add two super node, simplify progress

retain face choose face
add more info
2023-10-25 21:15:49 +08:00 · 2023-10-25 11:50:21 +08:00 · 2023-10-24 22:05:04 +08:00
9 changed files with 347 additions and 254 deletions
@@ -1,6 +1,6 @@
 # [Portrait-Maker](https://github.com/THtianhao/ComfyUI-Portrait-Maker)
 This project is an adaptation of [EasyPhoto](https://github.com/aigc-apps/sd-webui-EasyPhoto), which breaks down the process of [EasyPhoto](https://github.com/aigc-apps/sd-webui-EasyPhoto) and will add a series of operations on human portraits in the future.
-![](./images/easyphoto.png)
+![](./images/easyphoto.jpg)

 English | [简体中文](./README_zh-CN.md)

@@ -13,11 +13,18 @@ If you have any questions or suggestions, you can reach us through:
 - QQ Group: 10419777
 - WeChat Group: <img src="./images/wechat.jpg" width="200">

+## V1.2.0 Update
+1. Add PM_SuperColorTransfer node to simplify the color transfer process
+2. Add PM_SuperMakeUpTransfer node to simplify the process of makeup transfer 
+
+
 ## V1.1.0 Update
 1. faceskin adds blur option
-2. Add PM_FaceShapMatch node. See node introduction for details.
-3. Add PM_MakeUpTransfer node. See node introduction for details.
-3. Add a super-resolution model to the PM_PortraitEnhancement node. This super-resolution model can not highlight faces.
+2. Add PM_FaceShapMatch node. same as easyphot faceshap match
+3. Add PM_MakeUpTransfer node. same as easyphoto makeup transfer.
+4. Add a super-resolution model to the PM_PortraitEnhancement node. This super-resolution model can not highlight faces.
+5. Add v1.1.0 workflow
+6. RetinaFace supports face selection

 ## V1.0.0 Update
 1. Added log for model downloads.
@@ -67,6 +74,7 @@ Click "Load" in the right panel of ComfyUI and select the ./workflow/easyphoto_w
 * RetainFace PM: Perform matting using models from Model Scope. [Link](https://www.modelscope.cn/models/damo/cv_resnet50_face-detection_retinaface/summary)
  * image: Input image
  * multi_user_facecrop_ratio: Multiplicative factor for extracting the head region.
+  * face_index : Choose which face

 * FaceFusion PM: Merge faces from two images.
  * image: Input image
@@ -2,7 +2,7 @@

 这个项目改编于[EasyPhoto](https://github.com/aigc-apps/sd-webui-EasyPhoto)，对于[EasyPhoto](https://github.com/aigc-apps/sd-webui-EasyPhoto)进行了流程上的拆解，后续会加入其他项目处理人物头像上的系列操作。

-![](./images/easyphoto.png)
+![](./images/easyphoto.jpg)

 English | [简体中文](./README_zh-CN.md)

@@ -15,11 +15,17 @@ English | [简体中文](./README_zh-CN.md)
 - QQ 群：10419777
 - 微信群： <img src="./images/wechat.jpg" width="200">

+## V1.2.0 Update
+1. 增加PM_SuperColorTransfer 节点，简化了颜色迁移的流程
+2. 增加PM_SuperMakeUpTransfer 节点，简化了进行装扮迁移的流程
+
 ## v1.1.0 更新
 1. faceskin 增加模糊选项
-2. 增加 PM_FaceShapMatch节点 详情查看节点介绍
-3. 增加 PM_MakeUpTransfer节点 详情查看节点介绍
-3. PM_PortraitEnhancement节点增加一种超分模型，此超分模型可以对人脸不做高光
+2. 增加 PM_FaceShapMatch节点 与easyphoto的FaceshapMatch一致
+3. 增加 PM_MakeUpTransfer节点 与easyphoto的MakeupTransfer一致
+4. PM_PortraitEnhancement节点增加一种超分模型，此超分模型可以对人脸不做高光
+5. 增加v1.1.0 workflow
+6. RetinaFace 支持选择人脸

