import cv2 import mediapipe as mp import numpy as np import gradio as gr # Initialize MediaPipe Pose class. mp_pose = mp.solutions.pose pose = mp_pose.Pose(static_image_mode=True, min_detection_confidence=0.5) # Initialize drawing class mp_drawing = mp.solutions.drawing_utils def calculateAngle(landmark1, landmark2, landmark3): ''' This function calculates the angle between three landmarks. Args: landmark1: The first landmark containing the x, y, and z coordinates. landmark2: The second landmark containing the x, y, and z coordinates. landmark3: The third landmark containing the x, y, and z coordinates. Returns: angle: The calculated angle between the three landmarks. ''' x1, y1, _ = landmark1 x2, y2, _ = landmark2 x3, y3, _ = landmark3 angle = np.degrees(np.arctan2(y3 - y2, x3 - x2) - np.arctan2(y1 - y2, x1 - x2)) if angle < 0: angle += 360 return angle def classifyPose(landmarks, output_image, display=False): ''' This function classifies yoga poses depending upon the angles of various body joints. Args: landmarks: A list of detected landmarks of the person whose pose needs to be classified. output_image: A image of the person with the detected pose landmarks drawn. display: A boolean value that is if set to true the function displays the resultant image with the pose label written on it and returns nothing. Returns: output_image: The image with the detected pose landmarks drawn and pose label written. label: The classified pose label of the person in the output_image. ''' label = 'Unknown Pose' color = (0, 0, 255) left_elbow_angle = calculateAngle(landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value], landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value], landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value]) right_elbow_angle = calculateAngle(landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value], landmarks[mp_pose.PoseLandmark.RIGHT_ELBOW.value], landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value]) left_shoulder_angle = calculateAngle(landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value], landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value], landmarks[mp_pose.PoseLandmark.LEFT_HIP.value]) right_shoulder_angle = calculateAngle(landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value], landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value], landmarks[mp_pose.PoseLandmark.RIGHT_ELBOW.value]) left_knee_angle = calculateAngle(landmarks[mp_pose.PoseLandmark.LEFT_HIP.value], landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value], landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value]) right_knee_angle = calculateAngle(landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value], landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value], landmarks[mp_pose.PoseLandmark.RIGHT_ANKLE.value]) print(f"Left Elbow Angle: {left_elbow_angle}") print(f"Right Elbow Angle: {right_elbow_angle}") print(f"Left Shoulder Angle: {left_shoulder_angle}") print(f"Right Shoulder Angle: {right_shoulder_angle}") print(f"Left Knee Angle: {left_knee_angle}") print(f"Right Knee Angle: {right_knee_angle}") if abs(landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].y - landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].y) < 0.1 and \ abs(landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value].y - landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value].y) < 0.1 and \ abs(landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].x - landmarks[mp_pose.PoseLandmark.RIGHT_ANKLE.value].x) > 0.2 and \ abs(landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].x - landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value].x) > 0.2: label = "Five-Pointed Star Pose" if left_elbow_angle > 165 and left_elbow_angle < 195 and right_elbow_angle > 165 and right_elbow_angle < 195: if left_shoulder_angle > 80 and left_shoulder_angle < 110 and right_shoulder_angle > 80 and right_shoulder_angle < 110: if left_knee_angle > 165 and left_knee_angle < 195 or right_knee_angle > 165 and right_knee_angle < 195: if left_knee_angle > 90 and left_knee_angle < 120 or right_knee_angle > 90 and right_knee_angle < 120: label = 'Warrior II Pose' if left_knee_angle > 160 and left_knee_angle < 195 and right_knee_angle > 160 and right_knee_angle < 195: label = 'T Pose' if left_knee_angle > 165 and left_knee_angle < 195 or right_knee_angle > 165 and right_knee_angle < 195: if left_knee_angle > 315 and left_knee_angle < 335 or right_knee_angle > 25 and right_knee_angle < 45: label = 'Tree Pose' if abs(landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].x - landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].x) < 0.1 and \ abs(landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value].x - landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value].x) < 0.1 and \ landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].y < landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].y and \ landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value].y < landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value].y and \ abs(landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].y - landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value].y) < 0.05: label = "Upward Salute Pose" if landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].y > landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].y and \ landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value].y > landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].y and \ abs(landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].x - landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].x) < 0.05 and \ abs(landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value].x - landmarks[mp_pose.PoseLandmark.RIGHT_ANKLE.value].x) < 0.05: label = "Hands Under Feet Pose" if label != 'Unknown Pose': color = (0, 255, 0) cv2.putText(output_image, label, (10, 30), cv2.FONT_HERSHEY_PLAIN, 2, color, 2) if display: plt.figure(figsize=[10,10]) plt.imshow(output_image[:,:,::-1]) plt.title("Output Image") plt.axis('off') else: return output_image, label def run(image): image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) results = pose.process(image) if results.pose_landmarks: mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS) image, classification = classifyPose(results.pose_landmarks.landmark, image) return image, classification else: return image, "No Pose Detected" demo = gr.Interface(fn=run, inputs="image", outputs=["image", "text"], live=True) if __name__ == "__main__": demo.launch()