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https://github.com/nasdin/videorecognition-realtime-autotrainer-alerts

State of the art object detection in real-time using YOLOV3 algorithm. Augmented with a process that allows easy training of the classifier as a plug & play solution . Provides alert if an item in an alert list is detected.
https://github.com/nasdin/videorecognition-realtime-autotrainer-alerts

alerts automatic convolutional-neural-networks darknet deep-learning google-image-search image-processing image-recognition machine-learning object-detection real-time tensorflow video-recognition web-scraper webcam webscraping yolo yolo2 yolov2 yolov2-model

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State of the art object detection in real-time using YOLOV3 algorithm. Augmented with a process that allows easy training of the classifier as a plug & play solution . Provides alert if an item in an alert list is detected.

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README 

        

## Real-Time Video Recognition AI with auto-webscraping trainer + alerts, YoloV3

### By: Nasrudin Salim



#### Real-time object detection and classification with YoloV3



#### Test:

![ ](nas-demo.gif  "Algorithm Real-Time Demo")



### What is this:



- Automatically trains a model via webscraping image search results on a video recognition classifier with transfer learning.

- Enter a label, then enter a list of search queries to google for. It will then google for those search terms and fine-tunes a pretrained classifier.

- Detect objects as well as output alerts/colors differently if an object in your "alert list" is found.

- Can be performed on a video stream in real-time.

- Can be performed on a live-camera stream in real-time.



### Prerequisities

0. Anaconda 3

1.  Python 3

2. CUDNN and CUDA ToolKit Installed

3. GPU with CUDA 9.0 and above support



## Installation Instructions:

- A conda environment file has been provided, please make use of environment file to install the necessary requirement packages.

		conda create -n "YourEnvironmentName" -f environment.yml

- Project uses Cython, so build that too.

		python3 setup.py build_ext --inplace



Remember to download pretrained weights if you need to

See training section



# Usage

#### Getting Data

On the webscraper, indicate the labels in labels.txt, and change the parameters in config.py if needed then in terminal, type



		python3 downloadimages.py



#### Training

Once images are downloaded. You can download pretrained weights here: 

[darknet](https://pjreddie.com/darknet/yolo/) 

Or you can continue training your weights if you've done this before

Edit the parameters in train.py and then in batch type:



		python3 train.py -g



	-g : Whether to use GPU

	-e #: Epochs (optional)

	-s #: Save Rate (optional)

	-e #: Epochs (optional)

	-lr #: Learning Rate (optional)

	-b #: Batch (optional)



#### Testing/Using

Usage:



	python3 run.py "path_to_video_file" -g -s "test.avi"

	

#### Explanation



	Whether to use a video file, supply path

	If not video file, assume to use camera feed.

	-g /--gpu : Optional, whether to use GPU ( Defaults to config)

	-s /--save: Optional, whether to save results to a video file and where (Defaults to config)



### Configs



There is a config.py provided which helps to set the defaults for the following:

1. 	Edit the path to weight file

2. 	USE GPU? Bool

3. 	SAVE Result video file? Bool



You will be asked for the path of the video file. You can adjust the parameters as well as the paths of the weights by opening up the py files.



#### Setting Alerts

You can set alerts by editing the text file "alerts.txt" when a label found in this text file appears, it will generate an alert by drawing the box red and displaying "Alert x found in footage" when testing.



#### HELP AND CONTRIBUTION



	This project needs your help and contribution

	The Training Script needs work.

	The Alerts and Pinging requires work to finish.

	Downloading of images from google and seamless Training