update face detection method

app.py

@@ -3,16 +3,16 @@ import cvzone
 import numpy as np
 import os
 import gradio as gr
-import dlib
+import mediapipe as mp
 from datetime import datetime
 
 # Load the YuNet model
 model_path = 'face_detection_yunet_2023mar.onnx'
 face_detector = cv2.FaceDetectorYN.create(model_path, "", (320, 320))
 
-# Load dlib's shape predictor
-shape_predictor_path = 'shape_predictor_68_face_landmarks.dat'
-shape_predictor = dlib.shape_predictor(shape_predictor_path)
+# 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=0.5)
 
 # Initialize the glass number
 num = 1
@@ -58,19 +58,17 @@ def process_frame(frame):
     face_detector.setInputSize((width, height))
     _, faces = face_detector.detect(frame)
 
+    frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+    results = face_mesh.process(frame_rgb)
+
     face_shape = "Unknown"
-    if faces is not None:
+    if faces is not None and results.multi_face_landmarks:
         for face in faces:
             x, y, w, h = face[:4].astype(int)
             face_landmarks = face[4:14].reshape(5, 2).astype(int)
 
-            # Convert to dlib rectangle
-            dlib_rect = dlib.rectangle(x, y, x + w, y + h)
-            landmarks = shape_predictor(frame, dlib_rect)
-            landmarks = np.array([(p.x, p.y) for p in landmarks.parts()])
-
             # Determine face shape
-            face_shape = determine_face_shape(landmarks)
+            # face_shape = determine_face_shape(landmarks)
 
             # Get the nose position
             nose_x, nose_y = face_landmarks[2].astype(int)
@@ -102,6 +100,11 @@ def process_frame(frame):
             except Exception as e:
                 print(f"Error overlaying glasses: {e}")
 
+            for face_landmarks_mp in results.multi_face_landmarks:
+                landmarks = np.array([(lm.x * frame.shape[1], lm.y * frame.shape[0]) for lm in face_landmarks_mp.landmark])
+                # Determine face shape
+                face_shape = determine_face_shape(landmarks)
+
     return frame, face_shape
 
 # Transform function
@@ -135,9 +138,6 @@ def transform_cv2(frame, transform):
         return frame
     
 def refresh_interface():
-    # # Reset the transformation dropdown to its default value
-    # transform.update(value="none")
-
     # Reset the image to an empty state or a default image
     input_img.update(value=None)
 

requirements.txt

@@ -2,4 +2,4 @@ gradio
 cvzone
 opencv-python
 numpy
-dlib
+mediapipe>=0.10.11,<0.11