def __init__(self, root, ann_root, scale_size=None):
super(DRDetectionDS_predict_xml, self).__init__()
self.root = root
self.ann_root = ann_root
self.scale_size = scale_size
self.transform = transforms.Compose([
PILColorJitter(),
transforms.ToTensor(),
# Lighting(alphastd=0.01, eigval=eigen_values, eigvec=eigen_values),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
self.classid = ['optic_dis', 'macular']
ann_list = glob(os.path.join(ann_root, '*.xml'))
img_list = glob(os.path.join(root, '*.png'))
img_list = [i.split('.')[0] for i in img_list]
ann_list = [i.split('.')[0] for i in ann_list]
self.data_list = []
self.ann_info_list = []
self.bboxs_list = []
self.bboxs_c_list = []
for ann in ann_list:
if ann in img_list:
self.data_list.append(ann)
for index in self.data_list:
anns, bbox, bbox_c = self.__read_xml(index)
self.ann_info_list.append(anns)
self.bboxs_list.append(bbox)
self.bboxs_c_list.append(bbox_c)
def __init__(self, root, config, crop_size, scale_size, baseline=False):
super(MultiTaskClsDataSet, self).__init__()
self.root = root
self.config = config
self.crop_size = crop_size
self.scale_size = scale_size
self.baseline = baseline
df = pd.DataFrame.from_csv(config)
self.images_list = []
for index, row in df.iterrows():
self.images_list.append(row)
with open('info.json', 'r') as fp:
info = json.load(fp)
mean_values = torch.from_numpy(np.array(info['mean'], dtype=np.float32) / 255)
std_values = torch.from_numpy(np.array(info['std'], dtype=np.float32) / 255)
eigen_values = torch.from_numpy(np.array(info['eigval'], dtype=np.float32))
eigen_vectors = torch.from_numpy(np.array(info['eigvec'], dtype=np.float32))
if baseline:
self.transform = transforms.Compose([
transforms.RandomCrop(crop_size),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=mean_values, std=std_values),
])
else:
self.transform = transforms.Compose([
transforms.RandomCrop(crop_size),
transforms.RandomHorizontalFlip(),
PILColorJitter(),
transforms.ToTensor(),
Lighting(alphastd=0.01, eigval=eigen_values, eigvec=eigen_values),
transforms.Normalize(mean=mean_values, std=std_values),
])
def __init__(self, crop_size, scale_size, baseline):
super(kaggleClsTrain1, self).__init__()
self.image = ['data/kaggle1/train_images/train/' + line.strip() + '_' + str(scale_size) + '.png' for line in open('data/kaggle1/train_images/train/train_images.txt', 'r')]
self.label = torch.from_numpy(np.array(np.loadtxt('data/kaggle1/train_images/train/train_labels.txt'), np.int))
with open('data/kaggle/info.json', 'r') as fp:
info = json.load(fp)
mean_values = torch.from_numpy(np.array(info['mean'], dtype=np.float32) / 255)
std_values = torch.from_numpy(np.array(info['std'], dtype=np.float32) / 255)
eigen_values = torch.from_numpy(np.array(info['eigval'], dtype=np.float32))
eigen_vectors = torch.from_numpy(np.array(info['eigvec'], dtype=np.float32))
if baseline:
self.transform = transforms.Compose([
transforms.RandomCrop(crop_size),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=mean_values, std=std_values),
])
else:
self.transform = transforms.Compose([
transforms.RandomSizedCrop(crop_size),
transforms.RandomHorizontalFlip(),
PILColorJitter(),
transforms.ToTensor(),
#ColorJitter(),
Lighting(alphastd=0.1, eigval=eigen_values, eigvec=eigen_vectors),
#Affine(rotation_range=180, translation_range=None, shear_range=None, zoom_range=None),
transforms.Normalize(mean=mean_values, std=std_values),
])
def __init__(self, root, scale_size=None):
self.root = root
self.scale_size = scale_size
self.transform = transforms.Compose([
PILColorJitter(),
transforms.ToTensor(),
# Lighting(alphastd=0.01, eigval=eigen_values, eigvec=eigen_values),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
self.img_list = glob(os.path.join(root, '*.png'))
def DRDetection_predict_single_image(imagepath, scale_size=None):
transform = transforms.Compose([
PILColorJitter(),
transforms.ToTensor(),
# Lighting(alphastd=0.01, eigval=eigen_values, eigvec=eigen_values),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
img = Image.open(imagepath)
img, l, u, ratio = scale_image(img, scale_size)
img = transform(img)
return img, imagepath, [l, u, ratio]
def __init__(self, root, scale_size=None):
super(DRDetectionDS_predict, self).__init__()
self.root = root
self.scale_size = scale_size
self.transform = transforms.Compose([
PILColorJitter(),
transforms.ToTensor(),
# Lighting(alphastd=0.01, eigval=eigen_values, eigvec=eigen_values),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
self.img_list = glob(os.path.join(root, '*.png'))
self.data_list = []
self.ann_info_list = []
self.bboxs_list = []
self.bboxs_c_list = []