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pose2sim/Pose2Sim/calibration.py

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initial commit
2023-07-19 17:37:20 +08:00
#!/usr/bin/env python
# -*- coding: utf-8 -*-
'''
###########################################################################
## CAMERAS CALIBRATION ##
###########################################################################
Use this module to calibrate your cameras and save results to a .toml file.
It either converts a Qualisys calibration .qca.txt file,
Or calibrates cameras from checkerboard images.
Checkerboard calibration is based on
https://docs.opencv.org/3.4.15/dc/dbb/tutorial_py_calibration.html.
/!\ Beware that corners must be detected on all frames, or else extrinsic
parameters may be wrong. Set show_corner_detection to 1 to verify.
INPUTS:
- a calibration file in the 'calibration' folder (.qca.txt extension)
- OR folders 'calibration\intrinsics' (populated with video or about 30 images) and 'calibration\extrinsics' (populated with video or one image)
- a Config.toml file in the 'User' folder
OUTPUTS:
- a calibration file in the 'calibration' folder (.toml extension)
'''
## INIT
import os
import logging
import numpy as np
os.environ["OPENCV_LOG_LEVEL"]="FATAL"
import cv2
import glob
import toml
import re
from lxml import etree
import warnings
import matplotlib.pyplot as plt
from mpl_interactions import zoom_factory, panhandler
from Pose2Sim.common import RT_qca2cv, rotate_cam, euclidean_distance, natural_sort
## AUTHORSHIP INFORMATION
__author__ = "David Pagnon"
__copyright__ = "Copyright 2021, Pose2Sim"
__credits__ = ["David Pagnon"]
__license__ = "BSD 3-Clause License"
__version__ = "0.3"
__maintainer__ = "David Pagnon"
__email__ = "contact@david-pagnon.com"
__status__ = "Development"
## FUNCTIONS
def calib_qca_fun(file_to_convert_path, binning_factor=1):
'''
Convert a Qualisys .qca.txt calibration file
Converts from camera view to object view, Pi rotates cameras,
and converts rotation with Rodrigues formula
INPUTS:
- file_to_convert_path: path of the .qca.text file to convert
- binning_factor: when filming in 540p, one out of 2 pixels is binned so that the full frame is used
OUTPUTS:
- ret: residual reprojection error in _mm_: list of floats
- C: camera name: list of strings
- S: image size: list of list of floats
- D: distorsion: list of arrays of floats
- K: intrinsic parameters: list of 3x3 arrays of floats
- R: extrinsic rotation: list of arrays of floats
- T: extrinsic translation: list of arrays of floats
'''
ret, C, S, D, K, R, T = read_qca(file_to_convert_path, binning_factor)
RT = [RT_qca2cv(r,t) for r, t in zip(R, T)]
R = [rt[0] for rt in RT]
T = [rt[1] for rt in RT]
RT = [rotate_cam(r, t, ang_x=np.pi, ang_y=0, ang_z=0) for r, t in zip(R, T)]
R = [rt[0] for rt in RT]
T = [rt[1] for rt in RT]
R = [np.array(cv2.Rodrigues(r)[0]).flatten() for r in R]
T = np.array(T)
return ret, C, S, D, K, R, T
def read_qca(qca_path, binning_factor):
'''
Reads a Qualisys .qca.txt calibration file
Returns 6 lists of size N (N=number of cameras)
INPUTS:
- qca_path: path to .qca.txt calibration file: string
- binning_factor: usually 1: integer
OUTPUTS:
- ret: residual reprojection error in _mm_: list of floats
- C: camera name: list of strings
- S: image size: list of list of floats
- D: distorsion: list of arrays of floats
- K: intrinsic parameters: list of 3x3 arrays of floats
- R: extrinsic rotation: list of 3x3 arrays of floats
- T: extrinsic translation: list of arrays of floats
'''
root = etree.parse(qca_path).getroot()
ret, C, S, D, K, R, T = [], [], [], [], [], [], []
vid_id = []
# Camera name
for i, tag in enumerate(root.findall('cameras/camera')):
ret += [float(tag.attrib.get('avg-residual'))/1000]
C += [tag.attrib.get('serial')]
if tag.attrib.get('model') in ('Miqus Video', 'Miqus Video UnderWater', 'none'):
vid_id += [i]
# Image size
for tag in root.findall('cameras/camera/fov_video'):
w = (float(tag.attrib.get('right')) - float(tag.attrib.get('left')) +1) /binning_factor
h = (float(tag.attrib.get('bottom')) - float(tag.attrib.get('top')) +1) /binning_factor
S += [[w, h]]
# Intrinsic parameters: distorsion and intrinsic matrix
for i, tag in enumerate(root.findall('cameras/camera/intrinsic')):
k1 = float(tag.get('radialDistortion1'))/64/binning_factor
k2 = float(tag.get('radialDistortion2'))/64/binning_factor
p1 = float(tag.get('tangentalDistortion1'))/64/binning_factor
p2 = float(tag.get('tangentalDistortion2'))/64/binning_factor
D+= [np.array([k1, k2, p1, p2])]
fu = float(tag.get('focalLengthU'))/64/binning_factor
fv = float(tag.get('focalLengthV'))/64/binning_factor
cu = float(tag.get('centerPointU'))/64/binning_factor \
- float(root.findall('cameras/camera/fov_video')[i].attrib.get('left'))
cv = float(tag.get('centerPointV'))/64/binning_factor \
- float(root.findall('cameras/camera/fov_video')[i].attrib.get('top'))
K += [np.array([fu, 0., cu, 0., fv, cv, 0., 0., 1.]).reshape(3,3)]
# Extrinsic parameters: rotation matrix and translation vector
for tag in root.findall('cameras/camera/transform'):
tx = float(tag.get('x'))/1000
ty = float(tag.get('y'))/1000
tz = float(tag.get('z'))/1000
r11 = float(tag.get('r11'))
r12 = float(tag.get('r12'))
r13 = float(tag.get('r13'))
r21 = float(tag.get('r21'))
r22 = float(tag.get('r22'))
r23 = float(tag.get('r23'))
r31 = float(tag.get('r31'))
r32 = float(tag.get('r32'))
r33 = float(tag.get('r33'))
# Rotation (by-column to by-line)
R += [np.array([r11, r21, r31, r12, r22, r32, r13, r23, r33]).reshape(3,3)]
T += [np.array([tx, ty, tz])]
# Cameras names by natural order
C_vid = [C[v] for v in vid_id]
C_vid_id = [C_vid.index(c) for c in natural_sort(C_vid)]
C_id = [vid_id[c] for c in C_vid_id]
C = [C[c] for c in C_id]
ret = [ret[c] for c in C_id]
S = [S[c] for c in C_id]
D = [D[c] for c in C_id]
K = [K[c] for c in C_id]
R = [R[c] for c in C_id]
T = [T[c] for c in C_id]
return ret, C, S, D, K, R, T
def calib_optitrack_fun(config):
'''
Convert an Optitrack calibration file
INPUTS:
- a Config.toml file
OUTPUTS:
- ret: residual reprojection error in _mm_: list of floats
- C: camera name: list of strings
- S: image size: list of list of floats
- D: distorsion: list of arrays of floats
- K: intrinsic parameters: list of 3x3 arrays of floats
- R: extrinsic rotation: list of arrays of floats
- T: extrinsic translation: list of arrays of floats
'''
pass
def calib_vicon_fun(config):
'''
Convert a Vicon calibration file
INPUTS:
- a Config.toml file
OUTPUTS:
- ret: residual reprojection error in _mm_: list of floats
- C: camera name: list of strings
- S: image size: list of list of floats
- D: distorsion: list of arrays of floats
- K: intrinsic parameters: list of 3x3 arrays of floats
- R: extrinsic rotation: list of arrays of floats
- T: extrinsic translation: list of arrays of floats
'''
pass
def calib_board_fun(calib_dir, intrinsics_config_dict, extrinsics_config_dict):
'''
Calibrates intrinsic and extrinsic parameters
from images or videos of a checkerboard
or retrieve them from a file
INPUTS:
- calib_dir: directory containing intrinsic and extrinsic folders, each populated with camera directories
- intrinsics_config_dict: dictionary of intrinsics parameters (intrinsics_board_type, overwrite_intrinsics, show_detection_intrinsics, intrinsics_extension, extract_every_N_sec, intrinsics_corners_nb, intrinsics_square_size, intrinsics_marker_size, intrinsics_aruco_dict)
- extrinsics_config_dict: dictionary of extrinsics parameters (extrinsics_board_type, calculate_extrinsics, show_detection_extrinsics, extrinsics_extension, extrinsics_corners_nb, extrinsics_square_size, extrinsics_marker_size, extrinsics_aruco_dict, object_coords_3d)
OUTPUTS:
- ret: residual reprojection error in _px_: list of floats
- C: camera name: list of strings
- S: image size: list of list of floats
- D: distorsion: list of arrays of floats
- K: intrinsic parameters: list of 3x3 arrays of floats
- R: extrinsic rotation: list of arrays of floats (Rodrigues)
- T: extrinsic translation: list of arrays of floats
'''
ret, C, S, D, K, R, T = [], [], [], [], [], [], []
# retrieve intrinsics if calib_file found and if overwrite_intrinsics=False
overwrite_intrinsics = extrinsics_config_dict.get('overwrite_intrinsics')
try:
calib_file = glob.glob(os.path.join(calib_dir, f'Calib*.toml'))[0]
except:
pass
if not overwrite_intrinsics and 'calib_file' in locals():
logging.info(f'Preexisting intrinsics file found: {calib_file}. Retrieving intrinsics.')
calib_file = glob.glob(os.path.join(calib_dir, f'Calib*.toml'))[0]
calib_data = toml.load(calib_file)
K, D = [], []
for cam in calib_data:
if cam != 'metadata':
K += calib_data[cam]['matrix']
D += calib_data[cam]['distortions']
# calculate intrinsics otherwise
else:
logging.info(f'Calculating intrinsics.')
K, D = calibrate_intrinsics(calib_dir, intrinsics_config_dict)
# calculate extrinsics
calculate_extrinsic = extrinsics_config_dict.get('calculate_extrinsics')
if calculate_extrinsic:
R, T = calibrate_extrinsics(calib_dir, extrinsics_config_dict)
def calibrate_intrinsics(calib_dir, intrinsics_config_dict):
'''
Calculate intrinsic parameters
from images or videos of a checkerboard
Extract frames, then detect corners, then calibrate
INPUTS:
- calib_dir: directory containing intrinsic and extrinsic folders, each populated with camera directories
- intrinsics_config_dict: dictionary of intrinsics parameters (intrinsics_board_type, overwrite_intrinsics, show_detection_intrinsics, intrinsics_extension, extract_every_N_sec, intrinsics_corners_nb, intrinsics_square_size, intrinsics_marker_size, intrinsics_aruco_dict)
OUTPUTS:
- D: distorsion: list of arrays of floats
- K: intrinsic parameters: list of 3x3 arrays of floats
'''
try:
intrinsics_cam_listdirs_names = next(os.walk(os.path.join(calib_dir, 'intrinsics')))[1]
except StopIteration:
raise Exception(f'Error: No {os.path.join(calib_dir, "intrinsics")} folder found.')
