家に Raspberry Pi が落ちていたので腰痛を改善しようとした

def calculate_vertical_angle(a, b):
    
    a = np.array(a)
    b = np.array(b)

    vector = a - b

    vertical_vector = np.array([0, -1])
    cosine_angle = np.dot(vector, vertical_vector) / (np.linalg.norm(vector) * np.linalg.norm(vertical_vector))
    angle = np.degrees(np.arccos(np.clip(cosine_angle, -1.0, 1.0)))

    return angle

shoulders_mid = (left_shoulder + right_shoulder) / 2
hips_mid = (left_hip + right_hip) / 2

shoulder_to_hip_angle = calculate_vertical_angle(shoulders_mid, hips_mid)

def calculate_angle(a, b, c):
    a = np.array(a)
    b = np.array(b)
    c = np.array(c)

    ba = a - b
    bc = c - b

    cosine_angle = np.dot(ba, bc) / (np.linalg.norm(ba) * np.linalg.norm(bc))
    angle = np.arccos(np.clip(cosine_angle, -1.0, 1.0))
    return np.degrees(angle)

shoulders_mid = (left_shoulder + right_shoulder) / 2
hips_mid = (left_hip + right_hip) / 2

neck_angle = calculate_angle(nose, shoulders_mid, hips_mid)

import tensorflow as tf
import tflite_runtime.interpreter as tflite
import numpy as np
import cv2
import os
from matplotlib import pyplot as plt

model_path = "path_to_model"
input_size = 192

interpreter = tflite.Interpreter(model_path=model_path)
interpreter.allocate_tensors()

def movenet(input_image):
    input_image = tf.cast(input_image, dtype=tf.uint8)
    input_details = interpreter.get_input_details()
    output_details = interpreter.get_output_details()
    interpreter.set_tensor(input_details[0]['index'], input_image.numpy())
    interpreter.invoke()
    keypoints_with_scores = interpreter.get_tensor(output_details[0]['index'])
    return keypoints_with_scores


def draw_prediction_on_image(image, keypoints_with_scores, threshold=0.25):
    keypoints = keypoints_with_scores[0][0][:, :3]
    height, width, _ = image.shape


    face_indices = [0, 1, 2, 3, 4]  # [nose, left eye, right eye, left ear, right ear]
    body_indices = [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]  # shoulders, elbows, wrists, etc.

    for i, kp in enumerate(keypoints):
        y, x, confidence = kp
        if confidence > threshold:
            cx, cy = int(x * width), int(y * height)

            if i in face_indices:
                color = (0, 0, 255)
            elif i in body_indices:
                color = (0, 255, 0)
            else:
                color = (255, 255, 0)

            cv2.circle(image, (cx, cy), 5, color, -1)
    return image


def calculate_angle(a, b, c):
    a = np.array(a)
    b = np.array(b)
    c = np.array(c)

    ba = a - b
    bc = c - b

    cosine_angle = np.dot(ba, bc) / (np.linalg.norm(ba) * np.linalg.norm(bc))
    angle = np.arccos(np.clip(cosine_angle, -1.0, 1.0))
    return np.degrees(angle)


def calculate_vertical_angle(a, b):
    
    a = np.array(a)
    b = np.array(b)

    vector = a - b

    vertical_vector = np.array([0, -1])
    cosine_angle = np.dot(vector, vertical_vector) / (np.linalg.norm(vector) * np.linalg.norm(vertical_vector))
    angle = np.degrees(np.arccos(np.clip(cosine_angle, -1.0, 1.0)))

    return angle


def evaluate_posture(keypoints_with_scores):
    keypoints = keypoints_with_scores[0][0][:, :3]

    nose = keypoints[0][:2]
    left_shoulder = keypoints[5][:2]
    right_shoulder = keypoints[6][:2]
    left_hip = keypoints[11][:2]
    right_hip = keypoints[12][:2]

    shoulders_mid = (left_shoulder + right_shoulder) / 2
    hips_mid = (left_hip + right_hip) / 2

    shoulder_to_hip_angle = calculate_vertical_angle(shoulders_mid, hips_mid)
    neck_angle = calculate_angle(nose, shoulders_mid, hips_mid)

    posture_issues = []
    if neck_angle < 125:
        posture_issues.append("Forward head posture detected. Angle: {neck_angle:.2f} degrees")
    if shoulder_to_hip_angle < 80 or shoulder_to_hip_angle > 105:
        posture_issues.append(f"Rounded shoulders detected. Angle: {shoulder_to_hip_angle:.2f} degrees")
    
    return posture_issues


cap = cv2.VideoCapture(0)
while cap.isOpened():
    ret, frame = cap.read()
    if not ret:
        break

    input_image = tf.image.resize_with_pad(tf.convert_to_tensor(frame), input_size, input_size)
    input_image = tf.expand_dims(input_image, axis=0)

    keypoints_with_scores = movenet(input_image)
    posture_issues = evaluate_posture(keypoints_with_scores)
    output_overlay = draw_prediction_on_image(frame, keypoints_with_scores)

    y_offset = 50 
    for issue in posture_issues:
        cv2.putText(output_overlay, issue, (10, y_offset), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
        y_offset += 30

    cv2.imshow('MoveNet Pose Detection with Posture Evaluation', output_overlay)

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()