def __getitem__(self, idx):
"""
Gets batch at position `index`.
Arguments:
index: position of the batch in the Sequence.
Returns:
A batch
"""
indexes = self.indexes[idx * self.batch_size: (idx + 1) * self.batch_size]
image_batch = self.images[indexes]
image_batch = self.__load_image(image_batch)
if self.resize_shape:
# Resize images interpolation choice
# ``nearest`` (identical to ``cv2.INTER_NEAREST``)
# ``linear`` (identical to ``cv2.INTER_LINEAR``)
# ``area`` (identical to ``cv2.INTER_AREA``)
# ``cubic`` (identical to ``cv2.INTER_CUBIC``)
image_batch = ia.imresize_many_images(image_batch, self.resize_shape, interpolation='linear')
label_batch = image_batch
aug_batch = image_batch
if self.augment == True:
label_batch = self.aug_seq.augment_images(image_batch)
# aug_batch = random_mask.augment_images(label_batch)
return label_batch,label_batch
def __getitem__(self, idx):
"""
Gets batch at position `index`.
Arguments:
index: position of the batch in the Sequence.
Returns:
A batch
"""
indexes = self.indexes[idx * self.batch_size:(idx + 1) *
self.batch_size]
image_batch = self.images[indexes]
image_batch = self.__cache_data(indexes, image_batch)
# One-hot label
label_batch = np_utils.to_categorical(self.labels[indexes],
self.class_num)
if self.resize_shape:
# Resize images interpolation choice
# ``nearest`` (identical to ``cv2.INTER_NEAREST``)
# ``linear`` (identical to ``cv2.INTER_LINEAR``)
# ``area`` (identical to ``cv2.INTER_AREA``)
# ``cubic`` (identical to ``cv2.INTER_CUBIC``)
image_batch = ia.imresize_many_images(image_batch,
self.resize_shape,
interpolation='linear')
image_batch = image_batch
if self.augment == True:
image_batch = self.aug_seq.augment_images(image_batch)
return image_batch, label_batch
def encode(self, item, encode_y=False, treshold=0.5):
if isinstance(item, PredictionItem):
raise NotImplementedError(
"Instance segmentation is only capable to encode datasets")
if isinstance(item, DataSet):
res = []
for i in tqdm.tqdm(range(len(item)), "Encoding dataset"):
q = item[i]
imageId = q.id
if encode_y:
vl = q.y
else:
vl = q.prediction
labels = vl[0]
probs = vl[1]
masks = vl[3]
if masks.shape[1] != self.maskShape[0] or masks.shape[
2] != self.maskShape[1]:
masks = imgaug.imresize_many_images(
masks.astype(np.uint16), self.maskShape,
cv.INTER_CUBIC)
if len(labels) != len(masks):
raise Exception(
f"{imageId} does not have same ammount of masks and labels"
)
for i in range(len(masks)):
if probs[i] < treshold:
continue
mask = masks[i]
label = str(int(labels[i] + 0.5))
if label in self.classes:
rle = self._to_rle(mask)
res.append({
self.imColumn: imageId,
self.rleColumn: rle,
self.clazzColumn: label
})
res = self._recode(res)
clns = []
for c in self.splitColumns:
if not self.splitColumns[c] in clns:
clns.append(self.splitColumns[c])
r = [self.imColumn, self.clazzColumn, self.rleColumn]
for c in r:
if not c in self.splitColumns:
clns.append(c)
return pd.DataFrame(res, columns=clns)
def main():
parser = argparse.ArgumentParser(description="Contrast check script")
parser.add_argument("--per_channel", dest="per_channel", action="store_true")
args = parser.parse_args()
augs = []
for p in [0.25, 0.5, 1.0, 2.0, (0.5, 1.5), [0.5, 1.0, 1.5]]:
augs.append(("GammaContrast " + str(p), iaa.GammaContrast(p, per_channel=args.per_channel)))
for cutoff in [0.25, 0.5, 0.75]:
for gain in [5, 10, 15, 20, 25]:
augs.append(("SigmoidContrast " + str(cutoff) + " " + str(gain), iaa.SigmoidContrast(gain, cutoff, per_channel=args.per_channel)))
for gain in [0.0, 0.25, 0.5, 1.0, 2.0, (0.5, 1.5), [0.5, 1.0, 1.5]]:
augs.append(("LogContrast " + str(gain), iaa.LogContrast(gain, per_channel=args.per_channel)))
for alpha in [-1.0, 0.5, 0, 0.5, 1.0, 2.0, (0.5, 1.5), [0.5, 1.0, 1.5]]:
