posture_with_image.py

import cv2
import math as m
import mediapipe as mp

def findDistance(x1, y1, x2, y2):
    dist = m.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)
    print('distance: ', dist)
    return dist

def findAngle(x1, y1, x2, y2):
    theta = m.acos((y2 - y1) * (-y1) / (m.sqrt(
        (x2 - x1) ** 2 + (y2 - y1) ** 2) * y1))
    degree = int(180 / m.pi) * theta
    print('degree: ', degree)
    return degree

mp_pose = mp.solutions.pose
pose = mp_pose.Pose()

bad_image_path = 'images/bad_posture.jpg'
good_image_path = 'images\good_posture.jpeg'
image = cv2.imread(bad_image_path)

image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

keypoints = pose.process(image)

image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)

# Use lm and lmPose as representative of the following methods.
lm = keypoints.pose_landmarks
lmPose = mp_pose.PoseLandmark

# Acquire the landmark coordinates.
# Left shoulder.
l_shldr_x = int(lm.landmark[lmPose.LEFT_SHOULDER].x * image.shape[1])
l_shldr_y = int(lm.landmark[lmPose.LEFT_SHOULDER].y * image.shape[0])
# Right shoulder
r_shldr_x = int(lm.landmark[lmPose.RIGHT_SHOULDER].x * image.shape[1])
r_shldr_y = int(lm.landmark[lmPose.RIGHT_SHOULDER].y * image.shape[0])
# Left ear.
l_ear_x = int(lm.landmark[lmPose.LEFT_EAR].x * image.shape[1])
l_ear_y = int(lm.landmark[lmPose.LEFT_EAR].y * image.shape[0])
# Left hip.
l_hip_x = int(lm.landmark[lmPose.LEFT_HIP].x * image.shape[1])
l_hip_y = int(lm.landmark[lmPose.LEFT_HIP].y * image.shape[0])

# Calculate distance between left shoulder and right shoulder points.
offset = findDistance(l_shldr_x, l_shldr_y, r_shldr_x, r_shldr_y)

# Calculate angles.
neck_inclination = findAngle(l_shldr_x, l_shldr_y, l_ear_x, l_ear_y)
torso_inclination = findAngle(l_hip_x, l_hip_y, l_shldr_x, l_shldr_y)

cv2.circle(image, (l_shldr_x, l_shldr_y), 7, (255, 255, 0), -1)
cv2.circle(image, (l_ear_x, l_ear_y), 7, (0, 255, 255), -1)
cv2.circle(image, (r_shldr_x, r_shldr_y), 7, (255, 0, 255), -1)
cv2.circle(image, (l_hip_x, l_hip_y), 7, (0, 255, 0), -1)

# Draw lines between the landmarks
cv2.line(image, (l_shldr_x, l_shldr_y), (r_shldr_x, r_shldr_y), (255, 255, 0), 2)
cv2.line(image, (l_shldr_x, l_shldr_y), (l_ear_x, l_ear_y), (0, 255, 255), 2)
cv2.line(image, (l_ear_x, l_ear_y), (r_shldr_x, r_shldr_y), (255, 0, 255), 2)
cv2.line(image, (l_shldr_x, l_shldr_y), (l_hip_x, l_hip_y), (0, 255, 0), 2)
cv2.line(image, (l_hip_x, l_hip_y), (r_shldr_x, r_shldr_y), (255, 255, 0), 2)

# Put text, Posture and angle inclination.
angle_text_string = 'Neck : ' + str(int(neck_inclination)) + 'degrees.  Torso : ' + str(int(torso_inclination)) + 'degrees.'
cv2.putText(image, angle_text_string, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)

# Determine whether good posture or bad posture.
# The threshold angles have been set based on intuition.
if neck_inclination < 40 and torso_inclination < 10:
    cv2.putText(image, 'Good Posture', (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
else:
    cv2.putText(image, 'Bad Posture', (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)


cv2.resize(image, (600, 600))
cv2.imshow('Posture Analysis', image)
cv2.waitKey(0)
cv2.destroyAllWindows()