def image_segmentation(self, show=True, **kwargs):
"""Compute low-level segmentation methods like felzenswalb' efficient graph based segmentation or k-means based image segementation
https://scikit-image.org/docs/dev/auto_examples/segmentation/plot_segmentations.html#sphx-glr-auto-examples-segmentation-plot-segmentations-py
"""
# get settings of combobox and fields
param_seg = self._csbox_seg.get_dict()
# get the currently displayed image
img = imgtools.project_and_stack(self.get_obj().get_img_from_label(
param_seg["domain"], dtype=np.uint8, factor=255))
# img = self.get_obj().get_img_from_label("height")
# param_seg["reference"] = self.get_obj().get_img_from_label("height")
param_seg["boundaries"] = "find"
# define image list for visualization
img_list = [img]
mode = param_seg["mode"]
if mode == "SLIC" or mode == "SLIC-0" or mode == "Normalized Cuts":
param_model = self._csbox_slic.get_dict()
elif mode == "Felzenswalb":
param_model = self._csbox_felz.get_dict()
elif mode == "KMeans":
param_model = self._csbox_kmeans.get_dict()
seg = rsvis.utils.imgseg.ImgSeg(**param_seg)
seg.predict(img, **param_model)
img_list.extend([
seg.get_seg_map_color(),
seg.get_seg_map_boundaries(img=imgtools.project_and_stack(
self.get_obj().get_img_from_label(param_seg["reference"]),
dtype=np.uint8,
factor=255))
])
print("Number of segments: {}".format(seg.get_num_label()))
# open a topwindow with the segmentation results of the currently displayed image
if show:
self.get_obj().set_img(img_list[2], clear_mask=False)
self.set_img()
self._img_tw = tw.TopWindow(self,
title="Segmentation",
dtype="img",
value=img_list)
return img_list
def get_label_options():
return [{
"require":
"label",
"label":
"Image",
"name":
"Projection - Single Class",
"key":
None,
"description":
"Show the mask of one label in current image.",
"command":
lambda obj: obj.set_mask(imgtools.get_mask_image(
obj.get_img_from_label("{label}"),
index=obj.get_class(value=False)),
invert=True)
}, {
"require":
"label",
"label":
"Image",
"name":
"Projection - All Classes",
"key":
None,
"description":
"Show the mask of one label in current image.",
"command":
lambda obj: obj.set_img(
imgtools.get_color_map(
obj.get_img_from_label("{label}"), obj.get_img(), alpha=0.2))
}, {
"require":
"label",
"label":
"Image",
"name":
"Distance Transform",
"key":
None,
"description":
"Compute the distance transform map of a label given by current label index.",
"command":
lambda obj: obj.set_img(
imgtools.project_and_stack(imgtools.get_distance_transform(
obj.get_img_from_label("{label}"),
index=obj.get_class(value=False),
),
dtype=np.uint8,
factor=255))
}]
def set_img(self, img, clear_mask=True):
if not isinstance(img, np.ndarray):
return
self._img_size = [img.shape[1], img.shape[0]]
self._data_img = imgtools.expand_image_dim(img)
if not isinstance(img.dtype, np.uint8):
img = imgtools.project_and_stack(img, dtype=np.uint8, factor=255)
self._img = Image.fromarray(img)
if clear_mask:
self.set_mask(show=False)
self.create_image()