intrinsics_board_type = intrinsics_config_dict.get('intrinsics_board_type')
intrinsics_extension = intrinsics_config_dict.get('intrinsics_extension')
extract_every_N_sec = intrinsics_config_dict.get('extract_every_N_sec')
overwrite_extraction=False
show_detection_intrinsics = intrinsics_config_dict.get('show_detection_intrinsics')
intrinsics_corners_nb = intrinsics_config_dict.get('intrinsics_corners_nb')
intrinsics_square_size = intrinsics_config_dict.get('intrinsics_square_size')
C, S, D, K, R, T = [], [], [], [], [], []
for i,cam in enumerate(intrinsics_cam_listdirs_names):
# Prepare object points
objp = np.zeros((intrinsics_corners_nb[0]*intrinsics_corners_nb[1],3), np.float32)
objp[:,:2] = np.mgrid[0:intrinsics_corners_nb[0],0:intrinsics_corners_nb[1]].T.reshape(-1,2)
objp[:,:2] = objp[:,0:2]*intrinsics_square_size
objpoints = [] # 3d points in world space
imgpoints = [] # 2d points in image plane
logging.info(f'\nCamera {cam}:')
img_vid_files = glob.glob(os.path.join(calib_dir, 'intrinsics', cam, f'*.{intrinsics_extension}'))
if img_vid_files == []:
raise ValueError(f'The folder {os.path.join(calib_dir, "intrinsics", cam)} does not exist or does not contain any files with extension .{intrinsics_extension}.')
img_vid_files = sorted(img_vid_files, key=lambda c: [int(n) for n in re.findall(r'\d+', c)]) #sorting paths with numbers
# extract frames from video if video
try:
# check if file is a video rather than an image
cap = cv2.VideoCapture(img_vid_files[0])
cap.read()
if cap.read()[0] == False:
raise ValueError('No video in the folder or wrong extension.')
## extract frames from video
extract_frames(img_vid_files[0], extract_every_N_sec, overwrite_extraction)
img_vid_files = glob.glob(os.path.join(calib_dir, 'intrinsics', cam, f'*.png'))
img_vid_files = sorted(img_vid_files, key=lambda c: [int(n) for n in re.findall(r'\d+', c)])
except:
pass
# find corners
for img_path in img_vid_files:
imgp_confirmed, objp_confirmed = findCorners(img_path, intrinsics_corners_nb, show_detection_intrinsics=show_detection_intrinsics, objp=objp)
if isinstance(imgp_confirmed, np.ndarray):
objpoints.append(objp_confirmed)
imgpoints.append(imgp_confirmed)
if len(imgpoints) <= 10:
logging.info(f'Corners were detected only on {len(imgpoints)} images for camera {cam}. Calibration of intrinsic parameters may not be accurate with less than 20 good images of the board.')
# calculate intrinsics
img = cv2.imread(str(img_path))
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img.shape[1::-1],
None, None, flags=(cv2.CALIB_FIX_K3 + cv2.CALIB_FIX_PRINCIPAL_POINT))
h, w = [np.float32(i) for i in img.shape[:-1]]
C.append(f'cam_{str(i+1).zfill(2)}')
S.append([w, h])
D.append(dist[0])
K.append(mtx)
R.append([0,0,0])
T.append([0,0,0])
# Write calibration file with calculated intrinsic parameters and random extrinsicc parameters
calib_file = os.path.join(calib_dir, f'Calib_int{intrinsics_board_type}_extrandom.toml')
toml_write(calib_file, C, S, D, K, R, T)
return K, D
def calibrate_extrinsics(calib_dir, extrinsics_config_dict):
'''
Calibrates extrinsic parameters
from an image or the first frame of a video
of a checkerboard or of measured clues on the scene
INPUTS:
- calib_dir: directory containing intrinsic and extrinsic folders, each populated with camera directories
- extrinsics_config_dict: dictionary of extrinsics parameters (extrinsics_board_type, calculate_extrinsics, show_detection_extrinsics, extrinsics_extension, extrinsics_corners_nb, extrinsics_square_size, extrinsics_marker_size, extrinsics_aruco_dict, object_coords_3d)
OUTPUTS:
- R: extrinsic rotation: list of arrays of floats (Rodrigues)
- T: extrinsic translation: list of arrays of floats
'''
try:
extrinsics_cam_listdirs_names = next(os.walk(os.path.join(calib_dir, 'extrinsics')))[1]
except StopIteration:
raise Exception(f'Error: No {os.path.join(calib_dir, "extrinsics")} folder found.')
extrinsics_extension = extrinsics_config_dict.get('extrinsics_extension')
extrinsics_board_type = extrinsics_config_dict.get('extrinsics_board_type')
extrinsics_corners_nb = extrinsics_config_dict.get('extrinsics_corners_nb')
show_detection_extrinsics = True
for i,cam in enumerate(extrinsics_cam_listdirs_names):
logging.info(f'\nCamera {cam}:')
img_vid_files = glob.glob(os.path.join(calib_dir, 'extrinsics', cam, f'*.{extrinsics_extension}'))
if img_vid_files == []:
raise ValueError(f'The folder {os.path.join(calib_dir, "extrinsics", cam)} does not exist or does not contain any files with extension .{extrinsics_extension}.')
img_vid_files = sorted(img_vid_files, key=lambda c: [int(n) for n in re.findall(r'\d+', c)]) #sorting paths with numbers
if extrinsics_board_type == 'checkerboard':
# find corners
imgp = findCorners(img_vid_files[0], extrinsics_corners_nb, show_detection_extrinsics)
# CHANGE FINDCORNERS: 'O' for okay and next, 'D' for delete, 'C' for click
# DEFINE OBJECT POINTS
# IF CORNERS NOT FOUND: RAISE ERROR: 'set "show_detection_extrinsics" to true to click corners by hand, or change extrinsic_board_type to "scene"'
if isinstance(imgp, np.ndarray):
objpoints.append(objp)
imgpoints.append(imgp)
elif extrinsics_board_type == 'scene':
if len(objp) <= 10:
logging.info(f'Only {len(objp)} reference points for camera {cam}. Calibration of extrinsic parameters may not be accurate with less than 10 refeence points.')
