Added oneformer mask

__init__.py

@@ -7,6 +7,10 @@ import comfy.model_management as model_management
 import folder_paths
 from PIL import Image, ImageOps
 
+from transformers import AutoProcessor
+from transformers import OneFormerForUniversalSegmentation
+from transformers import OneFormerProcessor
+
 
 def from_torch_image(image):
     image = image.squeeze().cpu().numpy() * 255.0
@@ -53,6 +57,30 @@ def do_work_with_mask(image, mask, external_file_name):
     return image
 
 
+def get_segmentation_of_person(image):
+    NAME_MODEL_ONEFORMER = 'shi-labs/oneformer_ade20k_dinat_large'
+
+    processor = OneFormerProcessor.from_pretrained(NAME_MODEL_ONEFORMER)
+    model = OneFormerForUniversalSegmentation.from_pretrained(
+        NAME_MODEL_ONEFORMER)
+
+    width = image.shape[1]
+    height = image.shape[0]
+
+    inputs = processor(image, ["semantic"], return_tensors="pt")
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    predicted_semantic_map = processor.post_process_semantic_segmentation(
+        outputs, target_sizes=[(height, width)])[0]
+
+    img = predicted_semantic_map.detach().cpu().numpy()
+    img = (img == 12)
+    img = img.astype(np.uint8) * 255
+    return img
+
+
 class TRI3DATRParseBatch:
 
     def __init__(self):
@@ -1089,8 +1117,7 @@ class TRI3DDWPose_Preprocessor:
                                          include_face=detect_face,
                                          include_body=detect_body)
             cur_file_dir = os.path.dirname(os.path.realpath(__file__))
-            save_file_path = os.path.join(cur_file_dir,
-                                          filename_path)
+            save_file_path = os.path.join(cur_file_dir, filename_path)
             json.dump(pose_dict, open(save_file_path, 'w'))
             np_result = cv2.resize(np_result, (W, H),
                                    interpolation=cv2.INTER_AREA)
@@ -1131,9 +1158,12 @@ class TRI3DPosetoImage:
 
         canvas = np.zeros(shape=(height, width, 3), dtype=np.uint8)
         canvas = comfy_utils.draw_bodypose(canvas, keypoints)
-        canvas = comfy_utils.draw_handpose(canvas, keypoints[88:109])  #right hand
+        canvas = comfy_utils.draw_handpose(canvas,
+                                           keypoints[88:109])  #right hand
         canvas = comfy_utils.draw_handpose(canvas, keypoints[109:])  #left hand
-        canvas = torch.from_numpy(canvas.astype(np.float32)/255.0)[None,]
+        canvas = torch.from_numpy(canvas.astype(np.float32) / 255.0)[
+            None,
+        ]
         return (canvas, )
 
 
@@ -1155,44 +1185,34 @@ class TRI3DPoseAdaption:
                     "max": 15.0,
                     "step": 0.01
                 }),
-                "garment_category":(["no_sleeve_garment", "half_sleeve_garment", "full_sleeve_garment", \
-                                     "shorts", "trouser"], {"default": "no_sleeve_garment"}),
-                "output_save_path": ("STRING",{"default" : "dwpose/keypoints/adapted_pose.json"})
-            },
-            
-            "optional": {
-                "ref_face_pose_json_file": ("STRING",{"default" : ""}),
+                "garment_category":([" no_sleeve_garment", "half_sleeve_garment", "full_sleeve_garment", \
+                                     "shorts", "trouser"], {"default": " no_sleeve_garment"})
+
+
             }
         }
-        
 
     RETURN_TYPES = ("IMAGE", "BOOLEAN")
     FUNCTION = "main"
     CATEGORY = "TRI3D"
 
-    def main(self, input_pose_json_file, ref_pose_json_file, image_angle, rotation_threshold, garment_category,output_save_path, ref_face_pose_json_file=""):
+    def main(self, input_pose_json_file, ref_pose_json_file, image_angle,
+             rotation_threshold, garment_category):
         from .dwpose import comfy_utils
-        cur_file_dir = os.path.dirname(os.path.realpath(__file__))
-        output_save_path = os.path.join(cur_file_dir, output_save_path)
+
         if image_angle == "front":
 
             input_pose = json.load(open(input_pose_json_file))
             input_height = input_pose['height']
             input_width = input_pose['width']
             input_keypoints = input_pose['keypoints']
-            canvas = np.zeros(shape=(input_height, input_width, 3), dtype=np.uint8)
+            canvas = np.zeros(shape=(input_height, input_width, 3),
+                              dtype=np.uint8)
 
             ref_pose = json.load(open(ref_pose_json_file))
             ref_height = ref_pose['height']
             ref_width = ref_pose['width']
             ref_keypoints = ref_pose['keypoints']
-
-            if ref_face_pose_json_file != "":
-                ref_face_pose = json.load(open(ref_face_pose_json_file))
-                ref_face_height = ref_face_pose['height']
-                ref_face_width = ref_face_pose['width']
-                ref_face_keypoints = ref_face_pose['keypoints']
-                
             similar_torso = None
 
