Udacity Selft-Driving Car Engineer Nano Degree Project 2

ChengZhongShen, updated 🕥 2022-12-08 02:56:27

Advanced Lane Finding Project

Note: this is Udacity Nano Degree project, please refer to Udacity Repository for the project information and requirements.

The goals / steps of this project are the following:

Compute the camera calibration matrix and distortion coefficients given a set of chessboard images.
Apply a distortion correction to raw images.
Use color transforms, gradients, etc., to create a thresholded binary image.
Apply a perspective transform to rectify binary image ("birds-eye view").
Detect lane pixels and fit to find the lane boundary.
Determine the curvature of the lane and vehicle position with respect to center.
Warp the detected lane boundaries back onto the original image.
Output visual display of the lane boundaries and numerical estimation of lane curvature and vehicle position.

Camera Calibration

1. use the opencv `findChessboardCorners()` and `calibrateCamera()` funtion to implement the camera calibration.

The code for this step in the camera_calibration.py which located in camera_cal folder.
run the camera_calibration.py to process the calibration.

In the function calibriate(), the process as below: * prepare the objp[], which is same matrix as chessboard X6 * create list objepiont[] and imgpionts[] to hold the 3D and 2D pioints. * go through all the calibration image to search for conerner. use opencv findChessboardCorners function. * use opencv calibratteCamera() to get mtx and dist * write the calibration paramter to a pickle file camera_cal.p

To test if the pickle file which contain the 'mtx' and 'dist' work. use the test() function to test and visualize the result. * read the pickle and load the mtx and dist * read test image * use cv2.undistort() to undistort the test image. the result as below

undistort_image

Pipeline (single image)

pipeline.py include the class Pipeline, the function pipeline() which used to handle the image. You could run "pipeline.py" to see the pipeline effect.

The process described below:

1. distortion-corrected image.

use the paremeter from pickle file camera_cal.p (this is done by the function: get_camera_cal()) use the cv2.undistort to get the undistort image,the image showed below(resized to 640X360 to fit the file.): undistort_image

2. Threshold the image.

All the image process code is in the image_process.py in the pipeline it use the funtion import from image_process.py s_thresh=(170,255) sx_thresh=(20, 100) image_threshed = color_grid_thresh(image_undist, s_thresh=s_thresh, sx_thresh=sx_thresh) This is a combination of color and gradient thresholds to generate a binary image. in color: it use a HLS's s channel, for gradient, use x direction gradient. the detialed code is in the image_process.py, function color_grid_thresh, after apply the threshold, the image as below.

alt text

3. Perspective transform

To implement perspective tranform, first need get the tranform parameter M and Minv. This is done by the view_perspective.py, just like camera_cal, get the parameter then write into a pickle file for further use. You could run "view_perspective.py" to see the transform.

The view transform use manully adjust the 4 source piont. after serveral times adjust, the 4 poinst as below.

python src = np.float32( [[(img_size[0] / 2) - 63, img_size[1] / 2 + 100], [((img_size[0] / 6) - 20), img_size[1]], [(img_size[0] * 5 / 6) + 60, img_size[1]], [(img_size[0] / 2 + 65), img_size[1] / 2 + 100]]) dst = np.float32( [[(img_size[0] / 4), 0], [(img_size[0] / 4), img_size[1]], [(img_size[0] * 3 / 4), img_size[1]], [(img_size[0] * 3 / 4), 0]]) alt text

The wraped binary image as below

warpped_binary

4. Lane-line pixels detection and fit their positions with a polynomial

the lane_detection.py implement the lane-line pixels detection. the function find_lane_pixels() use a slide window to find the lane-line pixels. after get the lane_pixels, use np.polyfit() to get polynomial paratmeters, this is done in get_polynomial in the example, it like below. [A B C] python [ 1.42425935e-04 -3.09709625e-01 5.13026355e+02] [ 1.96100345e-04 -2.96906479e-01 1.12235500e+03]

To visualize the search result and fit polynomial, use fit_polynomial() function. the visualized result as below. show the search window/lane pixels/fit polynimial.

alt text

5. Calculate the radius of curvature of the lane and the position of the vehicle with respect to center.

The cacualtion is done in the cal_curv.py, the function measure_curv() used to calculate radius.

