81bbf66aab
* Add project code * Logger improvements * Improvements to web demo code * added create_wlasl_landmarks_dataset.py and xtract_mediapipe_landmarks.py * Fix rotation augmentation * fixed error in docstring, and removed unnecessary replace -1 -> 0 * Readme updates * Share base notebooks * Add notebooks and unify for different datasets * requirements update * fixes * Make evaluate more deterministic * Allow training with clearml * refactor preprocessing and apply linter * Minor fixes * Minor notebook tweaks * Readme updates * Fix PR comments * Remove unneeded code * Add banner to Readme --------- Co-authored-by: Gabriel Lema <gabriel.lema@xmartlabs.com>
73 lines
2.4 KiB
Python
73 lines
2.4 KiB
Python
import torch
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import numpy as np
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import torch.utils.data as torch_data
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from datasets.datasets_utils import load_dataset, tensor_to_dictionary, dictionary_to_tensor, \
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random_augmentation
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from normalization.body_normalization import normalize_single_dict as normalize_single_body_dict
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from normalization.hand_normalization import normalize_single_dict as normalize_single_hand_dict
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class CzechSLRDataset(torch_data.Dataset):
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"""Advanced object representation of the HPOES dataset for loading hand joints landmarks utilizing the Torch's
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built-in Dataset properties"""
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data: [np.ndarray]
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labels: [np.ndarray]
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def __init__(self, dataset_filename: str, num_labels=5, transform=None, augmentations=False,
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augmentations_prob=0.5, normalize=True):
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"""
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Initiates the HPOESDataset with the pre-loaded data from the h5 file.
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:param dataset_filename: Path to the h5 file
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:param transform: Any data transformation to be applied (default: None)
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"""
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loaded_data = load_dataset(dataset_filename)
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data, labels = loaded_data[0], loaded_data[1]
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self.data = data
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self.labels = labels
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self.targets = list(labels)
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self.num_labels = num_labels
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self.transform = transform
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self.augmentations = augmentations
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self.augmentations_prob = augmentations_prob
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self.normalize = normalize
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def __getitem__(self, idx):
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"""
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Allocates, potentially transforms and returns the item at the desired index.
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:param idx: Index of the item
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:return: Tuple containing both the depth map and the label
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"""
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depth_map = torch.from_numpy(np.copy(self.data[idx]))
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# label = torch.Tensor([self.labels[idx] - 1])
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label = torch.Tensor([self.labels[idx]])
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depth_map = tensor_to_dictionary(depth_map)
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# Apply potential augmentations
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depth_map = random_augmentation(self.augmentations, self.augmentations_prob, depth_map)
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if self.normalize:
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depth_map = normalize_single_body_dict(depth_map)
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depth_map = normalize_single_hand_dict(depth_map)
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depth_map = dictionary_to_tensor(depth_map)
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# Move the landmark position interval to improve performance
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depth_map = depth_map - 0.5
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if self.transform:
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depth_map = self.transform(depth_map)
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return depth_map, label
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def __len__(self):
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return len(self.labels)
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