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https://github.com/karolzak/keras-unet
Helper package with multiple U-Net implementations in Keras as well as useful utility tools helpful when working with image semantic segmentation tasks. This library and underlying tools come from multiple projects I performed working on semantic segmentation tasks
https://github.com/karolzak/keras-unet
deep-learning deep-neural-networks deeplearning image-segmentation keras keras-tensorflow satellite-imagery segmentation semantic-segmentation u-net unet unet-image-segmentation unet-keras
Last synced: 4 days ago
JSON representation
Helper package with multiple U-Net implementations in Keras as well as useful utility tools helpful when working with image semantic segmentation tasks. This library and underlying tools come from multiple projects I performed working on semantic segmentation tasks
- Host: GitHub
- URL: https://github.com/karolzak/keras-unet
- Owner: karolzak
- License: mit
- Created: 2019-03-13T07:50:21.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2021-03-01T10:12:42.000Z (almost 4 years ago)
- Last Synced: 2025-01-10T21:07:05.603Z (11 days ago)
- Topics: deep-learning, deep-neural-networks, deeplearning, image-segmentation, keras, keras-tensorflow, satellite-imagery, segmentation, semantic-segmentation, u-net, unet, unet-image-segmentation, unet-keras
- Language: Python
- Homepage:
- Size: 31.2 MB
- Stars: 339
- Watchers: 13
- Forks: 109
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Funding: .github/FUNDING.yml
- License: LICENSE
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# About
Helper package with multiple U-Net implementations in Keras as well as useful utility tools helpful when working with image segmentation tasks
# Features:
- [x] U-Net models implemented in Keras
- [x] Vanilla U-Net implementation based on [the original paper](https://arxiv.org/pdf/1505.04597.pdf)
- [x] Customizable U-Net
- [x] U-Net optimized for satellite images based on [DeepSense.AI Kaggle competition entry](https://deepsense.ai/deep-learning-for-satellite-imagery-via-image-segmentation/)
- [x] Utility functions:
- [x] Plotting images and masks with overlay
- [x] Plotting images masks and predictions with overlay (prediction on top of original image)
- [x] Plotting training history for metrics and losses
- [x] Cropping smaller patches out of bigger image (e.g. satellite imagery) using sliding window technique (also with overlap if needed)
- [x] Plotting smaller patches to visualize the cropped big image
- [x] Reconstructing smaller patches back to a big image
- [x] Data augmentation helper function
- [x] Notebooks (examples):
- [x] Training custom U-Net for whale tails segmentation
- [ ] Semantic segmentation for satellite images
- [x] Semantic segmentation for medical images [ISBI challenge 2015](https://biomedicalimaging.org/2015/program/isbi-challenges/)
# Installation:
```bash
pip install git+https://github.com/karolzak/keras-unet
```
or
```bash
pip install keras-unet
```
# Usage examples:
- U-Net implementations in Keras:
- [Vanilla U-Net](#Vanilla-U-Net)
- [Customizable U-Net](#Customizable-U-Net)
- [U-Net for satellite images](#U-Net-for-satellite-images)
- Utils:
- [Plot training history](#Plot-training-history)
- [Plot images and segmentation masks](#Plot-images-and-segmentation-masks)
- [Get smaller patches/crops from bigger image](#Get-smaller-patches/crops-from-bigger-image)
- [Plot small patches into single big image](#Plot-small-patches-into-single-big-image)
- [Reconstruct a bigger image from smaller patches/crops](#Reconstruct-a-bigger-image-from-smaller-patches/crops)
### Vanilla U-Net
[Model scheme can be viewed here](https://raw.githubusercontent.com/karolzak/keras-unet/master/docs/vanilla_unet.png)
```python
from keras_unet.models import vanilla_unet
model = vanilla_unet(input_shape=(512, 512, 3))
```
[[back to usage examples]](#usage-examples)
### Customizable U-Net
[Model scheme can be viewed here](https://raw.githubusercontent.com/karolzak/keras-unet/master/docs/custom_unet.png)
```python
from keras_unet.models import custom_unet
model = custom_unet(
input_shape=(512, 512, 3),
use_batch_norm=False,
num_classes=1,
filters=64,
dropout=0.2,
output_activation='sigmoid')
```
[[back to usage examples]](#usage-examples)
### U-Net for satellite images
[Model scheme can be viewed here](https://raw.githubusercontent.com/karolzak/keras-unet/master/docs/satellite_unet.png)
```python
from keras_unet.models import satellite_unet
model = satellite_unet(input_shape=(512, 512, 3))
```
[[back to usage examples]](#usage-examples)
### Plot training history
```python
history = model.fit_generator(...)
from keras_unet.utils import plot_segm_history
plot_segm_history(
history, # required - keras training history object
metrics=['iou', 'val_iou'], # optional - metrics names to plot
losses=['loss', 'val_loss']) # optional - loss names to plot
```
Output:
[[back to usage examples]](#usage-examples)
### Plot images and segmentation masks
```python
from keras_unet.utils import plot_imgs
plot_imgs(
org_imgs=x_val, # required - original images
mask_imgs=y_val, # required - ground truth masks
pred_imgs=y_pred, # optional - predicted masks
nm_img_to_plot=9) # optional - number of images to plot
```
Output:
[[back to usage examples]](#usage-examples)
### Get smaller patches/crops from bigger image
```python
from PIL import Image
import numpy as np
from keras_unet.utils import get_patches
x = np.array(Image.open("../docs/sat_image_1.jpg"))
print("x shape: ", str(x.shape))
x_crops = get_patches(
img_arr=x, # required - array of images to be cropped
size=100, # default is 256
stride=100) # default is 256
print("x_crops shape: ", str(x_crops.shape))
```
Output:
```output
x shape: (1000, 1000, 3)
x_crops shape: (100, 100, 100, 3)
```
[[back to usage examples]](#usage-examples)
### Plot small patches into single big image
```python
from keras_unet.utils import plot_patches
print("x_crops shape: ", str(x_crops.shape))
plot_patches(
img_arr=x_crops, # required - array of cropped out images
org_img_size=(1000, 1000), # required - original size of the image
stride=100) # use only if stride is different from patch size
```
Output:
```output
x_crops shape: (100, 100, 100, 3)
```
[[back to usage examples]](#usage-examples)
### Reconstruct a bigger image from smaller patches/crops
```python
import matplotlib.pyplot as plt
from keras_unet.utils import reconstruct_from_patches
print("x_crops shape: ", str(x_crops.shape))
x_reconstructed = reconstruct_from_patches(
img_arr=x_crops, # required - array of cropped out images
org_img_size=(1000, 1000), # required - original size of the image
stride=100) # use only if stride is different from patch size
print("x_reconstructed shape: ", str(x_reconstructed.shape))
plt.figure(figsize=(10,10))
plt.imshow(x_reconstructed[0])
plt.show()
```
Output:
```output
x_crops shape: (100, 100, 100, 3)
x_reconstructed shape: (1, 1000, 1000, 3)
```
[[back to usage examples]](#usage-examples)