def main():
parser = argparse.ArgumentParser(description='Train model.')
parser.add_argument('--tissue',
help='select tissue to train.',
default=None)
parser.add_argument('--inputFolder',
help='Select input folder.',
default=None)
parser.add_argument('--outputFolder',
help='select output folder',
default=None)
parser.add_argument('--scale', help='select output folder', default=None)
args = parser.parse_args()
config = Config
if args.tissue:
config.diagnosis = [args.tissue]
if args.outputFolder:
config.outputFolder = args.outputFolder
if args.scale:
config.scale = int(args.scale)
if args.tissue == 'Ganglioneuroma':
n_freq = 20
else:
n_freq = 30
print("Scale: " + args.scale)
print(config.diagnosis)
print(config.outputFolder)
tools = Tools()
annotated_nuclei = AnnotatedObjectSet()
ids_images = glob.glob(
os.path.join(args.inputFolder, config.diagnosis[0], 'images', '*.tif'))
ids_masks = glob.glob(
os.path.join(args.inputFolder, config.diagnosis[0], 'masks', '*.tif'))
# Create dataset for training the pix2pix-network based on image pairs
#for index,elem in enumerate(ids_paths):
for index, elem in enumerate(ids_images):
test = AnnotatedImage()
#test.readFromPath(tools.getLocalDataPath(elem[1],1),tools.getLocalDataPath(groundtruth_path[0],3))
test.readFromPath(ids_images[index], ids_masks[index], type='uint16')
enhanced_images = tools.enhanceImage(test,
flip_left_right=True,
flip_up_down=True,
deform=False)
for index, img in enumerate(enhanced_images):
annotated_nuclei.addObjectImage(
img, useBorderObjects=config.useBorderObjects)
# Create the image pairs
tools.createPix2pixDataset(annotated_nuclei,
config,
n_freq=n_freq,
tissue=args.tissue)
e = 1
def main():
parser = argparse.ArgumentParser(description='Train model.')
parser.add_argument('--tissue',
help='select tissue to train.',
default=None)
parser.add_argument('--inputFolder',
help='Select input folder.',
default=None)
parser.add_argument('--outputFolder',
help='select output folder',
default=None)
parser.add_argument('--scale', help='select output folder', default=None)
parser.add_argument('--mode', help='select output folder', default='train')
parser.add_argument('--resultsfile',
help='select output folder',
default=None)
parser.add_argument('--overlap', help='select output folder', default=None)
args = parser.parse_args()
config = Config
if args.tissue:
config.diagnosis = [args.tissue]
if args.outputFolder:
config.outputFolder = args.outputFolder
if args.scale == '1':
config.scale = True
if args.mode:
config.mode = args.mode
if args.resultsfile:
config.resultsfile = args.resultsfile
if args.overlap:
config.overlap = int(args.overlap)
print(config.diagnosis)
print(config.outputFolder)
print(config.scale)
tools = Tools()
annotated_nuclei = AnnotatedObjectSet()
ids_images = glob.glob(
os.path.join(args.inputFolder, config.diagnosis[0], 'images', '*.tif'))
ids_masks = glob.glob(
os.path.join(args.inputFolder, config.diagnosis[0], 'masks', '*.tif'))
# Create dataset for training the pix2pix-network based on image pairs
#for index,elem in enumerate(ids_paths):
for index, elem in enumerate(ids_images):
test = AnnotatedImage()
test.readFromPath(ids_images[index], ids_masks[index], type='uint16')
#enhanced_images = tools.enhanceImage(test,flip_left_right=True,flip_up_down=True,deform=True)
#for index,img in enumerate(enhanced_images):
# annotated_nuclei.addObjectImage(img,useBorderObjects=config.useBorderObjects)
annotated_nuclei.addObjectImage(
test,
useBorderObjects=config.useBorderObjects,
path_to_img=ids_images[index])
# Create and save the image tiles
tools.createAndSaveTiles(annotated_nuclei, config)
def main():
parser = argparse.ArgumentParser(description='Train model.')
