os x el capitan 10.11.6 color calibration

#Os x el capitan 10.11.6 color calibration windows

This leads to the computation of the lane curvature radius in meters The lane curve coefficients in world coordinates. We can then convert the binary lane marker pixel locations into world coordinates and recompute To estimate a conversion to meters, we use the U.S. Where y, x, A, B, and C are in pixel dimensions. This line is represented as a set of coefficients in the equation y = Ax^2 + Bx + C Once the pixels believed to belong to lane markings are identified, the least squares error polyomial fit for each lane line isĬomputed using NumPy's polyfit() function. An example of window-based search results is below:

A histogram of pixel values along columns is used to initialize Input image with the highest concentrations of non-zero pixels.

#Os x el capitan 10.11.6 color calibration windows

The sliding window search method identifies windows in the The pixels used for this calculation are identified via a window-based searchĪlgorithm implemented in the function estimate_lane_lines(). Using polynomial least squares error fit. Lane Detection: Lane Curve Equation and Curvature Radius EstimationĪfter the binary threshold image is warped above, each pixel in the resulting image must be used to compute lane curve equations To remove noise from the detected lane lines. Following this operation, OpenCV's erode() is used To the binary thresholded image using OpenCV's warpPerspective(). To compute a homography that transforms a forward facing image to a top-down facing image. This function uses a group of manually selected matching point pairs Using compute_forward_to_top_perspective_transform(). The following is an example of binary thresholding:Īfter binary thresholding, the function lane_detection_pipeline() in lane_detection.py computes a homography matrix Pixels which meet the requirements for either threshold are set to 1 The function then filters the Sobel image with two theresholds: (1)Ī gradient value threshold and (2) a color value threshold. Operator to compute the gradient in the x direction. The function first converts the image to grayscale, then applies the Sobel Image in which lane markers appear clearly. In the file lane_detection.py, the function lane_marker_detection() converts an undistorted RGB image into a binary When applied to images from the dashboard camera dataset, distortion correction has a much less noticeable effect: DistortionĬorrection makes straight lines in the scene appear as straight lines in the image: A GoProĬamera with a fisheye lens produces an image of a horizon in which the horizon has an impossible curvature. The figure below shows an exaggerated example of image distortion correction. Used to undistort the input image in the function undistort_image. The camera calibration step contain the distortion coefficients which when used to compose a transformation matrix, are Of camera parameters that minimize the reprojection errors of the chessboard corners. Given thatĮach image now has an associated set of corners, global Levenberg-Marquardt optimization can be used to find a combination Strong gradient at corners in the chessboard, all chessboard corners are reliably identifiable in each image. The method implemented closely follows the camera calibration procedure described inĪ set of points in the world space is first specified to match the 9圆 arrangement of chessboard squares. The functions compute_point_locations() and compute_calibration_matrix() in the file calibrate.py implement camera calibration. This part of the project is included as a submodule of the vehicle_perception project in the camera_calibration directory. Technical Report: Lane Detection Lane Detection: Camera Calibration and Distortion Correction Python main.py -p camera_info.p -vid datasets/test_videos/ -vod output/