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State-of-the-art Computer Vision and Object Detection for TensorFlow.

Project description



AUR license

State-of-the-art Computer Vision and Object Detection for TensorFlow.

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. If you're unfamiliar with Python virtual environments, check out this tutorial 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.

When your virtual environment is set up with TensorFlow, you can install sight using pip:

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:

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

1a. Loading images

from sight import Sightseer

ss = Sightseer()
image = ss.load_image("path/to/image")

1b. Loading videos

from sight import Sightseer

ss = Sightseer()
frames = ss.load_vidsource("path/to/video", return_data=True)

1c. Loading webcam footage

from sight import Sightseer

ss = Sightseer()
image = ss.load_webcam()

1d. Loading screen grab footage

from sight import Sightseer

ss = Sightseer()
image = ss.screen_grab()

2. Using models from sight.zoo

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:

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)

Project details


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