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| import logging | |
| import pandas as pd | |
| HAND_IDENTIFIERS = [ | |
| "wrist", | |
| "indexTip", | |
| "indexDIP", | |
| "indexPIP", | |
| "indexMCP", | |
| "middleTip", | |
| "middleDIP", | |
| "middlePIP", | |
| "middleMCP", | |
| "ringTip", | |
| "ringDIP", | |
| "ringPIP", | |
| "ringMCP", | |
| "littleTip", | |
| "littleDIP", | |
| "littlePIP", | |
| "littleMCP", | |
| "thumbTip", | |
| "thumbIP", | |
| "thumbMP", | |
| "thumbCMC" | |
| ] | |
| def normalize_hands_full(df: pd.DataFrame) -> pd.DataFrame: | |
| """ | |
| Normalizes the hands position data using the Bohacek-normalization algorithm. | |
| :param df: pd.DataFrame to be normalized | |
| :return: pd.DataFrame with normalized values for hand pose | |
| """ | |
| # TODO: Fix division by zero | |
| df.columns = [item.replace("_left_", "_0_").replace("_right_", "_1_") for item in list(df.columns)] | |
| normalized_df = pd.DataFrame(columns=df.columns) | |
| hand_landmarks = {"X": {0: [], 1: []}, "Y": {0: [], 1: []}} | |
| # Determine how many hands are present in the dataset | |
| range_hand_size = 1 | |
| if "wrist_1_X" in df.columns: | |
| range_hand_size = 2 | |
| # Construct the relevant identifiers | |
| for identifier in HAND_IDENTIFIERS: | |
| for hand_index in range(range_hand_size): | |
| hand_landmarks["X"][hand_index].append(identifier + "_" + str(hand_index) + "_X") | |
| hand_landmarks["Y"][hand_index].append(identifier + "_" + str(hand_index) + "_Y") | |
| # Iterate over all of the records in the dataset | |
| for index, row in df.iterrows(): | |
| # Treat each hand individually | |
| for hand_index in range(range_hand_size): | |
| sequence_size = len(row["wrist_" + str(hand_index) + "_X"]) | |
| # Treat each element of the sequence (analyzed frame) individually | |
| for sequence_index in range(sequence_size): | |
| # Retrieve all of the X and Y values of the current frame | |
| landmarks_x_values = [row[key][sequence_index] for key in hand_landmarks["X"][hand_index] if row[key][sequence_index] != 0] | |
| landmarks_y_values = [row[key][sequence_index] for key in hand_landmarks["Y"][hand_index] if row[key][sequence_index] != 0] | |
| # Prevent from even starting the analysis if some necessary elements are not present | |
| if not landmarks_x_values or not landmarks_y_values: | |
| logging.warning( | |
| " HAND LANDMARKS: One frame could not be normalized as there is no data present. Record: " + str(index) + | |
| ", Frame: " + str(sequence_index)) | |
| continue | |
| # Calculate the deltas | |
| width, height = max(landmarks_x_values) - min(landmarks_x_values), max(landmarks_y_values) - min( | |
| landmarks_y_values) | |
| if width > height: | |
| delta_x = 0.1 * width | |
| delta_y = delta_x + ((width - height) / 2) | |
| else: | |
| delta_y = 0.1 * height | |
| delta_x = delta_y + ((height - width) / 2) | |
| # Set the starting and ending point of the normalization bounding box | |
| starting_point = (min(landmarks_x_values) - delta_x, min(landmarks_y_values) - delta_y) | |
| ending_point = (max(landmarks_x_values) + delta_x, max(landmarks_y_values) + delta_y) | |
| # Normalize individual landmarks and save the results | |
| for identifier in HAND_IDENTIFIERS: | |
| key = identifier + "_" + str(hand_index) + "_" | |
| # Prevent from trying to normalize incorrectly captured points | |
| if row[key + "X"][sequence_index] == 0 or (ending_point[0] - starting_point[0]) == 0 or (starting_point[1] - ending_point[1]) == 0: | |
| continue | |
| normalized_x = (row[key + "X"][sequence_index] - starting_point[0]) / (ending_point[0] - | |
| starting_point[0]) | |