 ## v1.0.0 更新

@@ -70,6 +76,7 @@ Easyphoto工作位置: [./workflow/easyphoto.json](./workflows/easyphoto.json )
 * RetainFace PM:使用Model Scope中的模型进行抠图 [链接](https://www.modelscope.cn/models/damo/cv_resnet50_face-detection_retinaface/summary)
 	* image：输入图像
 	* multi_user_facecrop_ratio：提取头像区域的倍数
+    * face_index : 选择第几个人脸
 * FaceFusion PM:将两张图像的人脸进行融合
 	* image：输入图像
 	* user_image：要融合的头像
@@ -77,6 +77,8 @@ NODE_CLASS_MAPPINGS = {
    "PM_GetImageInfo": GetImageInfoPM,
    "PM_MakeUpTransfer": MakeUpTransferPM,
    "PM_FaceShapMatch": FaceShapMatchPM,
+    "PM_SuperColorTransfer": SuperColorTransferPM,
+    "PM_SuperMakeUpTransfer": SuperMakeUpTransferPM,
 }
 NODE_DISPLAY_NAME_MAPPINGS = {
    "PM_RetinaFace": "RetinaFace PM",
@@ -95,7 +97,9 @@ NODE_DISPLAY_NAME_MAPPINGS = {
    "PM_ImageResizeTarget": "ImageResizeTarget PM",
    "PM_GetImageInfo": "GetImageInfo PM",
    "PM_MakeUpTransfer": "MakeUpTransfer PM",
-    "PM_FaceShapMatch":"FaceShapMatch PM"
+    "PM_FaceShapMatch": "FaceShapMatch PM",
+    "PM_SuperColorTransfer": "SuperColorTransfer PM",
+    "PM_SuperMakeUpTransfer": "SuperMakeUpTransfer PM",
 }

 __all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
@@ -6,14 +6,16 @@ from .utils.face_process_utils import call_face_crop, color_transfer, Face_Skin
 from .utils.img_utils import img_to_tensor, tensor_to_img, tensor_to_np, np_to_tensor, np_to_mask, img_to_mask, img_to_np
 from .model_holder import *

-# import pydevd_pycharm
-# pydevd_pycharm.settrace('49.7.62.197', port=10090, stdoutToServer=True, stderrToServer=True)
+import pydevd_pycharm
+
+pydevd_pycharm.settrace('49.7.62.197', port=10090, stdoutToServer=True, stderrToServer=True)

 class RetinaFacePM:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {"image": ("IMAGE",),
-                             "multi_user_facecrop_ratio": ("FLOAT", {"default": 1, "min": 0, "max": 10, "step": 0.01})
+                             "multi_user_facecrop_ratio": ("FLOAT", {"default": 1, "min": 0, "max": 10, "step": 0.01}),
+                             "face_index": ("INT", {"default": 0, "min": 0, "max": 10, "step": 1})
                             }}

    RETURN_TYPES = ("IMAGE", "MASK", "BOX")
@@ -21,12 +23,14 @@ class RetinaFacePM:
    FUNCTION = "retain_face"
    CATEGORY = "protrait/model"

-    def retain_face(self, image, multi_user_facecrop_ratio):
+    def retain_face(self, image, multi_user_facecrop_ratio, face_index):
        np_image = np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
        image = Image.fromarray(np_image)
-        retinaface_boxes, retinaface_keypoints, retinaface_masks, retinaface_tensor = call_face_crop(get_retinaface_detection(), image, multi_user_facecrop_ratio)
-        crop_image = image.crop(retinaface_boxes[0])
-        return (img_to_tensor(crop_image), retinaface_tensor, retinaface_boxes[0])
+        retinaface_boxes, retinaface_keypoints, retinaface_masks, retinaface_mask_nps = call_face_crop(get_retinaface_detection(), image, multi_user_facecrop_ratio)
+        crop_image = image.crop(retinaface_boxes[face_index])
+        retinaface_mask = np_to_mask(retinaface_mask_nps[face_index])
+        retinaface_boxe = retinaface_boxes[face_index]
+        return (img_to_tensor(crop_image), retinaface_mask, retinaface_boxe)

 class FaceFusionPM:

@@ -345,8 +349,6 @@ class MakeUpTransferPM:
    CATEGORY = "protrait/model"

    def makeup_transfer(self, source_image, makeup_image):
-        source_image = tensor_to_img(source_image).resize([256, 256])
-        makeup_image = tensor_to_img(makeup_image).resize([256, 256])
        result = get_pagan_interface().transfer(source_image, makeup_image)
        return (img_to_tensor(result),)

