if True: #len(sys.argv)>2:
slidelist_test_1 = ['14', '18', '3', '22', '10', '15', '21']
slidelist_test_2 = ['1', '20', '17', '5', '2', '11', '16']
slidelist_test_3 = ['13', '7', '19', '8', '6', '9', '12']
# slidelist_test_1 = ['18','3','22','10','15','21','12']
# slidelist_test_2 = ['1','20','17','5','2','11','16']
# slidelist_test_3 = ['14','13','7','19','8','6','9']
if (sys.argv[2] == '1'):
slidelist_test = slidelist_test_1
elif (sys.argv[2] == '2'):
slidelist_test = slidelist_test_2
elif (sys.argv[2] == '3'):
slidelist_test = slidelist_test_3
DB = Database()
DB.open('databases/MITOS_WSI_CMC_ODAEL_TR.sqlite')
DB.execute('SELECT uid, filename FROM Slides')
slides = DB.fetchall()
slidelist_train = [y[1] for y in slides if str(y[0]) not in slidelist_test]
slidelist_test = [y[1] for y in slides if str(y[0]) in slidelist_test]
print('Training:', slidelist_train)
print('Test:', slidelist_test)
test = {x: f[x] for x in slidelist_test}
train = {x: f[x] for x in slidelist_train}
pickle.dump(train, open('trainval_' + sys.argv[1], 'wb'))
pickle.dump(test, open('test_' + sys.argv[1], 'wb'))
from SlideRunner.dataAccess.database import Database
delete_slides = [
"13,8,4,7,17,22,28,26,23,24,36,29,15,14,19,32,25,12", # 3 WSI
"13,8,4,7,17,22,28,26,23,24,36,29,15,14,19", #6 WSI
"13,8,4,7,17,22,28,26,23"
] #12 WSI
import os
DB = Database()
WSI_lists = ['3', '6', '12']
for i, k in enumerate(WSI_lists):
os.system(
'cp databases/MITOS_WSI_CMCT_ODAEL.sqlite databases/MITOS_WSI_CMCT_ODAEL_%sWSI.sqlite'
% k)
DB.open('databases/MITOS_WSI_CMCT_ODAEL_%sWSI.sqlite' % k)
cnt = DB.execute(
'DELETE FROM Annotations_label where annoid in (SELECT uid from Annotations where slide in (%s))'
% delete_slides[i])
cnt = DB.execute(
'DELETE FROM Annotations_coordinates where slide in (%s)' %
delete_slides[i])
cnt = DB.execute('DELETE FROM Annotations where slide in (%s)' %
delete_slides[i])
cnt = DB.execute('DELETE FROM Slides where uid in (%s)' % delete_slides[i])
DB.commit()
print('%20s: %20s' % ('Model', fname))
print('%20s: %20s' % ('Database', sys.argv[2]))
print('%20s: %20s' % ('Datasetname', datasetname))
print('%20s: %20s' % ('Validation/test', 'validation' if val=='-val' else 'test'))
# In[4]:
files=list()
train_slides=list()
val_slides=list()
test_slides=list()
slidenames = list()
getslides = """SELECT uid, filename FROM Slides"""
for idx, (currslide, filename) in enumerate(tqdm(database.execute(getslides).fetchall(), desc='Loading slides .. ')):
if (((sys.argv[4] == 'val') and (filename not in test_slide_filenames))
or (not (sys.argv[4] == 'val') and (filename in test_slide_filenames))):
slidenames += [currslide]
database.loadIntoMemory(currslide)
slide_path = path / slidedir / filename
slide = openslide.open_slide(str(slide_path))
level = 0#slide.level_count - 1
files.append(SlideContainer(file=slide_path, level=level, width=size, height=size, y=[[], []], annotations=dict()))
test_slides.append(idx)
patches_path = Path('D:/ProgProjekte/Python/Results-Exact-Study/Patches')
path = Path('./')
database = Database()
