def __init__(self, data_path, pickle_path, budget, augment=False):
self.path = data_path
self.augment = augment
if config.stain_normalized:
self.n = stainNorm_Reinhard.Normalizer()
i1 = cv2.imread('./data/source.png')
i1 = cv2.cvtColor(i1, cv2.COLOR_BGR2RGB)
self.n.fit(i1)
label = []
img_path = []
with open(pickle_path, 'rb') as f:
data_budget = pickle.load(f)
for i, class_name in enumerate(data_budget[budget]['patches'].keys()):
img_path = img_path + [
os.path.join(class_name, x)
for x in data_budget[budget]['patches'][class_name]
]
num_imgs = len(data_budget[budget]['patches'][class_name])
label = label + [i] * num_imgs
print(f'number of images in class {class_name} are {num_imgs}')
self.imgs = img_path
self.labels = label
def __init__(self, data_path, pickle_path, budget, augment=False):
self.path = data_path
self.augment = augment
if config.stain_normalized:
self.n = stainNorm_Reinhard.Normalizer()
i1 = cv2.imread('./data/source.png')
i1 = cv2.cvtColor(i1, cv2.COLOR_BGR2RGB)
self.n.fit(i1)
normal_label = []
tumour_label = []
normal_path = []
tumour_path = []
with open(pickle_path, 'rb') as f:
data_budget = pickle.load(f)
for image_name in data_budget[budget]['patches']['Normal']:
normal_path.append(os.path.join('Normal', image_name))
normal_label.append(0)
for image_name in data_budget[budget]['patches']['Tumour']:
tumour_path.append(os.path.join('Tumour', image_name))
tumour_label.append(1)
self.imgs = np.append(normal_path, tumour_path)
self.labels = np.append(normal_label, tumour_label)