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b004d6f | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 | import numpy as np
import imageio
import json
import torch
import sys
import os
import configargparse
import ast
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from loaders.utils import Rays
from utils import str2bool, load_args
def calc_psnr(img1, img2):
# Calculate the mean squared error
mse = np.mean((img1 - img2) ** 2)
# Calculate the maximum possible pixel value (for data scaled between 0 and 1)
max_pixel = 1.0
# Calculate the PSNR
psnr_value = 20 * np.log10(max_pixel / np.sqrt(mse))
return psnr_value
def get_rays(img_shape, c2w, K, device):
OPENGL_CAMERA = True
x, y = torch.meshgrid(
torch.arange(img_shape, device=device),
torch.arange(img_shape, device=device),
indexing="xy",
)
x = x.flatten()
y = y.flatten()
c2w = c2w.repeat(img_shape**2, 1, 1)
camera_dirs = torch.nn.functional.pad(
torch.stack(
[
(x - K[0, 2] + 0.5) / K[0, 0],
(y - K[1, 2] + 0.5)
/ K[1, 1]
* (-1.0 if OPENGL_CAMERA else 1.0),
],
dim=-1,
),
(0, 1),
value=(-1.0 if OPENGL_CAMERA else 1.0),
) # [num_rays, 3]
# [n_cams, height, width, 3]
directions = (camera_dirs[:, None, :] * c2w[:, :3, :3]).sum(dim=-1)
origins = torch.broadcast_to(c2w[:, :3, -1], directions.shape)
viewdirs = directions / torch.linalg.norm(
directions, dim=-1, keepdims=True
)
origins = torch.reshape(origins, (img_shape, img_shape, 3))
viewdirs = torch.reshape(viewdirs, (img_shape, img_shape, 3))
rays = Rays(origins=origins, viewdirs=viewdirs)
return rays
def read_json(json_path):
f = open(json_path)
positions = json.load(f)
f.close()
return positions
def generate_video(images, output_path, fps):
# Determine the width and height of the images
writer = imageio.get_writer(output_path, fps=fps)
for image in images:
writer.append_data(image)
writer.close()
def calc_iou(rgb, gt_tran):
intersection = np.minimum(rgb, gt_tran)
union = np.maximum(rgb, gt_tran)
iou = np.sum(intersection) / np.sum(union)
return iou
def load_eval_args():
parser = configargparse.ArgumentParser()
parser.add('-tc', '--test_config',
is_config_file=True,
default="./configs/test/captured/cinema_quantitative.ini",
help='Path to config file.'
)
parser.add_argument(
"--scene",
type=str,
default="cinema",
# choices=[
# # nerf transient
# "lego",
# "chair",
# "drums",
# "ficus",
# "hotdog",
# "bench",
# "boar",
# "benches"
# ],
help="scene to evaluate the models on",
)
parser.add_argument(
"--rep_number",
type=int,
default=30,
)
parser.add_argument(
"--step",
type=int,
default=290000,
)
parser.add_argument(
"--split",
type=str,
default="test",
)
parser.add_argument(
"--test_folder_path",
type=str,
default="test2",
)
parser.add_argument(
"--checkpoint_dir",
type=str,
default="/scratch/ondemand28/anagh/tnerf_release/multiview_transient/results/cinema_two_views_04-18_02:10:32",
)
parser.add_argument(
"--data_folder_path",
type=str,
default="./data",
)
parser.add_argument(
"--irf_path",
type=str,
default="",
help="Path to IRF file (.csv/.npy/.mat/.pt). If empty, fallback to --pulse_path.",
)
parser.add_argument(
"--irf_column",
type=str,
default="irf",
help="CSV column name for IRF values.",
)
parser.add_argument(
"--irf_half_window",
type=int,
default=50,
help="Half window around IRF peak. Set <=0 to disable cropping.",
)
parser.add_argument(
"--no_irf_reverse",
action="store_true",
help="Disable reverse before Conv1d kernel creation.",
)
parser.add_argument(
"--measurement_root",
type=str,
default="",
help="Optional measurement root for captured-ours loader.",
)
parser.add_argument(
"--data_exts",
type=str,
default=".npz,.txt,.pt,.h5,.hdf5",
help="Comma-separated measurement extension lookup order.",
)
parser.add_argument(
"--bin_width_s_loader",
type=float,
default=None,
help="Optional bin width in seconds for shift resampling.",
)
parser.add_argument(
"--img_height_test",
type=int,
default=None,
help="Test image height. If empty, use --img_shape_test.",
)
parser.add_argument(
"--img_width_test",
type=int,
default=None,
help="Test image width. If empty, use --img_shape_test.",
)
parser.add_argument(
"--invalid_mask_path",
type=str,
default="",
help="Optional offset map path for valid-pixel mask.",
)
parser.add_argument(
"--invalid_mask_invalid_gt",
type=float,
default=10.0,
help="Offset threshold: pixels with offset > threshold are invalid.",
)
parser.add_argument(
"--meas_peak_min",
type=float,
default=100.0,
help=(
"Minimum raw histogram peak per pixel to keep it in evaluation metrics. "
"<=0 disables this mask."
),
)
parser.add_argument(
"--scale_int",
type=float,
default=1.0,
help="Fixed scale for intensity normalisation (replaces per-image dynamic max).",
)
args = load_args(eval=True, parser=parser)
return args
num2words = {1: 'one', 2: 'two', 3: 'three', 4: 'four', 5: 'five',
6: 'six', 7: 'seven', 8: 'eight', 9: 'nine', 10: 'ten',
11: 'eleven', 12: 'twelve', 13: 'thirteen', 14: 'fourteen',
15: 'fifteen', 16: 'sixteen', 17: 'seventeen', 18: 'eighteen', 19: 'nineteen'}
if __name__=="__main__":
pass
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