Create posture_with_vid.py

posture_with_vid.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)
+    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
+    return degree
+
+mp_pose = mp.solutions.pose
+pose = mp_pose.Pose()
+
+cap = cv2.VideoCapture(0)
+
+while cap.isOpened():
+    ret, frame = cap.read()
+    if not ret:
+        break
+
+    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+
+    keypoints = pose.process(frame)
+
+    frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
+
+    if keypoints.pose_landmarks:
+        lm = keypoints.pose_landmarks
+        lmPose = mp_pose.PoseLandmark
+
+        # Acquire the landmark coordinates.
+        l_shldr_x = int(lm.landmark[lmPose.LEFT_SHOULDER].x * frame.shape[1])
+        l_shldr_y = int(lm.landmark[lmPose.LEFT_SHOULDER].y * frame.shape[0])
+        r_shldr_x = int(lm.landmark[lmPose.RIGHT_SHOULDER].x * frame.shape[1])
+        r_shldr_y = int(lm.landmark[lmPose.RIGHT_SHOULDER].y * frame.shape[0])
+        l_ear_x = int(lm.landmark[lmPose.LEFT_EAR].x * frame.shape[1])
+        l_ear_y = int(lm.landmark[lmPose.LEFT_EAR].y * frame.shape[0])
+        l_hip_x = int(lm.landmark[lmPose.LEFT_HIP].x * frame.shape[1])
+        l_hip_y = int(lm.landmark[lmPose.LEFT_HIP].y * frame.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)
+
+        # Draw landmarks and lines.
+        cv2.circle(frame, (l_shldr_x, l_shldr_y), 7, (255, 255, 0), -1)
+        cv2.circle(frame, (l_ear_x, l_ear_y), 7, (0, 255, 255), -1)
+        cv2.circle(frame, (r_shldr_x, r_shldr_y), 7, (255, 0, 255), -1)
+        cv2.circle(frame, (l_hip_x, l_hip_y), 7, (0, 255, 0), -1)
+
+        cv2.line(frame, (l_shldr_x, l_shldr_y), (r_shldr_x, r_shldr_y), (255, 255, 0), 2)
+        cv2.line(frame, (l_shldr_x, l_shldr_y), (l_ear_x, l_ear_y), (0, 255, 255), 2)
+        cv2.line(frame, (l_ear_x, l_ear_y), (r_shldr_x, r_shldr_y), (255, 0, 255), 2)
+        cv2.line(frame, (l_shldr_x, l_shldr_y), (l_hip_x, l_hip_y), (0, 255, 0), 2)
+        cv2.line(frame, (l_hip_x, l_hip_y), (r_shldr_x, r_shldr_y), (255, 255, 0), 2)
+
+        angle_text_string = 'Neck : ' + str(int(neck_inclination)) + 'degrees. Torso : ' + str(int(torso_inclination)) + 'degrees.'
+        cv2.putText(frame, angle_text_string, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
+
+        # Determine whether good posture or bad posture.
+        if neck_inclination < 40 and torso_inclination < 10:
+            cv2.putText(frame, 'Good Posture', (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
+        else:
+            cv2.putText(frame, 'Bad Posture', (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)
+
+    cv2.imshow('Posture Analysis', frame)
+
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
+
+cap.release()
+cv2.destroyAllWindows()