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>
242 lines
11 KiB
Python
242 lines
11 KiB
Python
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from typing import Tuple
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import pandas as pd
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from utils import get_logger
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BODY_IDENTIFIERS = [
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"nose",
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"neck",
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"rightEye",
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"leftEye",
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"rightEar",
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"leftEar",
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"rightShoulder",
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"leftShoulder",
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"rightElbow",
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"leftElbow",
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"rightWrist",
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"leftWrist"
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]
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def normalize_body_full(df: pd.DataFrame) -> Tuple[pd.DataFrame, list]:
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"""
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Normalizes the body position data using the Bohacek-normalization algorithm.
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:param df: pd.DataFrame to be normalized
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:return: pd.DataFrame with normalized values for body pose
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"""
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logger = get_logger(__name__)
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# TODO: Fix division by zero
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normalized_df = pd.DataFrame(columns=df.columns)
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invalid_row_indexes = []
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body_landmarks = {"X": [], "Y": []}
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# Construct the relevant identifiers
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for identifier in BODY_IDENTIFIERS:
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body_landmarks["X"].append(identifier + "_X")
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body_landmarks["Y"].append(identifier + "_Y")
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# Iterate over all of the records in the dataset
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for index, row in df.iterrows():
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sequence_size = len(row["leftEar_Y"])
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valid_sequence = True
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original_row = row
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last_starting_point, last_ending_point = None, None
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# Treat each element of the sequence (analyzed frame) individually
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for sequence_index in range(sequence_size):
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# Prevent from even starting the analysis if some necessary elements are not present
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if (row["leftShoulder_X"][sequence_index] == 0 or row["rightShoulder_X"][sequence_index] == 0) and \
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(row["neck_X"][sequence_index] == 0 or row["nose_X"][sequence_index] == 0):
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if not last_starting_point:
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valid_sequence = False
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continue
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else:
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starting_point, ending_point = last_starting_point, last_ending_point
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else:
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# NOTE:
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#
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# While in the paper, it is written that the head metric is calculated by halving the shoulder distance,
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# this is meant for the distance between the very ends of one's shoulder, as literature studying body
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# metrics and ratios generally states. The Vision Pose Estimation API, however, seems to be predicting
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# rather the center of one's shoulder. Based on our experiments and manual reviews of the data,
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# employing
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# this as just the plain shoulder distance seems to be more corresponding to the desired metric.
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#
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# Please, review this if using other third-party pose estimation libraries.
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if row["leftShoulder_X"][sequence_index] != 0 and row["rightShoulder_X"][sequence_index] != 0:
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left_shoulder = (row["leftShoulder_X"][sequence_index], row["leftShoulder_Y"][sequence_index])
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right_shoulder = (row["rightShoulder_X"][sequence_index], row["rightShoulder_Y"][sequence_index])
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shoulder_distance = ((((left_shoulder[0] - right_shoulder[0]) ** 2) + (
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(left_shoulder[1] - right_shoulder[1]) ** 2)) ** 0.5)
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head_metric = shoulder_distance
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else:
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neck = (row["neck_X"][sequence_index], row["neck_Y"][sequence_index])
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nose = (row["nose_X"][sequence_index], row["nose_Y"][sequence_index])
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neck_nose_distance = ((((neck[0] - nose[0]) ** 2) + ((neck[1] - nose[1]) ** 2)) ** 0.5)
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head_metric = neck_nose_distance
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# Set the starting and ending point of the normalization bounding box
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starting_point = [row["neck_X"][sequence_index] - 3 * head_metric,
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row["leftEye_Y"][sequence_index] + (head_metric / 2)]
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ending_point = [row["neck_X"][sequence_index] + 3 * head_metric, starting_point[1] - 6 * head_metric]
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last_starting_point, last_ending_point = starting_point, ending_point
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# Ensure that all of the bounding-box-defining coordinates are not out of the picture
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if starting_point[0] < 0:
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starting_point[0] = 0
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if starting_point[1] < 0:
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starting_point[1] = 0
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if ending_point[0] < 0:
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ending_point[0] = 0
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if ending_point[1] < 0:
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ending_point[1] = 0
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# Normalize individual landmarks and save the results
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for identifier in BODY_IDENTIFIERS:
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key = identifier + "_"
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# Prevent from trying to normalize incorrectly captured points
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if row[key + "X"][sequence_index] == 0:
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continue
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normalized_x = (row[key + "X"][sequence_index] - starting_point[0]) / (ending_point[0] -
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starting_point[0])
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normalized_y = (row[key + "Y"][sequence_index] - ending_point[1]) / (starting_point[1] -
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ending_point[1])
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row[key + "X"][sequence_index] = normalized_x
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row[key + "Y"][sequence_index] = normalized_y
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if valid_sequence:
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normalized_df = normalized_df.append(row, ignore_index=True)
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else:
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logger.warning(" BODY LANDMARKS: One video instance could not be normalized.")
