Update app.py

app.py

@@ -91,4 +91,52 @@ def classify_pose(landmarks, output_image, display=False):
         label = "Upward Salute Pose"
 
     # Check for Hands Under Feet Pose
-    if landmarks[mp_pose.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:
+        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:
+    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)
+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.",
+)
+
+iface.launch()