             #check torso similarity
@@ -1206,122 +1226,175 @@ class TRI3DPoseAdaption:
                 rs_angle_diff = abs(rs_angle_2 - rs_angle_1)
                 torso_angle_diff = abs(torso_angle_2 - torso_angle_1)
 
-                
-                similar_torso = False if (ls_angle_diff >= rotation_threshold) | (
-                    rs_angle_diff >= rotation_threshold) | (torso_angle_diff >= rotation_threshold) else True
+                similar_torso = False if (
+                    ls_angle_diff >= rotation_threshold) | (
+                        rs_angle_diff >= rotation_threshold) | (
+                            torso_angle_diff >= rotation_threshold) else True
 
                 if similar_torso == False:
-                    canvas = torch.from_numpy(canvas.astype(np.float32) / 255.0)[
-                        None,
-                    ]
+                    canvas = torch.from_numpy(
+                        canvas.astype(np.float32) / 255.0)[
+                            None,
+                        ]
                     return (canvas, similar_torso)
-            
-
-            #Scaling shoulder of input with respect to shoulder-torso ratio of ref-pose
-            input_keypoints = comfy_utils.scale(ref_keypoints, input_keypoints, 1, 2, 1, 2, ref_torso=True)             #left shoulder
-            input_keypoints = comfy_utils.scale(ref_keypoints, input_keypoints, 1, 5, 1, 5, ref_torso=True)             #right shoulder
 
             #Hands
-            if input_keypoints[4] == [-1,-1]: input_keypoints[4] = ref_keypoints[4]
-            if input_keypoints[7] == [-1,-1]: input_keypoints[7] = ref_keypoints[7]
-            
-            input_keypoints[88:] = ref_keypoints[88:]     #replace hands with reference hands
+            if input_keypoints[4] == [-1, -1]:
+                input_keypoints[4] = ref_keypoints[4]
+            if input_keypoints[7] == [-1, -1]:
+                input_keypoints[7] = ref_keypoints[7]
 
-            if garment_category not in ["half_sleeve_garment", "full_sleeve_garment"]:
-                input_keypoints = comfy_utils.rotate(ref_keypoints, input_keypoints, 2, 3)      # rotate left elbow
-            input_keypoints = comfy_utils.scale(ref_keypoints, input_keypoints, 2, 5, 2, 3) #scaling w.r.t to shoulder to elbow ratio of ref pose
+            input_keypoints[88:] = ref_keypoints[
+                88:]  #replace hands with reference hands
+
+            if garment_category not in [
+                    "half_sleeve_garment", "full_sleeve_garment"
+            ]:
+                input_keypoints = comfy_utils.rotate(ref_keypoints,
+                                                     input_keypoints, 2,
+                                                     3)  # rotate left elbow
+            input_keypoints = comfy_utils.scale(
+                ref_keypoints, input_keypoints, 2, 5, 2,
+                3)  #scaling w.r.t to shoulder to elbow ratio of ref pose
 
             prev_lw = input_keypoints[4]
             if garment_category != "full_sleeve_garment":
-                input_keypoints = comfy_utils.rotate(ref_keypoints, input_keypoints, 3, 4)      #rotate left wrist
-            input_keypoints = comfy_utils.scale(ref_keypoints, input_keypoints, 2, 3, 3, 4) #scaling w.r.t to elbow to wrist ratio of ref pose
+                input_keypoints = comfy_utils.rotate(ref_keypoints,
+                                                     input_keypoints, 3,
+                                                     4)  #rotate left wrist
+            input_keypoints = comfy_utils.scale(
+                ref_keypoints, input_keypoints, 2, 3, 3,
+                4)  #scaling w.r.t to elbow to wrist ratio of ref pose
 
