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facechain/common/model_processor.py

@@ -9,7 +9,7 @@ from facechain.utils.convert_utils import *
 def debug(*args):
     print(f"==== face chain debug ====", *args)
 
-def facechain_detect_crop(source_image_pil, face_index=0, crop_ratio=1, mode='normal'):
+def facechain_detect_crop(source_image_pil, face_index=0, crop_ratio=1, mode='normal', error_when_no_face=False):
     det_result = get_face_detection()(source_image_pil)
     mask = np.zeros_like(source_image_pil)
     bboxes = det_result['boxes']
@@ -24,6 +24,8 @@ def facechain_detect_crop(source_image_pil, face_index=0, crop_ratio=1, mode='no
     #         idx = i
     try:
         bbox = bboxes[face_index]
+        if error_when_no_face:
+            return source_image_pil, None, None, None
     except:
         raise RuntimeError('No face detected or face index error/没有检测到人脸或者人脸的索引错误')
 

facechain/nodes.py

@@ -33,6 +33,7 @@ class FaceDetectCrop:
                 "face_index": ("INT", {"default": 0, "min": 0, "max": 10, "step": 1}),
                 "crop_ratio": ("FLOAT", {"default": 1.0, "min": 0, "max": 10, "step": 0.1}),
                 "mode": (["normal", "square 512 width height"],),
+                "error_when_no_face": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
             }
         }
 
@@ -40,9 +41,11 @@ class FaceDetectCrop:
     FUNCTION = "face_detection"
     CATEGORY = "facechain/model"
 
-    def face_detection(self, source_image, face_index, crop_ratio, mode):
+    def face_detection(self, source_image, face_index, crop_ratio, mode, error_when_no_face):
         pil_image = tensor_to_img(source_image)
-        corp_img_pil, mask, bbox, points_array = facechain_detect_crop(pil_image, face_index, crop_ratio, mode)
+        corp_img_pil, mask, bbox, points_array = facechain_detect_crop(pil_image, face_index, crop_ratio, mode, error_when_no_face)
+        if mask is None and bbox is None and points_array is None:
+            return (source_image, source_image, None, None)
         return (image_to_tensor(corp_img_pil), mask_np3_to_mask_tensor(mask), bbox, points_array,)
 
 class FCFaceSegment: