app.py

import gradio as gr
import cv2
import mediapipe as mp
import numpy as np

# Initialize mediapipe pose class
mp_pose = mp.solutions.pose
pose = mp_pose.Pose(static_image_mode=False, min_detection_confidence=0.5, model_complexity=1)
mp_drawing = mp.solutions.drawing_utils

# Define a function to classify yoga poses
def classify_pose(landmarks):
    # Check if the both arms are straight.
    if left_elbow_angle > 165 and left_elbow_angle < 195 and right_elbow_angle > 165 and right_elbow_angle < 195:

        # Check if shoulders are at the required angle.
        if left_shoulder_angle > 80 and left_shoulder_angle < 110 and right_shoulder_angle > 80 and right_shoulder_angle < 110:

    # Check if it is the warrior II pose.
    #----------------------------------------------------------------------------------------------------------------

            # Check if one leg is straight.
            if left_knee_angle > 165 and left_knee_angle < 195 or right_knee_angle > 165 and right_knee_angle < 195:

                # Check if the other leg is bended at the required angle.
                if left_knee_angle > 90 and left_knee_angle < 120 or right_knee_angle > 90 and right_knee_angle < 120:

                    # Specify the label of the pose that is Warrior II pose.
                    label = 'Warrior II Pose'

    #----------------------------------------------------------------------------------------------------------------

    # Check if it is the T pose.
    #----------------------------------------------------------------------------------------------------------------

            # Check if both legs are straight
            if left_knee_angle > 160 and left_knee_angle < 195 and right_knee_angle > 160 and right_knee_angle < 195:

                # Specify the label of the pose that is tree pose.
                label = 'T Pose'
    
    #----------------------------------------------------------------------------------------------------------------

    # Check if it is the tree pose.
    #----------------------------------------------------------------------------------------------------------------

    # Check if one leg is straight
    if left_knee_angle > 165 and left_knee_angle < 195 or right_knee_angle > 165 and right_knee_angle < 195:

        # Check if the other leg is bended at the required angle.
        if left_knee_angle > 315 and left_knee_angle < 335 or right_knee_angle > 25 and right_knee_angle < 45:

            # Specify the label of the pose that is tree pose.
            label = 'Tree Pose'
    
    # Check for Upward Salute Pose
    if abs(landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value][0] - landmarks[mp_pose.PoseLandmark.LEFT_HIP.value][0]) < 100 and \
       abs(landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value][0] - landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value][0]) < 100 and \
       landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value][1] < landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value][1] and \
       landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value][1] < landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value][1] and \
       abs(landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value][1] - landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value][1]) < 50:
        label = "Upward Salute Pose"

    # Add more classification rules here
            return "Unknown Pose"

def detect_and_classify_pose(input_image):
    frame = cv2.cvtColor(input_image, cv2.COLOR_BGR2RGB)
    results = pose.process(frame)
    pose_classification = "No pose detected"
    if results.pose_landmarks:
        mp_drawing.draw_landmarks(frame, results.pose_landmarks, mp_pose.POSE_CONNECTIONS)
        pose_classification = classify_pose(results.pose_landmarks.landmark)
    return frame, pose_classification

iface = gr.Interface(
    fn=detect_and_classify_pose,
    inputs="image",
    outputs=["image", "text"],
    title="Live Yoga Pose Detection and Classification",
    description="This app detects and classifies yoga poses from the live camera feed using MediaPipe.",
)

iface.launch()