Firstly, transform the lane piont from pixels to meters, ```python

Transform pixel to meters

leftx = leftx * xm_per_pix lefty = lefty * ym_per_pix rightx = rightx * xm_per_pix righty = righty * ym_per_pix `` then fit these data usenp.polyfit()`

After get the polynomial parameter, use the function R = (1+(2Ay+B)^2)^3/2 / (|2A|)

For the offset, it is similar, tranfer pixel to meter, compare the lane center with picture center to get offse. these are in the function measure_offset()

6. Plot lane area and display the radius and offset.

In the Pipeline class, use below code to visualzie the lane and caculation. to avoid number quick jump in the screen, display the 15 frames average. python def project_fit_lane_info(self, image, color=(0,255,255)): """ project the fited lane information to the image use last 15 frame average data to avoid the number quick jump on screen. """ offset = np.mean(self.offset[-15:-1]) if len(self.offset) > self.smooth_number else np.mean(self.offset) curverad = np.mean(self.radius[-15:-1]) if len(self.radius) > self.smooth_number else np.mean(self.radius) direction = "right" if offset < 0 else "left" str_cur = "Radius of Curvature = {}(m)".format(int(curverad)) str_offset = "Vehicle is {0:.2f}m ".format(abs(offset)) + "{} of center".format(direction) cv2.putText(image, str_cur, (50,60), cv2.FONT_HERSHEY_SIMPLEX,2,color,2) cv2.putText(image, str_offset, (50,120), cv2.FONT_HERSHEY_SIMPLEX,2,color,2)

The result as below picture.

alt text

In the pipeline.py, you could go to line 487 to 498, choice test on one image or a batch of image to see how the pipeline work. ```python if name == 'main': """ image_test_tracker(), test pipeline on one image and show the image on screen images_test_tracker(), test pipeline on images and write the result to related folder """ image_test_tracker("./test_images/test6.jpg", "project", debug_window=False) # image_test_tracker("test_images/challenge/1.jpg", "challenge", debug_window=True) # image_test_tracker("test_images/harder/1.jpg", "harder", debug_window=True)

# images_test_tracker("test_images/", "output_images/", "project", debug_window=True)
# images_test_tracker("test_images/challenge/", "output_images/challenge/", "challenge", debug_window=True)
# images_test_tracker("test_images/harder/", "output_images/harder/", "harder", debug_window=True)

```

Pipeline (video)

To apply the pipeline on the video, you could run the gen_video.py. to generate video. the option is explained in the document description. the code is in line 66-84 ```python if name == "main": """ choise one line to uncoment and run the file, gen the video. the video will be output to ./outpu_videos/temp/ option: subclip = True, just use (0-5) second video, False, use total long video. option: debug_window = True, project the debug window on the up-right corner of the screen to visualize the image handle process and write the fit lane failure/search lane failure image to ./output_videos/temp/images """ # get_image("./test_video/challenge_video.mp4", "./test_images/challenge/", [i for i in range(1,16)]) # get_image("./test_video/harder_challenge_video.mp4", "./test_images/harder/", [i for i in range(1,47)])

gen_video_tracker("project_video.mp4", subclip=True, debug_window=True) 
# gen_video_tracker("project_video.mp4", subclip=False, debug_window=False)

# gen_video_tracker("challenge_video.mp4", subclip=True, debug_window=True) 
# gen_video_tracker("challenge_video.mp4", subclip=False, debug_window=True)

# gen_video_tracker("harder_challenge_video.mp4", subclip=True, debug_window=True)
# gen_video_tracker("harder_challenge_video.mp4", subclip=False, debug_window=False)

```

1. Pipeline issue

With the image process established on single image. We couldn't get the left/right lane correctly in whole video. there is always noise which will affect the lane detection.