parser.add_argument('--tissue', help='select tissue to train.', default="Ganglioneuroma")
parser.add_argument('--inputFolder', help='Select input folder.', default=None)
parser.add_argument('--outputFolder', help='select output folder', default=None)
parser.add_argument('--scale', help='select output folder', default=None)
parser.add_argument('--mode', help='select output folder', default='train')
parser.add_argument('--resultsfile', help='select output folder', default=None)
parser.add_argument('--overlap', help='select output folder', default=None)
parser.add_argument('--ending', help='select output folder', default=None)
parser.add_argument('--scalesize', help='select output folder', default=None)
args = parser.parse_args()
config = Config
if args.tissue:
config.diagnosis = [args.tissue]
if args.outputFolder:
config.outputFolder = args.outputFolder
if args.scale == '1':
config.scale=True
if args.mode:
config.mode=args.mode
if args.resultsfile:
config.resultsfile=args.resultsfile
if args.overlap:
config.overlap=int(args.overlap)
print(config.diagnosis)
print(config.outputFolder)
print(config.scale)
tools = Tools()
def takeSecond(elem):
return os.path.basename(elem)
annotated_nuclei = AnnotatedObjectSet()
print("Input folder: " + args.inputFolder)
ids_images = glob.glob(os.path.join(args.inputFolder,'*.' + args.ending))
ids_images.sort(key=takeSecond)
# Create dataset for training the pix2pix-network based on image pairs
#for index,elem in enumerate(ids_paths):
for index, elem in enumerate(ids_images):
print(ids_images[index])
test = AnnotatedImage()
test.readFromPathOnlyImage(ids_images[index])
#enhanced_images = tools.enhanceImage(test,flip_left_right=True,flip_up_down=True,deform=True)
#for index,img in enumerate(enhanced_images):
# annotated_nuclei.addObjectImage(img,useBorderObjects=config.useBorderObjects)
annotated_nuclei.addObjectImage(test, useBorderObjects=config.useBorderObjects,path_to_img=ids_images[index])
cv2.imwrite(r"/root/flo/tmp/test_before_tiling.jpg",test.getRaw())
# Create and save the image tiles
cv2.imwrite(r"/root/flo/tmp/test_before_tiling.jpg",test.getRaw())
tools.createAndSaveTilesForSampleSegmentation(annotated_nuclei,config,float(args.scalesize))
def main():
parser = argparse.ArgumentParser(description='Train model.')
parser.add_argument('--tissue',
help='select tissue to train.',
default=None)
parser.add_argument('--inputFolder',
help='Select input folder.',
default=None)
parser.add_argument('--outputFolder',
help='select output folder',
default=None)
parser.add_argument('--nr_images',
help='select number of images to create',
default=None)
parser.add_argument('--overlapProbability',
help='select overlapProbability',
default=None)
parser.add_argument('--scale', help='select output folder', default=None)
args = parser.parse_args()
tisquant = TisQuantExtract()
config = Config
if args.tissue:
config.diagnosis = [args.tissue]
if args.outputFolder:
config.outputFolder = args.outputFolder
if args.overlapProbability:
args.overlapProbability = float(args.overlapProbability)
else:
args.overlapProbability = 0.5
if args.scale == '1':
config.scale = True
print(config.diagnosis)
tools = Tools()
annotated_nuclei = AnnotatedObjectSet()
annotated_images = []
#ids_paths = tisquant.dbconnector.execute(query=tisquant.getLevel3AnnotatedImagesByDiagnosis_Query(diagnosis = config.diagnosis,magnification = config.magnification, staining_type = config.staining_type, staining = config.staining, segmentation_function = config.segmentation_function, annotator = config.annotator, device = config.device))
ids_images = glob.glob(
os.path.join(args.inputFolder, config.diagnosis[0], 'images', '*.tif'))
ids_masks = glob.glob(
os.path.join(args.inputFolder, config.diagnosis[0], 'masks', '*.tif'))
#for index,elem in enumerate(ids_paths):
for index, elem in enumerate(ids_images):
#groundtruth_paths = tisquant.dbconnector.execute(tisquant.getLevel3AnnotationByImageId_Query(elem[0],config.annotator))
#groundtruth_paths = tisquant.dbconnector.execute(tisquant.getLevel3AnnotationByImageIdUsingMaxExperience_Query(elem[0], config.annotator))
#for groundtruth_path in groundtruth_paths:
test = AnnotatedImage()
# test.readFromPath(tools.getLocalDataPath(elem[1],1),tools.getLocalDataPath(groundtruth_path[0],3))
test.readFromPath(ids_images[index], ids_masks[index])
annotated_images.append(test)
# Create artificial new dataset
scales = tools.getNormalizedScales(annotated_images)
for index, img in enumerate(annotated_images):
test = AnnotatedImage()
if config.scale:
test.createWithArguments(
tools.rescale_image(img.getRaw(),
(scales[index], scales[index])),
tools.rescale_mask(img.getMask(),
(scales[index], scales[index]),
make_labels=True))
else:
test.createWithArguments(img.getRaw(), img.getMask())
annotated_nuclei.addObjectImage(
test, useBorderObjects=config.useBorderObjects)
if config.scale == 0:
if args.tissue == 'Ganglioneuroma':
possible_numbers = [9, 16, 25, 36, 49]
else:
possible_numbers = [4, 4, 9]
else:
possible_numbers = [9, 16, 25, 36, 49]
