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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+shape_predictor_68_face_landmarks.dat filter=lfs diff=lfs merge=lfs -text

nodes.py

@@ -363,10 +363,8 @@ class RestoreFace:
 import numpy as np
 from ultralytics import YOLO
 from PIL import Image
-
-# Load a pretrained YOLOv8n model
-current_directory = os.getcwd()
-model = YOLO(task='detect', model=current_directory + '/custom_nodes/yolov8_face/yolov8m_200e.pt')
+import dlib
+import cv2
 
 class Mynode_2:
     @classmethod
@@ -377,7 +375,7 @@ class Mynode_2:
                 "source_image": ("IMAGE",),
             },
             "optional": {
-
+                "detection_type": (["Face", "Eyes"],{"default": "Face"},),
             }
         }
 
@@ -385,45 +383,93 @@ class Mynode_2:
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "method"
 
-    def method(self, input_image, source_image):
+    def method(self, input_image, source_image, detection_type):
         input_image_tmp = input_image.squeeze()
         # Pytorch张量转PIL对象
         input_image_pil = Image.fromarray(
             np.clip(255. * input_image_tmp.cpu().numpy(), 0, 255).astype(np.uint8)).convert('RGBA')
 
-        # Run inference on an image
-        results = model.predict(source=input_image_pil, conf=0.5)
-        # View results
-        tmp = results[0].boxes.shape
-        judge_face = tmp[0]
-        print(judge_face)
-
-        if judge_face == 0: # 等于0就是没检测出脸
-
-            return (input_image,)
-
-        else:
-            enabled = True
-
-            swap_model = "inswapper_128.onnx"
-            facedetection = "retinaface_resnet50"
-            face_restore_model = "GFPGANv1.4.pth"
-            face_restore_visibility = 1
-            codeformer_weight = 0.5
-            detect_gender_input = "no"
-            detect_gender_source = "no"
-            input_faces_index = "0"
-            source_faces_index = "0"
-            console_log_level = 1
-
-            class_reactor = reactor()
-            change_face_img, face_model = class_reactor.execute(enabled, input_image, swap_model, detect_gender_source, detect_gender_input,
-                                                                source_faces_index, input_faces_index, console_log_level, face_restore_model,
-                                                                face_restore_visibility, codeformer_weight, facedetection, source_image=source_image,
-                                                                face_model=None)
-
-            return (change_face_img,)
-
+        img = input_image_pil
+        if img.mode != 'RGB':
+            img_rgb = img.convert('RGB')
+        image_rgn_numpy = np.array(img_rgb)
+        # 因为dlib的探测器在灰度图像上运行,所以要转成灰度图像来减少计算复杂度
+        gray = cv2.cvtColor(image_rgn_numpy, cv2.COLOR_RGB2GRAY)
+
+        # 初始化Reactor参数
+        enabled = True
+        swap_model = "inswapper_128.onnx"
+        facedetection = "retinaface_resnet50"
+        face_restore_model = "GFPGANv1.4.pth"
+        face_restore_visibility = 1
+        codeformer_weight = 0.5
+        detect_gender_input = "no"
+        detect_gender_source = "no"
+        input_faces_index = "0"
+        source_faces_index = "0"
+        console_log_level = 1
+        
+        current_directory = os.getcwd()
+        # 加载dlib的正脸检测器
+        detector = dlib.get_frontal_face_detector()
+        # 加载官方提供的模型用于识别面部关键点
+        predictor = dlib.shape_predictor(current_directory + '/custom_nodes/yolov8_face/shape_predictor_68_face_landmarks.dat')
+
+        if detection_type == "Face":
+            # model = YOLO(task='detect', model=current_directory + '/custom_nodes/yolov8_face/yolov8m_200e.pt')
+
+            # # Run inference on an image
+            # results = model.predict(source=input_image_pil, conf=0.5)
+            # # View results
+            # tmp = results[0].boxes.shape
+            # judge_face = tmp[0]
+
+            # 检测灰度图像中的面部，第二个参数是图片的放大倍数
+            faces = detector(gray, 1)
+            if len(faces) == 0:
+                print("未检测到脸部，直接返回原图")
+                return (input_image,)
+            if len(faces) > 0:
+                print("已检测到脸部，准备开始换脸")
+                class_reactor = reactor()
+                change_face_img, face_model = class_reactor.execute(enabled, input_image, swap_model, detect_gender_source, detect_gender_input,
+                                                                    source_faces_index, input_faces_index, console_log_level, face_restore_model,
+                                                                    face_restore_visibility, codeformer_weight, facedetection, source_image=source_image,
+                                                                    face_model=None)
+                return (change_face_img,)
+        
+        if detection_type == "Eyes":
+            faces = detector(gray)
+            left_eye = []
+            right_eye = []
+            for face in faces:
+                landmarks = predictor(gray, face)
+                # 获得左眼的标志点（第37到第42点）
+                for n in range(36, 42):
+                    x = landmarks.part(n).x
+                    y = landmarks.part(n).y
+                    left_eye.append((x, y))
+
+                # 获得右眼的标志点（第43到第48点）
+                for n in range(42, 48):
+                    x = landmarks.part(n).x
+                    y = landmarks.part(n).y
+                    right_eye.append((x, y))
+
+            # 如果检测到至少有一个眼睛，就可以认为图片中存在眼睛
+            if len(left_eye) > 0 or len(right_eye) > 0:
+                print("已检测到眼睛，准备开始换脸")
+                class_reactor = reactor()
+                change_face_img, face_model = class_reactor.execute(enabled, input_image, swap_model, detect_gender_source, detect_gender_input,
+                                                                    source_faces_index, input_faces_index, console_log_level, face_restore_model,
+                                                                    face_restore_visibility, codeformer_weight, facedetection, source_image=source_image,
+                                                                    face_model=None)
+                return (change_face_img,)
+            
+            if len(left_eye) == 0 or len(right_eye) == 0:
+                print("未检测到眼睛，直接返回原图")
+                return (input_image,)
+                
 NODE_CLASS_MAPPINGS = {
     "ReActorFaceSwap": reactor,
     "ReActorLoadFaceModel": LoadFaceModel,

shape_predictor_68_face_landmarks.dat

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fbdc2cb80eb9aa7a758672cbfdda32ba6300efe9b6e6c7a299ff7e736b11b92f
+size 99693937