For example, in some frame, the lan detection failed, as below picuture shows detect fail

The full size picture is link to full size picture

2. add debug window on the pictures.

To solve this problem, we need to know what happed when the process not work. so I add a function project_debug_window() in the class, and we also need to check the fit lane(fitted polynomial) is OK or not. To check the lane at y postion 720/bot, 360/mid, 0/top the lane pixel distence and project to the final result picture for debug. build the function lane_sanity_check() in lane_detection.py

python lane_distance_bot = right_fitx[720] - left_fitx[720] lane_distance_mid = right_fitx[320] - left_fitx[320] lane_distance_top = right_fitx[0] - left_fitx[0]

The debug picture as below, the full size could find [here]

This is done by the class "Pipeline"'s function project_debug_window()

(./examples/project_detect_fail_with_debug.png) detect_fail_debug

When lane detect is False. use the recent data to project the lane area and culcualte the lane radius and car offset. one detect failure and use recent data. As below picture show project_with_previous_fit_lines

3 project video

Use the Pipeline.pipeline(), skip the noise frame, the pipeline work well on the project_video.mp4 well.

The project video is here project_video.mp4. The videos with debug window could find here project_video_with_debug_window.mp4.

4. challenge video

To solve the challenge video problem. Improve the pipeline with image process.

The challenge vidoe could be find here challenge_video.mp4.

The challenge video with debug window could be found here challenge_video_with_debug_window.

5. harder chanllenge

The main change of pipeline for harder_challenge compare with challenge is the image process, the search method is also changed.

The harder challenge vidoe could be find here harder_challenge_video.mp4.

The harder challenge video with debug window could be found here harder_challenge_video_with_debug_window.mp4.

Discussion

1. the time/efficiency issue

find_lane_pixels() (helpers/lane_detection.py) is used to search the whole warped image to find the lane pionts.

The pipeline handle the image off-line, so not consider the efficiency issue. In real application, the pipeline must hanle the image before the next image arrive. a quick search method should be applied.

2. lane_sanity check

The lane_sanity_check() (helpers/lane_detection.py) function is very simple. To check if the fitted polynomial lines, just compare the fitted lines three y postions x distence to check if the fitted lines is OK. this is not work very well when the lane curve change dramticlly just like the in the harder_challenge video.

Folders and Files

camera_cal the code which calibration the camera
examples some example pictures
helper all the functions which used in pipeline.py and video.py
output_images the images which processed by diff function.
output_video the video has finished
test_images the images used to test the pipeline
test_video three video for testing the lane detection
pipeline.py the code which use to hande the images. the actual lane dection happend.
requirements.txt the python package list in local machine
gen_video.py the code with use "pipeline" to handle the vidoe and generate the output video.

Issues

Bump certifi from 2018.8.24 to 2022.12.7

opened on 2022-12-08 02:56:27 by dependabot[bot]

Bumps certifi from 2018.8.24 to 2022.12.7.

Commits

9e9e840 2022.12.07
b81bdb2 2022.09.24
939a28f 2022.09.14
aca828a 2022.06.15.2
de0eae1 Only use importlib.resources's new files() / Traversable API on Python ≥3.11 ...
b8eb5e9 2022.06.15.1
47fb7ab Fix deprecation warning on Python 3.11 (#199)
b0b48e0 fixes #198 -- update link in license
9d514b4 2022.06.15
4151e88 Add py.typed to MANIFEST.in to package in sdist (#196)
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Bump pillow from 5.2.0 to 9.3.0

opened on 2022-11-22 02:53:09 by dependabot[bot]

Bumps pillow from 5.2.0 to 9.3.0.

Release notes

Sourced from pillow's releases.

