- 常用术语
内参矩阵: Intrinsic Matrix
焦距: Focal Length
主点: Principal Point
径向畸变: Radial Distortion
切向畸变: Tangential Distortion
旋转矩阵: Rotation Matrices
平移向量: Translation Vectors
平均重投影误差: Mean Reprojection Error
重投影误差: Reprojection Errors
重投影点: Reprojected Points
- 相机标定流程
1.从标定板图片或者视频流中读取标定板信息
2.取标定板中的关键点，保存在数组中
3.应用cv2.calibrateCamera()标定函数
4.应用cv2.getOptimalNewCameraMatrix()函数
5.保存摄像机内参信息
from pathlib import Path
import cv2
import numpy as np
from models.utils import VideoStreamer
import os.path as osp
import json
import datetime
class InitConfigs(object):
def dict_to_object(self, **data):
self.__dict__.update(data)
class CalibrateCamera(object):
def __init__(self, inputs):
configs = InitConfigs()
configs.dict_to_object(**inputs)
self.configs = configs
# input:0 resize=[-1] skip=1 image_glob=['*.png','*.jpg','*.jpeg'] max_length:1000000 pattern_size"(11,8)
self.vs = VideoStreamer(configs.input, configs.resize, configs.skip,
configs.image_glob, configs.max_length)
self.calibrate = False
self.image_size = None
self.quit = False
self.source_type = self.check_source_type()
def check_source_type(self):
basedir = self.configs.input
if isinstance(basedir, int) or basedir.isdigit():
print('==> USB webcam input: {}'.format(basedir))
source_type = "webcam"
elif basedir.startswith(('http', 'rtsp')):
print('==> IP camera input: {}'.format(basedir))
source_type = "IPcam"
elif Path(basedir).is_dir():
print('==> Image directory input: {}'.format(basedir))
source_type = "image"
elif Path(basedir).exists():
print('==> Video input: {}'.format(basedir))
source_type = "video"
else:
raise ValueError('VideoStreamer input \"{}\" not recognized.'.format(basedir))
return source_type
def keyboard_response(self):
key = chr(cv2.waitKey(1) & 0xFF)
if key == 'q':
self.vs.cleanup()
self.quit = True
print('Exiting (via q).')
elif key == 'c': # set the current frame as anchor
self.calibrate = True
print("self.calibrate",self.calibrate)
def draw_chessboard_corners(self, frame, corners, color=(0, 255, 0), thickness=1, radius=3):
if len(frame.shape) < 3:
frame = np.expand_dims(frame, axis=2)
frame = np.repeat(frame, 3, axis=2)
for i, corner in enumerate(corners):
cv2.circle(frame, tuple(corner[0].astype(np.int)), radius, color, thickness)
return frame
def get_keypoints(self):
objps, imgps = [], []
if self.source_type is "image":
while True:
self.keyboard_response()
if self.quit:
break
frame, ret = self.vs.next_frame()
if not ret:
print('Finished detection.')
break
objp, imgp, valid = self.detect_keypoints(frame)
out = frame
if valid:
objps.append(objp)
imgps.append(imgp)
out = self.draw_chessboard_corners(frame, imgp)
cv2.imshow('frame', out)
cv2.waitKey(500)
else:
frame, ret = self.vs.next_frame()
assert ret, 'Error when reading the first frame (try different --input?)'
while True:
self.keyboard_response()
if self.quit:
break
frame, ret = self.vs.next_frame()
if not ret:
print('Finished detection.')
break
if self.calibrate:
objp, imgp, valid = self.detect_keypoints(frame)
if valid:
print("==> Chessboard detected, press any key to continue.")
objps.append(objp)
imgps.append(imgp)
out = self.draw_chessboard_corners(frame, imgp)
cv2.imshow('frame', out)
cv2.waitKey(0)
st = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S.jpg")
filename = osp.join(self.configs.output_dir, st)
cv2.imwrite(filename, frame)
else:
print("==> Chessboard detection failed, check your chessboard.")
