Extract and Merge Batches/Image patches (tf/torch) for easy, fast and self-contained digital image processing and deep learning model training.
Project description
Extract and Merge Image Patches (EMPatches)
Extract and Merge Batches/Image patches (tf/torch), fast and self-contained digital image processing and deep learning model training.
- Extract patches
- Merge the extracted patches to obtain the original image back.
Update 0.2.0 (New Functionalities)
- Handling of
tensorflow
/pytorch
Batched images of shapeBxCxHxW
->pytorch
orBxHxWxC
->tf
. C can be any number not limited to just RGB channels. - Modes added for mergeing patches.
overwrite
: next patch will overwrite the overlapping area of the previous patch.max
: maximum value of overlapping area at each pixel will be written.min
: minimum value of overlapping area at each pixel will be written.avg
: mean/average value of overlapping area at each pixel will be written.
- Patching via providing Indices.
- Strided patching thanks to Andreasgejlm
Dependencies
python >= 3.6
numpy
math
Usage
Extracting Patches
from empatches import EMPatches
import imgviz # just for plotting
# get image either RGB or Grayscale
img = cv2.imread('../digits.jpg')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# load module
emp = EMPatches()
img_patches, indices = emp.extract_patches(img, patchsize=512, overlap=0.2)
# displaying 1st 10 image patches
tiled= imgviz.tile(list(map(np.uint8, img_patches)),border=(255,0,0))
plt.figure()
plt.imshow(tiled)
Image Processing
Now we can perform our operation on each patch independently and after we are done we can merge them back together.
'''
pseudo code
'''
# do some processing, just store the patches in the list in same order
img_patches_processed = some_processing_func(img_patches)
# or run your deep learning model on patches independently and then merge the predictions
img_patches_processed = model.predict(img_patches)
'''For now lets just flip channels'''
img_patches[1] = cv2.cvtColor(img_patches[1], cv2.COLOR_BGR2RGB)
Merging Patches via various modes
After processing the patches if you can merge all of them back in original form as follows,
merged_img = emp.merge_patches(img_patches, indices, mode='max') # or
merged_img = emp.merge_patches(img_patches, indices, mode='min') # or
merged_img = emp.merge_patches(img_patches, indices, mode='overwrite') # or
merged_img = emp.merge_patches(img_patches, indices, mode='avg') # or
# display
plt.figure()
plt.imshow(merged_img.astype(np.uint8))
plt.title(Your mode)
Strided Patching
img_patches, indices = emp.extract_patches(img, patchsize=512, overlap=0.2, stride=128)
tiled= imgviz.tile(list(map(np.uint8, img_patches)),border=(255,0,0))
plt.figure()
plt.imshow(tiled.astype(np.uint8))
plt.title('Strided patching')
Batched Patching
Things to know.
-
batch : Batch of images of shape either BxCxHxW -> pytorch or BxHxWxC -> tf to extract patches from in list(list1, list2, ...), where, list1->([H W C], [H W C], ...) and so on.
-
patchsize : size of patch to extract from image only square patches can be extracted for now.
-
overlap (Optional): overlap between patched in percentage a float between [0, 1].
-
stride (Optional): Step size between patches
-
type (Optional): Type of batched images tf or torch type
-
batch_patches : a list containing lists of extracted patches of images.
-
batch_indices : a list containing lists of indices of patches in order, whihc can be used at later stage for 'merging_patches'.
-
merged_batch : a np array of shape BxCxHxW -> pytorch or BxHxWxC -> tf.
Extraction
from empatches import BatchPatching
bp = BatchPatching(patchsize=512, overlap=0.2, stride=None, typ='torch')
# extracging
batch_patches, batch_indices = bp.patch_batch(batch) # batch of shape BxCxHxW, C can be any number 3 or greater
plt.imshow(batch_patches[1][2])
plt.title('3rd patch of 2nd image in batch')
Merging
# merging
# output will be of shpae depending on typ variable
# BxCxHxW -> torch or BxHxWxC -> tf
merged_batch = bp.merge_batch(batch_patches, batch_indices, mode='avg')
# accessing the merged images
plt.imshow(merged_batch[1,...].astype(np.uint8))
plt.title('2nd merged image in batch')
Patching via Providing Indices
NOTE in this case merging is not supported.
from empatches import patch_via_indices
img = cv2.imread('./digit.jpg')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.resize(img, (1024, 512))
i = [(0, 512, 0, 256), # 1st patch dims/indices
(0, 256, 310, 922),# 2nd patch dims/indices
(0, 512, 512, 768)]# 3rd patch dims/indices
img_patches = patch_via_indices(img, indices)
# plotting
tiled= imgviz.tile(list(map(np.uint8, img_patches)),border=(255,0,0))
plt.figure()
plt.imshow(tiled.astype(np.uint8))
plt.title('patching via providing indices')
Project details
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