-            #moving to hand ponts to wrist 
-            input_keypoints[109:] = comfy_utils.move(input_keypoints[109], input_keypoints[4], input_keypoints[109:]) 
-            input_keypoints[109:] = comfy_utils.move(ref_keypoints[4], ref_keypoints[109], input_keypoints[109:])
+            #moving to hand ponts to wrist
+            input_keypoints[109:] = comfy_utils.move(input_keypoints[109],
+                                                     input_keypoints[4],
+                                                     input_keypoints[109:])
+            input_keypoints[109:] = comfy_utils.move(ref_keypoints[4],
+                                                     ref_keypoints[109],
+                                                     input_keypoints[109:])
 
             # input_keypoints = comfy_utils.rotate_hand(ref_keypoints, input_keypoints, 109)    #rotating left hand
-            input_keypoints = comfy_utils.scale_hand(ref_keypoints, input_keypoints, 3, 4, 109)    #scaling left hand w.r.t left wrist
+            input_keypoints = comfy_utils.scale_hand(
+                ref_keypoints, input_keypoints, 3, 4,
+                109)  #scaling left hand w.r.t left wrist
 
-            if garment_category not in ["half_sleeve_garment", "full_sleeve_garment"]:            
-                input_keypoints = comfy_utils.rotate(ref_keypoints, input_keypoints, 5, 6)      #rotate right elbow
-            input_keypoints = comfy_utils.scale(ref_keypoints, input_keypoints, 2, 5, 5, 6) #scaling w.r.t to shoulder to elbow ratio of ref pose
+            if garment_category not in [
+                    "half_sleeve_garment", "full_sleeve_garment"
+            ]:
+                input_keypoints = comfy_utils.rotate(ref_keypoints,
+                                                     input_keypoints, 5,
+                                                     6)  #rotate right elbow
+            input_keypoints = comfy_utils.scale(
+                ref_keypoints, input_keypoints, 2, 5, 5,
+                6)  #scaling w.r.t to shoulder to elbow ratio of ref pose
 
             prev_rw = input_keypoints[7]
             if garment_category != "full_sleeve_garment":
-                input_keypoints = comfy_utils.rotate(ref_keypoints, input_keypoints, 6, 7)      #rotate right wrist
-            input_keypoints = comfy_utils.scale(ref_keypoints, input_keypoints, 5, 6, 6, 7)    #scaling w.r.t to elbow to wrist ratio of ref pose
+                input_keypoints = comfy_utils.rotate(ref_keypoints,
+                                                     input_keypoints, 6,
+                                                     7)  #rotate right wrist
+            input_keypoints = comfy_utils.scale(
+                ref_keypoints, input_keypoints, 5, 6, 6,
+                7)  #scaling w.r.t to elbow to wrist ratio of ref pose
 
             #moving hand points to wrist
-            input_keypoints[88:109] = comfy_utils.move(input_keypoints[88], input_keypoints[7], input_keypoints[88:109])
-            input_keypoints[88:109] = comfy_utils.move(ref_keypoints[7], ref_keypoints[88], input_keypoints[88:109])
+            input_keypoints[88:109] = comfy_utils.move(input_keypoints[88],
+                                                       input_keypoints[7],
+                                                       input_keypoints[88:109])
+            input_keypoints[88:109] = comfy_utils.move(ref_keypoints[7],
+                                                       ref_keypoints[88],
+                                                       input_keypoints[88:109])
 
             # input_keypoints = comfy_utils.rotate_hand(ref_keypoints, input_keypoints, 88)    #rotating right hand
-            input_keypoints = comfy_utils.scale_hand(ref_keypoints, input_keypoints, 6, 7, 88)    #scaling right hand w.r.t right wrist
+            input_keypoints = comfy_utils.scale_hand(
+                ref_keypoints, input_keypoints, 6, 7,
+                88)  #scaling right hand w.r.t right wrist
 