@@ -386,3 +388,73 @@ class FaceShapMatchPM:
        input_image_uint8 = np.array(source_image) * face_skin_mask + np.array(match_image) * (1 - face_skin_mask)

        return (np_to_tensor(input_image_uint8),)
+
+class SuperColorTransferPM:
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return \
+            {
+                "required": {
+                    "main_image": ("IMAGE",),
+                    "transfer_image": ("IMAGE",),
+                },
+                "optional": {
+                    "avatar_box": ("BOX",),
+                },
+            }
+
+    RETURN_TYPES = ("IMAGE",)
+
+    FUNCTION = "super_color_transfer"
+    CATEGORY = "protrait/model"
+
+    def super_color_transfer(self, main_image, transfer_image, avatar_box=None):
+        origin_np = tensor_to_np(main_image)
+        if avatar_box is not None:
+            main_image = main_image[:, avatar_box[1]:avatar_box[3], avatar_box[0]:avatar_box[2], :]
+            transfer_image = transfer_image[:, avatar_box[1]:avatar_box[3], avatar_box[0]:avatar_box[2], :]
+
+        transfer_result = color_transfer(tensor_to_np(main_image), tensor_to_np(transfer_image))  # 进行颜色迁移
+
+        face_skin_img = get_face_skin()(Image.fromarray(transfer_result), get_retinaface_detection(), [[1, 2, 3, 4, 5, 10, 12, 13]])[0]
+        face_skin_np = img_to_np(face_skin_img)
+        face_skin_np = cv2.blur(face_skin_np, (32, 32)) / 255
+
+        masked_img_np = tensor_to_np(main_image) * (1 - face_skin_np) + transfer_result * face_skin_np
+
+        origin_np[avatar_box[1]:avatar_box[3], avatar_box[0]:avatar_box[2], :] = masked_img_np
+
+        return (np_to_tensor(origin_np),)
+
+class SuperMakeUpTransferPM:
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return \
+            {
+                "required": {
+                    "main_image": ("IMAGE",),
+                    "makeup_image": ("IMAGE",),
+                },
+                "optional": {
+                    "avatar_box": ("BOX",),
+                },
+            }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "super_makeup_transfer"
+    CATEGORY = "protrait/model"
+
+    def super_makeup_transfer(self, main_image, makeup_image, avatar_box=None):
+        box_width, box_height = avatar_box[2] - avatar_box[0], avatar_box[3] - avatar_box[1]
+        origin_np = tensor_to_np(main_image)
+        if avatar_box is not None:
+            main_image = main_image[:, avatar_box[1]:avatar_box[3], avatar_box[0]:avatar_box[2], :]
+            makeup_image = makeup_image[:, avatar_box[1]:avatar_box[3], avatar_box[0]:avatar_box[2], :]
+        resize_source_box_image = tensor_to_img(main_image).resize([256, 256])
+        resize_makeup_box_image = tensor_to_img(makeup_image).resize([256, 256])
+        transfer_image = get_pagan_interface().transfer(resize_source_box_image, resize_makeup_box_image)
+        box_size_transfer = transfer_image.resize([box_width, box_height], Image.Resampling.LANCZOS)
+        origin_np[avatar_box[1]:avatar_box[3], avatar_box[0]:avatar_box[2], :] = img_to_np(box_size_transfer)
+        return (np_to_tensor(origin_np),)
@@ -65,9 +65,8 @@ def safe_get_box_mask_keypoints(image, retinaface_result, crop_ratio, face_seg,
        retinaface_boxs = [retinaface_boxs[index] for index in argindex]
        retinaface_keypoints = [retinaface_keypoints[index] for index in argindex]
        retinaface_mask_pils = [retinaface_mask_pils[index] for index in argindex]
-        retinaface_mask_np = [retinaface_masks[index] for index in argindex]
-        mask_tensor = np_to_mask(retinaface_mask_np[0])
-        return retinaface_boxs, retinaface_keypoints, retinaface_mask_pils, mask_tensor
+        retinaface_mask_nps = [retinaface_masks[index] for index in argindex]
+        return retinaface_boxs, retinaface_keypoints, retinaface_mask_pils, retinaface_mask_nps

