Spaces:
Running
Running
File size: 4,546 Bytes
499e141 |
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 |
#!/usr/bin/env python
"""
Copyright 2017, Zixin Luo, HKUST.
IO tools.
"""
from __future__ import print_function
import os
import re
import cv2
import numpy as np
from struct import unpack
def get_pose(R, t):
T = np.zeros((4, 4), dtype=R.dtype)
T[:3,:3] = R
T[:3,3:] = t
T[ 3, 3] = 1
return T
def load_pfm(pfm_path):
with open(pfm_path, 'rb') as fin:
color = None
width = None
height = None
scale = None
data_type = None
header = str(fin.readline().decode('UTF-8')).rstrip()
if header == 'PF':
color = True
elif header == 'Pf':
color = False
else:
raise Exception('Not a PFM file.')
dim_match = re.match(r'^(\d+)\s(\d+)\s$', fin.readline().decode('UTF-8'))
if dim_match:
width, height = map(int, dim_match.groups())
else:
raise Exception('Malformed PFM header.')
scale = float((fin.readline().decode('UTF-8')).rstrip())
if scale < 0: # little-endian
data_type = '<f'
else:
data_type = '>f' # big-endian
data_string = fin.read()
data = np.frombuffer(data_string, data_type)
shape = (height, width, 3) if color else (height, width)
data = np.reshape(data, shape)
data = np.flip(data, 0)
return data
def read_kpt(file_path):
"""Read the keypoint file.
Args:
file path: file path.
Returns:
kpt_data: keypoint data of Nx6 numpy array.
"""
kpt_data = np.fromfile(file_path, dtype=np.float32)
kpt_data = np.reshape(kpt_data, (-1, 6))
return kpt_data
def read_cams(cam_path):
"""
Args:
cam_path: Path to cameras.txt.
Returns:
cam_dict: A dictionary indexed by image index and composed of (K, t, R, dist, img_size).
K - 2x3, t - 3x1, R - 3x3, dist - 1x3, img_size - 1x2.
"""
cam_data = [i.split(' ') for i in read_list(cam_path)]
cam_dict = {}
for i in cam_data:
i = [float(j) for j in i if j != '']
K = np.array([(i[1], i[5], i[3]),
(0, i[2], i[4]), (0, 0, 1)])
t = np.array([(i[6], ), (i[7], ), (i[8], )])
R = np.array([(i[9], i[10], i[11]),
(i[12], i[13], i[14]),
(i[15], i[16], i[17])])
dist = np.array([i[18], i[19], i[20]])
img_size = np.array([i[21], i[22]])
cam_dict[i[0]]= (K, t, R, dist, img_size)
return cam_dict
def read_corr(file_path):
"""Read the match correspondence file.
Args:
file_path: file path.
Returns:
matches: list of match data, each consists of two image indices and Nx15 match matrix, of
which each line consists of two 2x3 transformations, geometric distance and two feature
indices.
"""
matches = []
with open(file_path, 'rb') as fin:
while True:
rin = fin.read(24)
if len(rin) == 0:
# EOF
break
idx0, idx1, num = unpack('L' * 3, rin)
bytes_theta = num * 60
corr = np.frombuffer(fin.read(bytes_theta), dtype=np.float32).reshape(-1, 15)
matches.append([idx0, idx1, corr])
return matches
def read_list(list_path):
"""Read list."""
if list_path is None or not os.path.exists(list_path):
print('Not exist', list_path)
exit(-1)
content= open(list_path).read().splitlines()
return content
def hash_int_pair(ind1, ind2):
"""Hash an int pair.
Args:
ind1: int1.
ind2: int2.
Returns:
hash_index: the hash index.
"""
assert ind1 <= ind2
return ind1 * 2147483647 + ind2
def read_mask(file_path, size=32):
"""Read the mask file.
Args:
file_path: file path.
size: mask size.
Returns:
mask_dict: mask data in dictionary, indexed by hashed pair index.
"""
mask_dict = {}
size = size * size * 2
record_size = 8 + size
with open(file_path, 'rb') as fin:
data = fin.read()
for i in range(0, len(data), record_size):
decoded = unpack('2i' + '?' * size, data[i: i + record_size])
mask = np.array(decoded[2:])
mask_dict[hash_int_pair(decoded[0], decoded[1])] = mask
return mask_dict
def resize_depth(depth, height, width):
H, W = depth.shape
if H < height or W < width:
depth = cv2.resize(depth, (height, width), interpolation=cv2.INTER_NEAREST)
return depth
|