# How many images?
if not args.nr_images:
args.nr_images = 10
else:
args.nr_images = int(args.nr_images)
for t in tqdm(range(0, args.nr_images)):
# Create artificial image
number_nuclei = random.randint(0, possible_numbers.__len__() - 1)
img = ArtificialAnnotatedImage(
width=256,
height=256,
number_nuclei=possible_numbers[number_nuclei],
probabilityOverlap=args.overlapProbability)
total_added = 0
for i in range(0, possible_numbers[number_nuclei]):
test = annotated_nuclei.returnArbitraryObject()
if (randint(0, 1)):
test = tools.arbitraryEnhance(test)
total_added += img.addImageAtGridPosition(test)
if (total_added > 0):
shape_y = img.getRaw().shape[0]
shape_x = img.getRaw().shape[1]
img_new = np.zeros((shape_y, shape_x * 2, 3), dtype=np.float32)
img_new[:, 0:shape_x,
0] = img_new[:, 0:shape_x,
1] = img_new[:, 0:shape_x,
2] = img_new[:,
shape_x:2 * shape_x,
0] = img_new[:,
shape_x:
2 *
shape_x,
1] = img_new[:,
shape_x:
2
*
shape_x,
2] = img.getRaw(
)
scipy.misc.toimage(
img_new, cmin=0.0,
cmax=1.0).save(config.outputFolder + config.diagnosis[0] +
'\\images\\Img_' + str(t) + '.jpg')
tifffile.imsave(config.outputFolder + config.diagnosis[0] +
'\\masks\\Mask_' + str(t) + '.tif',
img.getMask(),
dtype=np.uint8)
e = 1
def main():
parser = argparse.ArgumentParser(description='Train model.')
parser.add_argument('--tissue',
help='select tissue to train.',
default=None)
parser.add_argument('--outputFolder',
help='select output folder',
default=None)
args = parser.parse_args()
tisquant = TisQuantExtract()
config = Config
if args.tissue:
config.diagnosis = [args.tissue]
if args.outputFolder:
config.outputFolder = args.outputFolder
print(config.diagnosis)
print(config.outputFolder)
tools = Tools()
annotated_nuclei = AnnotatedObjectSet()
for fold in ['train', 'val', 'test', 'train_and_val']:
path_train = os.path.join(config.outputFolder, fold)
if not os.path.exists(path_train):
os.makedirs(path_train)
path_output = os.path.join(config.outputFolder, fold,
config.diagnosis[0])
if not os.path.exists(path_output):
os.makedirs(path_output)
path_output_images = os.path.join(config.outputFolder, fold,
config.diagnosis[0], 'images')
if not os.path.exists(path_output_images):
os.makedirs(path_output_images)
path_output_masks = os.path.join(config.outputFolder, fold,
config.diagnosis[0], 'masks')
if not os.path.exists(path_output_masks):
os.makedirs(path_output_masks)
# Create dataset for training the pix2pix-network based on image pairs
fold = 'train'
running_ind = 0
ids_paths = tisquant.dbconnector.execute(
query=tisquant.getLevel3AnnotatedImagesByDiagnosis_Query(
diagnosis=config.diagnosis,
magnification=config.magnification,
staining_type=config.staining_type,
staining=config.staining,
segmentation_function=config.segmentation_function,
annotator=config.annotator,
device=config.device))
random.shuffle(ids_paths)
nr_trainval = round(ids_paths.__len__() * 0.8)
nr_val = round(nr_trainval * 0.8)
for index, elem in enumerate(ids_paths):
groundtruth_paths = tisquant.dbconnector.execute(
tisquant.getLevel3AnnotationByImageIdUsingMaxExperience_Query(
elem[0], config.annotator))
if (index < nr_val):
folds = ['train', 'train_and_val']
elif (index < nr_trainval):
folds = ['val', 'train_and_val']
else:
folds = ['test']
for fold in folds:
path_output_images = os.path.join(config.outputFolder, fold,
config.diagnosis[0], 'images')
path_output_masks = os.path.join(config.outputFolder, fold,
config.diagnosis[0], 'masks')
for groundtruth_path in groundtruth_paths:
copyfile(
tools.getLocalDataPath(elem[1], 1),
os.path.join(
path_output_images,
config.diagnosis[0] + '_' + str(running_ind) + '.tif'))
print('Copying rawimage ' +
tools.getLocalDataPath(elem[1], 1) + '...')