    else:
        retinaface_box = np.array([])
@@ -120,9 +119,9 @@ def call_face_crop(retinaface_detection, image, crop_ratio, prefix="tmp"):
    # retinaface detect 
    retinaface_result = retinaface_detection(image)
    # get mask and keypoints
-    retinaface_box, retinaface_keypoints, retinaface_mask_pil, retinaface_mask_tensor = safe_get_box_mask_keypoints(image, retinaface_result, crop_ratio, None, "crop")
+    retinaface_box, retinaface_keypoints, retinaface_mask_pil, retinaface_mask_nps = safe_get_box_mask_keypoints(image, retinaface_result, crop_ratio, None, "crop")

-    return retinaface_box, retinaface_keypoints, retinaface_mask_pil, retinaface_mask_tensor
+    return retinaface_box, retinaface_keypoints, retinaface_mask_pil, retinaface_mask_nps

 def color_transfer(sc, dc):
    """
@@ -1,6 +1,6 @@
 {
  "last_node_id": 231,
-  "last_link_id": 419,
+  "last_link_id": 424,
  "nodes": [
    {
      "id": 50,
@@ -443,7 +443,7 @@
        "1": 46
      },
      "flags": {},
-      "order": 79,
+      "order": 77,
      "mode": 0,
      "inputs": [
        {
@@ -486,7 +486,7 @@
        "1": 46
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      "flags": {},
-      "order": 76,
+      "order": 74,
      "mode": 0,
      "inputs": [
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@@ -620,7 +620,7 @@
        "1": 82
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      "flags": {},
-      "order": 75,
+      "order": 73,
      "mode": 0,
      "inputs": [
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@@ -663,7 +663,7 @@
        "1": 246
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      "flags": {},
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+      "order": 76,
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      "inputs": [
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@@ -724,7 +724,7 @@
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      "flags": {},
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+      "order": 75,
      "mode": 0,
      "inputs": [
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@@ -793,7 +793,7 @@
        "1": 166
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      "flags": {},
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+      "order": 78,
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      "inputs": [
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@@ -856,7 +856,7 @@
        "1": 474.0000305175781
      },
      "flags": {},
-      "order": 81,
+      "order": 79,
      "mode": 0,
      "inputs": [
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@@ -907,33 +907,6 @@
      "color": "#323",
      "bgcolor": "#535"
    },
-    {
-      "id": 145,
-      "type": "PreviewImage",
-      "pos": [
-        4959.059430539544,
-        2873.60691902383
-      ],
-      "size": {
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-        "1": 542.2775268554688
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-      "flags": {},
-      "order": 83,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "images",
-          "type": "IMAGE",
-          "link": 246
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "PreviewImage"
-      },
-      "color": "#323",
-      "bgcolor": "#535"
-    },
    {
      "id": 147,
      "type": "LoraLoader",
@@ -1239,7 +1212,7 @@
        "1": 86
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+      "order": 64,
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@@ -1356,7 +1329,7 @@
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@@ -1383,7 +1356,7 @@
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@@ -1410,13 +1383,13 @@
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-      "order": 60,
+      "order": 58,
      "mode": 0,
      "inputs": [
        {
          "name": "image",
          "type": "IMAGE",
-          "link": 403
+          "link": 420
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        {
          "name": "box",
@@ -1483,7 +1456,7 @@
        "1": 313.27972412109375
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      "flags": {},
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+      "order": 70,
      "mode": 0,
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@@ -1510,7 +1483,7 @@
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+      "order": 62,
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@@ -1812,51 +1785,6 @@
      "color": "#323",
      "bgcolor": "#535"
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-    {
-      "id": 144,
-      "type": "VAEDecode",
-      "pos": [
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-      "mode": 0,
-      "inputs": [
-        {
-          "name": "samples",