# try:
# intrinsics_cam_listdirs_names = next(os.walk(os.path.join(calib_dir, 'intrinsics')))[1]
# except StopIteration:
# raise Exception(f'Error: No {os.path.join(calib_dir, "intrinsics")} folder found.')
# intrinsics_board_type = intrinsics_config_dict.get('intrinsics_board_type')
# intrinsics_extension = intrinsics_config_dict.get('intrinsics_extension')
# extract_every_N_sec = intrinsics_config_dict.get('extract_every_N_sec')
# overwrite_extraction=False
# show_detection_intrinsics = intrinsics_config_dict.get('show_detection_intrinsics')
# intrinsics_corners_nb = intrinsics_config_dict.get('intrinsics_corners_nb')
# intrinsics_square_size = intrinsics_config_dict.get('intrinsics_square_size')
# C, S, D, K, R, T = [], [], [], [], [], []
# for i,cam in enumerate(intrinsics_cam_listdirs_names):
# # Prepare object points
# objp = np.zeros((intrinsics_corners_nb[0]*intrinsics_corners_nb[1],3), np.float32)
# objp[:,:2] = np.mgrid[0:intrinsics_corners_nb[0],0:intrinsics_corners_nb[1]].T.reshape(-1,2)
# objp[:,:2] = objp[:,0:2]*intrinsics_square_size
# objpoints = [] # 3d points in world space
# imgpoints = [] # 2d points in image plane
# print(f'\nCamera {cam}:')
# img_vid_files = glob.glob(os.path.join(calib_dir, 'intrinsics', cam, f'*.{intrinsics_extension}'))
# if img_vid_files == []:
# raise ValueError(f'The folder {os.path.join(calib_dir, "intrinsics", cam)} does not exist or does not contain any files with extension .{intrinsics_extension}.')
# img_vid_files = sorted(img_vid_files, key=lambda c: [int(n) for n in re.findall(r'\d+', c)]) #sorting paths with numbers
# # extract frames from video if video
# try:
# # check if file is a video rather than an image
# cap = cv2.VideoCapture(img_vid_files[0])
# cap.read()
# if cap.read()[0] == False:
# raise ValueError('No video in the folder or wrong extension.')
# ## extract frames from video
# extract_frames(img_vid_files[0], extract_every_N_sec, overwrite_extraction)
# img_vid_files = glob.glob(os.path.join(calib_dir, 'intrinsics', cam, f'*.png'))
# img_vid_files = sorted(img_vid_files, key=lambda c: [int(n) for n in re.findall(r'\d+', c)])
# except:
# pass
# # find corners
# for img_path in img_vid_files:
# imgp = findCorners(img_path, intrinsics_corners_nb, show_detection_intrinsics)
# if isinstance(imgp, np.ndarray):
# objpoints.append(objp)
# imgpoints.append(imgp)
# if len(imgpoints) <= 10:
# print(f'Corners were detected only on {len(imgpoints)} images for camera {cam}. Calibration of intrinsic parameters may not be accurate with less than 20 good images of the board.')
# # calculate intrinsics
# img = cv2.imread(str(img_path))
# ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img.shape[1::-1],
# None, None, flags=(cv2.CALIB_FIX_K3 + cv2.CALIB_FIX_PRINCIPAL_POINT))
# h, w = [np.float32(i) for i in img.shape[:-1]]
# C.append(f'cam_{str(i+1).zfill(2)}')
# S.append([w, h])
# D.append(dist[0])
# K.append(mtx)
# R.append([0,0,0])
# T.append([0,0,0])
# # Write calibration file with calculated intrinsic parameters and random extrinsicc parameters
# calib_file = os.path.join(calib_dir, f'Calib_int{intrinsics_board_type}_extrandom.toml')
# toml_write(calib_file, C, S, D, K, R, T)
# return K, D
project_dir = config.get('project').get('project_dir')
if project_dir == '': project_dir = os.getcwd()
calib_folder_name = config.get('project').get('calib_folder_name')
calib_dir = os.path.join(project_dir, calib_folder_name)
cam_listdirs_names = next(os.walk(calib_dir))[1]
corners_nb = config.get('calibration').get('checkerboard').get('corners_nb')
square_size = config.get('calibration').get('checkerboard').get('square_size')
square_size = [square_size, square_size] if isinstance(square_size, int)==True else square_size
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001) # stop refining after 30 iterations or if error less than 0.001px
frame_for_origin = config.get('calibration').get('checkerboard').get('frame_for_origin')
show = config.get('calibration').get('checkerboard').get('show_corner_detection')
from_vid_or_img = config.get('calibration').get('checkerboard').get('from_vid_or_img')
vid_snapshot_every_N_frames = config.get('calibration').get('checkerboard').get('vid_snapshot_every_N_frames')
vid_extension = config.get('calibration').get('checkerboard').get('vid_extension')
img_extension = config.get('calibration').get('checkerboard').get('img_extension')
ret, C, S, D, K, R, T = [], [], [], [], [], [], []
for cam in cam_listdirs_names:
print(f'\nCamera {cam}:')
# Prepare object points
objp = np.zeros((corners_nb[0]*corners_nb[1],3), np.float32)
objp[:,:2] = np.mgrid[0:corners_nb[0],0:corners_nb[1]].T.reshape(-1,2)
objp[:,0] = objp[:,0]*square_size[0]
objp[:,1] = objp[:,1]*square_size[1]
objpoints = [] # 3d points in world space
imgpoints = [] # 2d points in image plane
# Find corners in vid
# Try videocapture
if from_vid_or_img=='vid':
video = glob.glob(os.path.join(calib_dir, cam, '*.'+ vid_extension))[0]
cap = cv2.VideoCapture(video)
ret_vid, img = cap.read()
while ret_vid:
count = int(round(cap.get(1)))
ret_vid, img_vid = cap.read()
if count % vid_snapshot_every_N_frames == 0:
img = img_vid
imgp = findCorners(img, corners_nb, criteria, show)
