Create posture_with_image.py

posture_with_image.py

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+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()