One of the important fields of Artificial Intelligence is Computer Vision. Computer Vision is the science of computers and software systems that can recognize and understand images and scenes. Computer Vision is also composed of various aspects such as image recognition, object detection, image generation, image super-resolution and more. Object detection is probably the most profound aspect of computer vision due the number practical use cases. In this tutorial, I will briefly introduce the concept of modern object detection, challenges faced by software developers, the solution my team has provided as well as code tutorials to perform high performance object detection.

Object detection refers to the capability of computer and software systems to locate objects in an image/scene and identify each object. Object detection has been widely used for face detection, vehicle detection, pedestrian counting, web images, security systems and driverless cars. There are many ways object detection can be used as well in many fields of practice. Like every other computer technology, a wide range of creative and amazing uses of object detection will definitely come from the efforts of computer programmers and software developers.

Getting to use modern object detection methods in applications and systems, as well as building new applications based on these methods is not a straight forward task. Early implementations of object detection involved the use of classical algorithms, like the ones supported in OpenCV, the popular computer vision library. However, these classical algorithms could not achieve enough performance to work under different conditions.

The breakthrough and rapid adoption of deep learning in 2012 brought into existence modern and highly accurate object detection algorithms and methods such as R-CNN, Fast-RCNN, Faster-RCNN, RetinaNet and fast yet highly accurate ones like SSD and YOLO. Using these methods and algorithms, based on deep learning which is also based on machine learning require lots of mathematical and deep learning frameworks understanding. There are millions of expert computer programmers and software developers that want to integrate and create new products that uses object detection. But this technology is kept out of their reach due to the extra and complicated path to understanding and making practical use of it.

ImageAI is a python library that lets programmers and software developers easily integrate state-of-the-art computer vision technologies into their existing and new applications, using just few lines of code.

An AI Commons project https://commons.specpal.science Developed and Maintained by Moses Olafenwa and John Olafenwa, brothers, creators of TorchFusion and Authors of Introduction to Deep Computer Vision

Built with simplicity in mind, ImageAI supports a list of state-of-the-art Machine Learning algorithms for image prediction, custom image prediction, object detection, video detection, video object tracking and image predictions trainings. ImageAI currently supports image prediction and training using 4 different Machine Learning algorithms trained on the ImageNet-1000 dataset. ImageAI also supports object detection, video detection and object tracking using RetinaNet, YOLOv3 and TinyYOLOv3 trained on COCO dataset.
Eventually, ImageAI will provide support for a wider and more specialized aspects of Computer Vision including and not limited to image recognition in special environments and special fields.

New Release : ImageAI 2.0.2
What's new:

• Option to state image size during custom image prediction model trainings
  
• Object Detection and Video Object detection now returns bounding box coordinates ('box points') (x1,y1,x2, y2) for each object detected in addition to object's 'name' and 'percentage probability'
  
• Options to hide 'percentage probability' and/or object 'name' from being shown in detected image or video
• Support for video object detection on video live stream from device camera, connected camera and IP camera
  
• Support for YOLOv3 and TinyYOLOv3 for all object detection and video object detection tasks.
• Video object detection for all input types (video file and camera) now allows defining custom functions to execute after each frame, each second and each minute of the video is detected and processed. Also include option to specify custom function at once video is fully detected and processed
  
• For each custom function specified, ImageAI returns the frame/seconds/minute/full video analysis of the detections that include the objects' details ( name , percentage probability, box_points), number of instance of each unique object detected (counts) and overall average count of the number of instance of each unique object detected in the case of second / minute / full video analysis
  
• Options to return detected frame at every frame, second or minute processed as a Numpy array.
  
  

- Python 3.5.1 (and later versions) Download (Support for Python 2.7 coming soon)
- pip3 Install
- Tensorflow 1.4.0 (and later versions) Install or install via pip

 pip3 install --upgrade tensorflow 

 pip3 install numpy 

 pip3 install scipy 

 pip3 install opencv-python 

 pip3 install pillow 

 pip3 install matplotlib 

 pip3 install h5py 

 pip3 install keras 

or download the Python Wheel imageai-2.0.2-py3-none-any.whl and run the python installation instruction in the command line to the path of the file like the one below:

pip3 install C:\User\MyUser\Downloads\imageai-2.0.2-py3-none-any.whl

convertible : 52.459555864334106
sports_car : 37.61284649372101
pickup : 3.1751200556755066
car_wheel : 1.817505806684494
minivan : 1.7487050965428352

ImageAI provides 4 different algorithms and model types to perform image prediction, trained on the ImageNet-1000 dataset. The 4 algorithms provided for image prediction include SqueezeNet, ResNet, InceptionV3 and DenseNet.

Click the link below to see the full sample codes, explanations and best practices guide.

>>> Tutorial & Guide

Input Image

Output Image

person : 91.946941614151

person : 73.61021637916565

laptop : 90.24320840835571

laptop : 73.6881673336029

laptop : 95.16398310661316

person : 87.10319399833679

Click the link below to see the full sample codes, explanations and best practices guide.

