CVT
Image transforms used in deep learning training.
installation
pip install cvt
git clone git@github.com:wangjie-ruc/cvt.git
cd cvt
python setup.py installusage
Please Refer to test folder.
Data should be put in a dict before transformed. 'image' for image data, 'mask' for pixel-wise annotation, for multi-label segmentation, mask are organised in a list. keypoint and bounding box are under developing.
We can transform a single image directly without putting it in a dict.
img = cv.imread("")
mask = cv.imread("")
data = {'image':img, 'mask':mask}cfg.json
{
"sequence":{
"sample": {
"bright": {"scale": 0.1},
"contrast": {"scale": 0.1},
"hue": {"scale": 0.1}
},
"shuffle": {
"vflip": {},
"hflip": {},
"rotate": {"degrees": 30}
}
}
}import cvt
import matplotlib.pyplot as plt
tfms = cvt.from_file('cfg.json')
img = cv.imread('image.jpg')[:,:,[2,1,0]]
data = {'image':img}
data = tfms(data)
plt.imshow(data['image'])
plt.show()generate json config from python code
from cvt.transform import (RandomBright, RandomContrast, RandomHorizontalFlip,
RandomHue, RandomRotation, RandomVerticalFlip,
Sample, Sequence, Shuffle)
tfms = Sequence([
Sample([RandomBright(0.2), RandomContrast(0.2), RandomHue(0.2)]),
Shuffle([RandomHorizontalFlip(), RandomVerticalFlip(), RandomRotation(30)])
])
print(tfms.to_json())
''' expected output
{
"sequence": {
"sample": {
"bright": {
"scale": [
0.8,
1.2
]
},
"contrast": {
"scale": [
0.8,
1.2
]
},
"hue": {
"scale": [
-0.2,
0.2
]
}
},
"shuffle": {
"hflip": {
"p": 0.5
},
"vflip": {
"p": 0.5
},
"rotate": {
"degrees": [
-30,
30
],
"interpolation": "nearest",
"expand": false,
"center": null,
"fill": 0
}
}
}
}
'''modules
- compose transforms
- Sequence
- Shuffle
- Sample
- color transforms
- RandomBright
- RandomContrast
- RandomSaturation
- RandomHue
- RandomGamma
- geometry transforms
- RandomHorizontalFlip
- RandomVerticalFlip
- RandomRotation
- Resize
- RandomResizedCrop
- RandomCrop
- Pad
- tensor
- ToTensor
- is_numpy
- is_pil
- is_tensor
- to_numpy
- to_pil
- to_tensor