9.3.0

https://pillow.readthedocs.io/en/stable/releasenotes/9.3.0.html

Changes

Initialize libtiff buffer when saving #6699 [@radarhere]

Limit SAMPLESPERPIXEL to avoid runtime DOS #6700 [@wiredfool]

Inline fname2char to fix memory leak #6329 [@nulano]

Fix memory leaks related to text features #6330 [@nulano]

Use double quotes for version check on old CPython on Windows #6695 [@hugovk]

GHA: replace deprecated set-output command with GITHUB_OUTPUT file #6697 [@nulano]

Remove backup implementation of Round for Windows platforms #6693 [@cgohlke]

Upload fribidi.dll to GitHub Actions #6532 [@nulano]

Fixed set_variation_by_name offset #6445 [@radarhere]

Windows build improvements #6562 [@nulano]

Fix malloc in _imagingft.c:font_setvaraxes #6690 [@cgohlke]

Only use ASCII characters in C source file #6691 [@cgohlke]

Release Python GIL when converting images using matrix operations #6418 [@hmaarrfk]

Added ExifTags enums #6630 [@radarhere]

Do not modify previous frame when calculating delta in PNG #6683 [@radarhere]

Added support for reading BMP images with RLE4 compression #6674 [@npjg]

Decode JPEG compressed BLP1 data in original mode #6678 [@radarhere]

pylint warnings #6659 [@marksmayo]

Added GPS TIFF tag info #6661 [@radarhere]

Added conversion between RGB/RGBA/RGBX and LAB #6647 [@radarhere]

Do not attempt normalization if mode is already normal #6644 [@radarhere]

Fixed seeking to an L frame in a GIF #6576 [@radarhere]

Consider all frames when selecting mode for PNG save_all #6610 [@radarhere]

Don't reassign crc on ChunkStream close #6627 [@radarhere]

Raise a warning if NumPy failed to raise an error during conversion #6594 [@radarhere]

Only read a maximum of 100 bytes at a time in IMT header #6623 [@radarhere]

Show all frames in ImageShow #6611 [@radarhere]

Allow FLI palette chunk to not be first #6626 [@radarhere]

If first GIF frame has transparency for RGB_ALWAYS loading strategy, use RGBA mode #6592 [@radarhere]

Round box position to integer when pasting embedded color #6517 [@radarhere]

Removed EXIF prefix when saving WebP #6582 [@radarhere]

Pad IM palette to 768 bytes when saving #6579 [@radarhere]

Added DDS BC6H reading #6449 [@ShadelessFox]

Added support for opening WhiteIsZero 16-bit integer TIFF images #6642 [@JayWiz]

Raise an error when allocating translucent color to RGB palette #6654 [@jsbueno]

Moved mode check outside of loops #6650 [@radarhere]

Added reading of TIFF child images #6569 [@radarhere]

Improved ImageOps palette handling #6596 [@PososikTeam]

Defer parsing of palette into colors #6567 [@radarhere]

Apply transparency to P images in ImageTk.PhotoImage #6559 [@radarhere]

Use rounding in ImageOps contain() and pad() #6522 [@bibinhashley]

Fixed GIF remapping to palette with duplicate entries #6548 [@radarhere]

Allow remap_palette() to return an image with less than 256 palette entries #6543 [@radarhere]

Corrected BMP and TGA palette size when saving #6500 [@radarhere]

... (truncated)

Changelog

Sourced from pillow's changelog.

9.3.0 (2022-10-29)

Limit SAMPLESPERPIXEL to avoid runtime DOS #6700 [wiredfool]

Initialize libtiff buffer when saving #6699 [radarhere]

Inline fname2char to fix memory leak #6329 [nulano]

Fix memory leaks related to text features #6330 [nulano]

Use double quotes for version check on old CPython on Windows #6695 [hugovk]

Remove backup implementation of Round for Windows platforms #6693 [cgohlke]

Fixed set_variation_by_name offset #6445 [radarhere]

Fix malloc in _imagingft.c:font_setvaraxes #6690 [cgohlke]

Release Python GIL when converting images using matrix operations #6418 [hmaarrfk]

Added ExifTags enums #6630 [radarhere]

Do not modify previous frame when calculating delta in PNG #6683 [radarhere]

Added support for reading BMP images with RLE4 compression #6674 [npjg, radarhere]

Decode JPEG compressed BLP1 data in original mode #6678 [radarhere]

