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camera.py
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camera.py
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"""
* This file is part of PYSLAM
*
* Copyright (C) 2016-present Luigi Freda <luigi dot freda at gmail dot com>
*
* PYSLAM is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* PYSLAM is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with PYSLAM. If not, see <http://www.gnu.org/licenses/>.
"""
from enum import Enum
import numpy as np
import cv2
import json
#import g2o
from utils_geom import add_ones
from utils_sys import Printer
class CameraTypes(Enum):
NONE = 0
PINHOLE = 1
class CameraBase:
def __init__(self):
self.type = CameraTypes.NONE
self.width, self.height = None, None
self.fx, self.fy = None, None
self.cx, self.cy = None, None
self.D = None
self.is_distorted = None
self.fps = None
self.bf = None
self.b = None
self.u_min = None
self.u_max = None
self.v_min = None
self.v_max = None
self.initialized = False
class Camera(CameraBase):
def __init__(self, config):
super().__init__()
if config is None:
return
width = config.cam_settings['Camera.width']
height = config.cam_settings['Camera.height']
fx = config.cam_settings['Camera.fx']
fy = config.cam_settings['Camera.fy']
cx = config.cam_settings['Camera.cx']
cy = config.cam_settings['Camera.cy']
D = config.DistCoef # D = [k1, k2, p1, p2, k3]
fps = config.cam_settings['Camera.fps']
self.width = width
self.height = height
self.fx = fx
self.fy = fy
self.cx = cx
self.cy = cy
self.D = np.array(D,dtype=np.float32) # np.array([k1, k2, p1, p2, k3]) distortion coefficients
self.is_distorted = np.linalg.norm(self.D) > 1e-10
self.fps = fps
# If stereo camera => assuming rectified images as input at present (so no need of left-right transformation matrix Tlr)
if 'Camera.bf' in config.cam_settings:
self.bf = config.cam_settings['Camera.bf']
self.b = self.bf/self.fx
if config.sensor_type == 'stereo' and self.bf is None:
raise ValueError('Expecting the field Camera.bf in the camera config file')
self.depth_factor = 1.0 # Deptmap values factor
if 'DepthMapFactor' in config.cam_settings:
self.depth_factor = 1.0/float(config.cam_settings['DepthMapFactor'])
print('Using DepthMapFactor = %f' % self.depth_factor)
if config.sensor_type == 'rgbd' and self.depth_factor is None:
raise ValueError('Expecting the field DepthMapFactor in the camera config file')
self.depth_threshold = None # Close/Far threshold. Baseline times.
if 'ThDepth' in config.cam_settings:
depth_threshold = float(config.cam_settings['ThDepth'])
assert(self.bf is not None)
self.depth_threshold = self.bf * depth_threshold / self.fx
print('Using depth_threshold = %f' % self.depth_threshold)
if (config.sensor_type == 'rgbd' or config.sensor_type == 'stereo') and self.depth_threshold is None:
raise ValueError('Expecting the field ThDepth in the camera config file')
def is_stereo(self):
return self.bf is not None
def to_json(self):
return {'type':int(self.type.value),
'width':int(self.width),
'height':int(self.height),
'fx':float(self.fx),
'fy':float(self.fy),
'cx':float(self.cx),
'cy':float(self.cy),
'D':json.dumps(self.D.astype(float).tolist() if self.D is not None else None),
'fps':int(self.fps),
'bf':float(self.bf),
'b':float(self.b),
'depth_factor':float(self.depth_factor),
'depth_threshold':float(self.depth_threshold),
'is_distorted':bool(self.is_distorted),
'u_min':float(self.u_min),
'u_max':float(self.u_max),
'v_min':float(self.v_min),
'v_max':float(self.v_max),
'initialized':bool(self.initialized)
}
def init_from_json(self, json_str):
self.type = CameraTypes(int(json_str['type']))
self.width = int(json_str['width'])
self.height = int(json_str['height'])
self.fx = float(json_str['fx'])
self.fy = float(json_str['fy'])
self.cx = float(json_str['cx'])
self.cy = float(json_str['cy'])
self.D = np.array(json.loads(json_str['D'])) if json_str['D'] is not None else None
self.fps = int(json_str['fps'])
self.bf = float(json_str['bf'])
self.b = float(json_str['b'])
self.depth_factor = float(json_str['depth_factor'])
self.depth_threshold = float(json_str['depth_threshold'])
self.is_distorted = bool(json_str['is_distorted'])
self.u_min = float(json_str['u_min'])
self.u_max = float(json_str['u_max'])
self.v_min = float(json_str['v_min'])