>>> Tutorial & Guide

Video Object Detection & Analysis

Below is a snapshot of a video with objects detected.

Video Custom Object Detection (Object Tracking)

Below is a snapshot of a video with only person, bicycle and motorcyle detected.

Video Analysis Visualization

Below is a visualization of video analysis returned by **ImageAI** into a 'per_second' function.

ImageAI provides very convenient and powerful methods to perform object detection in videos and track specific object(s). The video object detection class provided only supports the current state-of-the-art RetinaNet, but with options to adjust for state of the art performance or real time processing. Click the link to see the full videos, sample codes, explanations and best practices guide.

>>> Tutorial & Guide

A sample from the IdenProf Dataset used to train a Model for predicting professionals.

ImageAI provides classes and methods for you to train a new model that can be used to perform prediction on your own custom objects. You can train your custom models using SqueezeNet, ResNet50, InceptionV3 and DenseNet in 5 lines of code. Click the link below to see the guide to preparing training images, sample training codes, explanations and best practices.

>>> Tutorials & Documentation

Prediction from a sample model trained on IdenProf, for predicting professionals

mechanic : 76.82620286941528
chef : 10.106072574853897
waiter : 4.036874696612358
police : 2.6663416996598244
pilot : 2.239348366856575

ImageAI provides classes and methods for you to run image prediction your own custom objects using your own model trained with ImageAI Model Training class. You can use your custom models trained with SqueezeNet, ResNet50, InceptionV3 and DenseNet and the JSON file containing the mapping of the custom object names. Click the link below to see the guide to sample training codes, explanations, and best practices guide.

>>> Tutorials & Documentation

We have provided full documentation for all ImageAI classes and functions in 2 major languages. Find links below:

>> Documentation - English Version https://imageai.readthedocs.io
>> Documentation - Chinese Version https://imageai-cn.readthedocs.io

FOR macOS
https://www.tensorflow.org/install/install_mac

FOR UBUNTU
https://www.tensorflow.org/install/install_linux

We also welcome submissions of applications and systems built by you and powered by ImageAI for listings here. Should you want your ImageAI powered developments listed here, you can reach to us via our Contacts below.

For anyone interested in building AI systems and using them for business, economic, social and research purposes, it is critical that the person knows the likely positive, negative and unprecedented impacts the use of such technologies will have. They must also be aware of approaches and practices recommended by experienced industry experts to ensure every use of AI brings overall benefit to mankind. We therefore recommend to everyone that wishes to use ImageAI and other AI tools and resources to read Microsoft's January 2018 publication on AI titled "The Future Computed : Artificial Intelligence and its role in society ". Kindly follow the link below to download the publication.

https://blogs.microsoft.com/blog/2018/01/17/future-computed-artificial-intelligence-role-society/

Moses Olafenwa
Email: guymodscientist@gmail.com
Website: https://moses.specpal.science
Twitter: @OlafenwaMoses
Medium : @guymodscientist
Facebook : moses.olafenwa


John Olafenwa
Email: johnolafenwa@gmail.com
Website: https://john.specpal.science
Twitter: @johnolafenwa
Medium : @johnolafenwa
Facebook : olafenwajohn

We give special thanks to Kang vcar for his incredible and excellent work in translating ImageAI's documentation to the Chinese language. Find below the contact details of those who have contributed immensely to the ImageAI project.

Kang vcar
Email: kangvcar@mail.com
Website: http://www.kangvcar.com
Twitter: @kangvcar

1. Somshubra Majumdar, DenseNet Implementation of the paper, Densely Connected Convolutional Networks in Keras
   https://github.com/titu1994/DenseNet/
   
   

2. Broad Institute of MIT and Harvard, Keras package for deep residual networks
   https://github.com/broadinstitute/keras-resnet
   
   

3. Fizyr, Keras implementation of RetinaNet object detection
   https://github.com/fizyr/keras-retinanet
   
   

4. Francois Chollet, Keras code and weights files for popular deeplearning models
   https://github.com/fchollet/deep-learning-models
   
   

5. Forrest N. et al, SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
   https://arxiv.org/abs/1602.07360
   
   

6. Kaiming H. et al, Deep Residual Learning for Image Recognition
   https://arxiv.org/abs/1512.03385
   
   

7. Szegedy. et al, Rethinking the Inception Architecture for Computer Vision
   https://arxiv.org/abs/1512.00567
   
   

8. Gao. et al, Densely Connected Convolutional Networks
   https://arxiv.org/abs/1608.06993
   
   

9. Tsung-Yi. et al, Focal Loss for Dense Object Detection
   https://arxiv.org/abs/1708.02002
   
   

10. O Russakovsky et al, ImageNet Large Scale Visual Recognition Challenge
    https://arxiv.org/abs/1409.0575
    
    

11. TY Lin et al, Microsoft COCO: Common Objects in Context
    https://arxiv.org/abs/1405.0312
    
    

12. Moses & John Olafenwa, A collection of images of identifiable professionals.
    https://github.com/OlafenwaMoses/IdenProf