def addingIntersection(self, blobA, blobB, blobC):
"""
Update the blobA by adding to it the intersection between the blobB and the blobC
"""
mask_intersect, bbox_intersect = Mask.intersectMask(
blobB.getMask(), blobB.bbox, blobC.getMask(), blobC.bbox)
bbox = Mask.jointBox([blobA.bbox, bbox_intersect])
(mask, bbox) = Mask.jointMask(bbox, bbox)
Mask.paintMask(mask, bbox, blobA.getMask(), blobA.bbox, 1)
Mask.paintMask(mask, bbox, mask_intersect, bbox_intersect, 1)
if mask.any():
blobA.updateUsingMask(bbox, mask.astype(int))
def union(self, blobs):
"""
Create a new blob that is the union of the (two) blobs given
"""
#boxs are in image space, masks invert x and y.
boxes = []
for blob in blobs:
boxes.append(blob.bbox)
box = Mask.jointBox(boxes)
(mask, box) = Mask.jointMask(box, box)
for blob in blobs:
Mask.paintMask(mask, box, blob.getMask(), blob.bbox, 1)
if mask.any():
# measure is brutally slower with non int types (factor 4), while byte&bool would be faster by 25%, conversion is fast.
blob = blobs[0].copy()
blob.updateUsingMask(box, mask.astype(int))
return blob
return None
def segmentation(self):
# compute bbox of scribbles (working area)
bboxes = []
for i, curve in enumerate(self.scribbles.points):
bbox = Mask.pointsBox(curve, int(self.scribbles.size[i] / 2))
bboxes.append(bbox)
working_area = Mask.jointBox(bboxes)
if working_area[0] < 0:
working_area[0] = 0
if working_area[1] < 0:
working_area[1] = 0
if working_area[0] + working_area[3] > self.viewerplus.img_map.height(
) - 1:
working_area[3] = self.viewerplus.img_map.height(
) - 1 - working_area[0]
if working_area[1] + working_area[2] > self.viewerplus.img_map.width(
) - 1:
working_area[2] = self.viewerplus.img_map.width(
) - 1 - working_area[1]
crop_img = utils.cropQImage(self.viewerplus.img_map, working_area)
crop_imgnp = utils.qimageToNumpyArray(crop_img)
# create markers
mask = np.zeros((working_area[3], working_area[2], 3), dtype=np.int32)
color_codes = dict()
counter = 1
for i, curve in enumerate(self.scribbles.points):
col = self.scribbles.label[i].fill
b = col[2]
g = col[1]
r = col[0]
color = (b, g, r)
color_code = b + 256 * g + 65536 * r
color_key = str(color_code)
if color_codes.get(color_key) is None:
name = self.scribbles.label[i].name
color_codes[color_key] = (counter, name)
counter = counter + 1
curve = np.int32(curve)
curve[:, 0] = curve[:, 0] - working_area[1]
curve[:, 1] = curve[:, 1] - working_area[0]
curve = curve.reshape((-1, 1, 2))
mask = cv2.polylines(mask,
pts=[curve],
isClosed=False,
color=color,
thickness=self.scribbles.size[i],
lineType=cv2.LINE_8)
mask = np.uint8(mask)
markers = np.zeros((working_area[3], working_area[2]), dtype='int32')
for label in self.scribbles.label:
col = label.fill
b = col[2]
g = col[1]
r = col[0]
color_code = b + 256 * g + 65536 * r
color_key = str(color_code)
idx = np.where((mask[:, :, 0] == b) & (mask[:, :, 1] == g)
& (mask[:, :, 2] == r))
(value, name) = color_codes[color_key]
markers[idx] = value
# markers = np.int32(255*rgb2gray(mask))
# markersprint = 255*rgb2gray(mask)
markersprint = markers
cv2.imwrite('mask.png', markersprint)
# watershed segmentation
segmentation = cv2.watershed(crop_imgnp, markers)
segmentation = filters.median(segmentation, disk(5), mode="mirror")
cv2.imwrite('segmentation.png', segmentation)
# the result of the segmentation must be converted into labels again
lbls = measure.label(segmentation)
blobs = []
for region in measure.regionprops(lbls):
blob = Blob(region, working_area[1], working_area[0],
self.viewerplus.annotations.getFreeId())
color_index = segmentation[region.coords[0][0],
region.coords[0][1]]
data = list(color_codes.items())
index = 0
for i in range(len(data)):
(color_code, t) = data[i]
if t[0] == color_index:
color_code = int(color_code)
r = int(color_code / 65536)
g = int(int(color_code - r * 65536) / 256)
b = int(color_code - r * 65536 - g * 256)
color = [r, g, b]
name = t[1]
break
blob.class_color = color
blob.class_name = name
blobs.append(blob)
return blobs