-          "type": "LATENT",
-          "link": 244
-        },
-        {
-          "name": "vae",
-          "type": "VAE",
-          "link": 245
-        }
-      ],
-      "outputs": [
-        {
-          "name": "IMAGE",
-          "type": "IMAGE",
-          "links": [
-            246,
-            271,
-            312
-          ],
-          "shape": 3,
-          "slot_index": 0
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "VAEDecode"
-      },
-      "color": "#323",
-      "bgcolor": "#535"
-    },
    {
      "id": 178,
      "type": "PreviewImage",
@@ -1893,7 +1821,7 @@
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+        "1": 58
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      "flags": {},
      "order": 110,
@@ -1919,6 +1847,9 @@
      "properties": {
        "Node name for S&R": "PM_PortraitEnhancement"
      },
+      "widgets_values": [
+        "pgen"
+      ],
      "color": "#2a363b",
      "bgcolor": "#3f5159"
    },
@@ -2061,13 +1992,13 @@
        "1": 66
      },
      "flags": {},
-      "order": 69,
+      "order": 67,
      "mode": 0,
      "inputs": [
        {
          "name": "origin_image",
          "type": "IMAGE",
-          "link": 402
+          "link": 421
        },
        {
          "name": "box_area",
@@ -2112,7 +2043,7 @@
        "1": 78
      },
      "flags": {},
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+      "order": 69,
      "mode": 0,
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@@ -2158,7 +2089,7 @@
        "1": 325.4287414550781
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@@ -2672,7 +2603,7 @@
        {
          "name": "image",
          "type": "IMAGE",
-          "link": 271
+          "link": 423
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        {
          "name": "box",
@@ -2968,7 +2899,7 @@
        "1": 26
      },
      "flags": {},
-      "order": 65,
+      "order": 63,
      "mode": 0,
      "inputs": [
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@@ -3033,7 +2964,7 @@
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+      "order": 61,
      "mode": 0,
      "inputs": [
        {
@@ -3095,8 +3026,8 @@
      "id": 6,
      "type": "LoadImage",
      "pos": [
-        683,
-        370
+        249.94248304953703,
+        302.33370767033864
      ],
      "size": {
        "0": 303.5721130371094,
@@ -3301,7 +3232,7 @@
        "1": 289.9919128417969
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@@ -3459,10 +3390,10 @@
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        1020
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-      "size": [
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+        "1": 444.23541259765625
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@@ -3845,7 +3776,7 @@
        {
          "name": "origin_image",
          "type": "IMAGE",
-          "link": 312
+          "link": 424
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        {
          "name": "box_area",
@@ -3932,10 +3863,10 @@
        7570.006255877076,
        1602.40493563089
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-      ],
+      "size": {
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+        "1": 246
+      },
      "flags": {},
      "order": 103,
      "mode": 0,
@@ -4353,95 +4284,28 @@
      "bgcolor": "#535"
    },
    {
-      "id": 61,
-      "type": "VAEDecode",
+      "id": 145,
+      "type": "PreviewImage",
      "pos": [
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+        4605,
+        2907
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      "size": {
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+      "order": 81,
      "mode": 0,
      "inputs": [
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-          "name": "samples",
-          "type": "LATENT",
-          "link": 79
-        },
-        {
-          "name": "vae",
-          "type": "VAE",
-          "link": 142
-        }
-      ],
-      "outputs": [
-        {
-          "name": "IMAGE",
+          "name": "images",
          "type": "IMAGE",
-          "links": [
-            82,
-            419
-          ],
-          "shape": 3,
-          "slot_index": 0
+          "link": 246
        }
      ],
      "properties": {
-        "Node name for S&R": "VAEDecode"
-      },
-      "color": "#323",
-      "bgcolor": "#535"
-    },
-    {
-      "id": 224,
-      "type": "PM_FaceShapMatch",
-      "pos": [
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-      "flags": {},
-      "order": 58,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "source_image",
-          "type": "IMAGE",
-          "link": 419
-        },
-        {
-          "name": "match_image",