self.calibrate = False
cv2.imshow('frame', frame)
cv2.destroyAllWindows()
self.vs.cleanup()
# print("objps",objps)
# print("imgps",imgps)
return objps, imgps
def detect_keypoints(self, img):
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
if len(img.shape) > 2:
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
else:
gray = img
self.image_size = gray.shape
ret, corners = cv2.findChessboardCorners(gray, self.configs.pattern_size, None)
objp = None
if ret:
corners = cv2.cornerSubPix(gray, corners, (5, 5), (-1, -1), criteria)
objp = np.zeros((self.configs.pattern_size[0] * self.configs.pattern_size[1], 3), np.float32)
objp[:, :2] = np.mgrid[0:self.configs.pattern_size[1], 0:self.configs.pattern_size[0]].T.reshape(-1, 2)
return objp, corners, ret
def run(self):
objpoints, imgpoints = self.get_keypoints()
print("true")
print(len(objpoints),len(imgpoints))
if len(objpoints) > 1 and len(imgpoints) > 1:
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, self.image_size[::-1], None, None)
if ret:
h, w = self.image_size
cameramtx, _ = cv2.getOptimalNewCameraMatrix(mtx, dist, (w, h), 1, (w, h))
if self.configs.output_dir is not None:
out_name = osp.join(self.configs.output_dir, 'intrinsics2.json')
with open(out_name, 'w') as fp:
json.dump(dict({"camera_matrix": cameramtx.reshape(1, -1).tolist(),
"distortions": dist.reshape(1, -1).tolist()}), fp)
else:
print("==> Not enough corners detected. Please retry the chessboard detection.")
if __name__ == '__main__':
inputs = dict(
{
"input": "output", //输入为标定板图片存放的路径
"output_dir": 'output',
"image_glob": ['*.png', '*.jpg', '*.jpeg'],
"skip": 1,
"max_length": 1000000,
"resize": [-1],
"pattern_size": (11, 8),
}
)
runner = CalibrateCamera(inputs)
runner.run()
class VideoStreamer:
""" Class to help process image streams. Four types of possible inputs:"
1.) USB Webcam.
2.) An IP camera
3.) A directory of images (files in directory matching 'image_glob').
4.) A video file, such as an .mp4 or .avi file.
"""
def __init__(self, basedir, resize, skip, image_glob, max_length=1000000):
self._ip_grabbed = False
self._ip_running = False
self._ip_camera = False
self._ip_image = None
self._ip_index = 0
self.cap = []
self.camera = True
self.video_file = False
self.listing = []
self.resize = resize
self.interp = cv2.INTER_AREA
self.i = 0
self.skip = skip
self.max_length = max_length
if isinstance(basedir, int) or basedir.isdigit():
print('==> Processing USB webcam input: {}'.format(basedir))
self.cap = cv2.VideoCapture(int(basedir))
self.listing = range(0, self.max_length)
elif basedir.startswith(('http', 'rtsp')):
print('==> Processing IP camera input: {}'.format(basedir))
self.cap = cv2.VideoCapture(basedir)
self.start_ip_camera_thread()
self._ip_camera = True
self.listing = range(0, self.max_length)
elif Path(basedir).is_dir():
print('==> Processing image directory input: {}'.format(basedir))
self.listing = list(Path(basedir).glob(image_glob[0]))
for j in range(1, len(image_glob)):
image_path = list(Path(basedir).glob(image_glob[j]))
self.listing = self.listing + image_path
self.listing.sort()
self.listing = self.listing[::self.skip]
self.max_length = np.min([self.max_length, len(self.listing)])
if self.max_length == 0:
raise IOError('No images found (maybe bad \'image_glob\' ?)')
self.listing = self.listing[:self.max_length]
self.camera = False
elif Path(basedir).exists():
print('==> Processing video input: {}'.format(basedir))
self.cap = cv2.VideoCapture(basedir)
self.cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)
num_frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))
self.listing = range(0, num_frames)
self.listing = self.listing[::self.skip]
self.video_file = True
self.max_length = np.min([self.max_length, len(self.listing)])
self.listing = self.listing[:self.max_length]
else:
raise ValueError('VideoStreamer input \"{}\" not recognized.'.format(basedir))
if self.camera and not self.cap.isOpened():
raise IOError('Could not read camera')
- 标定板图片保存在output目录中，最终的摄像机内参信息也保存在该文件夹，文件名为intrinsics.json，目录如下图所示。
