Update app.py

app.py

@@ -23,7 +23,7 @@ def calculate_angle(a, b, c):
     return angle
 
 # Define a function to classify yoga poses
-def classify_pose(landmarks, output_image, display=False):
+def classify_pose(landmarks, output_image):
     label = 'Unknown Pose'
 
     # Calculate the required angles
@@ -91,51 +91,53 @@ def classify_pose(landmarks, output_image, display=False):
         label = "Upward Salute Pose"
 
     # Check for Hands Under Feet Pose
-    if landmarks[mp_pose.Pose.Landmark.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:
+    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 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:
+    # 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
+    # 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)
-return input_image, pose_classification
+    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)
+    
+    return input_image, pose_classification
 
 iface = gr.Interface(
-fn=detect_and_classify_pose,
-inputs=[
-gr.inputs.Image(type="numpy", label="Upload an Image"),
-gr.inputs.Slider(minimum=0, maximum=2, default=1, label="Model Complexity"),
-gr.inputs.Slider(minimum=0.1, maximum=1.0, default=0.5, label="Detection Confidence"),
-gr.inputs.Radio(choices=['White', 'Green', 'Black'], default='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.",
+    fn=detect_and_classify_pose,
+    inputs=[
+        gr.inputs.Image(type="numpy", label="Upload an Image"),
+        gr.inputs.Slider(minimum=0, maximum=2, default=1, label="Model Complexity"),
+        gr.inputs.Slider(minimum=0.1, maximum=1.0, default=0.5, label="Detection Confidence"),
+        gr.inputs.Radio(choices=['White', 'Green', 'Black'], default='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()