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 = None
mp_drawing = mp.solutions.drawing_utils

# Function to calculate the angle between three points
def calculate_angle(a, b, c):
    a = np.array([a.x, a.y])  # First point
    b = np.array([b.x, b.y])  # Mid point
    c = np.array([c.x, c.y])  # End point
    
    radians = np.arctan2(c[1] - b[1], c[0] - b[0]) - np.arctan2(a[1] - b[1], a[0] - b[0])
    angle = np.abs(radians * 180.0 / np.pi)
    
    if angle > 180.0:
        angle = 360 - angle
        
    return angle

# Define a function to classify yoga poses
def classify_pose(landmarks, output_image):
    label = 'Unknown Pose'

    # Calculate the required angles
    left_elbow_angle = calculate_angle(
        landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value],
        landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value],
        landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value])

    right_elbow_angle = calculate_angle(
        landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value],
        landmarks[mp_pose.PoseLandmark.RIGHT_ELBOW.value],
        landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value])

    left_shoulder_angle = calculate_angle(
        landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value],
        landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value],
        landmarks[mp_pose.PoseLandmark.LEFT_HIP.value])

    right_shoulder_angle = calculate_angle(
        landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value],
        landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value],
        landmarks[mp_pose.PoseLandmark.RIGHT_ELBOW.value])

    left_knee_angle = calculate_angle(
        landmarks[mp_pose.PoseLandmark.LEFT_HIP.value],
        landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value],
        landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value])

    right_knee_angle = calculate_angle(
        landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value],
        landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value],
        landmarks[mp_pose.PoseLandmark.RIGHT_ANKLE.value])

    # Check for Five-Pointed Star Pose
    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"
    
    # Check for Warrior II pose
    if 165 < left_elbow_angle < 195 and 165 < right_elbow_angle < 195 and \
       80 < left_shoulder_angle < 110 and 80 < right_shoulder_angle < 110:
        if (165 < left_knee_angle < 195 or 165 < right_knee_angle < 195) and \
           (90 < left_knee_angle < 120 or 90 < right_knee_angle < 120):
            label = 'Warrior II Pose'

    # Check for T pose
    if 165 < left_elbow_angle < 195 and 165 < right_elbow_angle < 195 and \
       80 < left_shoulder_angle < 110 and 80 < right_shoulder_angle < 110 and \
       160 < left_knee_angle < 195 and 160 < right_knee_angle < 195:
        label = 'T Pose'

    # Check for Tree Pose
    if (165 < left_knee_angle < 195 or 165 < right_knee_angle < 195) and \
       (315 < left_knee_angle < 335 or 25 < right_knee_angle < 45):
        label = 'Tree Pose'
    
    # Check for Upward Salute 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"

    # Check for Hands Under Feet 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"

    # Check for Plank Pose
    if 160 < left_shoulder_angle < 200 and 160 < right_shoulder_angle < 200 and \
       160 < left_knee_angle < 200 and 160 < right_knee_angle < 200:
        label = "Plank Pose"

    # Write the label on the output image
    cv2.putText(output_image, label, (10, 30), cv2.FONT_HERSHEY_PLAIN, 2, (0, 255, 0), 2)
    
    return output_image, label

def detect_and_classify_pose(input_image, complexity, confidence, background_color):
    global pose
    pose = mp_pose.Pose(static_image_mode=False, min_detection_confidence=confidence, model_complexity=complexity)
    
    input_image = cv2.cvtColor(input_image, cv2.COLOR_BGR2RGB)
    if background_color == 'White':
        input_image[:] = [255, 255, 255]
    elif background_color == 'Green':
        input_image[:] = [0, 255, 0]
    elif background_color == 'Black':
        input_image[:] = [0, 0, 0]

    results = pose.process(input_image)
    pose_classification = "No pose detected"
    if results.pose_landmarks:
        mp_drawing.draw_landmarks(input_image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS)
        input_image, pose_classification = classify_pose(results.pose_landmarks.landmark, input_image)
    
    input_image = cv2.cvtColor(input_image, cv2.COLOR_RGB2BGR)  # Convert back to BGR for correct display in Gradio
    return input_image, pose_classification

iface = gr.Interface(
    fn=detect_and_classify_pose,
    inputs=[
        gr.Image(type="numpy", label="Upload an Image"),
        gr.Slider(minimum=0, maximum=2, value=1, label="Model Complexity"),
        gr.Slider(minimum=0.1, maximum=1.0, value=0.5, label="Detection Confidence"),
        gr.Radio(choices=['White', 'Green', 'Black'], value='White', label="Background Color")
    ],
    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()