copyfile(
tools.getLocalDataPath(groundtruth_path[0], 3),
os.path.join(
path_output_masks,
config.diagnosis[0] + '_' + str(running_ind) + '.tif'))
print('Copying mask ' +
tools.getLocalDataPath(groundtruth_path[0], 3) + '...')
running_ind = running_ind + 1
def main():
parser = argparse.ArgumentParser(description='Train model.')
parser.add_argument('--tissue',
help='select tissue to train.',
default=None)
parser.add_argument('--inputFolder',
help='Select input folder.',
default=None)
parser.add_argument('--outputFolder',
help='select output folder',
default=None)
parser.add_argument('--scale', help='select output folder', default=None)
args = parser.parse_args()
tisquant = TisQuantExtract()
config = Config
if args.tissue:
config.diagnosis = [args.tissue]
if args.outputFolder:
config.outputFolder = args.outputFolder
if args.scale:
config.scale = int(args.scale)
print("Scale: " + args.scale)
print(config.diagnosis)
print(config.outputFolder)
tools = Tools()
annotated_nuclei = AnnotatedObjectSet()
#ids_paths = tisquant.dbconnector.execute(query=tisquant.getLevel3AnnotatedImagesByDiagnosis_Query(diagnosis = config.diagnosis,magnification = config.magnification, staining_type = config.staining_type, staining = config.staining, segmentation_function = config.segmentation_function, annotator = config.annotator, device = config.device))
ids_images = glob.glob(
os.path.join(args.inputFolder, config.diagnosis[0], 'images', '*.tif'))
ids_masks = glob.glob(
os.path.join(args.inputFolder, config.diagnosis[0], 'masks', '*.tif'))
# Create dataset for training the pix2pix-network based on image pairs
#for index,elem in enumerate(ids_paths):
for index, elem in enumerate(ids_images):
#groundtruth_paths = tisquant.dbconnector.execute(tisquant.getLevel3AnnotationByImageId_Query(elem[0],config.annotator)) # Pathes from groundtruth from all annotators
#groundtruth_paths = tisquant.dbconnector.execute(tisquant.getLevel3AnnotationByImageIdUsingMaxExperience_Query(elem[0], config.annotator)) # Pathes from groundtruth from most experienced annotator
#for groundtruth_path in groundtruth_paths:
test = AnnotatedImage()
#test.readFromPath(tools.getLocalDataPath(elem[1],1),tools.getLocalDataPath(groundtruth_path[0],3))
test.readFromPath(ids_images[index], ids_masks[index])
enhanced_images = tools.enhanceImage(test,
flip_left_right=True,
flip_up_down=True,
deform=True)
for index, img in enumerate(enhanced_images):
annotated_nuclei.addObjectImage(
img, useBorderObjects=config.useBorderObjects)
# Create the image pairs
tools.createPix2pixDataset(annotated_nuclei, config)
"""
#img = ArtificialAnnotatedImage.transformToArtificialImage(annotated_nuclei.images[0])
img = ArtificialAnnotatedImage(width=256,height=256)
for i in range(0,10):
test = annotated_nuclei.returnArbitraryObject()
img.addImageAtRandomPosition(test)
plt.figure(1)
plt.imshow(img.getRaw(),cmap='gray')
plt.figure(2)
plt.imshow(img.getMask())
plt.show()
"""
e = 1
def main():
parser = argparse.ArgumentParser(description='Train model.')
