Update app.py

app.py

@@ -1,106 +1,106 @@
-import cv2
-import mediapipe as mp
-from mediapipe.tasks import python
-from mediapipe.tasks.python import vision
-from mediapipe.framework.formats import landmark_pb2
-import numpy as np
-import gradio as gr
-
-# Mediapipe FaceLandmarker seçeneklerini belirleyin
-base_options = python.BaseOptions(model_asset_path='c:\\face_landmarker.task')
-options = vision.FaceLandmarkerOptions(
-    base_options=base_options,
-    output_face_blendshapes=True,
-    output_facial_transformation_matrixes=True,
-    num_faces=1
-)
-detector = vision.FaceLandmarker.create_from_options(options)
-
-# Landmark noktalarını çizmek için fonksiyon
-def draw_landmarks_on_image(rgb_image, detection_result):
-    face_landmarks_list = detection_result.face_landmarks
-    annotated_image = np.copy(rgb_image)
-    
-    for idx in range(len(face_landmarks_list)):
-        face_landmarks = face_landmarks_list[idx]
-        face_landmarks_proto = landmark_pb2.NormalizedLandmarkList()
-        face_landmarks_proto.landmark.extend([
-            landmark_pb2.NormalizedLandmark(x=landmark.x, y=landmark.y, z=landmark.z) for landmark in face_landmarks
-        ])
-
-        mp.solutions.drawing_utils.draw_landmarks(
-            image=annotated_image,
-            landmark_list=face_landmarks_proto,
-            connections=mp.solutions.face_mesh.FACEMESH_TESSELATION,
-            landmark_drawing_spec=None,
-            connection_drawing_spec=mp.solutions.drawing_styles
-            .get_default_face_mesh_tesselation_style())
-        mp.solutions.drawing_utils.draw_landmarks(
-            image=annotated_image,
-            landmark_list=face_landmarks_proto,
-            connections=mp.solutions.face_mesh.FACEMESH_CONTOURS,
-            landmark_drawing_spec=None,
-            connection_drawing_spec=mp.solutions.drawing_styles
-            .get_default_face_mesh_contours_style())
-        mp.solutions.drawing_utils.draw_landmarks(
-            image=annotated_image,
-            landmark_list=face_landmarks_proto,
-            connections=mp.solutions.face_mesh.FACEMESH_IRISES,
-            landmark_drawing_spec=None,
-            connection_drawing_spec=mp.solutions.drawing_styles
-            .get_default_face_mesh_iris_connections_style())
-    return annotated_image
-
-# Gradio için gerçek zamanlı video akışı işleme fonksiyonu
-def process_frame(frame):
-    # OpenCV görüntüsünü Mediapipe formatına dönüştür
-    rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-    mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=rgb_frame)
-
-    # Yüz yer işaretlerini algıla
-    detection_result = detector.detect(mp_image)
-
-    # Çerçeveyi güncelle
-    if detection_result.face_blendshapes:
-        # İlk yüzün blendshape skorlarını al
-        face_blendshapes = detection_result.face_blendshapes[0]
-
-        # eyeBlinkLeft ve eyeBlinkRight blendshape skorlarını bul
-        blink_left = next((bs.score for bs in face_blendshapes if bs.category_name == "eyeBlinkLeft"), 0)
-        blink_right = next((bs.score for bs in face_blendshapes if bs.category_name == "eyeBlinkRight"), 0)
-
-        # Göz durumunu belirle
-        left_eye_status = "Kapalı" if blink_left > 0.5 else "Açık"
-        right_eye_status = "Kapalı" if blink_right > 0.5 else "Açık"
-
-        # Landmarkları çizin
-        annotated_image = draw_landmarks_on_image(rgb_frame, detection_result)
-
-        # # Çerçeveye göz durumunu yaz
-        # cv2.putText(annotated_image, f"Sol Goz: {left_eye_status}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
-        # cv2.putText(annotated_image, f"Sag Goz: {right_eye_status}", (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
-
-        return cv2.cvtColor(annotated_image, cv2.COLOR_RGB2BGR), left_eye_status, right_eye_status
-    else:
-        return frame, "Göz Tespiti Yok", "Göz Tespiti Yok"
-
-# Gradio arayüzü
-def video_feed():
-    cap = cv2.VideoCapture(0)
-    while True:
-        success, frame = cap.read()
-        if not success:
-            break
-        
-        frame, left_eye_status, right_eye_status = process_frame(frame)
-        yield frame, left_eye_status, right_eye_status
-
-iface = gr.Interface(fn=video_feed,
-                     inputs=None,  # Giriş yok, sadece video akışı
-                     outputs=[gr.Image(type="numpy", label="Yüz Tespiti Sonucu"),
-                              gr.Textbox(label="Sol Göz Durumu"),
-                              gr.Textbox(label="Sağ Göz Durumu")],
-                     live=True)
-
-# Gradio arayüzünü başlat
