Update app.py

app.py

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+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
+
+# 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, display=False):
+    label = 'Unknown Pose'
+    color = (0, 0, 255)
+
+    # 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
+    # The body should be in a straight line from head to heels, 
+    # so the shoulder and hip angles should be close to 180 degrees
+    if left_shoulder_angle > 160 and left_shoulder_angle < 200 and \
+       right_shoulder_angle > 160 and right_shoulder_angle < 200 and \
+       left_knee_angle > 160 and left_knee_angle < 200 and \
+       right_knee_angle > 160 and right_knee_angle < 200:
+        label = "Plank Pose"
+    
+    # Update the color to green if pose is classified
+    if label != 'Unknown Pose':
+        color = (0, 255, 0)
+    
+    # Write the label on the output image
+    cv2.putText(output_image, label, (10, 30), cv2.FONT_HERSHEY_PLAIN, 2, color, 2)
+
+    return output_image, label
+
+def detect_and_classify_pose(input_image):
+    input_image = cv2.cvtColor(input_image, cv2.COLOR_BGR2RGB)
+    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)
+    return cv2.cvtColor(input_image, cv2.COLOR_RGB2BGR), pose_classification
+
+iface = gr.Interface(
+    fn=detect_and_classify_pose,
+    inputs=gr.inputs.Video(source="webcam", streaming=True),
+    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()