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>
48 lines
1.3 KiB
Python
48 lines
1.3 KiB
Python
import os
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import ast
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import pandas as pd
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from normalization.hand_normalization import normalize_hands_full
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from normalization.body_normalization import normalize_body_full
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DATASET_PATH = './data'
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# Load the dataset
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df = pd.read_csv(os.path.join(DATASET_PATH, "WLASL_test_15fps.csv"), encoding="utf-8")
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# Retrieve metadata
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video_size_heights = df["video_size_height"].to_list()
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video_size_widths = df["video_size_width"].to_list()
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# Delete redundant (non-related) properties
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del df["video_size_height"]
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del df["video_size_width"]
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# Temporarily remove other relevant metadata
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labels = df["labels"].to_list()
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video_fps = df["video_fps"].to_list()
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del df["labels"]
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del df["video_fps"]
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# Convert the strings into lists
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def convert(x): return ast.literal_eval(str(x))
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for column in df.columns:
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df[column] = df[column].apply(convert)
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# Perform the normalizations
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df = normalize_hands_full(df)
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df, invalid_row_indexes = normalize_body_full(df)
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# Clear lists of items from deleted rows
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# labels = [t for i, t in enumerate(labels) if i not in invalid_row_indexes]
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# video_fps = [t for i, t in enumerate(video_fps) if i not in invalid_row_indexes]
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# Return the metadata back to the dataset
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df["labels"] = labels
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df["video_fps"] = video_fps
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df.to_csv(os.path.join(DATASET_PATH, "WLASL_test_15fps_normalized.csv"), encoding="utf-8", index=False)
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