Added GPS TIFF tag info #6661 [radarhere]

Added conversion between RGB/RGBA/RGBX and LAB #6647 [radarhere]

Do not attempt normalization if mode is already normal #6644 [radarhere]

... (truncated)

Commits

d594f4c Update CHANGES.rst [ci skip]
909dc64 9.3.0 version bump
1a51ce7 Merge pull request #6699 from hugovk/security-libtiff_buffer
2444cdd Merge pull request #6700 from hugovk/security-samples_per_pixel-sec
744f455 Added release notes
0846bfa Add to release notes
799a6a0 Fix linting
00b25fd Hide UserWarning in logs
05b175e Tighter test case
13f2c5a Prevent DOS with large SAMPLESPERPIXEL in Tiff IFD
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Bump numpy from 1.15.0 to 1.22.0

opened on 2022-06-21 21:28:35 by dependabot[bot]

Bumps numpy from 1.15.0 to 1.22.0.

Release notes

Sourced from numpy's releases.

v1.22.0

NumPy 1.22.0 Release Notes

NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are:

Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release.

A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across application such as CuPy and JAX.

NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data.

New methods for quantile, percentile, and related functions. The new methods provide a complete set of the methods commonly found in the literature.

A new configurable allocator for use by downstream projects.

These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation.

The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays.

Expired deprecations

Deprecated numeric style dtype strings have been removed

Using the strings "Bytes0", "Datetime64", "Str0", "Uint32", and "Uint64" as a dtype will now raise a TypeError.

(gh-19539)

Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

numpy.loads was deprecated in v1.15, with the recommendation that users use pickle.loads instead. ndfromtxt and mafromtxt were both deprecated in v1.17 - users should use numpy.genfromtxt instead with the appropriate value for the usemask parameter.

(gh-19615)

... (truncated)

Commits

4adc87d Merge pull request #20685 from charris/prepare-for-1.22.0-release
fd66547 REL: Prepare for the NumPy 1.22.0 release.
125304b wip
c283859 Merge pull request #20682 from charris/backport-20416
5399c03 Merge pull request #20681 from charris/backport-20954
f9c45f8 Merge pull request #20680 from charris/backport-20663
794b36f Update armccompiler.py
d93b14e Update test_public_api.py
7662c07 Update init.py
311ab52 Update armccompiler.py
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Bump ipython from 6.5.0 to 7.16.3

opened on 2022-01-21 19:33:34 by dependabot[bot]

Bumps ipython from 6.5.0 to 7.16.3.

Commits

d43c7c7 release 7.16.3
5fa1e40 Merge pull request from GHSA-pq7m-3gw7-gq5x
8df8971 back to dev
9f477b7 release 7.16.2
138f266 bring back release helper from master branch
5aa3634 Merge pull request #13341 from meeseeksmachine/auto-backport-of-pr-13335-on-7...
bcae8e0 Backport PR #13335: What's new 7.16.2
8fcdcd3 Pin Jedi to <0.17.2.
2486838 release 7.16.1
20bdc6f fix conda build
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Bump opencv-python from 3.4.2.17 to 4.2.0.32

opened on 2021-10-12 22:55:36 by dependabot[bot]

Bumps opencv-python from 3.4.2.17 to 4.2.0.32.

Release notes

Sourced from opencv-python's releases.

4.2.0.32

opencv-python: https://pypi.org/project/opencv-python/

opencv-contrib-python: https://pypi.org/project/opencv-contrib-python/

opencv-python-headless: https://pypi.org/project/opencv-python-headless/

opencv-contrib-python-headless: https://pypi.org/project/opencv-contrib-python-headless/

OpenCV version 4.2.0.

Changes:

macOS environment updated from xcode8.3 to xcode 9.4

macOS uses now Qt 5 instead of Qt 4

Nasm version updated to Docker containers

multibuild updated

Fixes:

don't use deprecated brew tap-pin, instead refer to the full package name when installing #267

replace get_config_var() with get_config_vars() in setup.py #274

add workaround for DLL errors in Windows Server #264

3.4.9.31

opencv-python: https://pypi.org/project/opencv-python/

opencv-contrib-python: https://pypi.org/project/opencv-contrib-python/

opencv-python-headless: https://pypi.org/project/opencv-python-headless/

opencv-contrib-python-headless: https://pypi.org/project/opencv-contrib-python-headless/

OpenCV version 3.4.9.

