]
error_noc += np.sum(epe_map * noc_mask) / np.sum(noc_mask)
error_occ += np.sum(epe_map * (gt_flow[:, :, 2] - noc_mask)) / max(
np.sum(gt_flow[:, :, 2] - noc_mask), 1.0
)
error_rate += calculate_error_rate(
epe_map, gt_flow[:, :, 0:2], gt_flow[:, :, 2]
)
error_rate_noc += calculate_error_rate(
epe_map, gt_flow[:, :, 0:2], noc_mask
)
if moving_masks is not None:
move_mask = moving_masks[i]
error_move_rate += calculate_error_rate(
epe_map, gt_flow[:, :, 0:2], gt_flow[:, :, 2] * move_mask
)
error_static_rate += calculate_error_rate(
epe_map, gt_flow[:, :, 0:2], gt_flow[:, :, 2] * (1.0 - move_mask)
)
error_move += np.sum(epe_map * gt_flow[:, :, 2] * move_mask) / np.sum(
gt_flow[:, :, 2] * move_mask
)
error_static += np.sum(
epe_map * gt_flow[:, :, 2] * (1.0 - move_mask)
) / np.sum(gt_flow[:, :, 2] * (1.0 - move_mask))
if gt_flows[0].shape[-1] == 4:
res = [
error / B,
error_noc / B,
error_occ / B,
error_rate / B,
error_rate_noc / B,
]
if moving_masks is not None:
res += [error_move / B, error_static / B]
return res
else:
return [error / B]
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