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ratulKabir / Custom-Object-Detection-using-Darkflow

Make custom objects dataset and detect them using darkflow. Darkflow is a tensorflow translation of Darknet.
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custom-dataset custom-object-detection darkflow deep-learning deep-neural-networks image-processing labelimg machine-learning real-time tensorflow yolo yolov2

readme

Intro

While learning YOLO I have gone through a lot of blogs, github codes, blogs, courses. I have tried to combine all of them and see how to work with my own dataset set.

I have used Anaconda and jupyter notebook. Here I have used Darkflow to detect custom object.
Also I use Windows. Therefore all my tips are likely to run well on Windows.

Requirements

Python3, tensorflow 1.0, numpy, opencv 3. Links for installation below:

Getting started

You can choose one of the following three ways to get started with darkflow. If you are using Python 3 on windows you will need to install Microsoft Visual C++ 14.0. Here you can find installation process, why it is required, references etc or you can try stackoverflow.

  1. Just build the Cython extensions in place. NOTE: If installing this way you will have to use ./flow in the cloned darkflow directory instead of flow as darkflow is not installed globally.

    python3 setup.py build_ext --inplace

  2. Let pip install darkflow globally in dev mode (still globally accessible, but changes to the code immediately take effect)

    pip install -e .

  3. Install with pip globally

    pip install .

Download a weights file

Make own Dataset

I have run the model on around 250 images. I recommend to have a much bigger dataset for better performance.

Dataset

To make a dataset of objects around you

Annotation

Training on your own dataset

The steps below assume we want to use tiny YOLO and our dataset has 3 classes

  1. Create a copy of the configuration file tiny-yolo-voc.cfg and rename it according to your preference tiny-yolo-voc-3c.cfg (It is crucial that you leave the original tiny-yolo-voc.cfg file unchanged, see below for explanation). Here tiny-yolo-voc-3c.cfg is for 3 classes, you can change the name as you wish.

  2. In tiny-yolo-voc-3c.cfg, change classes in the [region] layer (the last layer) to the number of classes you are going to train for. In our case, classes are set to 3.

    ...

[region]
anchors = 1.08,1.19,  3.42,4.41,  6.63,11.38,  9.42,5.11,  16.62,10.52
bias_match=1
classes=3  ## 3 classes
coords=4
num=5
softmax=1

...

  3. In tiny-yolo-voc-3c.cfg, change filters in the [convolutional] layer (the second to last layer) to num (classes + 5). In our case, num is 5 and classes are 3 so 5 (3 + 5) = 40 therefore filters are set to 40.

    ...

[convolutional]
size=1
stride=1
pad=1
filters=40  ## 5 * (3 + 5) = 40
activation=linear

[region]
anchors = 1.08,1.19,  3.42,4.41,  6.63,11.38,  9.42,5.11,  16.62,10.52

...

  4. Change labels.txt to include the label(s) you want to train on (number of labels should be the same as the number of classes you set in tiny-yolo-voc-3c.cfg file). In my case, labels.txt will contain 3 labels.

    king
ace
ten

  5. Reference the tiny-yolo-voc-3c.cfg model when you train.

    python flow --model cfg/tiny-yolo-voc-3c.cfg --load weights/tiny-yolo-voc.weights --train --annotation train/Annotations --dataset train/Images --gpu 1.0 --epochs 300

    In windows you need to type python at the beginning otherwise it does not recognise the flow command. Next spesify the model --model cfg/tiny-yolo-voc-3c.cfg and the weights --load weights/tiny-yolo-voc.weights. After that specify the path for the annatations --annotation train/Annotations and images --dataset train/Images. Use --gpu 1.0 to use gpu for speed, if you do not have GPU just don't use this part. You can specify the number of epochs. By default it is 1000. However it can be stopped anytime. I recommend to keep the lose below 1.

Object Detection using YOLO

Open the object-detection-with-YOLO.ipynb file. I have tried to add comments to make it easy to understand.

Image

To detect object from images:

  1. Go to the Object Detection from Image section.
  2. Change the image name with your image name from the following line
    img = cv2.imread('images/img_2386.jpg', cv2.IMREAD_COLOR)
  3. If you have multiple object in your image then you have to define all the tl (Top left), br (Bottom right) for different ofjects and their labels.

Video

To detect object from video:

  1. Go to the Object Detection from Video section.
  2. Change the image name with your image name from the following line
    capture = cv2.VideoCapture('test2.mkv')
  3. Run.
  4. Press Q to quit

Webcam

To detect object from webcam just run the code from Object Detection from Webcam section. If you have multiple webcams you may need to specify the number correctly for your desired webcam. I have my laptops default webcam. Thats why I have used 0. To change the nummber edit this line
capture = cv2.VideoCapture(0)

References