# HOG-SVM-python

**Repository Path**: fidder/HOG-SVM-python

## Basic Information

- **Project Name**: HOG-SVM-python
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2019-12-18
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# object-detector
Object Detector using HOG as descriptor and Linear SVM as classifier. | [Video](https://www.youtube.com/watch?v=SPXocFBjr70)

## Run the code

I have created a single python script that can be used to test the code. To test the code, run the lines below in your terminal.

```shell
git clone https://github.com/bikz05/object-detector.git
cd object-detector/bin
test-object-detector
```

_The `test-object-detector` will download the [UIUC Image Database for Car Detection](https://cogcomp.cs.illinois.edu/Data/Car/) and train a classifier to detect cars in an image. The SVM model files will be stored in `data/models`, so that they can be resused later on._

### Configuration File

All the configurations are in the `data/config/config.cfg` configuration files. You can change it as per your need. Here is what the default configuration file looks like (which I have set for Car Detector)-

```bash
[hog]
min_wdw_sz: [100, 40]
step_size: [10, 10]
orientations: 9
pixels_per_cell: [8, 8]
cells_per_block: [3, 3]
visualize: False
normalize: True

[nms]
threshold: .3

[paths]
pos_feat_ph: ../data/features/pos
neg_feat_ph: ../data/features/neg
model_path: ../data/models/svm.model
```

### About the modules

* `extract-features.py` -- This module is used to extract HOG features of the training images.
* `train-classifier.py` -- This module is used to train the classifier.
* `nms.py` -- This module performs Non Maxima Suppression.
* `test-classifier.py` -- This module is used to test the classifier using a test image.
* `config.py` -- Imports the configuration variables from `config.cfg`.

## Some of the results

#### Test Image 1
_Detections before NMS_

![Image 1](data/images/test-im-1.png)

_Detections after NMS_

![](data/images/test-im-1-nms.png)
#### Test Image 2
_Detections before NMS_

![](data/images/test-im-2.png)

_Detections after NMS_

![](data/images/test-im-2-nms.png)
#### Test Image 3
_Detections before NMS_

![](data/images/test-im-3.png)

_Detections after NMS_

![](data/images/test-im-3-nms.png)
#### Test Image 4
_Detections before NMS_

![](data/images/test-im-4.png)

_Detections after NMS_

![](data/images/test-im-4-nms.png)

## TODO

Here is list of tasks that I am planning to implement in the future -

* Optimize code to use more `numpy` vectorized codes.
* Faster NMS code.
* Add bootstrapping (Hard Negative Mining) code.


## Useful tutorials

1. [Histogram of Oriented Gradients and Object Detection](http://www.pyimagesearch.com/2014/11/10/histogram-oriented-gradients-object-detection/)
2. [Image Pyramids with Python and OpenCV](http://www.pyimagesearch.com/2015/03/16/image-pyramids-with-python-and-opencv/)
3. [Sliding Windows for Object Detection with Python and OpenCV](http://www.pyimagesearch.com/2015/03/23/sliding-windows-for-object-detection-with-python-and-opencv/)
4. [Non-Maximum Suppression for Object Detection in Python](http://www.pyimagesearch.com/2014/11/17/non-maximum-suppression-object-detection-python/)
5. [(Faster) Non-Maximum Suppression in Python](http://www.pyimagesearch.com/2015/02/16/faster-non-maximum-suppression-python/)
6. [Texture Matching using Local Binary Patterns (LBP), OpenCV, scikit-learn and Python](http://hanzratech.in/2015/05/30/local-binary-patterns.html)
7. [Detección de objetos Course by Coursera](https://www.coursera.org/course/deteccionobjetos)