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PR to fix #342 #347

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Jun 9, 2020
Merged

PR to fix #342 #347

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fab234c
intermediate work for normalization
fazamani Jun 4, 2020
bf9e4f0
Merge branch 'staging' into image-normalization
fazamani Jun 4, 2020
cd575ff
1) normalize function runs based on global MIN and MAX 2) has a error…
fazamani Jun 4, 2020
6b1ebdf
global normalization added to test.py
fazamani Jun 5, 2020
c67a322
Merge branch 'staging' into image-normalization
fazamani Jun 5, 2020
684e225
increasing the threshold on timeout
fazamani Jun 5, 2020
f699e99
trigger
fazamani Jun 5, 2020
0a050b1
revert
fazamani Jun 5, 2020
18d66cb
idk what happened
fazamani Jun 5, 2020
a9b0aa9
Merge branch 'staging' into image-normalization
fazamani Jun 8, 2020
627afe8
increase timeout
fazamani Jun 8, 2020
b015317
picking up global min and max
fazamani Jun 9, 2020
39a861a
passing config to TrainPatchLoader to facilitate access to global min…
fazamani Jun 9, 2020
ea9f319
removed print statement
fazamani Jun 9, 2020
7818b73
changed section loaders
fazamani Jun 9, 2020
18f4e02
updated test for min and max from config too
fazamani Jun 9, 2020
380f217
adde MIN and MAX to config
fazamani Jun 9, 2020
4f3b503
notebook modified for loaders
fazamani Jun 9, 2020
ff797b7
another dataloader in notebook
fazamani Jun 9, 2020
c92ce78
readme update
fazamani Jun 9, 2020
6140e23
changed the default values for min max, updated the docstring for loa…
fazamani Jun 9, 2020
ab184d8
debug
fazamani Jun 9, 2020
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8 changes: 4 additions & 4 deletions cv_lib/cv_lib/event_handlers/tensorboard_handlers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,9 +20,9 @@ def _transform_image(output_tensor):
return torchvision.utils.make_grid(output_tensor, normalize=True, scale_each=True)


def _transform_pred(output_tensor):
def _transform_pred(output_tensor, n_classes):
output_tensor = output_tensor.squeeze().cpu().numpy()
decoded = decode_segmap(output_tensor)
decoded = decode_segmap(output_tensor, n_classes)
return torchvision.utils.make_grid(np_to_tb(decoded), normalize=False, scale_each=False)


Expand Down Expand Up @@ -111,5 +111,5 @@ def log_results(engine, evaluator, summary_writer, n_classes, stage):
y_pred[mask == 255] = 255

summary_writer.add_image(f"{stage}/Image", _transform_image(image), epoch)
summary_writer.add_image(f"{stage}/Mask", _transform_pred(mask), epoch)
summary_writer.add_image(f"{stage}/Pred", _transform_pred(y_pred), epoch)
summary_writer.add_image(f"{stage}/Mask", _transform_pred(mask, n_classes), epoch)
summary_writer.add_image(f"{stage}/Pred", _transform_pred(y_pred, n_classes), epoch)
21 changes: 12 additions & 9 deletions cv_lib/cv_lib/utils.py
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Expand Up @@ -8,13 +8,16 @@
from matplotlib import pyplot as plt


def normalize(array):
def normalize(array, MIN, MAX):
"""
Normalizes a segmentation mask array to be in [0,1] range
for use with PIL.Image
Normalizes a segmentation image array by the global range of the data,
MIN and MAX, for use with PIL.Image
"""
min = array.min()
return (array - min) / (array.max() - min)

den = MAX - MIN
if den==0:
den += np.finfo(float).eps
return (array - MIN) / den


def mask_to_disk(mask, fname, n_classes, cmap_name="rainbow"):
Expand All @@ -30,15 +33,15 @@ def mask_to_disk(mask, fname, n_classes, cmap_name="rainbow"):
Image.fromarray(cmap(mask / n_classes, bytes=True)).save(fname)


def image_to_disk(mask, fname, cmap_name="seismic"):
def image_to_disk(image, fname, MIN, MAX, cmap_name="seismic"):
"""
write segmentation image to disk using a particular colormap
"""
cmap = plt.get_cmap(cmap_name)
Image.fromarray(cmap(normalize(mask), bytes=True)).save(fname)
Image.fromarray(cmap(normalize(image, MIN, MAX), bytes=True)).save(fname)


