https://github.com/ycc789741ycc/hog-bgr-with-visualization
HOG feature descriptor, the kind of feature transform before we put our image into SVM. This repository also provides hog visualization both before and after doing block normalization.
https://github.com/ycc789741ycc/hog-bgr-with-visualization
computer-vision hog-features hog-features-extraction numpy opencv python
Last synced: 2 months ago
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HOG feature descriptor, the kind of feature transform before we put our image into SVM. This repository also provides hog visualization both before and after doing block normalization.
- Host: GitHub
- URL: https://github.com/ycc789741ycc/hog-bgr-with-visualization
- Owner: ycc789741ycc
- License: other
- Created: 2021-08-30T06:32:22.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2021-08-30T11:04:40.000Z (over 3 years ago)
- Last Synced: 2025-03-20T15:03:39.590Z (2 months ago)
- Topics: computer-vision, hog-features, hog-features-extraction, numpy, opencv, python
- Language: Python
- Homepage:
- Size: 2.33 MB
- Stars: 6
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# HOG-BGR-with-visualization
HOG feature descriptor, a kind of feature transform before we put our image into SVM or NN.
In this API, I split hog feature into BGR channel respectively, it's a little different from hog in skimage, but this API still support grayscale image.
This API divides orients of gradient into 9 bins and each bin represent 0 20 40 60 ... 160.
Furthermore, due to hog's performance has some relationship with tuning parameters, so this repository provides hog visualization both before and after doing block normalization.
## Environment
Python>=3.9.1
numpy>=1.20.0
opencv-python>=4.5.1.48
## Installation
```
pip install hogBGR
```
## Demo
Original Picture
```
import cv2
from hogBGR import HOG
from hogBGR import HOGvisualized
from hogBGR import util
```
### HOG
> Let's create a 16x16 (pixels) cell, 2x2 (cells) block hog image.
```
img = cv2.imread(r'hogBGR/example/rose.png')
cell_hogfeature = HOG.Cell_HOG(img,cell_size=(16,16))
hog = HOG.BlockNorm_HOG(cell_hogfeature,block_size=(2,2))
```
or
```
img = cv2.imread(r'hogBGR/example/rose.png')
hog = HOG.HOGfeature(img,cell_size=(16,16),block_size=(2,2),flatten=False)
```
> Take a visualization
```
res = HOGvisualized.visualize_HOG(hog[:,:,:,0,0,:],15)
cv2.imshow("HOG feature with block normaliztion",res)
```
or
```
util.BlockNormaliztionHOG_Show(img,one_cell=True)
```
> Below shows hog image in each cell of blocks. (First method would only show first cell of blocks)
### HOG without block normalization
> Let's create a 16x16 (pixels) cell hog image.
```
img = cv2.imread(r'hogBGR/example/rose.png')
cell_hogfeature = HOG.Cell_HOG(img,cell_size=(16,16))
```
> Take a visualization
```
res = HOGvisualized.visualize_HOG(cell_hogfeature,15)
cv2.imshow("part1 - HOG feature without block normaliztion",res)
```
or
```
util.NonBlockNormaliztionHOG_Show(img)
```
> Below shows hog image before block normalization
### Gray scale input image is also supported
Orginal Picture
Visualization
### Simplified visualization
> You can try the result with yourself if you want a faster visualization.
```
res = HOGvisualized.Fast_visualize_HOG(hogfeature,7)
```