Metadata-Version: 1.2
Name: torchtraps
Version: 0.1.4
Summary: Python package for computer vision on camera trap images.
Home-page: https://github.com/winzurk/torchtraps
Author: Zac Winzurk
Author-email: zwinzurk@asu.edu
License: MIT license
Description: =====================================
        Torch Traps :leopard: :camera_flash:
        =====================================
        
        
        .. image:: https://img.shields.io/pypi/v/torchtraps.svg
                :target: https://pypi.python.org/pypi/torchtraps
        
        .. image:: https://img.shields.io/travis/winzurk/torchtraps.svg
                :target: https://travis-ci.com/winzurk/torchtraps
        
        .. image:: https://readthedocs.org/projects/torchtraps/badge/?version=latest
                :target: https://torchtraps.readthedocs.io/en/latest/?badge=latest
                :alt: Documentation Status
        
        
        Torch Traps is python package for *lighting* :zap: *fast* wildlife camera trap image annotation based on PyTorch. :fire:
        
        * Documentation: https://torchtraps.readthedocs.io.
        * GitHub: https://github.com/winzurk/torchtraps
        * PyPI: https://pypi.python.org/pypi/torchtraps
        * MIT License
        
        .. torchtraps/sample_images/NJP-2.JPG
        
        .. image:: https://github.com/winzurk/torchtraps/blob/master/torchtraps/sample_images/NJP-2.JPG
                :target: https://github.com/winzurk/torchtraps/blob/master/torchtraps/sample_images/NJP-2.JPG
                :width: 300
        
        Photo Credit: Northern Jaguar Project
        
        Over the past several decades, biologists all over the world have widely adopted camera traps as a standard tool for
        monitoring biodiversity, resulting in backlogs often on the order of millions of images waiting to be manually reviewed
        by humans to assess wildlife population densities. The application of modern computer vision and deep learning methods
        to accelerate the processing of wildlife camera trap data has the potential to alleviate existing bottlenecks for large
        scale biodiversity monitoring, thus dramatically increasing the speed at which researchers can obtain data-driven
        insights about ecosystems, and ultimately leading to more efficient resource allocation and better informed policy
        making by NGOs and government agencies.
        
        Torch Traps aims to provide a simple tool (as little as 1 line of code) to bring state-of-the-art computer vision models
        into the hands of biologists to accelerate the speed at which they can review camera trap imagery.
        
        
        Install
        --------
        .. code-block:: bash
        
            $ pip install torchtraps
        
        Fast Inference on Folder of Images
        -------------------------------------------------
        
        Classify an entire folder of camera trap images in one line of code by simply passing the relative path to the folder
        containing images. Outputs are automatically saved to a csv file which can be further processed opened in an application
        like Excel.
        
        .. code-block:: python
        
            import torchtraps.lightning import kachow
        
            kachow('path/to/image/folder')
        
        
        .. csv-table:: Example Output File
            :header: "image", "prediction", "confidence"
        
                "image1.jpg", "jaguar", 0.99
                "image2.jpg", "empty", 0.98
                "image3.jpg", "agouti", 0.91
                "image4.jpg", "empty", 0.95
                "image5.jpg", "ocelot", 0.87
        
        
        
        Features
        --------
        
        * Module for fast computer vision on camera trap images.
        * Train and fine-tune classification models on your own dataset.
        * Based on PyTorch
        * MIT license
        
        
        Complete Installation Tutorial from Scratch
        ----------------------------------------------
        This is a full tutorial on how to install and get up and running with Torch Traps. Zero programming knowledge is
        assumed in the attempt to make Torch Traps as accessible as possible. If you do run into any problems, please email
        me at zwinzurk@asu.edu
        
        * Step 1: Install Anaconda
        
            Go to https://www.anaconda.com/distribution/
        
            Download Anaconda Python 3.7 version for the operating system you are using (Windows, MacOS, or Linux).
        
            Click on 64-Bit Graphical Installer (442 MB) to download the version with a Graphical User Interface.
        
            .. image:: tutorial/AnacondaDownload.jpg
                :width: 300
        
            Why do I need Anaconda?
        
                Torch Traps is a module written in `Python <http://www.python.org/>`_ (a programming language), so we first need to have Python installed
                on our computer. There are several ways to install python, but Anaconda allows us to install Python and it comes
                pre-installed with many of the common modules used for Data Science, and optionally comes with a GUI which can
                be used to open notebooks.
        
            After download is complete, double-click to install and follow installation instructions.
        
            .. image:: tutorial/InstallAnaconda.jpg
                :width: 300
        
        
        * Step 2: Open Anaconda Navigator
        
            After installing Anaconda, open the Anaconda Navigator application on your computer.
        
            .. image:: tutorial/OpenNavigator.jpg
                :width: 300
        
        * Step 3: Launch Jupyter Lab
        
            We will then launch a Jupyter Lab. Your web browser will open but the Jupyter server is running locally as you can
            see the url should be http://localhost:8889/lab
        
            .. image:: tutorial/LaunchJupyter.jpg
                :width: 300
        
        * Step 4: Navigate to Working Folder on Left
        
            By clicking on the folder icon in the upper-left corner we can navigate the file system.
        
            Navigate to the directory on your computer where your camera trap image folder is located.
        
        * Step 5: Open Python3 Notebook
        
            Now that we are working in the right directory, we can launch a new Python notebook. This will create a new file in
            our working directory called Untitled.ipynb. We can right-click on the file name to re-name it.
        
            .. image:: tutorial/CreateNotebook.jpg
                :width: 300
        
        * Step 6: Install Torch Traps
        
            Jupyter notebooks allow us to run python code one 'cell' at a time. So the first thing we need to do is install
            torch traps, if we have not before. Copy the code below into the first cell, and then run the cell by either
            clicking the play button or hitting SHIFT+ENTER at the same.
        
            .. code-block:: bash
        
                !pip install torch traps
        
            .. image:: tutorial/InstallTorchTraps.jpg
                :width: 300
        
         * Step 7: Run Torch Traps on Folder of Images
        
            Now that the Torch Traps is installed, you can copy the code below into a new code cell.
        
            Change the 'path/to/image/folder' to the name of your folder containing camera trap images (ex. 'camera_trap_images')
        
            Run the cell. (SHIFT + ENTER)
        
            Note: If running for the first time, an internet connection will be required to download the model file.
        
            When complete an output.csv file will appear in the directory you are working in. You can double-click csv files to
            view in Jupyter Lab or open with another application like Excel.
        
            .. code-block:: python
        
                import torchtraps.lightning import kachow
                kachow('path/to/image/folder')
        
        
            .. image:: tutorial/RunTorchTraps.jpg
                :width: 300
        
        * Step 8: Open CSV File To See Classification Results
        
        .. Future: Step 9: View Images of Particular Class
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        =======
        History
        =======
        
        0.1.0 (2020-03-30)
        ------------------
        
        * First release on PyPI.
        
Keywords: torchtraps
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.5
