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random_image_generation.py
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random_image_generation.py
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import numpy as np
import h5py
from skimage import io
import random
d = h5py.File("/hpc/pmc_rios/Brain/190910_rl57_fungi_16bit_25x_125um_corr-stitching_bfc_nucl-nuclei_shifted.ims", "r")
m = h5py.File("/hpc/pmc_rios/Brain/190910_rl57_fungi_16bit_25x_125um_corr-stitching_bfc_memb-mean.ims", "r")
trainsize = 1000
testsize = 100
valsize = 100
image_width=16128
image_height=16896
image_size = 512
empt = np.zeros((image_size, image_size), dtype = 'uint8')
extraLoopX = False
extraLoopY = False
quotentX, remainderX = divmod(image_width, image_size)
xGrids = quotentX
XGrids = xGrids -1
if remainderX != 0:
xGrids = xGrids -1
quotentY, remainderY = divmod(image_height, image_size)
yGrids = quotentY
yGrids = yGrids -1
if remainderY != 0:
yGrids = yGrids -1
coord_list = []
#Choose number of interest
while count != trainsize:
x_start_cor = random.randint(1,xGrids-1)
x_end = x_start_cor * 512
x_start = x_end - 512
y_start_cor = random.randint(1, yGrids-1)
y_end = y_start_cor * 512
y_start = y_end - 512
i = random.randint(1,110)
if (x_start_cor, y_start_cor, i) is not in coord_list:
# Input baselayer
nuclei = d['DataSet/ResolutionLevel 0/TimePoint 0/Channel 0/Data'][i, x_start:x_end, y_start:y_end]
memb = m['DataSet/ResolutionLevel 0/TimePoint 0/Channel 0/Data'][i, x_start:x_end, y_start:y_end]
nuclei = (nuclei.astype(np.float64) / 30000) * 255 #TODO: change value
nuclei[nuclei > 255] = 255 # clipping
memb = (memb.astype(np.float64) / 8000) * 255
memb[memb > 255] = 255 # clipping
inputs = (nuclei + memb) / 2 # TODO: need division by 2?
inputs = inputs.astype(np.uint8)
# Combine the inputs
full_input = np.zeros((image_size, image_size, 3))
n = 10
if count%10 == 0:
n = random.randint(0,1)
if n == 0:
# Stack the input 3 times
full_input[:,:,0] = memb.astype(np.uint8)
full_input[:,:,1] = memb.astype(np.uint8)
full_input[:,:,2] = memb.astype(np.uint8)
full_input = full_input.astype(np.uint8)
if n ==1:
# Stack the input 3 times
full_input[:,:,0] = nuclei.astype(np.uint8)
full_input[:,:,1] = nuclei.astype(np.uint8)
full_input[:,:,2] = nuclei.astype(np.uint8)
full_input = full_input.astype(np.uint8)
if count%10 != 0:
# Stack the input 3 times
full_input[:,:,0] = inputs
full_input[:,:,1] = inputs
full_input[:,:,2] = inputs
full_input = full_input.astype(np.uint8)
### Output
# Convert float to int
nuclei = nuclei.astype(np.uint8)
memb = memb.astype(np.uint8)
if n == 0:
# Stack the slices and the empty array above each other
output = np.stack((empt, memb, empt), axis=2)
# Save image
new = np.concatenate([full_input, output],1)
io.imsave('/hpc/pmc_rios/Brain/2dunet/AB_nuclei_meanmemb/train/{0}_{1}_{2}_{3}_input_memb.png'.format(x_start, x_end, i, y_start), new)
if n == 1:
# Stack the slices and the empty array above each other
output = np.stack((nuclei, empt, empt), axis=2)
# Save image
new = np.concatenate([full_input, output],1)
io.imsave('/hpc/pmc_rios/Brain/2dunet/AB_nuclei_meanmemb/train/{0}_{1}_{2}_{3}_input_nuclei.png'.format(x_start, x_end, i, y_start), new)
if count%10 != 0:
# Stack the slices and the empty array above each other
output = np.stack((nuclei, memb, empt), axis=2)
# Save image
new = np.concatenate([full_input, output],1)
io.imsave('/hpc/pmc_rios/Brain/2dunet/AB_nuclei_meanmemb/train/{0}_{1}_{2}_{3}_input_nuclmemb.png'.format(x_start, x_end, i, y_start), new)
coord_list.append((x_start_cor, y_start_cor, i))
count = count + 1
else:
pass
test_count = 0
while test_count != testsize:
print(test_count)
x_start_cor = random.randint(1,xGrids-1)
x_end = x_start_cor * 512
x_start = x_end - 512
y_start_cor = random.randint(1, yGrids-1)
y_end = y_start_cor * 512
y_start = y_end - 512
i = random.randint(1,110)
if (x_start_cor, y_start_cor, i) is not in coord_list:
# Input baselayer
nuclei = d['DataSet/ResolutionLevel 0/TimePoint 0/Channel 0/Data'][i, x_start:x_end, y_start:y_end]
memb = m['DataSet/ResolutionLevel 0/TimePoint 0/Channel 0/Data'][i, x_start:x_end, y_start:y_end]
nuclei = (nuclei.astype(np.float64) / 30000) * 255 #TODO: change value
nuclei[nuclei > 255] = 255 # clipping
memb = (memb.astype(np.float64) / 8000) * 255
memb[memb > 255] = 255 # clipping
inputs = (nuclei + memb) / 2 # TODO: need division by 2?
