Initial codebase (#1)

* 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

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Co-authored-by: Gabriel Lema <gabriel.lema@xmartlabs.com>

datasets/czech_slr_dataset.py

@@ -0,0 +1,72 @@
+import torch
+import numpy as np
+import torch.utils.data as torch_data
+
+from datasets.datasets_utils import load_dataset, tensor_to_dictionary, dictionary_to_tensor, \
+    random_augmentation
+from normalization.body_normalization import normalize_single_dict as normalize_single_body_dict
+from normalization.hand_normalization import normalize_single_dict as normalize_single_hand_dict
+
+
+class CzechSLRDataset(torch_data.Dataset):
+    """Advanced object representation of the HPOES dataset for loading hand joints landmarks utilizing the Torch's
+    built-in Dataset properties"""
+
+    data: [np.ndarray]
+    labels: [np.ndarray]
+
+    def __init__(self, dataset_filename: str, num_labels=5, transform=None, augmentations=False,
+                 augmentations_prob=0.5, normalize=True):
+        """
+        Initiates the HPOESDataset with the pre-loaded data from the h5 file.
+
+        :param dataset_filename: Path to the h5 file
+        :param transform: Any data transformation to be applied (default: None)
+        """
+
+        loaded_data = load_dataset(dataset_filename)
+        data, labels = loaded_data[0], loaded_data[1]
+
+        self.data = data
+        self.labels = labels
+        self.targets = list(labels)
+        self.num_labels = num_labels
+        self.transform = transform
+
+        self.augmentations = augmentations
+        self.augmentations_prob = augmentations_prob
+        self.normalize = normalize
+
+    def __getitem__(self, idx):
+        """
+        Allocates, potentially transforms and returns the item at the desired index.
+
+        :param idx: Index of the item
+        :return: Tuple containing both the depth map and the label
+        """
+
+        depth_map = torch.from_numpy(np.copy(self.data[idx]))
+        # label = torch.Tensor([self.labels[idx] - 1])
+        label = torch.Tensor([self.labels[idx]])
+
+        depth_map = tensor_to_dictionary(depth_map)
+
+        # Apply potential augmentations
+        depth_map = random_augmentation(self.augmentations, self.augmentations_prob, depth_map)
+
+        if self.normalize:
+            depth_map = normalize_single_body_dict(depth_map)
+            depth_map = normalize_single_hand_dict(depth_map)
+
+        depth_map = dictionary_to_tensor(depth_map)
+
+        # Move the landmark position interval to improve performance
+        depth_map = depth_map - 0.5
+
+        if self.transform:
+            depth_map = self.transform(depth_map)
+
+        return depth_map, label
+
+    def __len__(self):
+        return len(self.labels)