def decode_segmap(label_mask, colormap_name="rainbow"):
def decode_segmap(label_mask, n_classes, colormap_name="rainbow"):
"""
Decode segmentation class labels into a colour image
Args:
Expand All @@ -51,7 +54,7 @@ def decode_segmap(label_mask, colormap_name="rainbow"):
cmap = plt.get_cmap(colormap_name)
# loop over the batch
for i in range(label_mask.shape[0]):
im = Image.fromarray(cmap(normalize(label_mask[i, :, :]), bytes=True)).convert("RGB")
im = Image.fromarray(cmap((label_mask[i, :, :] / n_classes), bytes=True)).convert("RGB")
out[i, :, :, :] = np.array(im).swapaxes(0, 2).swapaxes(1, 2)

return out
Expand Down
7 changes: 4 additions & 3 deletions experiments/interpretation/dutchf3_patch/local/test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -205,6 +205,8 @@ def _patch_label_2d(
):
"""Processes a whole section
"""


img = torch.squeeze(img)
h, w = img.shape[-2], img.shape[-1] # height and width

Expand Down Expand Up @@ -234,14 +236,14 @@ def _patch_label_2d(
path_prefix = f"{outdir}/{batch_indexes[i][0]}_{batch_indexes[i][1]}"
model_output = model_output.detach().cpu()
# save image:
image_to_disk(np.array(batch[i, 0, :, :]), path_prefix + "_img.png")
image_to_disk(np.array(batch[i, 0, :, :]), path_prefix + "_img.png", float(img.min()), float(img.max()))
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Is it possible to use the max and min values here from the config file?

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do you mean now, or in the next attempt to address the "global MIN and MAX" next sprint? Currently we don't have MIN and MAX defined in the cofing, I reverted those changes!

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okay that makes sense.. sorry if I misunderstood.

# dump model prediction:
mask_to_disk(model_output[i, :, :, :].argmax(dim=0).numpy(), path_prefix + "_pred.png", num_classes)
# dump model confidence values
for nclass in range(num_classes):
image_to_disk(
model_output[i, nclass, :, :].numpy(), path_prefix + f"_class_{nclass}_conf.png",
)
model_output[i, nclass, :, :].numpy().min(), model_output[i, nclass, :, :].numpy().max())

# crop the output_p in the middle
output = output_p[:, :, ps:-ps, ps:-ps]
Expand Down Expand Up @@ -288,7 +290,6 @@ def _evaluate_split(
for i, (images, labels) in enumerate(test_loader):
logger.info(f"split: {split}, section: {i}")
total_iteration = total_iteration + 1

outputs = _patch_label_2d(
model,
images,
Expand Down
20 changes: 10 additions & 10 deletions interpretation/deepseismic_interpretation/dutchf3/data.py
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Original file line number Diff line number Diff line change
Expand Up @@ -155,7 +155,7 @@ def __getitem__(self, index):
outdir = f"debug/sectionLoader_{self.split}_raw"
generate_path(outdir)
path_prefix = f"{outdir}/index_{index}_section_{section_name}"
image_to_disk(im, path_prefix + "_img.png")
image_to_disk(im, path_prefix + "_img.png", self.seismic.min(), self.seismic.max())
mask_to_disk(lbl, path_prefix + "_lbl.png", self.n_classes)

if self.augmentations is not None:
Expand All @@ -169,7 +169,7 @@ def __getitem__(self, index):
outdir = f"debug/sectionLoader_{self.split}_{'aug' if self.augmentations is not None else 'noaug'}"
generate_path(outdir)
path_prefix = f"{outdir}/index_{index}_section_{section_name}"
image_to_disk(np.array(im[0]), path_prefix + "_img.png")
image_to_disk(np.array(im[0]), path_prefix + "_img.png", self.seismic.min(), self.seismic.max())
mask_to_disk(np.array(lbl[0]), path_prefix + "_lbl.png", self.n_classes)

