import os
import ast
import pandas as pd

from normalization.hand_normalization import normalize_hands_full
from normalization.body_normalization import normalize_body_full

DATASET_PATH = './data/processed'
# Load the dataset
df = pd.read_csv(os.path.join(DATASET_PATH, "spoter_train.csv"), encoding="utf-8")

print(df.head())
print(df.columns)

# Retrieve metadata
# video_size_heights = df["video_height"].to_list()
# video_size_widths = df["video_width"].to_list()

# Delete redundant (non-related) properties
# del df["video_height"]
# del df["video_width"]

# Temporarily remove other relevant metadata
labels = df["labels"].to_list()
signs = df["sign"].to_list()

del df["labels"]
del df["sign"]
del df["path"]
del df["participant_id"]
del df["sequence_id"]

# Convert the strings into lists


def convert(x): return ast.literal_eval(str(x))


for column in df.columns:
    df[column] = df[column].apply(convert)

# Perform the normalizations
df = normalize_hands_full(df)
# df, invalid_row_indexes = normalize_body_full(df)

# Clear lists of items from deleted rows
# labels = [t for i, t in enumerate(labels) if i not in invalid_row_indexes]
# video_fps = [t for i, t in enumerate(video_fps) if i not in invalid_row_indexes]

# Return the metadata back to the dataset
df["labels"] = labels
df["sign"] = signs

df.to_csv(os.path.join(DATASET_PATH, "spoter_train_norm.csv"), encoding="utf-8", index=False)