| normalized_y = (row[key + "Y"][sequence_index] - ending_point[1]) / (starting_point[1] - | |
| ending_point[1]) | |
| row[key + "X"][sequence_index] = normalized_x | |
| row[key + "Y"][sequence_index] = normalized_y | |
| normalized_df = normalized_df.append(row, ignore_index=True) | |
| return normalized_df | |
| def normalize_single_dict(row: dict): | |
| """ | |
| Normalizes the skeletal data for a given sequence of frames with signer's hand pose data. The normalization follows | |
| the definition from our paper. | |
| :param row: Dictionary containing key-value pairs with joint identifiers and corresponding lists (sequences) of | |
| that particular joints coordinates | |
| :return: Dictionary with normalized skeletal data (following the same schema as input data) | |
| """ | |
| hand_landmarks = {0: [], 1: []} | |
| # Determine how many hands are present in the dataset | |
| range_hand_size = 1 | |
| if "wrist_1" in row.keys(): | |
| range_hand_size = 2 | |
| # Construct the relevant identifiers | |
| for identifier in HAND_IDENTIFIERS: | |
| for hand_index in range(range_hand_size): | |
| hand_landmarks[hand_index].append(identifier + "_" + str(hand_index)) | |
| # Treat each hand individually | |
| for hand_index in range(range_hand_size): | |
| sequence_size = len(row["wrist_" + str(hand_index)]) | |
| # Treat each element of the sequence (analyzed frame) individually | |
| for sequence_index in range(sequence_size): | |
| # Retrieve all of the X and Y values of the current frame | |
| landmarks_x_values = [row[key][sequence_index][0] for key in hand_landmarks[hand_index] if | |
| row[key][sequence_index][0] != 0] | |
| landmarks_y_values = [row[key][sequence_index][1] for key in hand_landmarks[hand_index] if | |
| row[key][sequence_index][1] != 0] | |
| # Prevent from even starting the analysis if some necessary elements are not present | |
| if not landmarks_x_values or not landmarks_y_values: | |
| continue | |
| # Calculate the deltas | |
| width, height = max(landmarks_x_values) - min(landmarks_x_values), max(landmarks_y_values) - min( | |
| landmarks_y_values) | |
| if width > height: | |
| delta_x = 0.1 * width | |
| delta_y = delta_x + ((width - height) / 2) | |
| else: | |
| delta_y = 0.1 * height | |
| delta_x = delta_y + ((height - width) / 2) | |
| # Set the starting and ending point of the normalization bounding box | |
| starting_point = [min(landmarks_x_values) - delta_x, min(landmarks_y_values) - delta_y] | |
| ending_point = [max(landmarks_x_values) + delta_x, max(landmarks_y_values) + delta_y] | |
| # Ensure that all of the bounding-box-defining coordinates are not out of the picture | |
| if starting_point[0] < 0: starting_point[0] = 0 | |
| if starting_point[1] > 1: starting_point[1] = 1 | |
| if ending_point[0] < 0: ending_point[0] = 0 | |
| if ending_point[1] > 1: ending_point[1] = 1 | |
| # Normalize individual landmarks and save the results | |
| for identifier in HAND_IDENTIFIERS: | |
| key = identifier + "_" + str(hand_index) | |
| # Prevent from trying to normalize incorrectly captured points | |
| if row[key][sequence_index][0] == 0 or (ending_point[0] - starting_point[0]) == 0 or ( | |
| starting_point[1] - ending_point[1]) == 0: | |
| continue | |
| normalized_x = (row[key][sequence_index][0] - starting_point[0]) / (ending_point[0] - starting_point[0]) | |
| normalized_y = (row[key][sequence_index][1] - starting_point[1]) / (ending_point[1] - starting_point[1]) | |
| row[key][sequence_index] = list(row[key][sequence_index]) | |
| row[key][sequence_index][0] = normalized_x | |
| row[key][sequence_index][1] = normalized_y | |
| return row | |
| if __name__ == "__main__": | |
| pass | |