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normalized_df = normalized_df.append(original_row, ignore_index=True)
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invalid_row_indexes.append(index)
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logger.info("The normalization of body is finished.")
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logger.info("\t-> Original size:", df.shape[0])
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logger.info("\t-> Normalized size:", normalized_df.shape[0])
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logger.info("\t-> Problematic videos:", len(invalid_row_indexes))
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return normalized_df, invalid_row_indexes
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def normalize_single_dict(row: dict):
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"""
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Normalizes the skeletal data for a given sequence of frames with signer's body pose data. The normalization follows
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the definition from our paper.
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:param row: Dictionary containing key-value pairs with joint identifiers and corresponding lists (sequences) of
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that particular joints coordinates
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:return: Dictionary with normalized skeletal data (following the same schema as input data)
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"""
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sequence_size = len(row["leftEar"])
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valid_sequence = True
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original_row = row
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logger = get_logger(__name__)
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last_starting_point, last_ending_point = None, None
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# Treat each element of the sequence (analyzed frame) individually
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for sequence_index in range(sequence_size):
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left_shoulder = (row["leftShoulder"][sequence_index][0], row["leftShoulder"][sequence_index][1])
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right_shoulder = (row["rightShoulder"][sequence_index][0], row["rightShoulder"][sequence_index][1])
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neck = (row["neck"][sequence_index][0], row["neck"][sequence_index][1])
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nose = (row["nose"][sequence_index][0], row["nose"][sequence_index][1])
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# Prevent from even starting the analysis if some necessary elements are not present
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if (left_shoulder[0] == 0 or right_shoulder[0] == 0
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or (left_shoulder[0] == right_shoulder[0] and left_shoulder[1] == right_shoulder[1])) and (
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neck[0] == 0 or nose[0] == 0 or (neck[0] == nose[0] and neck[1] == nose[1])):
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if not last_starting_point:
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valid_sequence = False
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continue
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else:
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starting_point, ending_point = last_starting_point, last_ending_point
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else:
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# NOTE:
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#
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# While in the paper, it is written that the head metric is calculated by halving the shoulder distance,
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# this is meant for the distance between the very ends of one's shoulder, as literature studying body
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# metrics and ratios generally states. The Vision Pose Estimation API, however, seems to be predicting
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# rather the center of one's shoulder. Based on our experiments and manual reviews of the data, employing
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# this as just the plain shoulder distance seems to be more corresponding to the desired metric.
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#
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# Please, review this if using other third-party pose estimation libraries.
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if left_shoulder[0] != 0 and right_shoulder[0] != 0 and \
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(left_shoulder[0] != right_shoulder[0] or left_shoulder[1] != right_shoulder[1]):
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shoulder_distance = ((((left_shoulder[0] - right_shoulder[0]) ** 2) + (
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(left_shoulder[1] - right_shoulder[1]) ** 2)) ** 0.5)
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head_metric = shoulder_distance
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else:
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neck_nose_distance = ((((neck[0] - nose[0]) ** 2) + ((neck[1] - nose[1]) ** 2)) ** 0.5)
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head_metric = neck_nose_distance
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# Set the starting and ending point of the normalization bounding box
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# starting_point = [row["neck"][sequence_index][0] - 3 * head_metric,
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# row["leftEye"][sequence_index][1] + (head_metric / 2)]
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starting_point = [row["neck"][sequence_index][0] - 3 * head_metric,
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row["leftEye"][sequence_index][1] + head_metric]
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ending_point = [row["neck"][sequence_index][0] + 3 * head_metric, starting_point[1] - 6 * head_metric]
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last_starting_point, last_ending_point = starting_point, ending_point
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# Ensure that all of the bounding-box-defining coordinates are not out of the picture
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if starting_point[0] < 0:
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starting_point[0] = 0
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if starting_point[1] < 0:
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starting_point[1] = 0
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if ending_point[0] < 0:
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ending_point[0] = 0
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if ending_point[1] < 0:
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ending_point[1] = 0
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# Normalize individual landmarks and save the results
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for identifier in BODY_IDENTIFIERS:
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key = identifier
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# Prevent from trying to normalize incorrectly captured points
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if row[key][sequence_index][0] == 0:
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continue
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if (ending_point[0] - starting_point[0]) == 0 or (starting_point[1] - ending_point[1]) == 0:
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logger.warning("Problematic normalization")
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valid_sequence = False
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break
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normalized_x = (row[key][sequence_index][0] - starting_point[0]) / (ending_point[0] - starting_point[0])
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normalized_y = (row[key][sequence_index][1] - ending_point[1]) / (starting_point[1] - ending_point[1])
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row[key][sequence_index] = list(row[key][sequence_index])
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row[key][sequence_index][0] = normalized_x
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row[key][sequence_index][1] = normalized_y
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if valid_sequence:
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return row
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else:
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return original_row
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if __name__ == "__main__":
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pass
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