parser.add_argument('--tissue', help='select tissue to train.', default=None)
parser.add_argument('--inputFolder', help='Select input folder.', default=None)
parser.add_argument('--outputFolder', help='select output folder', default=None)
parser.add_argument('--nr_images', help='select number of images to create', default=None)
parser.add_argument('--overlapProbability', help='select overlapProbability', default=None)
parser.add_argument('--scale', help='select output folder', default=None)
parser.add_argument('--img_prefix', help='select output folder', default='Img_')
parser.add_argument('--mask_prefix', help='select output folder', default='Mask_')
#random.seed(13431)
args = parser.parse_args()
config = Config
if args.tissue:
config.diagnosis = [args.tissue]
if args.outputFolder:
config.outputFolder = args.outputFolder
if args.overlapProbability:
args.overlapProbability = float(args.overlapProbability)
else:
args.overlapProbability = 0.5
if args.tissue == 'Ganglioneuroma':
n_freq = 20#15
else:
n_freq = 30
if args.scale == '1':
config.scale=True
print(config.diagnosis)
tools = Tools()
annotated_nuclei =[]
annotated_images = []
ids_images = glob.glob(os.path.join(args.inputFolder,config.diagnosis[0],'images','*.tif'))
ids_masks = glob.glob(os.path.join(args.inputFolder, config.diagnosis[0], 'masks', '*.tif'))
for index, elem in enumerate(ids_images):
test = AnnotatedImage()
test.readFromPath(ids_images[index], ids_masks[index],type='uint16')
annotated_images.append(test)
# Create artificial new dataset
scales = tools.getNormalizedScales(annotated_images)
running = 0
for index,img in enumerate(annotated_images):
test = AnnotatedImage()
annotated_nuclei.append(AnnotatedObjectSet())
if config.scale:
test.createWithArguments(tools.rescale_image(img.getRaw(),(scales[index],scales[index])),tools.rescale_mask(img.getMask(),(scales[index],scales[index]), make_labels=True))
else:
test.createWithArguments(img.getRaw(),img.getMask())
annotated_nuclei[running].addObjectImage(test, useBorderObjects=config.useBorderObjects, tissue=args.tissue, scale=Config.scale)
running += 1
del test
if config.scale == 0:
if args.tissue == 'Ganglioneuroma':
possible_numbers = [9, 16, 25, 36, 49]
else:
possible_numbers = [4, 4, 9]
else:
possible_numbers = [9,16,25,36,49]
# How many images?
if not args.nr_images:
args.nr_images=10
else:
args.nr_images=int(args.nr_images)
for t in tqdm(range(0,args.nr_images)):
nr_img = random.randint(0,annotated_nuclei.__len__()-1)
# Create artificial image
number_nuclei = random.randint(0, possible_numbers.__len__()-1)
# calculate Background
tmp_image = annotated_nuclei[nr_img].images[0].getRaw()
tmp_mask = annotated_nuclei[nr_img].images[0].getMask()
kernel = np.ones((15, 15), np.uint8)
bg = cv2.erode((tmp_mask == 0).astype(np.uint8), kernel, iterations=1)
bg = np.sort(tmp_image[np.where(bg>0)])
img = ArtificialAnnotatedImage(width=256,height=256,number_nuclei=possible_numbers[number_nuclei],probabilityOverlap=args.overlapProbability,background=bg)
total_added = 0
for i in range(0,possible_numbers[number_nuclei]):
test = annotated_nuclei[nr_img].returnArbitraryObject()
if (randint(0,1)):
test = tools.arbitraryEnhance(test)
total_added += img.addImageAtGridPosition(test)
if (total_added > 0):
shape_y = img.getRaw().shape[0]
shape_x = img.getRaw().shape[1]
img_new = np.zeros((shape_y,shape_x*2,3),dtype=np.float32)
img_new[:,0:shape_x,0] = img_new[:,0:shape_x,1] = img_new[:,0:shape_x,2] = img_new[:,shape_x:2*shape_x,0] = img_new[:,shape_x:2*shape_x,1] = img_new[:,shape_x:2*shape_x,2] = img.getRaw()
scipy.misc.toimage(img_new, cmin=0.0, cmax=1.0).save(config.outputFolder + config.diagnosis[0] + '\\images\\' + args.img_prefix + str(t) + '.jpg')
tifffile.imsave(config.outputFolder + config.diagnosis[0] + '\\masks\\' + args.mask_prefix + str(t) + '.tif',img.getMask(),dtype=np.uint8)
e=1