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https://github.com/cj-mills/cjm-torchvision-tfms
Some custom Torchvision tranforms.
https://github.com/cj-mills/cjm-torchvision-tfms
Last synced: 3 days ago
JSON representation
Some custom Torchvision tranforms.
- Host: GitHub
- URL: https://github.com/cj-mills/cjm-torchvision-tfms
- Owner: cj-mills
- License: apache-2.0
- Created: 2023-07-29T23:31:57.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-02-23T00:54:00.000Z (9 months ago)
- Last Synced: 2024-08-25T09:42:05.616Z (3 months ago)
- Language: Jupyter Notebook
- Homepage: https://cj-mills.github.io/cjm-torchvision-tfms/
- Size: 46.8 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# cjm-torchvision-tfms
## Install
``` sh
pip install cjm_torchvision_tfms
```
## How to use
``` python
from PIL import Image
img_path = './images/call-hand-gesture.png'
# Open the associated image file as a RGB image
sample_img = Image.open(img_path).convert('RGB')
# Print the dimensions of the image
print(f"Image Dims: {sample_img.size}")
# Show the image
sample_img
```
Image Dims: (384, 512)
``` python
from cjm_torchvision_tfms.core import ResizeMax, PadSquare, CustomTrivialAugmentWide
import torch
from torchvision import transforms
from cjm_pytorch_utils.core import tensor_to_pil
from cjm_pil_utils.core import stack_imgs
```
``` python
target_sz = 384
```
``` python
print(f"Source image: {sample_img.size}")
# Create a `ResizeMax` object
resize_max = ResizeMax(max_sz=target_sz)
# Convert the cropped image to a tensor
img_tensor = transforms.PILToTensor()(sample_img)[None]
print(f"Image tensor: {img_tensor.shape}")
# Resize the tensor
resized_tensor = resize_max(img_tensor)
print(f"Padded tensor: {resized_tensor.shape}")
# Display the updated image
tensor_to_pil(resized_tensor)
```
Source image: (384, 512)
Image tensor: torch.Size([1, 3, 512, 384])
Padded tensor: torch.Size([1, 3, 384, 288])
``` python
print(f"Resized tensor: {resized_tensor.shape}")
# Create a `PadSquare` object
pad_square = PadSquare(shift=True)
# Pad the tensor
padded_tensor = pad_square(resized_tensor)
print(f"Padded tensor: {padded_tensor.shape}")
# Display the updated image
stack_imgs([tensor_to_pil(pad_square(resized_tensor)) for i in range(3)])
```
Resized tensor: torch.Size([3, 384, 288])
Padded tensor: torch.Size([3, 384, 384])
``` python
num_bins = 31
custom_augmentation_space = {
# Identity operation doesn't change the image
"Identity": (torch.tensor(0.0), False),
# Distort the image along the x or y axis, respectively.
"ShearX": (torch.linspace(0.0, 0.25, num_bins), True),
"ShearY": (torch.linspace(0.0, 0.25, num_bins), True),
# Move the image along the x or y axis, respectively.
"TranslateX": (torch.linspace(0.0, 32.0, num_bins), True),
"TranslateY": (torch.linspace(0.0, 32.0, num_bins), True),
# Rotate operation: rotates the image.
"Rotate": (torch.linspace(0.0, 45.0, num_bins), True),
# Adjust brightness, color, contrast,and sharpness respectively.
"Brightness": (torch.linspace(0.0, 0.75, num_bins), True),
"Color": (torch.linspace(0.0, 0.99, num_bins), True),
"Contrast": (torch.linspace(0.0, 0.99, num_bins), True),
"Sharpness": (torch.linspace(0.0, 0.99, num_bins), True),
# Reduce the number of bits used to express the color in each channel of the image.
"Posterize": (8 - (torch.arange(num_bins) / ((num_bins - 1) / 6)).round().int(), False),
# Invert all pixel values above a threshold.
"Solarize": (torch.linspace(255.0, 0.0, num_bins), False),
# Maximize the image contrast by setting the darkest color to black and the lightest to white.
"AutoContrast": (torch.tensor(0.0), False),
# Equalize the image histogram to improve its contrast.
"Equalize": (torch.tensor(0.0), False),
}
# Create a `CustomTrivialAugmentWide` object
trivial_aug = CustomTrivialAugmentWide(op_meta=custom_augmentation_space)
# Pad the tensor
aug_tensor = trivial_aug(resized_tensor)
print(f"Augmented tensor: {aug_tensor.shape}")
# Display the updated image
stack_imgs([tensor_to_pil(trivial_aug(resized_tensor)) for i in range(3)])
```
Augmented tensor: torch.Size([3, 384, 288])
