# patchify
patchfy can split images into small overlappable patches by given patch cell size, and merge patches into original image.
This library provides two functions: `patchify`, `unpatchify`.
## Installation
```
pip install patchify
```
## Usage
### Split image to patches
`patchify(image_to_patch, patch_shape, step=1)`
2D image:
```python
#This will split the image into small images of shape [3,3]
patches = patchify(image, (3, 3), step=1)
```
3D image:
```python
#This will split the image into small images of shape [3,3,3]
patches = patchify(image, (3, 3, 3), step=1)
```
### Merge patches into original image
`unpatchify(patches_to_merge, merged_image_size)`
```python
reconstructed_image = unpatchify(patches, image.shape)
```
This will reconstruct the original image that was patchified in previous code.
**Caveat**: in order for `unpatchify` to work, you need to create patchies with equal step size. e.g. if the original image has width 3 and the patch has width 2, you cannot really create equal step size patches with step size 2. (first patch [elem0, elem1] and second patch [elem2, elem3], which is out of bound).
The required condition for unpatchify to success is to have (width - patch_width) mod step_size = 0.
### Full running examples
#### 2D image patchify and merge
```python
import numpy as np
from patchify import patchify, unpatchify
image = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])
patches = patchify(image, (2,2), step=1) # split image into 2*3 small 2*2 patches.
assert patches.shape == (2, 3, 2, 2)
reconstructed_image = unpatchify(patches, image.shape)
assert (reconstructed_image == image).all()
```
#### 3D image patchify and merge
```python
import numpy as np
from patchify import patchify, unpatchify
image = np.random.rand(512,512,3)
patches = patchify(image, (2,2,3), step=1) # patch shape [2,2,3]
print(patches.shape) # (511, 511, 1, 2, 2, 3). Total patches created: 511x511x1
assert patches.shape == (511, 511, 1, 2, 2, 3)
reconstructed_image = unpatchify(patches, image.shape)
print(reconstructed_image.shape) # (512, 512, 3)
assert (reconstructed_image == image).all()
```
Raw data
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"description": "# patchify\n\npatchfy can split images into small overlappable patches by given patch cell size, and merge patches into original image.\n\nThis library provides two functions: `patchify`, `unpatchify`.\n\n## Installation\n```\npip install patchify\n```\n\n## Usage\n\n### Split image to patches\n\n`patchify(image_to_patch, patch_shape, step=1)`\n\n2D image:\n```python\n#This will split the image into small images of shape [3,3]\npatches = patchify(image, (3, 3), step=1)\n```\n\n3D image:\n```python\n#This will split the image into small images of shape [3,3,3]\npatches = patchify(image, (3, 3, 3), step=1)\n```\n\n### Merge patches into original image\n\n`unpatchify(patches_to_merge, merged_image_size)`\n\n```python\nreconstructed_image = unpatchify(patches, image.shape)\n```\nThis will reconstruct the original image that was patchified in previous code.\n\n**Caveat**: in order for `unpatchify` to work, you need to create patchies with equal step size. e.g. if the original image has width 3 and the patch has width 2, you cannot really create equal step size patches with step size 2. (first patch [elem0, elem1] and second patch [elem2, elem3], which is out of bound).\n\nThe required condition for unpatchify to success is to have (width - patch_width) mod step_size = 0.\n\n### Full running examples\n\n#### 2D image patchify and merge\n\n```python\nimport numpy as np\nfrom patchify import patchify, unpatchify\n\nimage = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])\n\npatches = patchify(image, (2,2), step=1) # split image into 2*3 small 2*2 patches.\n\nassert patches.shape == (2, 3, 2, 2)\nreconstructed_image = unpatchify(patches, image.shape)\n\nassert (reconstructed_image == image).all()\n```\n\n#### 3D image patchify and merge\n\n```python\nimport numpy as np\nfrom patchify import patchify, unpatchify\n\nimage = np.random.rand(512,512,3)\n\npatches = patchify(image, (2,2,3), step=1) # patch shape [2,2,3]\nprint(patches.shape) # (511, 511, 1, 2, 2, 3). Total patches created: 511x511x1\n\nassert patches.shape == (511, 511, 1, 2, 2, 3)\nreconstructed_image = unpatchify(patches, image.shape)\nprint(reconstructed_image.shape) # (512, 512, 3)\n\nassert (reconstructed_image == image).all()\n```\n",
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