File size: 6,496 Bytes
872b0a0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 | import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
import numpy as np
from flowlib import read_flow_png, flow_to_image
import cv2
import multiprocessing
import functools
def get_scaled_intrinsic_matrix(calib_file, zoom_x, zoom_y):
intrinsics = load_intrinsics_raw(calib_file)
intrinsics = scale_intrinsics(intrinsics, zoom_x, zoom_y)
intrinsics[0, 1] = 0.0
intrinsics[1, 0] = 0.0
intrinsics[2, 0] = 0.0
intrinsics[2, 1] = 0.0
return intrinsics
def load_intrinsics_raw(calib_file):
filedata = read_raw_calib_file(calib_file)
if "P_rect_02" in filedata:
P_rect = filedata['P_rect_02']
else:
P_rect = filedata['P2']
P_rect = np.reshape(P_rect, (3, 4))
intrinsics = P_rect[:3, :3]
return intrinsics
def read_raw_calib_file(filepath):
# From https://github.com/utiasSTARS/pykitti/blob/master/pykitti/utils.py
"""Read in a calibration file and parse into a dictionary."""
data = {}
with open(filepath, 'r') as f:
for line in f.readlines():
key, value = line.split(':', 1)
# The only non-float values in these files are dates, which
# we don't care about anyway
try:
data[key] = np.array([float(x) for x in value.split()])
except ValueError:
pass
return data
def scale_intrinsics(mat, sx, sy):
out = np.copy(mat)
out[0, 0] *= sx
out[0, 2] *= sx
out[1, 1] *= sy
out[1, 2] *= sy
return out
def read_flow_gt_worker(dir_gt, i):
flow_true = read_flow_png(
os.path.join(dir_gt, "flow_occ", str(i).zfill(6) + "_10.png"))
flow_noc_true = read_flow_png(
os.path.join(dir_gt, "flow_noc", str(i).zfill(6) + "_10.png"))
return flow_true, flow_noc_true[:, :, 2]
def load_gt_flow_kitti(gt_dataset_dir, mode):
gt_flows = []
noc_masks = []
if mode == "kitti_2012":
num_gt = 194
dir_gt = gt_dataset_dir
elif mode == "kitti_2015":
num_gt = 200
dir_gt = gt_dataset_dir
else:
num_gt = None
dir_gt = None
raise ValueError('Mode {} not found.'.format(mode))
fun = functools.partial(read_flow_gt_worker, dir_gt)
pool = multiprocessing.Pool(5)
results = pool.imap(fun, range(num_gt), chunksize=10)
pool.close()
pool.join()
for result in results:
gt_flows.append(result[0])
noc_masks.append(result[1])
return gt_flows, noc_masks
def calculate_error_rate(epe_map, gt_flow, mask):
bad_pixels = np.logical_and(
epe_map * mask > 3,
epe_map * mask / np.maximum(
np.sqrt(np.sum(np.square(gt_flow), axis=2)), 1e-10) > 0.05)
return bad_pixels.sum() / mask.sum()
def eval_flow_avg(gt_flows,
noc_masks,
pred_flows,
cfg,
moving_masks=None,
write_img=False):
error, error_noc, error_occ, error_move, error_static, error_rate = 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
error_move_rate, error_static_rate = 0.0, 0.0
num = len(gt_flows)
for gt_flow, noc_mask, pred_flow, i in zip(gt_flows, noc_masks, pred_flows,
range(len(gt_flows))):
H, W = gt_flow.shape[0:2]
pred_flow = np.copy(pred_flow)
pred_flow[:, :, 0] = pred_flow[:, :, 0] / cfg.img_hw[1] * W
pred_flow[:, :, 1] = pred_flow[:, :, 1] / cfg.img_hw[0] * H
flo_pred = cv2.resize(
pred_flow, (W, H), interpolation=cv2.INTER_LINEAR)
if write_img:
if not os.path.exists(os.path.join(cfg.model_dir, "pred_flow")):
os.mkdir(os.path.join(cfg.model_dir, "pred_flow"))
cv2.imwrite(
os.path.join(cfg.model_dir, "pred_flow",
str(i).zfill(6) + "_10.png"),
flow_to_image(flo_pred))
cv2.imwrite(
os.path.join(cfg.model_dir, "pred_flow",
str(i).zfill(6) + "_10_gt.png"),
flow_to_image(gt_flow[:, :, 0:2]))
cv2.imwrite(
os.path.join(cfg.model_dir, "pred_flow",
str(i).zfill(6) + "_10_err.png"),
flow_to_image(
(flo_pred - gt_flow[:, :, 0:2]) * gt_flow[:, :, 2:3]))
epe_map = np.sqrt(
np.sum(np.square(flo_pred[:, :, 0:2] - gt_flow[:, :, 0:2]),
axis=2))
error += np.sum(epe_map * gt_flow[:, :, 2]) / np.sum(gt_flow[:, :, 2])
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])
if moving_masks:
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 moving_masks:
result = "{:>10}, {:>10}, {:>10}, {:>10}, {:>10}, {:>10}, {:>10}, {:>10} \n".format(
'epe', 'epe_noc', 'epe_occ', 'epe_move', 'epe_static',
'move_err_rate', 'static_err_rate', 'err_rate')
result += "{:10.4f}, {:10.4f}, {:10.4f}, {:10.4f}, {:10.4f}, {:10.4f}, {:10.4f}, {:10.4f} \n".format(
error / num, error_noc / num, error_occ / num, error_move / num,
error_static / num, error_move_rate / num, error_static_rate / num,
error_rate / num)
return result
else:
result = "{:>10}, {:>10}, {:>10}, {:>10} \n".format(
'epe', 'epe_noc', 'epe_occ', 'err_rate')
result += "{:10.4f}, {:10.4f}, {:10.4f}, {:10.4f} \n".format(
error / num, error_noc / num, error_occ / num, error_rate / num)
return result
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