Metadata-Version: 2.1
Name: sightseer
Version: 1.0.7
Summary: State-of-the-art Computer Vision and Object Detection for TensorFlow.
Home-page: https://github.com/rish-16/sight
Author: Rishabh Anand
Author-email: mail.rishabh.anand@gmail.com
License: ASF
Download-URL: https://github.com/rish-16/sight/archive/1.0.0.tar.gz
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        	<img src="https://github.com/rish-16/sight/blob/master/Assets/logo.png?raw=true" width=200>
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            <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">
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        <h3 align="center">
        <p>State-of-the-art Computer Vision and Object Detection for TensorFlow.</p>
        </h3>
        
        *sightseer* provides state-of-the-art general-purpose architectures (YOLOv3, MaskRCNN, Fast/Faster RCNN, SSD...) for Computer Vision and Object Detection tasks with 30+ pretrained models written in TensorFlow 1.15.
        
        ## Installation
        
        `sightseer` is written in Python 3.5+ and TensorFlow 1.15. 
        
        Ideally, `sightseer` 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 `sightseer`, 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 `sightseer` using `pip`:
        
        ```bash
        pip install sightseer
        ```
        
        ### Model Architectures
        
        1. YOLOv3 (Darknet by Joseph Redmon)
        2. More upcoming!
        
        ## Features
        
        <strong>1. Loading images</strong>
        
        ```python
        from sightseer import Sightseer
        
        ss = Sightseer()
        image = ss.load_image("path/to/image")
        ```
        
        <strong>2. Using models from `sightseer.zoo`</strong>
        
        Once installed, any model offered by `sightseer` 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 sightseer import Sightseer
        from sightseer.zoo import YOLOv3Client
        from pprint import pprint
        
        yolo = YOLOv3Client()
        yolo.load_model() # downloads weights
        
        # loading image from local system
        ss = Sightseer()
        image = ss.load_image("./images/road.jpg")
        
        # getting labels, confidence scores, and bounding box data
        preds, pred_img = yolo.predict(image, return_img=True)
        pprint (preds)
        ss.render_image(pred_img)
        ```
        
        ## Contributing
        
        Suggestions, improvements, and enhancements are always welcome! If you have any issues, please do raise one in the Issues section. If you have an improvement, do file an issue to discuss the suggestion before creating a PR.
        
        All ideas – no matter how outrageous – welcome!
        
        ## Licence
        
        [Apache Licencse 2.0](https://github.com/rish-16/sight/blob/master/LICENSE)
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