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Train_Model.py
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Train_Model.py
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import cv2
import mediapipe as mp
import numpy as np
import time
import pandas as pd
mp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles
mp_pose = mp.solutions.pose
prevTime = 0
keyPoint = [
"nose",
"left_eye_inner",
"left_eye",
"left_eye_outer",
"right_eye_inner",
"right_eye",
"right_eye_outer",
"left_ear",
"right_ear",
"mouth_left",
"mouth_right",
"left_shoulder",
"right_shoulder",
"left_elbow",
"right_elbow",
"left_wrist",
"right_wrist",
"left_pinky",
"right_pinky",
"left_index",
"right_index",
"left_thumb",
"right_thumb",
"left_hip",
"right_hip",
"left_knee",
"right_knee",
"left_ankle",
"right_ankle",
"left_heel",
"right_heel",
"left_foot_index",
"right_foot_index"
]
keyXYZ = [
"nose_x",
"nose_y",
"nose_z",
"left_eye_inner_x",
"left_eye_inner_y",
"left_eye_inner_z",
"left_eye_x",
"left_eye_y",
"left_eye_z",
"left_eye_outer_x",
"left_eye_outer_y",
"left_eye_outer_z",
"right_eye_inner_x",
"right_eye_inner_y",
"right_eye_inner_z",
"right_eye_x",
"right_eye_y",
"right_eye_z",
"right_eye_outer_x",
"right_eye_outer_y",
"right_eye_outer_z",
"left_ear_x",
"left_ear_y",
"left_ear_z",
"right_ear_x",
"right_ear_y",
"right_ear_z",
"mouth_left_x",
"mouth_left_y",
"mouth_left_z",
"mouth_right_x",
"mouth_right_y",
"mouth_right_z",
"left_shoulder_x",
"left_shoulder_y",
"left_shoulder_z",
"right_shoulder_x",
"right_shoulder_y",
"right_shoulder_z",
"left_elbow_x",
"left_elbow_y",
"left_elbow_z",
"right_elbow_x",
"right_elbow_y",
"right_elbow_z",
"left_wrist_x",
"left_wrist_y",
"left_wrist_z",
"right_wrist_x",
"right_wrist_y",
"right_wrist_z",
"left_pinky_x",
"left_pinky_y",
"left_pinky_z",
"right_pinky_x",
"right_pinky_y",
"right_pinky_z",
"left_index_x",
"left_index_y",
"left_index_z",
"right_index_x",
"right_index_y",
"right_index_z",
"left_thumb_x",
"left_thumb_y",
"left_thumb_z",
"right_thumb_x",
"right_thumb_y",
"right_thumb_z",
"left_hip_x",
"left_hip_y",
"left_hip_z",
"right_hip_x",
"right_hip_y",
"right_hip_z",
"left_knee_x",
"left_knee_y",
"left_knee_z",
"right_knee_x",
"right_knee_y",
"right_knee_z",
"left_ankle_x",
"left_ankle_y",
"left_ankle_z",
"right_ankle_x",
"right_ankle_y",
"right_ankle_z",
"left_heel_x",
"left_heel_y",
"left_heel_z",
"right_heel_x",
"right_heel_y",
"right_heel_z",
"left_foot_index_x",
"left_foot_index_y",
"left_foot_index_z",
"right_foot_index_x",
"right_foot_index_y",
"right_foot_index_z"
]
print(len(keyXYZ))
res_point = []
print(len(keyPoint))
cap = cv2.VideoCapture("Fall_Trim.mp4")
with mp_pose.Pose(
static_image_mode=True,
min_detection_confidence=0.5,
min_tracking_confidence=0.5) as pose:
while cap.isOpened():
success, image = cap.read()
if not success:
print("Ignoring empty camera frame.")
continue
image.flags.writeable = False
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
results = pose.process(image)
if results.pose_landmarks:
for index, landmarks in enumerate(results.pose_landmarks.landmark):
# print(keyPoint[index], landmarks.x, landmarks.y, landmarks.z)
# print(len(results.pose_landmarks.landmark))
res_point.append(landmarks.x)
res_point.append(landmarks.y)
res_point.append(landmarks.z)
# Draw the pose annotation on the image.
image.flags.writeable = True
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
mp_drawing.draw_landmarks(
image,
results.pose_landmarks,
mp_pose.POSE_CONNECTIONS,
landmark_drawing_spec=mp_drawing_styles.get_default_pose_landmarks_style())
# Flip the image horizontally for a selfie-view display.
currTime = time.time()
fps = 1 / (currTime - prevTime)
prevTime = currTime
cv2.putText(image, f'FPS: {int(fps)}', (20, 70), cv2.FONT_HERSHEY_PLAIN, 3, (0, 196, 255), 2)
# cv2.imshow('MediaPipe Pose', cv2.flip(image, 1))
cv2.imshow('MediaPipe Pose', image)
if cv2.waitKey(0) & 0xFF == 27:
shape1 = int(len(res_point) / len(keyXYZ))
print(shape1)
print(res_point)
print(len(res_point))
res_point = np.array(res_point).reshape(shape1, len(keyXYZ))
print(res_point)
print(res_point.shape)
data = pd.DataFrame(data=res_point, columns=keyXYZ, index=None)
data.to_csv("fall_point.csv", encoding='utf-8')
break
cap.release()