if __name__ == "__main__":
execution_id = datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S')
results_dir = root_dir(FOLDER_EXPERIMENTS(version=5), execution_id)
os.makedirs(results_dir, exist_ok=True)
logging.basicConfig(
level=logging.INFO,
handlers=[
logging.FileHandler(os.path.join(results_dir, 'log.txt')),
logging.StreamHandler()
]
)
p_names = sample_names()
for idx_p, p_name in enumerate(p_names[0:MAX_PATIENTS]):
for idx_img, (path_image, original_image) in enumerate(images(patient_name=p_name, max_images=MAX_IMAGES_PER_PATIENT)):
results_p_dir = os.path.join(results_dir, p_name, str(idx_img))
os.makedirs(results_p_dir, exist_ok=True)
logging.info(f'Processing: {p_name}-{idx_img}')
if SCALE:
logging.info('Resizing image')
sz = [ceil(d*SCALE) for d in original_image.shape[:2]] + [3]
original_image = resize(img_as_float(original_image), sz)
io.imsave(fname=os.path.join(results_p_dir, '01 01 Original.jpg'),
arr=original_image)
image = original_image
logging.info('Gaussian filter')
image = apply_on_normalized_luminance(
if __name__ == "__main__":
execution_id = datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S')
results_dir = root_dir(FOLDER_EXPERIMENTS(version=6), execution_id)
os.makedirs(results_dir, exist_ok=True)
logging.basicConfig(level=logging.INFO,
handlers=[
logging.FileHandler(
os.path.join(results_dir, 'log.txt')),
logging.StreamHandler()
])
p_names = sample_names()
for idx_p, p_name in enumerate(p_names[0:MAX_PATIENTS]):
for idx_img, (path_image, original_image) in enumerate(
images(patient_name=p_name,
max_images=MAX_IMAGES_PER_PATIENT)):
Ki67 = get_expert_Ki67(p_name)
if SCALE:
logging.info('Resizing image')
sz = [ceil(d * SCALE) for d in original_image.shape[:2]] + [3]
original_image = resize(img_as_float(original_image), sz)
io.imsave(fname=os.path.join(
results_dir,
f'{idx_p:02d} {p_name} - Image {idx_img:02d} - Ki67: {Ki67}.jpg'
),
arr=original_image)
image = original_image
results_dir = root_dir(FOLDER_EXPERIMENTS(version=7), execution_id)
os.makedirs(results_dir, exist_ok=True)
logging.basicConfig(level=logging.INFO,
handlers=[
logging.FileHandler(
os.path.join(results_dir, 'log.txt')),
logging.StreamHandler()
])
s_names = sample_names()
df = None
for idx_s, s_name in enumerate(s_names[0:MAX_PATIENTS]):
Ki67_gt = get_expert_Ki67(s_name)
for path_image, original_image in images(
patient_name=s_name, max_images=MAX_IMAGES_PER_PATIENT):
m = re.search(f'/{s_name}-(?P<n_img>[0-9]+).(?P<ext>[a-zA-Z]+)$',
path_image)
img_ext = m.group('ext')
img_number = int(m.group('n_img'))
img_name = f'{s_name}-{img_number}'
img_filename = f'{img_name}.{img_ext}'
results_p_dir = os.path.join(results_dir, s_name, img_name)
os.makedirs(results_p_dir, exist_ok=True)
result = {
'execution_id': execution_id,
'sample_name': s_name,
'img_number': img_number,
'img_file': img_filename,
'Ki67_gt': Ki67_gt,