if isinstance(imgp, np.ndarray):
objpoints.append(objp)
imgpoints.append(imgp)
cap.release()
# Find corners in images
elif from_vid_or_img=='img':
images = glob.glob(os.path.join(calib_dir, cam, '*.'+ img_extension))
images_sorted = sorted(images, key=lambda c: [int(n) for n in re.findall(r'\d+', c)]) #sorting paths with numbers
for image_f in images_sorted:
img = cv2.imread(image_f)
imgp = findCorners(img, corners_nb, criteria, show)
if isinstance(imgp, np.ndarray):
objpoints.append(objp)
imgpoints.append(imgp)
# Calibration
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img.shape[1::-1],
None, None, flags=(cv2.CALIB_FIX_K3 + cv2.CALIB_FIX_PRINCIPAL_POINT))
h, w = [np.float32(i) for i in img.shape[:-1]]
print(ret, repr(mtx), repr(dist))
print(w,h)
ret.append(r)
C.append(cam)
S.append([w, h])
D.append(dist[0])
K.append(mtx)
R.append(rvecs[frame_for_origin].squeeze())
T.append(tvecs[frame_for_origin].squeeze())
# Object view to camera view
RT = [rotate_cam(r, t, ang_x=np.pi, ang_y=0, ang_z=0) for r, t in zip(R, T)]
R = [rt[0] for rt in RT]
T = [rt[1] for rt in RT]
R = [np.array(cv2.Rodrigues(r)[0]).flatten() for r in R]
T = np.array(T)/1000
return ret, C, S, D, K, R, T
def extract_frames(video_path, extract_every_N_sec=1, overwrite_extraction=False):
'''
Extract frames if don't exist yet or if overwrite==True
INPUT:
- video_path: path to video whose frames need to be extracted
- extract_every_N_sec: extract one frame every N seconds (can be <1)
- overwrite_extraction: if True, overwrite even if frames have already been extracted
OUTPUT:
- extracted frames in folder
'''
if not os.path.exists(os.path.splitext(video_path)[0] + '_00000.png') or overwrite_extraction:
cap = cv2.VideoCapture(str(video_path))
if cap.isOpened():
fps = round(cap.get(cv2.CAP_PROP_FPS))
frame_nb = 0
logging.info(f'Extracting frames...')
while cap.isOpened():
ret, frame = cap.read()
if ret == True:
if frame_nb % (fps*extract_every_N_sec) == 0:
img_path = (os.path.splitext(video_path)[0] + '_' +str(frame_nb).zfill(5)+'.png')
cv2.imwrite(str(img_path), frame)
frame_nb+=1
else:
break
def findCorners(img_path, corner_nb, show=True, objp=[]):
'''
Find corners in the photo of a checkerboard.
Press 'Y' to accept detection, 'N' to dismiss this image, 'C' to click points by hand.
Left click to add a point, right click to remove the last point.
Use mouse wheel to zoom in and out and to pan.
Make sure that:
- the checkerboard is surrounded by a white border
- rows != lines, and row is even if lines is odd (or conversely)
- it is flat and without reflections
- corner_nb correspond to _internal_ corners
INPUTS:
- img_path: path to image (or video)
- corner_nb: [H, W] internal corners in checkerboard: list of two integers [9,6]
- optionnal: show: choose whether to show corner detections
- optionnal: objp: array [[3d corner coordinates]]
OUTPUTS:
- imgp_confirmed: array of [[2d corner coordinates]]
- only if objp!=[]: objp_confirmed: array of [[3d corner coordinates]]
'''
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001) # stop refining after 30 iterations or if error less than 0.001px
# reads image if image, or first frame if video
try:
img = cv2.imread(img_path)
except:
with warnings.catch_warnings():
warnings.simplefilter("ignore")
cap = cv2.VideoCapture(img_path)
ret, img = cap.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# Find corners
ret, corners = cv2.findChessboardCorners(gray, corner_nb, None)
# If corners are found, refine corners
if ret == True:
imgp = cv2.cornerSubPix(gray, corners, (11,11), (-1,-1), criteria)
logging.info(f'{os.path.basename(img_path)}: Corners found.')
if show:
# Draw corners
cv2.drawChessboardCorners(img, corner_nb, imgp, ret)
# Add corner index
for i, corner in enumerate(imgp):
x, y = corner.ravel()
cv2.putText(img, str(i), (int(x)-5, int(y)-5), cv2.FONT_HERSHEY_SIMPLEX, .5, (255, 255, 255), 2)
cv2.putText(img, str(i), (int(x)-5, int(y)-5), cv2.FONT_HERSHEY_SIMPLEX, .5, (0,0,0), 1)
# Visualizer and key press event handler
for var_to_delete in ['imgp_confirmed', 'objp_confirmed']:
if var_to_delete in globals():
del var_to_delete
imgp_objp_confirmed = imgp_objp_visualizer_clicker(img, imgp=imgp, objp=objp, img_path=img_path)
# If corners are not found, dismiss or click points by hand
else:
logging.info(f'{os.path.basename(img_path)}: Corners not found.')
if show:
# Visualizer and key press event handler
imgp_objp_confirmed = imgp_objp_visualizer_clicker(img, imgp=[], objp=objp, img_path=img_path)
# if len(imgp_objp_confirmed) == 1:
# imgp_confirmed = imgp_objp_confirmed
# objp_confirmed = objp
# else:
# imgp_confirmed, objp_confirmed = imgp_objp_confirmed
return imgp_objp_confirmed
def imgp_objp_visualizer_clicker(img, imgp=[], objp=[], img_path=''):
'''
Shows image img.
If imgp is given, displays them in green
If objp is given, can be displayed in a 3D plot if 'C' is pressed.
If img_path is given, just uses it to name the window
If 'Y' is pressed, closes all and returns confirmed imgp and (if given) objp
If 'N' is pressed, closes all and returns nothing
If 'C' is pressed, allows clicking imgp by hand. If objp is given:
Displays them in 3D as a helper.