inputs = inputs.astype(np.uint8)
# Combine the inputs
full_input = np.zeros((image_size, image_size, 3))
n = 10
if test_count%10 == 0:
n = random.randint(0,1)
if n == 0:
# Stack the input 3 times
full_input[:,:,0] = memb.astype(np.uint8)
full_input[:,:,1] = memb.astype(np.uint8)
full_input[:,:,2] = memb.astype(np.uint8)
full_input = full_input.astype(np.uint8)
if n ==1:
# Stack the input 3 times
full_input[:,:,0] = nuclei.astype(np.uint8)
full_input[:,:,1] = nuclei.astype(np.uint8)
full_input[:,:,2] = nuclei.astype(np.uint8)
full_input = full_input.astype(np.uint8)
if test_count%10 != 0:
# Stack the input 3 times
full_input[:,:,0] = inputs
full_input[:,:,1] = inputs
full_input[:,:,2] = inputs
full_input = full_input.astype(np.uint8)
### Output
# Convert float to int
nuclei = nuclei.astype(np.uint8)
memb = memb.astype(np.uint8)
if n == 0:
# Stack the slices and the empty array above each other
output = np.stack((empt, memb, empt), axis=2)
# Save image
new = np.concatenate([full_input, output],1)
io.imsave('/hpc/pmc_rios/Brain/2dunet/AB_nuclei_meanmemb/test/{0}_{1}_{2}_{3}_input_memb.png'.format(x_start, x_end, i, y_start), new)
if n == 1:
# Stack the slices and the empty array above each other
output = np.stack((nuclei, empt, empt), axis=2)
# Save image
new = np.concatenate([full_input, output],1)
io.imsave('/hpc/pmc_rios/Brain/2dunet/AB_nuclei_meanmemb/test/{0}_{1}_{2}_{3}_input_nuclei.png'.format(x_start, x_end, i, y_start), new)
else:
# Stack the slices and the empty array above each other
output = np.stack((nuclei, memb, empt), axis=2)
# Save image
new = np.concatenate([full_input, output],1)
io.imsave('/hpc/pmc_rios/Brain/2dunet/AB_nuclei_meanmemb/test/{0}_{1}_{2}_{3}_input_nuclmemb.png'.format(x_start, x_end, i, y_start), new)
coord_list.append((x_start_cor, y_start_cor, i))
test_count = test_count + 1
else:
pass
val_count = 0
while val_count != valsize:
x_start_cor = random.randint(1,xGrids-1)
x_end = x_start_cor * 512
x_start = x_end - 512
y_start_cor = random.randint(1, yGrids-1)
y_end = y_start_cor * 512
y_start = y_end - 512
i = random.randint(1,110)
if (x_start_cor, y_start_cor, i) is not in coord_list:
# Input baselayer
nuclei = d['DataSet/ResolutionLevel 0/TimePoint 0/Channel 0/Data'][i, x_start:x_end, y_start:y_end]
memb = m['DataSet/ResolutionLevel 0/TimePoint 0/Channel 0/Data'][i, x_start:x_end, y_start:y_end]
nuclei = (nuclei.astype(np.float64) / 30000) * 255 #TODO: change value
nuclei[nuclei > 255] = 255 # clipping
memb = (memb.astype(np.float64) / 8000) * 255
memb[memb > 255] = 255 # clipping
inputs = (nuclei + memb) / 2 # TODO: need division by 2?
inputs = inputs.astype(np.uint8)
# Combine the inputs
full_input = np.zeros((image_size, image_size, 3))
n = 10
if count%10 == 0:
n = random.randint(0,2)
if n == 0:
# Stack the input 3 times
full_input[:,:,0] = memb.astype(np.uint8)
full_input[:,:,1] = memb.astype(np.uint8)
full_input[:,:,2] = memb.astype(np.uint8)
full_input = full_input.astype(np.uint8)
if n ==1:
# Stack the input 3 times
full_input[:,:,0] = nuclei.astype(np.uint8)
full_input[:,:,1] = nuclei.astype(np.uint8)
full_input[:,:,2] = nuclei.astype(np.uint8)
full_input = full_input.astype(np.uint8)
if count%10 != 0:
# Stack the input 3 times
full_input[:,:,0] = inputs
full_input[:,:,1] = inputs
full_input[:,:,2] = inputs
full_input = full_input.astype(np.uint8)
### Output
# Convert float to int
nuclei = nuclei.astype(np.uint8)
memb = memb.astype(np.uint8)
if n == 0:
# Stack the slices and the empty array above each other
output = np.stack((empt, memb, empt), axis=2)
# Save image
new = np.concatenate([full_input, output],1)
io.imsave('/hpc/pmc_rios/Brain/2dunet/AB_nuclei_meanmemb/val/{0}_{1}_{2}_{3}_input_memb.png'.format(x_start, x_end, i, y_start), new)
if n == 1:
# Stack the slices and the empty array above each other
output = np.stack((nuclei, empt, empt), axis=2)
# Save image
new = np.concatenate([full_input, output],1)
io.imsave('/hpc/pmc_rios/Brain/2dunet/AB_nuclei_meanmemb/val/{0}_{1}_{2}_{3}_input_nuclei.png'.format(x_start, x_end, i, y_start), new)
else:
# Stack the slices and the empty array above each other
output = np.stack((nuclei, memb, empt), axis=2)
# Save image
new = np.concatenate([full_input, output],1)
io.imsave('/hpc/pmc_rios/Brain/2dunet/AB_nuclei_meanmemb/val/{0}_{1}_{2}_{3}_input_nuclmemb.png'.format(x_start, x_end, i, y_start), new)
coord_list.append((x_start_cor, y_start_cor, i))
val_count = val_count + 1
else:
pass