-iface.launch(share=True)
+import cv2
+import mediapipe as mp
+from mediapipe.tasks import python
+from mediapipe.tasks.python import vision
+from mediapipe.framework.formats import landmark_pb2
+import numpy as np
+import gradio as gr
+
+# Mediapipe FaceLandmarker seçeneklerini belirleyin
+base_options = python.BaseOptions(model_asset_path='face_landmarker.task')
+options = vision.FaceLandmarkerOptions(
+    base_options=base_options,
+    output_face_blendshapes=True,
+    output_facial_transformation_matrixes=True,
+    num_faces=1
+)
+detector = vision.FaceLandmarker.create_from_options(options)
+
+# Landmark noktalarını çizmek için fonksiyon
+def draw_landmarks_on_image(rgb_image, detection_result):
+    face_landmarks_list = detection_result.face_landmarks
+    annotated_image = np.copy(rgb_image)
+    
+    for idx in range(len(face_landmarks_list)):
+        face_landmarks = face_landmarks_list[idx]
+        face_landmarks_proto = landmark_pb2.NormalizedLandmarkList()
+        face_landmarks_proto.landmark.extend([
+            landmark_pb2.NormalizedLandmark(x=landmark.x, y=landmark.y, z=landmark.z) for landmark in face_landmarks
+        ])
+
+        mp.solutions.drawing_utils.draw_landmarks(
+            image=annotated_image,
+            landmark_list=face_landmarks_proto,
+            connections=mp.solutions.face_mesh.FACEMESH_TESSELATION,
+            landmark_drawing_spec=None,
+            connection_drawing_spec=mp.solutions.drawing_styles
+            .get_default_face_mesh_tesselation_style())
+        mp.solutions.drawing_utils.draw_landmarks(
+            image=annotated_image,
+            landmark_list=face_landmarks_proto,
+            connections=mp.solutions.face_mesh.FACEMESH_CONTOURS,
+            landmark_drawing_spec=None,
+            connection_drawing_spec=mp.solutions.drawing_styles
+            .get_default_face_mesh_contours_style())
+        mp.solutions.drawing_utils.draw_landmarks(
+            image=annotated_image,
+            landmark_list=face_landmarks_proto,
+            connections=mp.solutions.face_mesh.FACEMESH_IRISES,
+            landmark_drawing_spec=None,
+            connection_drawing_spec=mp.solutions.drawing_styles
+            .get_default_face_mesh_iris_connections_style())
+    return annotated_image
+
+# Gradio için gerçek zamanlı video akışı işleme fonksiyonu
+def process_frame(frame):
+    # OpenCV görüntüsünü Mediapipe formatına dönüştür
+    rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+    mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=rgb_frame)
+
+    # Yüz yer işaretlerini algıla
+    detection_result = detector.detect(mp_image)
+
+    # Çerçeveyi güncelle
+    if detection_result.face_blendshapes:
+        # İlk yüzün blendshape skorlarını al
+        face_blendshapes = detection_result.face_blendshapes[0]
+
+        # eyeBlinkLeft ve eyeBlinkRight blendshape skorlarını bul
+        blink_left = next((bs.score for bs in face_blendshapes if bs.category_name == "eyeBlinkLeft"), 0)
+        blink_right = next((bs.score for bs in face_blendshapes if bs.category_name == "eyeBlinkRight"), 0)
+
+        # Göz durumunu belirle
+        left_eye_status = "Kapalı" if blink_left > 0.5 else "Açık"
+        right_eye_status = "Kapalı" if blink_right > 0.5 else "Açık"
+
+        # Landmarkları çizin
+        annotated_image = draw_landmarks_on_image(rgb_frame, detection_result)
+
+        # # Çerçeveye göz durumunu yaz
+        # cv2.putText(annotated_image, f"Sol Goz: {left_eye_status}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
+        # cv2.putText(annotated_image, f"Sag Goz: {right_eye_status}", (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
+
+        return cv2.cvtColor(annotated_image, cv2.COLOR_RGB2BGR), left_eye_status, right_eye_status
+    else:
+        return frame, "Göz Tespiti Yok", "Göz Tespiti Yok"
+
+# Gradio arayüzü
+def video_feed():
+    cap = cv2.VideoCapture(0)
+    while True:
+        success, frame = cap.read()
+        if not success:
+            break
+        
+        frame, left_eye_status, right_eye_status = process_frame(frame)
+        yield frame, left_eye_status, right_eye_status
+
+iface = gr.Interface(fn=video_feed,
+                     inputs=None,  # Giriş yok, sadece video akışı
+                     outputs=[gr.Image(type="numpy", label="Yüz Tespiti Sonucu"),
+                              gr.Textbox(label="Sol Göz Durumu"),
+                              gr.Textbox(label="Sağ Göz Durumu")],
+                     live=True)
+
+# Gradio arayüzünü başlat
+iface.launch(share=True)