# Pedestrian_Detection

**Repository Path**: lowchardcz3/Pedestrian_Detection

## Basic Information

- **Project Name**: Pedestrian_Detection
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: GPL-3.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-03-24
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Pedestrian Detection


## Dependencies

* OpenCV
* scikit-image ```pip install scikit-image==0.14.1```
* scikkit-learn ```pip install scikit-learn==0.20.2```

## Running Detection

To test on images, simply run, `python detectmulti.py -i <path to image>`

For example, `python detectmulti.py -i sample_images/pedestrian.jpg`


For more options run, `python detectmulti.py -h`

Following are some examples,


![Pedestrian](.readme_images/before_nms1.png?raw=true "Sample Results")
![Pedestrian](.readme_images/after_nms1.png?raw=true "Sample Results")

![Pedestrian](.readme_images/before_nms2.png?raw=true "Sample Results")
![Pedestrian](.readme_images/after_nms2.png?raw=true "Sample Results")


## Training
This code is meant for Training a Pedestrian Detector using [INRIA Person Dataset](http://pascal.inrialpes.fr/data/human/).

Download, Uncompress and place it in the root of this repository.

Then just run:
```
sudo sh fixpng.sh # To fix the broken png files in this dataset.
```
**Note:** *The above step is necessary to fix the broken pngs in the dataset, without running this the classifer won't be correctly trained.*

To train just run:
```
python train.py --pos <path to positive images> --neg <path to negative images>
```
For INRIA dataset, this would be probably,
```
python train.py --pos INRIAPerson/train_64x128_H96/pos --neg INRIAPerson/train_64x128_H96/neg
```

After training, two new files would be created, namely, `person.pkl` and `person_final.pkl`. The former is the pre eliminary detector and the latter is the improved (hard negatively mined) detector.

### Note on Training:
Training can use high amounts of memory, so be sure to have a swap space in case of RAM overflows. Also, memory consumption can be reduced by decreasing the maximum number of hard negative windows to be mined. This is defined by the `MAX_HARD_NEGATIVES` global variable in `train.py`.


## Testing
After successful training just run:
```
python test.py --pos <path to positive images> --neg <path to negative images>
```
For INRIA dataset, this would be probably,
```
python test.py --pos INRIAPerson/test_64x128_H96/pos --neg INRIAPerson/test_64x128_H96/neg
```

This would print `True Positives`, `True Negatives`, `False Positives`, `False Negatives`, `Precision`, `Recall` and `F1 Score`.