self.v_max = float(json_str['v_max'])
self.initialized = bool(json_str['initialized'])
class PinholeCamera(Camera):
def __init__(self, config):
super().__init__(config)
self.type = CameraTypes.PINHOLE
if config is None:
return
fx = self.fx
fy = self.fy
cx = self.cx
cy = self.cy
self.K = np.array([[fx, 0,cx],
[ 0,fy,cy],
[ 0, 0, 1]])
self.Kinv = np.array([[1/fx, 0,-cx/fx],
[ 0, 1/fy,-cy/fy],
[ 0, 0, 1]])
#print(f'PinholeCamera: K = {self.K}')
self.u_min, self.u_max = 0, self.width
self.v_min, self.v_max = 0, self.height
self.init()
def to_json(self):
camera_json = super().to_json()
camera_json['K'] = json.dumps(self.K.astype(float).tolist())
camera_json['Kinv'] = json.dumps(self.Kinv.astype(float).tolist())
return camera_json
@staticmethod
def from_json(json_str):
c = PinholeCamera(None)
c.init_from_json(json_str)
c.K = np.array(json.loads(json_str['K']))
c.Kinv = np.array(json.loads(json_str['Kinv']))
return c
def init(self):
if not self.initialized:
self.initialized = True
self.undistort_image_bounds()
# project a 3D point or an array of 3D points (w.r.t. camera frame), of shape [Nx3]
# out: Nx2 image points, [Nx1] array of map point depths
def project(self, xcs):
# u = self.fx * xc[0]/xc[2] + self.cx
# v = self.fy * xc[1]/xc[2] + self.cy
projs = self.K @ xcs.T
zs = projs[-1]
projs = projs[:2]/ zs
return projs.T, zs
# stereo-project a 3D point or an array of 3D points (w.r.t. camera frame), of shape [Nx3]
# (assuming rectified stereo images)
# out: Nx3 image points, [Nx1] array of map point depths
def project_stereo(self, xcs):
# u = self.fx * xc[0]/xc[2] + self.cx
# v = self.fy * xc[1]/xc[2] + self.cy
# ur = u - bf//xc[2]
projs = self.K @ xcs.T
zs = projs[-1]
projs = projs[:2]/ zs
ur = projs[0] - self.bf/zs
projs = np.concatenate((projs.T,ur[:, np.newaxis]),axis=1)
return projs, zs
# unproject 2D point uv (pixels on image plane) on
def unproject(self, uv):
x = (uv[0] - self.cx)/self.fx
y = (uv[1] - self.cy)/self.fy
return x,y
# in: uvs [Nx2]
# out: xcs array [Nx2] of normalized coordinates
def unproject_points(self, uvs):
return np.dot(self.Kinv, add_ones(uvs).T).T[:, 0:2]
# in: uvs [Nx2], depths [Nx1]
# out: xcs array [Nx3] of normalized coordinates
def unproject_points_3d(self, uvs, depths):
return np.dot(self.Kinv, add_ones(uvs).T * depths).T[:, 0:3]
# in: uvs [Nx2]
# out: uvs_undistorted array [Nx2] of undistorted coordinates
def undistort_points(self, uvs):
if self.is_distorted:
#uvs_undistorted = cv2.undistortPoints(np.expand_dims(uvs, axis=1), self.K, self.D, None, self.K) # => Error: while undistorting the points error: (-215:Assertion failed) src.isContinuous()
uvs_contiguous = np.ascontiguousarray(uvs[:, :2]).reshape((uvs.shape[0], 1, 2))
uvs_undistorted = cv2.undistortPoints(uvs_contiguous, self.K, self.D, None, self.K)
return uvs_undistorted.ravel().reshape(uvs_undistorted.shape[0], 2)
else:
return uvs
# update image bounds
def undistort_image_bounds(self):
uv_bounds = np.array([[self.u_min, self.v_min],
[self.u_min, self.v_max],
[self.u_max, self.v_min],
[self.u_max, self.v_max]], dtype=np.float32).reshape(4,2)
#print('uv_bounds: ', uv_bounds)
if self.is_distorted:
uv_bounds_undistorted = cv2.undistortPoints(np.expand_dims(uv_bounds, axis=1), self.K, self.D, None, self.K)
uv_bounds_undistorted = uv_bounds_undistorted.ravel().reshape(uv_bounds_undistorted.shape[0], 2)
else:
uv_bounds_undistorted = uv_bounds
#print('uv_bounds_undistorted: ', uv_bounds_undistorted)
self.u_min = min(uv_bounds_undistorted[0][0],uv_bounds_undistorted[1][0])
self.u_max = max(uv_bounds_undistorted[2][0],uv_bounds_undistorted[3][0])
self.v_min = min(uv_bounds_undistorted[0][1],uv_bounds_undistorted[2][1])
self.v_max = max(uv_bounds_undistorted[1][1],uv_bounds_undistorted[3][1])
# print('camera u_min: ', self.u_min)
# print('camera u_max: ', self.u_max)
# print('camera v_min: ', self.v_min)
# print('camera v_max: ', self.v_max)
def is_in_image(self, uv, z):
return (uv[0] > self.u_min) & (uv[0] < self.u_max) & \
(uv[1] > self.v_min) & (uv[1] < self.v_max) & \
(z > 0)
# input: [Nx2] array of uvs, [Nx1] of zs
# output: [Nx1] array of visibility flags
def are_in_image(self, uvs, zs):
return (uvs[:, 0] > self.u_min) & (uvs[:, 0] < self.u_max) & \
(uvs[:, 1] > self.v_min) & (uvs[:, 1] < self.v_max) & \
(zs > 0 )