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@@ -5,23 +5,30 @@ 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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DATASET_PATH = './data/wlasl'
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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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df = pd.read_csv(os.path.join(DATASET_PATH, "WLASL100_train.csv"), encoding="utf-8")
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print(df.head())
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print(df.columns)
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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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video_size_heights = df["video_height"].to_list()
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video_size_widths = df["video_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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del df["video_height"]
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del df["video_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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video_fps = df["fps"].to_list()
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del df["labels"]
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del df["video_fps"]
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del df["fps"]
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del df["split"]
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del df["video_id"]
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del df["label_name"]
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del df["length"]
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# Convert the strings into lists
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@@ -42,6 +49,6 @@ df, invalid_row_indexes = normalize_body_full(df)
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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["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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df.to_csv(os.path.join(DATASET_PATH, "wlasl_train_norm.csv"), encoding="utf-8", index=False)
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