Changes:

macOS environment updated from xcode8.3 to xcode 9.4

macOS uses now Qt 5 instead of Qt 4

Nasm version updated to Docker containers

multibuild updated

Fixes:

don't use deprecated brew tap-pin, instead refer to the full package name when installing #267

replace get_config_var() with get_config_vars() in setup.py #274

add workaround for DLL errors in Windows Server #264

4.1.2.30

opencv-python: https://pypi.org/project/opencv-python/

opencv-contrib-python: https://pypi.org/project/opencv-contrib-python/

opencv-python-headless: https://pypi.org/project/opencv-python-headless/

opencv-contrib-python-headless: https://pypi.org/project/opencv-contrib-python-headless/

OpenCV version 4.1.2.

Changes:

... (truncated)

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Bump urllib3 from 1.23 to 1.26.5

opened on 2021-06-01 22:45:58 by dependabot[bot]

Bumps urllib3 from 1.23 to 1.26.5.

Release notes

Sourced from urllib3's releases.

1.26.5

:warning: IMPORTANT: urllib3 v2.0 will drop support for Python 2: Read more in the v2.0 Roadmap

Fixed deprecation warnings emitted in Python 3.10.

Updated vendored six library to 1.16.0.

Improved performance of URL parser when splitting the authority component.

If you or your organization rely on urllib3 consider supporting us via GitHub Sponsors

1.26.4

:warning: IMPORTANT: urllib3 v2.0 will drop support for Python 2: Read more in the v2.0 Roadmap

Changed behavior of the default SSLContext when connecting to HTTPS proxy during HTTPS requests. The default SSLContext now sets check_hostname=True.

If you or your organization rely on urllib3 consider supporting us via GitHub Sponsors

1.26.3

:warning: IMPORTANT: urllib3 v2.0 will drop support for Python 2: Read more in the v2.0 Roadmap

Fixed bytes and string comparison issue with headers (Pull #2141)

Changed ProxySchemeUnknown error message to be more actionable if the user supplies a proxy URL without a scheme (Pull #2107)

If you or your organization rely on urllib3 consider supporting us via GitHub Sponsors

1.26.2

:warning: IMPORTANT: urllib3 v2.0 will drop support for Python 2: Read more in the v2.0 Roadmap

Fixed an issue where wrap_socket and CERT_REQUIRED wouldn't be imported properly on Python 2.7.8 and earlier (Pull #2052)

1.26.1

:warning: IMPORTANT: urllib3 v2.0 will drop support for Python 2: Read more in the v2.0 Roadmap

Fixed an issue where two User-Agent headers would be sent if a User-Agent header key is passed as bytes (Pull #2047)

1.26.0

:warning: IMPORTANT: urllib3 v2.0 will drop support for Python 2: Read more in the v2.0 Roadmap

Added support for HTTPS proxies contacting HTTPS servers (Pull #1923, Pull #1806)

Deprecated negotiating TLSv1 and TLSv1.1 by default. Users that still wish to use TLS earlier than 1.2 without a deprecation warning should opt-in explicitly by setting ssl_version=ssl.PROTOCOL_TLSv1_1 (Pull #2002) Starting in urllib3 v2.0: Connections that receive a DeprecationWarning will fail

Deprecated Retry options Retry.DEFAULT_METHOD_WHITELIST, Retry.DEFAULT_REDIRECT_HEADERS_BLACKLIST and Retry(method_whitelist=...) in favor of Retry.DEFAULT_ALLOWED_METHODS, Retry.DEFAULT_REMOVE_HEADERS_ON_REDIRECT, and Retry(allowed_methods=...) (Pull #2000) Starting in urllib3 v2.0: Deprecated options will be removed

... (truncated)

Changelog

Sourced from urllib3's changelog.