augs.append(("LinearContrast " + str(alpha), iaa.LinearContrast(alpha, per_channel=args.per_channel)))
augs.append(("AllChannelsHistogramEqualization", iaa.AllChannelsHistogramEqualization()))
augs.append(("HistogramEqualization (Lab)", iaa.HistogramEqualization(to_colorspace=iaa.HistogramEqualization.Lab)))
augs.append(("HistogramEqualization (HSV)", iaa.HistogramEqualization(to_colorspace=iaa.HistogramEqualization.HSV)))
augs.append(("HistogramEqualization (HLS)", iaa.HistogramEqualization(to_colorspace=iaa.HistogramEqualization.HLS)))
for clip_limit in [0.1, 1, 5, 10]:
for tile_grid_size_px in [3, 7]:
augs.append(("AllChannelsCLAHE %d %dx%d" % (clip_limit, tile_grid_size_px, tile_grid_size_px),
iaa.AllChannelsCLAHE(clip_limit=clip_limit, tile_grid_size_px=tile_grid_size_px,
per_channel=args.per_channel)))
for clip_limit in [1, 5, 10, 100, 200]:
for tile_grid_size_px in [3, 7, 15]:
augs.append(("CLAHE %d %dx%d" % (clip_limit, tile_grid_size_px, tile_grid_size_px),
iaa.CLAHE(clip_limit=clip_limit, tile_grid_size_px=tile_grid_size_px)))
images = [data.astronaut()] * 16
images = ia.imresize_many_images(np.uint8(images), (128, 128))
for name, aug in augs:
print("-----------")
print(name)
print("-----------")
images_aug = aug.augment_images(images)
images_aug[0] = images[0]
grid = ia.draw_grid(images_aug, rows=4, cols=4)
ia.imshow(grid)
def __getitem__(self, idx):
"""
Gets batch at position `index`.
Arguments:
index: position of the batch in the Sequence.
Returns:
A batch
"""
indexes = self.indexes[idx * self.batch_size:(idx + 1) *
self.batch_size]
image_batch = self.images[indexes]
image_batch = self.__cache_data(indexes, image_batch)
# One-hot label, E.g. if self.class_num = 5
# Before: [[1,2,...],[3,4,...]...]
# After: [[[0,1,0,0,0],[0,0,1,0,0]...] , [[0,0,0,1,0],[0,0,0,0,1]...]...]
label_batch = np_utils.to_categorical(self.labels[indexes],
self.class_num)
# For the multi label fixed-length OCR problem, the label shape must be [BATCH,FIXED_LENGTH*CLASS]
label_batch = np.reshape(label_batch,
(-1, self.fixed_length * self.class_num))
if self.resize_shape:
# Resize images interpolation choice
# ``nearest`` (identical to ``cv2.INTER_NEAREST``)
# ``linear`` (identical to ``cv2.INTER_LINEAR``)
# ``area`` (identical to ``cv2.INTER_AREA``)
# ``cubic`` (identical to ``cv2.INTER_CUBIC``)
image_batch = ia.imresize_many_images(image_batch,
self.resize_shape,
interpolation='linear')
image_batch = image_batch
if self.augment == True:
image_batch = self.aug_seq.augment_images(image_batch)
return image_batch, label_batch
def main():
parser = argparse.ArgumentParser(description="Contrast check script")
parser.add_argument("--per_channel",
dest="per_channel",
action="store_true")
args = parser.parse_args()
augs = []
for p in [0.25, 0.5, 1.0, 2.0, (0.5, 1.5), [0.5, 1.0, 1.5]]:
augs.append(("GammaContrast " + str(p),
iaa.GammaContrast(p, per_channel=args.per_channel)))
for cutoff in [0.25, 0.5, 0.75]:
for gain in [5, 10, 15, 20, 25]:
augs.append(("SigmoidContrast " + str(cutoff) + " " + str(gain),
iaa.SigmoidContrast(gain,
cutoff,
per_channel=args.per_channel)))
for gain in [-1.0, 0.0, 0.25, 0.5, 1.0, 2.0, (0.5, 1.5), [0.5, 1.0, 1.5]]:
augs.append(("LogContrast " + str(gain),
iaa.LogContrast(gain, per_channel=args.per_channel)))
for alpha in [-1.0, 0.5, 0, 0.5, 1.0, 2.0, (0.5, 1.5), [0.5, 1.0, 1.5]]:
augs.append(("LinearContrast " + str(alpha),
iaa.LinearContrast(alpha, per_channel=args.per_channel)))
images = [data.astronaut()] * 16
images = ia.imresize_many_images(np.uint8(images), (128, 128))
for name, aug in augs:
print("-----------")
print(name)
print("-----------")
images_aug = aug.augment_images(images)
grid = ia.draw_grid(images_aug, rows=4, cols=4)
ia.imshow(grid)