Left click to add a point, right click to remove the last point.
Press 'H' to indicate that one of the objp is not visible on image
Closes all and returns imgp and objp if all points have been clicked
Allows for zooming and panning with middle click
INPUTS:
- img: image opened with openCV
- optional: imgp: detected image points, to be accepted or not. Array of [[2d corner coordinates]]
- optionnal: objp: array of [[3d corner coordinates]]
- optional: img_path: path to image
OUTPUTS:
- imgp_confirmed: image points that have been correctly identified. array of [[2d corner coordinates]]
- only if objp!=[]: objp_confirmed: array of [[3d corner coordinates]]
'''
def on_key(event):
'''
Handles key press events:
'Y' to return imgp, 'N' to dismiss image, 'C' to click points by hand.
Left click to add a point, 'H' to indicate it is not visible, right click to remove the last point.
'''
global imgp_confirmed, objp_confirmed, objp_confirmed_notok, scat, ax_3d, fig_3d, events, count
if event.key == 'y':
# If 'y', close all
# If points have been clicked, imgp_confirmed is returned, else imgp
# If objp is given, objp_confirmed is returned in addition
if 'scat' not in globals():
imgp_confirmed = imgp
# close all, del all global variables except imgp_confirmed and objp_confirmed
plt.close('all')
for var_to_delete in ['events', 'count', 'scat', 'fig_3d', 'ax_3d', 'objp_confirmed_notok']:
if var_to_delete in globals():
del var_to_delete
if objp == []:
if 'objp_confirmed' in globals():
del objp_confirmed
if event.key == 'n' or event.key == 'q':
# If 'n', close all and return nothing
plt.close('all')
for var_to_delete in ['events', 'count', 'scat', 'fig_3d', 'ax_3d', 'objp_confirmed_notok', 'imgp_confirmed', 'objp_confirmed']:
if var_to_delete in globals():
del var_to_delete
if event.key == 'c':
# If 'c', allows retrieving imgp_confirmed by clicking them on the image
scat = ax.scatter([],[],marker='+',color='g')
plt.connect('button_press_event', on_click)
# If objp is given, display 3D object points in black
if objp != [] and not plt.fignum_exists(2):
fig_3d = plt.figure()
fig_3d.tight_layout()
fig_3d.canvas.manager.set_window_title('Object points to be clicked')
ax_3d = fig_3d.add_subplot(projection='3d')
for i, (xs,ys,zs) in enumerate(np.float32(objp)):
ax_3d.scatter(xs,ys,zs, marker='.', color='k')
ax_3d.text(xs,ys,zs, f'{str(i+1)}', size=10, zorder=1, color='k')
set_axes_equal(ax_3d)
fig_3d.show()
if event.key == 'h':
# If 'h', indicates that one of the objp is not visible on image
# Displays it in red on 3D plot
if objp != [] and 'ax_3d' in globals():
count = [0 if 'count' not in globals() else count+1][0]
if 'events' not in globals():
# retrieve first objp_confirmed_notok and plot 3D
events = [event]
objp_confirmed_notok = objp[count]
ax_3d.scatter(*objp_confirmed_notok, marker='o', color='r')
fig_3d.canvas.draw()
elif count == len(objp)-1:
# if all objp have been clicked or indicated as not visible, close all
plt.close('all')
for var_to_delete in ['events', 'count', 'scat', 'fig_3d', 'ax_3d', 'objp_confirmed_notok']:
if var_to_delete in globals():
del var_to_delete
else:
# retrieve other objp_confirmed_notok and plot 3D
events.append(event)
objp_confirmed_notok = objp[count]
ax_3d.scatter(*objp_confirmed_notok, marker='o', color='r')
fig_3d.canvas.draw()
else:
pass
def on_click(event):
'''
Detect click position on image
If right click, last point is removed
'''
global imgp_confirmed, objp_confirmed, objp_confirmed_notok, scat, ax_3d, fig_3d, events, count, xydata
# Left click: Add clicked point to imgp_confirmed
# Display it on image and on 3D plot
if event.button == 1:
# To remember the event to cancel after right click
if 'events' in globals():
events.append(event)
else:
events = [event]
# Add clicked point to image
xydata = scat.get_offsets()
new_xydata = np.concatenate((xydata,[[event.xdata,event.ydata]]))
scat.set_offsets(new_xydata)
imgp_confirmed = np.expand_dims(scat.get_offsets(), axis=1)
plt.draw()
# Add clicked point to 3D object points if given
if objp != []:
count = [0 if 'count' not in globals() else count+1][0]
if count==0:
# retrieve objp_confirmed and plot 3D
objp_confirmed = [objp[count]]
ax_3d.scatter(*objp[count], marker='o', color='g')
fig_3d.canvas.draw()
elif count == len(objp)-1:
# retrieve objp_confirmed
objp_confirmed = [[objp[count]] if 'objp_confirmed' not in globals() else objp_confirmed+[objp[count]]][0]
# close all, delete all
plt.close('all')
for var_to_delete in ['events', 'count', 'scat', 'scat_3d', 'fig_3d', 'ax_3d', 'objp_confirmed_notok']:
if var_to_delete in globals():
del var_to_delete
else:
# retrieve objp_confirmed and plot 3D
objp_confirmed = [[objp[count]] if 'objp_confirmed' not in globals() else objp_confirmed+[objp[count]]][0]
ax_3d.scatter(*objp[count], marker='o', color='g')
fig_3d.canvas.draw()
# Right click:
# If last event was left click, remove last point and if objp given, from objp_confirmed
# If last event was 'H' and objp given, remove last point from objp_confirmed_notok
elif event.button == 3: # right click
# If last event was left click:
if 'button' in dir(events[-1]):
if events[-1].button == 1:
# Remove lastpoint from image
new_xydata = scat.get_offsets()[:-1]
scat.set_offsets(new_xydata)
plt.draw()
# Remove last point from imgp_confirmed
imgp_confirmed = imgp_confirmed[:-1]
if objp != []:
if count >= 1: count -= 1
# Remove last point from objp_confirmed
objp_confirmed = objp_confirmed[:-1]
# remove from plot
if len(ax_3d.collections) > len(objp):
ax_3d.collections[-1].remove()
fig_3d.canvas.draw()
# If last event was 'h' key
elif events[-1].key == 'h':
if objp != []:
if count >= 1: count -= 1
# Remove last point from objp_confirmed_notok
objp_confirmed_notok = objp_confirmed_notok[:-1]
# remove from plot
if len(ax_3d.collections) > len(objp):
ax_3d.collections[-1].remove()
fig_3d.canvas.draw()
def set_axes_equal(ax):
"""
Make axes of 3D plot have equal scale so that spheres appear as spheres,
cubes as cubes, etc.
