Create app.py

app.py

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+from flask import Flask, render_template, request, jsonify
+import cv2
+import mediapipe as mp
+import numpy as np
+import statistics
+import pyrealsense2 as rs
+from sklearn.linear_model import LinearRegression
+
+app = Flask(__name__)
+
+# Initialize MediaPipe Face Mesh
+mp_face_mesh = mp.solutions.face_mesh
+face_mesh = mp_face_mesh.FaceMesh(static_image_mode=False, max_num_faces=1, min_detection_confidence=0.7)
+
+def initialize_realsense():
+    pipeline = rs.pipeline()
+    config = rs.config()
+    config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)
+    config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)
+    pipeline.start(config)
+    return pipeline
+
+def process_frame(color_image, depth_frame):
+    rgb_frame = cv2.cvtColor(color_image, cv2.COLOR_BGR2RGB)
+    results = face_mesh.process(rgb_frame)
+    
+    if results.multi_face_landmarks:
+        for face_landmarks in results.multi_face_landmarks:
+            upper_lip = face_landmarks.landmark[13]
+            lower_lip = face_landmarks.landmark[14]
+            
+            h, w, _ = color_image.shape
+            upper_lip_coords = (int(upper_lip.x * w), int(upper_lip.y * h))
+            lower_lip_coords = (int(lower_lip.x * w), int(lower_lip.y * h))
+            
+            return upper_lip_coords, lower_lip_coords
+    return None, None
+
+@app.route('/')
+def home():
+    return render_template('index.html')
+
+@app.route('/analyze', methods=['POST'])
+def analyze():
+    pipeline = initialize_realsense()
+    try:
+        frames = pipeline.wait_for_frames()
+        color_frame = frames.get_color_frame()
+        depth_frame = frames.get_depth_frame()
+        
+        color_image = np.asanyarray(color_frame.get_data())
+        upper_lip_coords, lower_lip_coords = process_frame(color_image, depth_frame)
+        
+        if upper_lip_coords and lower_lip_coords:
+            # Process measurements and calculate distance
+            distance = calculate_distance(upper_lip_coords, lower_lip_coords, depth_frame)
+            stage = determine_stage(distance)
+            
+            return jsonify({
+                'distance': float(distance),
+                'stage': stage,
+                'status': 'success'
+            })
+        
+        return jsonify({'status': 'error', 'message': 'No face detected'})
+    
+    finally:
+        pipeline.stop()
+
+def calculate_distance(upper_lip_coords, lower_lip_coords, depth_frame):
+    # Your distance calculation logic here
+    pass
+
+def determine_stage(distance):
+    if distance >= 45:
+        return "Stage I"
+    elif 20 <= distance < 45:
+        return "Stage II"
+    else:
+        return "Stage III"
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=7860)