return im, lbl
Expand Down Expand Up @@ -411,7 +411,7 @@ def __getitem__(self, index):
generate_path(outdir)
# this needs to take the first dimension of image (no depth) but lbl only has 1 dim
path_prefix = f"{outdir}/index_{index}_section_{section_name}"
image_to_disk(im[0, :, :], path_prefix + "_img.png")
image_to_disk(im[0, :, :], path_prefix + "_img.png", self.seismic.min(), self.seismic.max())
mask_to_disk(lbl, path_prefix + "_lbl.png", self.n_classes)

if self.augmentations is not None:
Expand All @@ -430,7 +430,7 @@ def __getitem__(self, index):
)
generate_path(outdir)
path_prefix = f"{outdir}/index_{index}_section_{section_name}"
image_to_disk(np.array(im[0, :, :]), path_prefix + "_img.png")
image_to_disk(np.array(im[0, :, :]), path_prefix + "_img.png", self.seismic.min(), self.seismic.max())
mask_to_disk(np.array(lbl[0, :, :]), path_prefix + "_lbl.png", self.n_classes)

return im, lbl
Expand Down Expand Up @@ -500,7 +500,7 @@ def __getitem__(self, index):
outdir = f"debug/patchLoader_{self.split}_raw"
generate_path(outdir)
path_prefix = f"{outdir}/index_{index}_section_{patch_name}"
image_to_disk(im, path_prefix + "_img.png")
image_to_disk(im, path_prefix + "_img.png", self.seismic.min(), self.seismic.max())
mask_to_disk(lbl, path_prefix + "_lbl.png", self.n_classes)

if self.augmentations is not None:
Expand All @@ -512,7 +512,7 @@ def __getitem__(self, index):
outdir = f"patchLoader_{self.split}_{'aug' if self.augmentations is not None else 'noaug'}"
generate_path(outdir)
path_prefix = f"{outdir}/{index}"
image_to_disk(im, path_prefix + "_img.png")
image_to_disk(im, path_prefix + "_img.png", self.seismic.min(), self.seismic.max())
mask_to_disk(lbl, path_prefix + "_lbl.png", self.n_classes)

if self.is_transform:
Expand All @@ -523,7 +523,7 @@ def __getitem__(self, index):
outdir = f"debug/patchLoader_{self.split}_{'aug' if self.augmentations is not None else 'noaug'}"
generate_path(outdir)
path_prefix = f"{outdir}/index_{index}_section_{patch_name}"
image_to_disk(np.array(im[0, :, :]), path_prefix + "_img.png")
image_to_disk(np.array(im[0, :, :]), path_prefix + "_img.png", self.seismic.min(), self.seismic.max())
mask_to_disk(np.array(lbl[0, :, :]), path_prefix + "_lbl.png", self.n_classes)

return im, lbl
Expand Down Expand Up @@ -769,7 +769,7 @@ def __getitem__(self, index):
outdir = f"debug/patchLoaderWithSectionDepth_{self.split}_raw"
generate_path(outdir)
path_prefix = f"{outdir}/index_{index}_section_{patch_name}"
image_to_disk(im[0, :, :], path_prefix + "_img.png")
image_to_disk(im[0, :, :], path_prefix + "_img.png", self.seismic.min(), self.seismic.max())
mask_to_disk(lbl, path_prefix + "_lbl.png", self.n_classes)

if self.augmentations is not None:
Expand All @@ -783,7 +783,7 @@ def __getitem__(self, index):
outdir = f"patchLoaderWithSectionDepth_{self.split}_{'aug' if self.augmentations is not None else 'noaug'}"
generate_path(outdir)
path_prefix = f"{outdir}/{index}"
image_to_disk(im[0, :, :], path_prefix + "_img.png")
image_to_disk(im[0, :, :], path_prefix + "_img.png", self.seismic.min(), self.seismic.max())
mask_to_disk(lbl, path_prefix + "_lbl.png", self.n_classes)

if self.is_transform:
Expand All @@ -796,7 +796,7 @@ def __getitem__(self, index):
)
generate_path(outdir)
path_prefix = f"{outdir}/index_{index}_section_{patch_name}"
image_to_disk(np.array(im[0, :, :]), path_prefix + "_img.png")
image_to_disk(np.array(im[0, :, :]), path_prefix + "_img.png", self.seismic.min(), self.seismic.max())
mask_to_disk(np.array(lbl[0, :, :]), path_prefix + "_lbl.png", self.n_classes)

return im, lbl
Expand Down