Metadata-Version: 2.1
Name: sightseer
Version: 1.0.1
Summary: State-of-the-art Computer Vision and Object Detection for TensorFlow.
Home-page: UNKNOWN
Author: 
Author-email: 
License: ASF
Description: <p align="center">
            <br>
        	<img src="./Assets/logo.png" width=200>
            <br>
        <p>
        
        <p align="center">
            <a href="https://github.com/rish-16/sight/blob/master/LICENSE">
        		<img alt="AUR license" src="https://img.shields.io/badge/License-Apache%202.0-yellow.svg">
            </a>
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        <h3 align="center">
        <p>State-of-the-art Computer Vision and Object Detection for TensorFlow.</p>
        </h3>
        
        *Sight* provides state-of-the-art general-purpose architectures (YOLO9000, MaskRCNN, Fast/Faster RCNN, SSD...) for Computer Vision and Object Detection tasks with 30+ pretrained models written in TensorFlow 1.15.
        
        ## Installation
        
        `sight` is written in Python 3.5+ and TensorFlow 1.15. 
        
        Ideally, `sight` should be installed in a [virtual environments](https://docs.python.org/3/library/venv.html). If you're unfamiliar with Python virtual environments, check out this [tutorial](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/) on getting started.
        
        ### Via PyPi
        
        To use `sight`, you must first have TensorFlow installed. To do so, follow the instructions on the [TensorFlow installation page](https://www.tensorflow.org/install/pip?lang=python3).
        
        When your virtual environment is set up with TensorFlow, you can install `sight` using `pip`:
        
        ```bash
        pip install sight
        ```
        
        ### From Source
        
        Again, to install from source, you need TensorFlow 1.15 and above running in a virtual environment. You can install the package by cloning the repo and installing the dependencies:
        
        ```bash
        git clone https://github.com/rish-16/sight
        cd sight
        pip install .
        ```
        
        ### Model Architectures
        
        1. YOLOv3 (Darknet by Joseph Redmon)
        2. Mask R-CNN (Facebook AI Research)
        
        ## Usage
        
        <strong>1a. Loading images</strong>
        
        ```python
        from sight import Sightseer
        
        ss = Sightseer()
        image = ss.load_image("path/to/image")
        ```
        
        <strong>1b. Loading videos</strong>
        
        ```python
        from sight import Sightseer
        
        ss = Sightseer()
        frames = ss.load_vidsource("path/to/video", return_data=True)
        ```
        
        <strong>1c. Loading webcam footage</strong>
        
        ```python
        from sight import Sightseer
        
        ss = Sightseer()
        image = ss.load_webcam()
        ```
        
        <strong>1d. Loading screen grab footage</strong>
        
        ```python
        from sight import Sightseer
        
        ss = Sightseer()
        image = ss.screen_grab()
        ```
        
        <strong>2. Using models from `sight.zoo`</strong>
        
        Once installed, any model offered by `sight` can be accessed in less than 10 lines of code. For instance, the code to use the YOLOv3 (Darknet) model is as follows:
        
        ```python
        from sight import Sightseer
        from sight.zoo import YOLOv3Client
        
        yolo = YOLOv3Client()
        yolo.load_model() # downloads weights
        
        # loading images from local system
        ss = Sightseer("path/to/img")
        image = ss.load_image()
        
        # returns array of labels, confidence, and bounding box info
        preds, pred_img = yolo.predict(image, return_image=True)
        ss.render_image(pred_img)
        ```
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