database.open(str('C:/Users/c.marzahl/Downloads/MITOS_WSI_CCMCT_MEL.sqlite'))
slide_names = [
'f26e9fcef24609b988be.svs', 'f3741e764d39ccc4d114.svs',
'fff27b79894fe0157b08.svs'
]
slidelist_test = ['27', '30', '31', '6', '18', '20', '1', '2', '3', '9', '11']
nr_target_cells = 350
getslides = """SELECT uid, filename FROM Slides"""
for idx, (currslide, filename) in enumerate(
tqdm(database.execute(getslides).fetchall(),
desc='Loading slides .. ')):
if (str(filename) not in slide_names):
continue
database.loadIntoMemory(currslide)
slide_path = Path('D:/Datasets/WSI-CCMCT') / filename
slide = openslide.open_slide(str(slide_path))
level = 0
down_factor = 1
classes = {4: "non-mitotic cell"}
nr_mitotic_figures = 0
slidelist_test = ['27', '30', '31', '6', '18', '20', '1', '2', '3', '9', '11']
clause = ','.join(slidelist_test)
files = [
'MITOS_WSI_CCMCT_ODAEL_50HPF.sqlite', 'MITOS_WSI_CCMCT_ODAEL_10HPF.sqlite',
'MITOS_WSI_CCMCT_ODAEL_5HPF.sqlite', 'MITOS_WSI_CCMCT_ODAEL_12WSI.sqlite',
'MITOS_WSI_CCMCT_ODAEL_6WSI.sqlite', 'MITOS_WSI_CCMCT_ODAEL_3WSI.sqlite',
'MITOS_WSI_CCMCT_ODAEL.sqlite'
]
for f in files:
DB.open('databases/' + f)
cnt = DB.execute(
'SELECT COUNT(*) FROM Annotations where agreedClass==2 and slide not in (%s)'
% clause).fetchone()[0]
print('%40s: %d mitotic figures in training set' % (f, cnt))
DB.open(files[-1])
slidelist_train = [
s[0] for s in DB.execute(
'SELECT Slides.filename, count(*) as cnt FROM Slides left join Annotations on Annotations.slide == Slides.uid where Slides.uid not in (%s) and Annotations.agreedClass==2 group by slide order by cnt asc'
% clause).fetchall()
]
slidelist_cnt = {
s[0]: s[1]
for s in DB.execute(
'SELECT Slides.filename, count(*) as cnt FROM Slides left join Annotations on Annotations.slide == Slides.uid where Slides.uid not in (%s) and Annotations.agreedClass==2 group by slide order by cnt asc'
def calculate_F1_fromCSV(databasefile,
csv_directory,
hotclass=1,
verbose=False):
DB = Database()
DB = DB.open(databasefile)
import os
import csv
result_boxes = {}
for root, dirs, files in os.walk(csv_directory):
if len(dirs) > 0: continue
slidenr = int(root.split('/')[-1])
result_boxes['%02d_test.tif' % slidenr] = []
f = open(root + '/' + files[0], 'r')
for y, x in csv.reader(f, delimiter=','):
result_boxes['%02d_test.tif' % slidenr].append([int(x), int(y), 1])
sTP, sFN, sFP = 0, 0, 0
F1dict = dict()
sP = 0
print('Calculating F1 for test set of %d files' % len(result_boxes), ':',
result_boxes.keys())
mitcount = DB.execute(
f'SELECT COUNT(*) FROM Annotations where agreedClass={hotclass}'
).fetchall()
print('Official count of mitotic figures in DB: ', mitcount)
slideids = []
for resfile in result_boxes:
boxes = np.array(result_boxes[resfile])
TP, FP, FN, F1 = 0, 0, 0, 0
slide_id = DB.findSlideWithFilename(resfile, '')
slideids.append(str(slide_id))
DB.loadIntoMemory(slide_id)
annoList = []
for annoI in DB.annotations:
anno = DB.annotations[annoI]
if anno.agreedClass == hotclass:
annoList.append([anno.x1, anno.y1])
centers_DB = np.array(annoList)
if boxes.shape[0] > 0:
score = boxes[:, -1]
F1, TP, FP, FN = _F1_core(centers_DB, boxes, score, 0)
if (centers_DB.shape[0] != TP + FN):
print(resfile, centers_DB.shape[0], TP + FN)
else: # no detections --> missed all
FN = centers_DB.shape[0]
if (verbose):
print(f'{resfile}: F1:{F1}, TP:{TP}, FP:{FP}, FN:{FN}')
sTP += TP
sFP += FP
sP += centers_DB.shape[0]
sFN += FN
F1dict[resfile] = F1
print('Overall: ')
sF1 = 2 * sTP / (2 * sTP + sFP + sFN)
print('F1: ', sF1)
print('Precision: %.3f ' % (sTP / (sTP + sFP)))
print('Recall: %.3f' % (sTP / (sTP + sFN)))
return sF1, F1dict
def calculate_F1(databasefile,
result_boxes=None,
resfile=None,
det_thres=0.5,
hotclass=2,
verbose=False):
DB = Database()
DB = DB.open(databasefile)
if (result_boxes is None):
if resfile is None:
raise ValueError(
'At least one of resfile/result_boxes must be given')
if (resfile[-3:] == 'bz2'):
f = bz2.BZ2File(resfile, 'rb')
else:
f = open(resfile, 'rb')
result_boxes = pickle.load(f)
sTP, sFN, sFP = 0, 0, 0
F1dict = dict()
sP = 0
result_boxes = nms(result_boxes, det_thres)
print('Calculating F1 for test set of %d files' % len(result_boxes), ':',
result_boxes.keys())
mitcount = DB.execute(
f'SELECT COUNT(*) FROM Annotations where agreedClass={hotclass}'
).fetchall()
print('Official count of mitotic figures in DB: ', mitcount)
slideids = []
for resfile in result_boxes:
boxes = np.array(result_boxes[resfile])
TP, FP, FN, F1 = 0, 0, 0, 0
slide_id = DB.findSlideWithFilename(resfile, '')
slideids.append(str(slide_id))
DB.loadIntoMemory(slide_id)
annoList = []
for annoI in DB.annotations:
anno = DB.annotations[annoI]
if anno.agreedClass == hotclass:
annoList.append([anno.x1, anno.y1])
centers_DB = np.array(annoList)
if boxes.shape[0] > 0:
score = boxes[:, -1]
F1, TP, FP, FN = _F1_core(centers_DB, boxes, score, det_thres)
if (centers_DB.shape[0] != TP + FN):
print(resfile, centers_DB.shape[0], TP + FN)
else: # no detections --> missed all
FN = centers_DB.shape[0]
if (verbose):
print(f'{resfile}: F1:{F1}, TP:{TP}, FP:{FP}, FN:{FN}')
sTP += TP
sFP += FP
sP += centers_DB.shape[0]
sFN += FN
F1dict[resfile] = F1
print('Overall: ')
sF1 = 2 * sTP / (2 * sTP + sFP + sFN)
print('F1: ', sF1)
print('Precision: %.3f ' % (sTP / (sTP + sFP)))
print('Recall: %.3f' % (sTP / (sTP + sFN)))
return sF1, F1dict
hpf = int(sys.argv[1])
os.system('mkdir -p %dHPF' % hpf)
DB = Database()
DB.open('../databases/MITOS_WSI_CCMCT_ODAEL.sqlite')
DBRK = Database()
DBRK.open('databases/HighMCAreas.sqlite')
os.system(
'cp ../databases/MITOS_WSI_CCMCT_ODAEL.sqlite MITOS_WSI_CCMCT_ODAEL_%dHPF.sqlite'
% hpf)
DBnew = Database()
DBnew.open('MITOS_WSI_CCMCT_ODAEL_%dHPF.sqlite' % hpf)
DBnew.execute('ATTACH `../databases/MITOS_WSI_CCMCT_ODAEL.sqlite` as orig')
for uid, filename in DB.listOfSlides():
print(uid, filename)
DB.loadIntoMemory(uid)
uidRK = DBRK.findSlideWithFilename(filename, '')
DBRK.loadIntoMemory(uidRK)
slide = openslide.open_slide('../WSI/' + filename)
A = 0.237 * hpf # mm^2
W_hpf_microns = np.sqrt(A * 4 / 3) * 1000 # in microns
H_hpf_microns = np.sqrt(A * 3 / 4) * 1000 # in microns