-          "type": "IMAGE",
-          "link": 400
-        },
-        {
-          "name": "face_box",
-          "type": "BOX",
-          "link": 398
-        }
-      ],
-      "outputs": [
-        {
-          "name": "IMAGE",
-          "type": "IMAGE",
-          "links": [
-            401,
-            402,
-            403
-          ],
-          "shape": 3,
-          "slot_index": 0
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "PM_FaceShapMatch"
+        "Node name for S&R": "PreviewImage"
      },
      "color": "#323",
      "bgcolor": "#535"
@@ -4450,8 +4314,8 @@
      "id": 223,
      "type": "Reroute",
      "pos": [
-        4088,
-        -28
+        5004,
+        2846
      ],
      "size": [
        75,
@@ -4484,19 +4348,158 @@
      "color": "#323",
      "bgcolor": "#535"
    },
+    {
+      "id": 61,
+      "type": "VAEDecode",
+      "pos": [
+        3798,
+        -70
+      ],
+      "size": {
+        "0": 210,
+        "1": 46
+      },
+      "flags": {},
+      "order": 55,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "samples",
+          "type": "LATENT",
+          "link": 79
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": 142
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            82,
+            420,
+            421
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VAEDecode"
+      },
+      "color": "#323",
+      "bgcolor": "#535"
+    },
+    {
+      "id": 224,
+      "type": "PM_FaceShapMatch",
+      "pos": [
+        5097,
+        2791
+      ],
+      "size": {
+        "0": 229.20001220703125,
+        "1": 66
+      },
+      "flags": {},
+      "order": 82,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "source_image",
+          "type": "IMAGE",
+          "link": 422
+        },
+        {
+          "name": "match_image",
+          "type": "IMAGE",
+          "link": 400
+        },
+        {
+          "name": "face_box",
+          "type": "BOX",
+          "link": 398
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            401,
+            423,
+            424
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "PM_FaceShapMatch"
+      },
+      "color": "#323",
+      "bgcolor": "#535"
+    },
+    {
+      "id": 144,
+      "type": "VAEDecode",
+      "pos": [
+        4678,
+        2799
+      ],
+      "size": {
+        "0": 210,
+        "1": 46
+      },
+      "flags": {},
+      "order": 80,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "samples",
+          "type": "LATENT",
+          "link": 244
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": 245
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            246,
+            422
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VAEDecode"
+      },
+      "color": "#323",
+      "bgcolor": "#535"
+    },
    {
      "id": 225,
      "type": "PreviewImage",
      "pos": [
-        4148,
-        101
-      ],
-      "size": [
-        370.333740234375,
-        482.09925079345703
+        5014,
+        2933
      ],
+      "size": {
+        "0": 370.333740234375,
+        "1": 482.0992431640625
+      },
      "flags": {},
-      "order": 59,
+      "order": 83,
      "mode": 0,
      "inputs": [
        {
@@ -5201,14 +5204,6 @@
      1,
      "MASK"
    ],
-    [
-      271,
-      144,
-      0,
-      159,
-      0,
-      "IMAGE"
-    ],
    [
      272,
      159,
@@ -5385,14 +5380,6 @@
      2,
      "IMAGE"
    ],
-    [
-      312,
-      144,
-      0,
-      168,
-      0,
-      "IMAGE"
-    ],
    [
      319,
      17,
@@ -5737,22 +5724,6 @@
      0,
      "IMAGE"
    ],
-    [
-      402,
-      224,
-      0,
-      131,
-      0,
-      "IMAGE"
-    ],
-    [
-      403,
-      224,
-      0,
-      112,
-      0,
-      "IMAGE"
-    ],
    [
      414,
      195,
@@ -5794,20 +5765,52 @@
      "IMAGE"
    ],
    [
-      419,
+      420,
      61,
      0,
+      112,
+      0,
+      "IMAGE"
+    ],
+    [
+      421,
+      61,
+      0,
+      131,
+      0,
+      "IMAGE"
+    ],
+    [
+      422,
+      144,
+      0,
      224,
      0,
      "IMAGE"
+    ],
+    [
+      423,
+      224,
+      0,
+      159,
+      0,
+      "IMAGE"
+    ],
+    [
+      424,
+      224,
+      0,
+      168,
+      0,
+      "IMAGE"
    ]
  ],
  "groups": [
    {
      "title": "Roop Image",
      "bounding": [
-        672,
-        295,
+        239,
+        227,
        333,
        410
      ],
Author	SHA1	Message	Date
toto	d9ade2e531	add two super node, simplify progress	2023-10-25 21:15:49 +08:00
toto	4abaf4cd92	retain face choose face	2023-10-25 11:50:21 +08:00
toto	60bebf7b02	add more info	2023-10-24 22:05:04 +08:00