1.26.5 (2021-05-26)

Fixed deprecation warnings emitted in Python 3.10.

Updated vendored six library to 1.16.0.

Improved performance of URL parser when splitting the authority component.

1.26.4 (2021-03-15)

Changed behavior of the default SSLContext when connecting to HTTPS proxy during HTTPS requests. The default SSLContext now sets check_hostname=True.

1.26.3 (2021-01-26)

Fixed bytes and string comparison issue with headers (Pull #2141)

Changed ProxySchemeUnknown error message to be more actionable if the user supplies a proxy URL without a scheme. (Pull #2107)

1.26.2 (2020-11-12)

Fixed an issue where wrap_socket and CERT_REQUIRED wouldn't be imported properly on Python 2.7.8 and earlier (Pull #2052)

1.26.1 (2020-11-11)

Fixed an issue where two User-Agent headers would be sent if a User-Agent header key is passed as bytes (Pull #2047)

1.26.0 (2020-11-10)

NOTE: urllib3 v2.0 will drop support for Python 2. Read more in the v2.0 Roadmap <https://urllib3.readthedocs.io/en/latest/v2-roadmap.html>_.

Added support for HTTPS proxies contacting HTTPS servers (Pull #1923, Pull #1806)

Deprecated negotiating TLSv1 and TLSv1.1 by default. Users that still wish to use TLS earlier than 1.2 without a deprecation warning

... (truncated)

Commits

d161647 Release 1.26.5
2d4a3fe Improve performance of sub-authority splitting in URL
2698537 Update vendored six to 1.16.0
07bed79 Fix deprecation warnings for Python 3.10 ssl module
d725a9b Add Python 3.10 to GitHub Actions
339ad34 Use pytest==6.2.4 on Python 3.10+
f271c9c Apply latest Black formatting
1884878 [1.26] Properly proxy EOF on the SSLTransport test suite
a891304 Release 1.26.4
8d65ea1 Merge pull request from GHSA-5phf-pp7p-vc2r
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ChengZhong.Shen

GitHub Repository

Udacity Selft-Driving Car Engineer Nano Degree Project 2

Camera Calibration

1. use the opencv findChessboardCorners() and calibrateCamera() funtion to implement the camera calibration.

Pipeline (single image)

1. distortion-corrected image.

2. Threshold the image.

3. Perspective transform

4. Lane-line pixels detection and fit their positions with a polynomial

5. Calculate the radius of curvature of the lane and the position of the vehicle with respect to center.

Transform pixel to meters

6. Plot lane area and display the radius and offset.

Pipeline (video)

1. Pipeline issue

2. add debug window on the pictures.

3 project video

4. challenge video

5. harder chanllenge

Discussion

1. the time/efficiency issue

2. lane_sanity check

Folders and Files

Issues

Bump certifi from 2018.8.24 to 2022.12.7

Bump pillow from 5.2.0 to 9.3.0

9.3.0

Changes

9.3.0 (2022-10-29)

Bump numpy from 1.15.0 to 1.22.0

v1.22.0

NumPy 1.22.0 Release Notes

Expired deprecations

Deprecated numeric style dtype strings have been removed

Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

Bump ipython from 6.5.0 to 7.16.3

Bump opencv-python from 3.4.2.17 to 4.2.0.32

4.2.0.32

3.4.9.31

4.1.2.30

Bump urllib3 from 1.23 to 1.26.5

1.26.5

1.26.4

1.26.3

1.26.2

1.26.1

1.26.0

1.26.5 (2021-05-26)

1.26.4 (2021-03-15)

1.26.3 (2021-01-26)

1.26.2 (2020-11-12)

1.26.1 (2020-11-11)

1.26.0 (2020-11-10)

ChengZhong.Shen

1. use the opencv `findChessboardCorners()` and `calibrateCamera()` funtion to implement the camera calibration.

Expired deprecations for `loads`, `ndfromtxt`, and `mafromtxt` in npyio