MiddleBury相机参数（calib.txt）
cam0=[1758.23 0 953.34; 0 1758.23 552.29; 0 0 1]
cam1=[1758.23 0 953.34; 0 1758.23 552.29; 0 0 1]
doffs=0
baseline=111.53
width=1920
height=1080
ndisp=290
isint=0
vmin=75
vmax=262
- cam0,1: 相机内参：[f 0 cx; 0 f cy; 0 0 1]
- f: 焦距
- cx, cy: 主点坐标
- doffs: 主点在各自图像坐标系的x坐标差：doffs = cx1 – cx0 (here always == 0)
- baseline: 基线长度（单位：mm）
- width, height:图像尺寸
- ndisp: 视差范围d ∈ [ 0 ， 1 ， . . . ， n d i s p − 1 ] d \in[ 0，1， … ，ndisp-1]d ∈[0 ，1 ，…，n d i s p −1 ]
- vmin, vmax: 在视差边界，用于颜色可视化（立体视觉算法永不上这个值）;
SceneFlow相机参数
相机内参：
大多数的场景模拟焦距为35mm的相机成像，这些视图下的相机内参为：
[ f x 0.0 c x 0.0 f y c y 0.0 0.0 1.0 ] = [ 1050.0 0.0 479.5 0.0 1050.0 269.5 0.0 0.0 1.0 ] \begin{bmatrix} f_x & 0.0 & c_x\ 0.0 & f_y & c_y\ 0.0 & 0.0 &1.0 \end{bmatrix}=\begin{bmatrix} 1050.0 & 0.0 & 479.5\ 0.0 & 1050.0 & 269.5\ 0.0 & 0.0 &1.0 \end{bmatrix}⎣⎡f x 0 .0 0 .0 0 .0 f y 0 .0 c x c y 1 .0 ⎦⎤=⎣⎡1 0 5 0 .0 0 .0 0 .0 0 .0 1 0 5 0 .0 0 .0 4 7 9 .5 2 6 9 .5 1 .0 ⎦⎤
某些场景下模拟焦距为15.0mm的相机城乡，这些场景下的相机内参为：
[ f x 0.0 c x 0.0 f y c y 0.0 0.0 1.0 ] = [ 450.0 0.0 479.5 0.0 450.0 269.5 0.0 0.0 1.0 ] \begin{bmatrix} f_x & 0.0 & c_x\ 0.0 & f_y & c_y\ 0.0 & 0.0 &1.0 \end{bmatrix}=\begin{bmatrix} 450.0 & 0.0 & 479.5\ 0.0 & 450.0 & 269.5\ 0.0 & 0.0 &1.0 \end{bmatrix}⎣⎡f x 0 .0 0 .0 0 .0 f y 0 .0 c x c y 1 .0 ⎦⎤=⎣⎡4 5 0 .0 0 .0 0 .0 0 .0 4 5 0 .0 0 .0 4 7 9 .5 2 6 9 .5 1 .0 ⎦⎤
相机外参(camera_data.txt)：
Frame 1\n
L 0.000180073388037 -4.3711384734e-08 -0.999999880791 47.8838249108 -0.999999880791 -6.77291538409e-08 -0.000180141098099 39.5000647903 -2.96019105055e-15 1.0 -1.11432655103e-07 1.0 0.0 0.0 0.0 1.0\n
R 0.000180073388037 -4.3711384734e-08 -0.999999880791 47.8840049842 -0.999999880791 -6.77291538409e-08 -0.000180141098099 38.5000649095 -2.96019105055e-15 1.0 -1.11432655103e-07 1.0 0.0 0.0 0.0 1.0\n
\n
Frame 2\n
…
4*4的矩阵，按行展开
MiddleBury(SceneFlow)视差图pfm文件
pfm文件由两部分组成：
PFM header（文件头区域）
PF
1080 1920
-0.003922
元数据
- Identifier Line：”Pf” ：单通道灰度图像 ，”PF” ：3通道RGB图像
- Dimensions Line：图像尺寸
- Scale Factor / Endianness： scale
数据集文件读取
from pathlib import Path
import numpy as np
import csv
import re
import cv2
def read_calib(calib_file_path):
with open(calib_file_path, 'r') as calib_file:
calib = {}
csv_reader = csv.reader(calib_file, delimiter='=')
for attr, value in csv_reader:
calib.setdefault(attr, value)
return calib
def read_pfm(pfm_file_path):
with open(pfm_file_path, 'rb') as pfm_file:
header = pfm_file.readline().decode().rstrip()
channels = 3 if header == 'PF' else 1
dim_match = re.match(r'^(\d+)\s(\d+)\s$', pfm_file.readline().decode('utf-8'))
if dim_match:
width, height = map(int, dim_match.groups())
else:
raise Exception("Malformed PFM header.")
scale = float(pfm_file.readline().decode().rstrip())
if scale < 0:
endian = '
scale = -scale
else:
endian = '>'
dispariy = np.fromfile(pfm_file, endian + 'f')
img = np.reshape(dispariy, newshape=(height, width, channels))
img = np.flipud(img).astype('uint8')
show(img, "disparity")
return dispariy, [(height, width, channels), scale]
def create_depth_map(pfm_file_path, calib=None):
dispariy, [shape, scale] = read_pfm(pfm_file_path)
if calib is None:
raise Exception("Loss calibration information.")
else:
fx = float(calib['cam0'].split(' ')[0].lstrip('['))
base_line = float(calib['baseline'])
doffs = float(calib['doffs'])
depth_map = fx * base_line / (dispariy / scale + doffs)
depth_map = np.reshape(depth_map, newshape=shape)
depth_map = np.flipud(depth_map).astype('uint8')
return depth_map
def show(img, win_name='image'):
if img is None:
raise Exception("Can't display an empty image.")
else:
cv2.namedWindow(win_name, cv2.WINDOW_NORMAL)
cv2.imshow(win_name, img)
cv2.waitKey()
cv2.destroyWindow(win_name)
def main():
pfm_file_dir = Path(r'./middlebury2021/data/ladder1')
calib_file_path = pfm_file_dir.joinpath('calib.txt')
disp_left = pfm_file_dir.joinpath('disp0.pfm')
calib = read_calib(calib_file_path)
depth_map_left = create_depth_map(disp_left, calib)
show(depth_map_left, "depth_map")
if __name__ == '__main__':
main()
Original: https://blog.csdn.net/weixin_40957452/article/details/124376988
Author: CV科研随想录
Title: MiddleBury与SceneFlow 数据集相机参数与pfm文件解析
原创文章受到原创版权保护。转载请注明出处：https://www.johngo689.com/533557/
转载文章受原作者版权保护。转载请注明原作者出处！