def debug_segmentation(ROI):
# image init, and conversion to gray and then threshold it
img = ROI[:, :, 0:3]
cell_list = []
# Watershed to find individual cells
#img_fine, ret_fine, markers_fine, markers_nuc, joined_mask, cells_to_remove = Klara_test.cell_watershed(img)
cytoplasm_cont, nuclei_mask = Klara_test.cell_watershed(img)
# Put all the objects in the cell_list
#cell_list = modify_cell_list(ROI,ret_fine,markers_fine,markers_nuc,cell_list)
cell_list = Klara_test.modify_cell_list(img, cytoplasm_cont, nuclei_mask)
# Basic classification to RBC and labels these cells
cell_list = RBC_classification(cell_list)
rbc_counter = rbc_cell_extraction(cell_list)
# Also extract image
for cell in cell_list:
if cell.label == "RBC":
subroi = ROI[cell.y:cell.y+cell.h, cell.x:cell.x+cell.w, :]
subroi[:, :, 0] = subroi[:, :, 0]*np.invert(cell.mask.astype(bool))
subroi[:, :, 1] = subroi[:, :, 1]*np.invert(cell.mask.astype(bool))
subroi[:, :, 2] = subroi[:, :, 2]*np.invert(cell.mask.astype(bool))
ROI[cell.y:cell.y+cell.h, cell.x:cell.x+cell.w, :] = subroi
#ROI[cell.mask.astype(int)] = 0
return rbc_counter, ROI
def segment_and_remove_from_roi(ROI):
# First run the usual segmentation algor
# image init, and conversion to gray and then threshold it
img = np.copy(ROI[:, :, 0:3])
# Watershed to find individual cells
#img_fine, ret_fine, markers_fine, markers_nuc, joined_mask, cells_to_remove = Klara_test.cell_watershed(img)
cytoplasm_cont, nuclei_mask, removed_cells, exchanged_cells = Klara_test.cell_watershed(img)
# Put all the objects in the cell_list
#cell_list = modify_cell_list(ROI,ret_fine,markers_fine,markers_nuc,cell_list)
cell_list = Klara_test.modify_cell_list(img, cytoplasm_cont, nuclei_mask)
# Basic classification to RBC and labels these cells
cell_list = RBC_classification(cell_list)
# Smooth the contours
cell_list = smooth_contours(cell_list)
# Now loop through and remove the segmented rois from the ROI
for cell in cell_list:
if cell.label == "RBC":
subroi = img[cell.y:cell.y+cell.h, cell.x:cell.x+cell.w, :]
subroi0 = subroi[:, :, 0]
subroi1 = subroi[:, :, 1]
subroi2 = subroi[:, :, 2]
subroi0 = subroi0*np.invert(cell.mask.astype(bool))
subroi1 = subroi1*np.invert(cell.mask.astype(bool))
subroi2 = subroi2*np.invert(cell.mask.astype(bool))
subroi[:, :, 0] = subroi0
subroi[:, :, 1] = subroi1
subroi[:, :, 2] = subroi2
img[cell.y:cell.y+cell.h, cell.x:cell.x+cell.w, :] = subroi
# Remove all the cells classified to RBC, cell_list now only contains unknown cells
cell_list = wbc_cell_extraction(cell_list)
# Before returning, make sure that the image is a bigger bounding box (for viewing purposes)
for cell in cell_list:
# First add center coordinates for the bounding box
xf = cell.x + float(cell.w)/2.
yf = cell.y + float(cell.h)/2.
# Get the size of the ROI
roi_rows = ROI.shape[0]
roi_cols = ROI.shape[1]
# Check so that the image is not outside of the ROI
if (yf - 50 < 0) or (yf + 50 > roi_rows) or (xf - 50 < 0) or (xf + 50 > roi_cols):
# If it is, then simply use what we already have.. IDGAF
cell.big_img = ROI[cell.y:cell.y+cell.h, cell.x:cell.x+cell.w, :]
else:
cell.big_img = ROI[int(yf - 50):int(yf + 50), int(xf - 50):int(xf + 50), :]
return cell_list, img
def segmentation(ROI):
# image init, and conversion to gray and then threshold it
img = ROI[:, :, 0:3]
cell_list = []
# Watershed to find individual cells
#img_fine, ret_fine, markers_fine, markers_nuc, joined_mask, cells_to_remove = Klara_test.cell_watershed(img)
cytoplasm_cont, nuclei_mask, removed_cells, exchanged_cells = Klara_test.cell_watershed(img)
# Put all the objects in the cell_list
#cell_list = modify_cell_list(ROI,ret_fine,markers_fine,markers_nuc,cell_list)
cell_list = Klara_test.modify_cell_list(img, cytoplasm_cont, nuclei_mask)
#fff = open('../others_1.pik', 'w+')
#pickle.dump(cell_list, fff)
# Basic classification to RBC and labels these cells
cell_list = RBC_classification(cell_list)
# Remove all the cells classified to RBC, cell_list now only contains unknown cells
cell_list = wbc_cell_extraction(cell_list)
#smooth contours to remove tentacles
cell_list = smooth_contours(cell_list)
# Before returning, make sure that the image is a bigger bounding box (for viewing purposes)
for cell in cell_list:
# First add center coordinates for the bounding box
xf = cell.x + float(cell.w)/2.
yf = cell.y + float(cell.h)/2.
# Get the size of the ROI
roi_rows = ROI.shape[0]
roi_cols = ROI.shape[1]
# Check so that the image is not outside of the ROI
if (yf - 50 < 0) or (yf + 50 > roi_rows) or (xf - 50 < 0) or (xf + 50 > roi_cols):
# If it is, then simply use what we already have.. IDGAF
cell.big_img = cell.img
else:
cell.big_img = ROI[int(yf - 50):int(yf + 50), int(xf - 50):int(xf + 50), :]
return cell_list
