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import warnings
import pandas as pd
from os import path
import cv2
import mediapipe as mp
import json
from spoter_mod.pose_model_identifier import BODY_IDENTIFIERS, HAND_IDENTIFIERS, mp_holistic_data
mp_drawing = mp.solutions.drawing_utils
mp_holistic = mp.solutions.holistic
mp_drawing_styles = mp.solutions.drawing_styles
holistic = mp_holistic.Holistic()
column_names = []
column_names.append('video_id')
for id_name in BODY_IDENTIFIERS.keys():
for xy in ["_X", "_Y"]:
column_names.append(id_name + xy)
for lr in ["_Right", "_Left"]:
for id_name in HAND_IDENTIFIERS.keys():
for xy in ["_X", "_Y"]:
column_names.append(id_name + lr + xy)
column_names.append('labels')
def create_df(flnm, column_names):
df = pd.DataFrame(columns=column_names)
return df
def save_data(df, data, flnm):
df = df.append(data.get_series(), ignore_index=True)
df.to_pickle(flnm)
def obtain_pose_data(path):
cap = cv2.VideoCapture(path)
data = mp_holistic_data(column_names)
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
# Recolor image to RGB
image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# Make detection
holistic_results = holistic.process(image)
# Extract feature and save to mp_pose_data class
data.extract_data(holistic_results)
cap.release()
return data
if __name__ == '__main__':
pass
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