classifier-pipeline | Exports tracked animals through thermal vision | Machine Learning library

by TheCacophonyProject Python Version: 0.0.12 License: GPL-3.0

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | classifier-pipeline Summary

classifier-pipeline is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. classifier-pipeline has no bugs, it has no vulnerabilities, it has build file available, it has a Strong Copyleft License and it has low support. You can install using 'pip install classifier-pipeline' or download it from GitHub, PyPI.

These scripts handle the data pre-processing, training, and execution of a Convolutional Neural Network based classifier for thermal vision. The output is a TensorFlow model that can identify thermal video clips of animals.

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classifier-pipeline has a low active ecosystem.

It has 17 star(s) with 14 fork(s). There are 11 watchers for this library.

There were 4 major release(s) in the last 6 months.

There are 16 open issues and 11 have been closed. On average issues are closed in 139 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of classifier-pipeline is 0.0.12

Quality

classifier-pipeline has 0 bugs and 0 code smells.

Security

classifier-pipeline has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

classifier-pipeline code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

classifier-pipeline is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

classifier-pipeline releases are available to install and integrate.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions are available. Examples and code snippets are not available.

It has 11773 lines of code, 789 functions and 88 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed classifier-pipeline and discovered the below as its top functions. This is intended to give you an instant insight into classifier-pipeline implemented functionality, and help decide if they suit your requirements.

Read video from cv2 file
Detects the images in the image
Shows the component labels
Apply morphological saliency to an image
Add a track
Compute the track movement statistics
Compute the euclidean distance between two vectors
Add prediction data
Create a Track object from a metadata object
Print the thumbnail statistics
Create a list of segment headers
Parse command line arguments
Handle a connection
Parse command line arguments
Get the thermals of the track
Add tracking to a preview
Classify a single job
Display the saliency map
Start recording
Calculates the accuracy of a given dataset
Create TF records for training
Get a resampled dataset
Track a video
Evaluate DBclips
Compute the score of a track
Create tag for a clip

Get all kandi verified functions for this library.

classifier-pipeline Key Features

No Key Features are available at this moment for classifier-pipeline.

classifier-pipeline Examples and Code Snippets

No Code Snippets are available at this moment for classifier-pipeline.

Community Discussions

Trending Discussions on classifier-pipeline

Why is SparkContext being shutdown during Logistic Regression?

QUESTION

Why is SparkContext being shutdown during Logistic Regression?

Asked 2017-Aug-04 at 15:58

I think it has something to do with memory, because it was working fine for smaller data sets. The program utilizes, and prematurely shuts down, while using Logistic Regression from Spark-Mllib. I am running this command below to start my spark program on HDFS.

...

ANSWER

Answered 2017-Aug-04 at 15:58

The driver memory wasn't large enough. Increasing it prevented these errors.

Source https://stackoverflow.com/questions/45471790

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install classifier-pipeline

Install the following prerequisites (tested with Ubuntu 18.0 and Python Python 3.6.9) apt-get install -y tzdata git python3 python3-dev python3-venv libcairo2-dev build-essential libgirepository1.0-dev libjpeg-dev python-cairo libhdf5-dev. Copy classifier_TEMPLATE.yaml to classifier.yaml. Edit.
Create a virtual environment in python3 and install the necessary prerequisites pip install -r requirements.txt
Copy the classifier_Template.yaml to classifier.yaml and then edit this file with your own settings. You will need to set up the paths for it work on your system. (Note: Currently these settings only apply to classify.py and extract.py)
Optionally install GPU support for tensorflow (note this requires additional setup) pip install tensorflow-gpu
MPEG4 output requires FFMPEG to be installed which can be found here On linux apt-get install ffmpeg. On windows the installation path will need to be added to the system path.
Create a classifier configuration

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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