lanenet-lane-detection | Use tensorflow to implement a Deep Neural Network | Machine Learning library
kandi X-RAY | lanenet-lane-detection Summary
kandi X-RAY | lanenet-lane-detection Summary
lanenet-lane-detection is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow applications. lanenet-lane-detection has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.
Use tensorflow to implement a Deep Neural Network for real time lane detection mainly based on the IEEE IV conference paper "Towards End-to-End Lane Detection: an Instance Segmentation Approach".You can refer to their paper for details This model consists of a encoder-decoder stage, binary semantic segmentation stage and instance semantic segmentation using discriminative loss function for real time lane detection task.
Use tensorflow to implement a Deep Neural Network for real time lane detection mainly based on the IEEE IV conference paper "Towards End-to-End Lane Detection: an Instance Segmentation Approach".You can refer to their paper for details This model consists of a encoder-decoder stage, binary semantic segmentation stage and instance semantic segmentation using discriminative loss function for real time lane detection task.
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lanenet-lane-detection has a low active ecosystem.
It has 6 star(s) with 3 fork(s). There are 3 watchers for this library.
It had no major release in the last 6 months.
There are 1 open issues and 0 have been closed. On average issues are closed in 129 days. There are 1 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of lanenet-lane-detection is current.
Quality
lanenet-lane-detection has 0 bugs and 0 code smells.
Security
lanenet-lane-detection has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
lanenet-lane-detection code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
lanenet-lane-detection is licensed under the Apache-2.0 License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
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lanenet-lane-detection releases are not available. You will need to build from source code and install.
Build file is available. You can build the component from source.
Installation instructions, examples and code snippets are available.
It has 4839 lines of code, 215 functions and 28 files.
It has high code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed lanenet-lane-detection and discovered the below as its top functions. This is intended to give you an instant insight into lanenet-lane-detection implemented functionality, and help decide if they suit your requirements.
- Compute the loss of the classification loss
- 2D convolution layer
- Layer normalization layer
- Build a relu
- Train lanenet dataset
- Load pretrained weights
- Generate input tensors
- Takes a list of images and returns a dataset
- Returns the next batch of data
- Distribute an image using AutoAugment
- Augment image for training
- Calculate sharpness
- Embed inference
- Shear image with bboxes
- Show training data for a TF record
- Compute recall
- Rotate an image
- Records training intermediate results
- Generate tf records
- Apply cutout to an image
- Copy Tusimple Datasimplements
- Writes example tf records
- Translate a bounding box
- Decode a single block
- Compute image acc
- Compute discriminative loss
Get all kandi verified functions for this library.
lanenet-lane-detection Key Features
No Key Features are available at this moment for lanenet-lane-detection.
lanenet-lane-detection Examples and Code Snippets
No Code Snippets are available at this moment for lanenet-lane-detection.
Community Discussions
Trending Discussions on lanenet-lane-detection
QUESTION
How can I replace `plt.imsave` with `cmap` option set to `gray` with opencv operations?
Asked 2020-Jan-12 at 08:46
This is the source image I am working with:
I am using this github repository (the file I'm using is tools/test_lanenet.py) to do binary lane segmentation. now I get this image:
The second image is actually an image resulted from this command:
...ANSWER
Answered 2020-Jan-12 at 07:36Your images are not the same size. To mask the black/white image onto the color image, they need to align. I tried to simply crop them to the same minimum dimensions at the top left corner, but that did not align them properly.
However, this Python/OpenCV code will give you some idea how to start once you figure out how to align them.
Color Input:
B/W Lane Image:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install lanenet-lane-detection
This software has only been tested on ubuntu 16.04(x64), python3.5, cuda-9.0, cudnn-7.0 with a GTX-1070 GPU. To install this software you need tensorflow 1.10.0 and other version of tensorflow has not been tested but I think it will be able to work properly in tensorflow above version 1.10. Other required package you may install them by.
Support
For any new features, suggestions and bugs create an issue on GitHub.
If you have any questions check and ask questions on community page Stack Overflow .
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