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detecting-road-features/source/lanetracker/camera.py
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import numpy as np | |
import cv2 | |
import matplotlib.image as mpimg | |
class CameraCalibration(object): | |
""" | |
Prepares camera calibration pipeline based on a set of calibration images. | |
""" | |
def __init__(self, calibration_images, pattern_size=(9, 6), retain_calibration_images=False): | |
""" | |
Initialises camera calibration pipeline based on a set of calibration images. | |
Parameters | |
---------- | |
calibration_images : Calibration images. | |
pattern_size : Shape of the calibration pattern. | |
retain_calibration_images : Flag indicating if we need to preserve calibration images. | |
""" | |
self.camera_matrix = None | |
self.dist_coefficients = None | |
self.calibration_images_success = [] | |
self.calibration_images_error = [] | |
self.calculate_calibration(calibration_images, pattern_size, retain_calibration_images) | |
def __call__(self, image): | |
""" | |
Calibrates an image based on saved settings. | |
Parameters | |
---------- | |
image : Image to calibrate. | |
Returns | |
------- | |
Calibrated image. | |
""" | |
if self.camera_matrix is not None and self.dist_coefficients is not None: | |
return cv2.undistort(image, self.camera_matrix, self.dist_coefficients, None, self.camera_matrix) | |
else: | |
return image | |
def calculate_calibration(self, images, pattern_size, retain_calibration_images): | |
""" | |
Prepares calibration settings. | |
Parameters | |
---------- | |
images : Set of calibration images. | |
pattern_size : Calibration pattern shape. | |
retain_calibration_images : Flag indicating if we need to preserve calibration images. | |
""" | |
# Prepare object points: (0,0,0), (1,0,0), (2,0,0), ... | |
pattern = np.zeros((pattern_size[1] * pattern_size[0], 3), np.float32) | |
pattern[:, :2] = np.mgrid[0:pattern_size[0], 0:pattern_size[1]].T.reshape(-1, 2) | |
pattern_points = [] # 3d points in real world space | |
image_points = [] # 2d points in image plane. | |
image_size = None | |
# Step through the list and search for chessboard corners | |
for i, path in enumerate(images): | |
image = mpimg.imread(path) | |
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY) | |
# Find the chessboard corners | |
found, corners = cv2.findChessboardCorners(gray, pattern_size, None) | |
# If found, add object points and image points | |
if found: | |
pattern_points.append(pattern) | |
image_points.append(corners) | |
image_size = (image.shape[1], image.shape[0]) | |
if retain_calibration_images: | |
cv2.drawChessboardCorners(image, pattern_size, corners, True) | |
self.calibration_images_success.append(image) | |
else: | |
if retain_calibration_images: | |
self.calibration_images_error.append(image) | |
if pattern_points and image_points: | |
_, self.camera_matrix, self.dist_coefficients, _, _ = cv2.calibrateCamera( | |
pattern_points, image_points, image_size, None, None | |
) |