From https://stackoverflow.com/questions/13685386/how-to-set-the-equal-aspect-ratio-for-all-axes-x-y-z
Input
ax: a matplotlib axis, e.g., as output from plt.gca().
"""
x_limits = ax.get_xlim3d()
y_limits = ax.get_ylim3d()
z_limits = ax.get_zlim3d()
x_range = abs(x_limits[1] - x_limits[0])
x_middle = np.mean(x_limits)
y_range = abs(y_limits[1] - y_limits[0])
y_middle = np.mean(y_limits)
z_range = abs(z_limits[1] - z_limits[0])
z_middle = np.mean(z_limits)
# The plot bounding box is a sphere in the sense of the infinity
# norm, hence I call half the max range the plot radius.
plot_radius = 0.5*max([x_range, y_range, z_range])
ax.set_xlim3d([x_middle - plot_radius, x_middle + plot_radius])
ax.set_ylim3d([y_middle - plot_radius, y_middle + plot_radius])
ax.set_zlim3d([z_middle - plot_radius, z_middle + plot_radius])
# Write instructions
cv2.putText(img, 'Type "Y" to accept point detection.', (20, 20), cv2.FONT_HERSHEY_SIMPLEX, .5, (255,255,255), 2, lineType = cv2.LINE_AA)
cv2.putText(img, 'Type "Y" to accept point detection.', (20, 20), cv2.FONT_HERSHEY_SIMPLEX, .5, (0,0,0), 1, lineType = cv2.LINE_AA)
cv2.putText(img, 'If points are wrongfully (or not) detected:', (20, 40), cv2.FONT_HERSHEY_SIMPLEX, .5, (255,255,255), 2, lineType = cv2.LINE_AA)
cv2.putText(img, 'If points are wrongfully (or not) detected:', (20, 40), cv2.FONT_HERSHEY_SIMPLEX, .5, (0,0,0), 1, lineType = cv2.LINE_AA)
cv2.putText(img, '- type "N" to dismiss this image,', (20, 60), cv2.FONT_HERSHEY_SIMPLEX, .5, (255,255,255), 2, lineType = cv2.LINE_AA)
cv2.putText(img, '- type "N" to dismiss this image,', (20, 60), cv2.FONT_HERSHEY_SIMPLEX, .5, (0,0,0), 1, lineType = cv2.LINE_AA)
cv2.putText(img, '- type "C" to click points by hand (beware of their order).', (20, 80), cv2.FONT_HERSHEY_SIMPLEX, .5, (255,255,255), 2, lineType = cv2.LINE_AA)
cv2.putText(img, '- type "C" to click points by hand (beware of their order).', (20, 80), cv2.FONT_HERSHEY_SIMPLEX, .5, (0,0,0), 1, lineType = cv2.LINE_AA)
cv2.putText(img, ' left click to add a point, right click to remove it, "H" to indicate it is not visible. ', (20, 100), cv2.FONT_HERSHEY_SIMPLEX, .5, (255,255,255), 2, lineType = cv2.LINE_AA)
cv2.putText(img, ' left click to add a point, right click to remove it, "H" to indicate it is not visible. ', (20, 100), cv2.FONT_HERSHEY_SIMPLEX, .5, (0,0,0), 1, lineType = cv2.LINE_AA)
cv2.putText(img, ' Confirm with "Y", cancel with "N".', (20, 120), cv2.FONT_HERSHEY_SIMPLEX, .5, (255,255,255), 2, lineType = cv2.LINE_AA)
cv2.putText(img, ' Confirm with "Y", cancel with "N".', (20, 120), cv2.FONT_HERSHEY_SIMPLEX, .5, (0,0,0), 1, lineType = cv2.LINE_AA)
cv2.putText(img, 'Use mouse wheel to zoom in and out and to pan', (20, 140), cv2.FONT_HERSHEY_SIMPLEX, .5, (255,255,255), 2, lineType = cv2.LINE_AA)
cv2.putText(img, 'Use mouse wheel to zoom in and out and to pan', (20, 140), cv2.FONT_HERSHEY_SIMPLEX, .5, (0,0,0), 1, lineType = cv2.LINE_AA)
# Put image in a matplotlib figure for more controls
plt.rcParams['toolbar'] = 'None'
fig, ax = plt.subplots()
fig = plt.gcf()
fig.canvas.manager.set_window_title(os.path.basename(img_path))
ax.axis("off")
for corner in imgp:
x, y = corner.ravel()
cv2.drawMarker(img, (int(x),int(y)), (0,255,0), cv2.MARKER_CROSS, 15, 2)
ax.imshow(img)
figManager = plt.get_current_fig_manager()
figManager.window.showMaximized()
plt.tight_layout()
# Allow for zoom and pan in image
zoom_factory(ax)
ph = panhandler(fig, button=2)
# Handles key presses to Accept, dismiss, or click points by hand
cid = fig.canvas.mpl_connect('key_press_event', on_key)
plt.draw()
plt.show(block=True)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
plt.rcParams['toolbar'] = 'toolmanager'
if 'imgp_confirmed' in globals() and 'objp_confirmed' in globals():
return imgp_confirmed, objp_confirmed
elif 'imgp_confirmed' in globals() and not 'objp_confirmed' in globals():
return imgp_confirmed
else:
return
def calib_points_fun(config):
pass
def toml_write(calib_path, C, S, D, K, R, T):
'''
Writes calibration parameters to a .toml file
INPUTS:
- calib_path: path to the output calibration file: string
- C: camera name: list of strings
- S: image size: list of list of floats
- D: distorsion: list of arrays of floats
- K: intrinsic parameters: list of 3x3 arrays of floats
- R: extrinsic rotation: list of arrays of floats (Rodrigues)
- T: extrinsic translation: list of arrays of floats
