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 = []