OUTPUTS:
- a .toml file cameras calibrations
'''
with open(os.path.join(calib_path), 'w+') as cal_f:
for c in range(len(C)):
cam=f'[cam_{c+1}]\n'
name = f'name = "{C[c]}"\n'
size = f'size = [ {S[c][0]}, {S[c][1]}]\n'
mat = f'matrix = [ [ {K[c][0,0]}, 0.0, {K[c][0,2]}], [ 0.0, {K[c][1,1]}, {K[c][1,2]}], [ 0.0, 0.0, 1.0]]\n'
dist = f'distortions = [ {D[c][0]}, {D[c][1]}, {D[c][2]}, {D[c][3]}]\n'
rot = f'rotation = [ {R[c][0]}, {R[c][1]}, {R[c][2]}]\n'
tran = f'translation = [ {T[c][0]}, {T[c][1]}, {T[c][2]}]\n'
fish = f'fisheye = false\n\n'
cal_f.write(cam + name + size + mat + dist + rot + tran + fish)
meta = '[metadata]\nadjusted = false\nerror = 0.0\n'
cal_f.write(meta)
def recap_calibrate(ret, calib_path, calib_full_type):
'''
Print a log message giving calibration results. Also stored in User/logs.txt.
OUTPUT:
- Message in console
'''
calib = toml.load(calib_path)
ret_m, ret_px = [], []
for c, cam in enumerate(calib.keys()):
if cam != 'metadata':
fm = calib[cam]['matrix'][0][0]
Dm = euclidean_distance(calib[cam]['translation'], [0,0,0])
if calib_full_type=='convert_qualisys':
ret_m.append( np.around(ret[c]*1000, decimals=3) )
ret_px.append( np.around(ret[c] / Dm * fm, decimals=3) )
elif calib_full_type=='calculate_board':
ret_px.append( np.around(ret[c], decimals=3) )
ret_m.append( np.around(ret[c]*1000 * Dm / fm, decimals=3) )
logging.info(f'\n--> Residual (RMS) calibration errors for each camera are respectively {ret_px} px, which corresponds to {ret_m} mm.\n')
logging.info(f'Calibration file is stored at {calib_path}.')
def calibrate_cams_all(config):
'''
Either converts a preexisting calibration file,
or calculates calibration from scratch (from a board or from points).
Stores calibration in a .toml file
Prints recap.
INPUTS:
- a Config.toml file
OUTPUT:
- a .toml camera calibration file
'''
# Read config
project_dir = config.get('project').get('project_dir')
if project_dir == '': project_dir = os.getcwd()
calib_folder_name = config.get('project').get('calib_folder_name')
calib_dir = os.path.join(project_dir, calib_folder_name)
calib_type = config.get('calibration').get('calibration_type')
if calib_type=='convert':
convert_filetype = config.get('calibration').get('convert').get('convert_from')
if convert_filetype=='qualisys':
convert_ext = '.qca.txt'
binning_factor = config.get('calibration').get('convert').get('qualisys').get('binning_factor')
elif convert_filetype=='optitrack':
print('See Readme.md to retrieve calibration values.')
elif convert_filetype=='vicon':
print('Not supported yet')
file_to_convert_path = glob.glob(os.path.join(calib_dir, f'*{convert_ext}*'))[0]
calib_output_path = os.path.join(calib_dir, f'Calib_{convert_filetype}.toml')
calib_full_type = '_'.join([calib_type, convert_filetype])
args_calib_fun = [file_to_convert_path, binning_factor]
elif calib_type=='calculate':
calculate_method = config.get('calibration').get('calculate').get('calculate_method')
if calculate_method=='board':
intrinsics_board_type = config.get('calibration').get('calculate').get('board').get('intrinsics').get('intrinsics_board_type')
intrinsics_config_dict = config.get('calibration').get('calculate').get('board').get('intrinsics')
extrinsics_board_type = config.get('calibration').get('calculate').get('board').get('extrinsics').get('extrinsics_board_type')
extrinsics_config_dict = config.get('calibration').get('calculate').get('board').get('extrinsics')
calib_output_path = os.path.join(calib_dir, f'Calib_int{intrinsics_board_type}_ext{extrinsics_board_type}.toml')
calib_full_type = '_'.join([calib_type, calculate_method])
args_calib_fun = [calib_dir, intrinsics_config_dict, extrinsics_config_dict]
elif calculate_method=='points':
print('Not supported yet')
else:
print('Wrong calculate_method in Config.toml')
else:
print('Wrong calibration_type in Config.toml')
# Map calib function
calib_mapping = {
'convert_qualisys': calib_qca_fun,
'convert_optitrack': calib_optitrack_fun,
'convert_vicon': calib_vicon_fun,
'calculate_board': calib_board_fun,
'calculate_points': calib_points_fun
}
calib_fun = calib_mapping[calib_full_type]
# Calibrate
ret, C, S, D, K, R, T = calib_fun(*args_calib_fun)
# Write calibration file
toml_write(calib_output_path, C, S, D, K, R, T)
# Recap message
recap_calibrate(ret, calib_output_path, calib_full_type)
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