             #legs
             if garment_category not in ["trouser", "shorts"]:
-                input_keypoints = comfy_utils.rotate(ref_keypoints, input_keypoints, 8, 9)      #rotate left knee
-            input_keypoints = comfy_utils.scale(ref_keypoints, input_keypoints, 1, 8, 8, 9, ref_torso=True)      #scale left knee
-            
+                input_keypoints = comfy_utils.rotate(ref_keypoints,
+                                                     input_keypoints, 8,
+                                                     9)  #rotate left knee
+            input_keypoints = comfy_utils.scale(ref_keypoints, input_keypoints,
+                                                1, 8, 8, 9)  #scale left knee
+
             if garment_category != "trouser":
-                input_keypoints = comfy_utils.rotate(ref_keypoints, input_keypoints, 9, 10)     #rotate left foot
-            input_keypoints = comfy_utils.scale(ref_keypoints, input_keypoints, 8, 9, 9, 10)    #scaling w.r.t to knee to foot ratio of ref pose
-            
+                input_keypoints = comfy_utils.rotate(ref_keypoints,
+                                                     input_keypoints, 9,
+                                                     10)  #rotate left foot
+            input_keypoints = comfy_utils.scale(
+                ref_keypoints, input_keypoints, 8, 9, 9,
+                10)  #scaling w.r.t to knee to foot ratio of ref pose
+
             if garment_category not in ["trouser", "shorts"]:
-                input_keypoints = comfy_utils.rotate(ref_keypoints, input_keypoints, 11, 12)     #rotate right knee
-            input_keypoints = comfy_utils.scale(ref_keypoints, input_keypoints, 1, 11, 11, 12, ref_torso=True)      #scale right knee
-            
+                input_keypoints = comfy_utils.rotate(ref_keypoints,
+                                                     input_keypoints, 11,
+                                                     12)  #rotate right knee
+            input_keypoints = comfy_utils.scale(ref_keypoints, input_keypoints,
+                                                1, 11, 11,
+                                                12)  #scale right knee
+
             if garment_category != "trouser":
-                input_keypoints = comfy_utils.rotate(ref_keypoints, input_keypoints, 12, 13)    #rotate right foot
-            input_keypoints = comfy_utils.scale(ref_keypoints, input_keypoints, 11, 12, 12, 13)    #scaling w.r.t to knee to foot ratio of ref pose
+                input_keypoints = comfy_utils.rotate(ref_keypoints,
+                                                     input_keypoints, 12,
+                                                     13)  #rotate right foot
+            input_keypoints = comfy_utils.scale(
+                ref_keypoints, input_keypoints, 11, 12, 12,
+                13)  #scaling w.r.t to knee to foot ratio of ref pose
 
             #face
             prev_nose = input_keypoints[0]
+            input_keypoints = comfy_utils.rotate(ref_keypoints,
+                                                 input_keypoints, 1,
+                                                 0)  #rotate nose
+            input_keypoints = comfy_utils.scale(
+                ref_keypoints, input_keypoints, 2, 5, 1, 0
+            )  #scale neck to nose w.r.t to shoulder neck-nose len ratio of ref pose
 
-            if ref_face_pose_json_file != "":
-                reference = ref_face_keypoints
-            else:
-                reference = ref_keypoints
-
-            input_keypoints = comfy_utils.rotate(reference, input_keypoints, 1, 0)      #rotate nose
-            input_keypoints = comfy_utils.scale(reference, input_keypoints, 2, 5, 1, 0)      #scale neck to nose w.r.t to shoulder neck-nose len ratio of ref pose
-                
             #changing face points to w.r.t to new nose point after rotation
-            input_keypoints[14:18] = comfy_utils.move(prev_nose, input_keypoints[0], input_keypoints[14:18])
+            input_keypoints[14:18] = comfy_utils.move(prev_nose,
+                                                      input_keypoints[0],
+                                                      input_keypoints[14:18])
 
-            input_keypoints = comfy_utils.rotate(reference, input_keypoints, 0,
-                                                14)  #rotate left eye
+            input_keypoints = comfy_utils.rotate(ref_keypoints,
+                                                 input_keypoints, 0,
+                                                 14)  #rotate left eye
             input_keypoints = comfy_utils.scale(
-                reference, input_keypoints, 1, 0, 0,
-                14)  #scaling w.r.t to neck len to eye_nose len ratio of ref pose
+                ref_keypoints, input_keypoints, 1, 0, 0, 14
+            )  #scaling w.r.t to neck len to eye_nose len ratio of ref pose
 
-            input_keypoints = comfy_utils.rotate(reference, input_keypoints, 0,
-                                                15)  #rotate right eye
+            input_keypoints = comfy_utils.rotate(ref_keypoints,
+                                                 input_keypoints, 0,
+                                                 15)  #rotate right eye
             input_keypoints = comfy_utils.scale(
-                reference, input_keypoints, 1, 0, 0,
-                15)  #scaling w.r.t to neck len to eye_nose len ratio of ref pose
+                ref_keypoints, input_keypoints, 1, 0, 0, 15
+            )  #scaling w.r.t to neck len to eye_nose len ratio of ref pose
 
-            input_keypoints = comfy_utils.rotate(reference, input_keypoints,
-                                                14, 16)  #rotate left ear
+            input_keypoints = comfy_utils.rotate(ref_keypoints,
+                                                 input_keypoints, 14,
+                                                 16)  #rotate left ear
             input_keypoints = comfy_utils.scale(
-                reference, input_keypoints, 1, 0, 14,
-                16)  #scaling w.r.t to neck len to ear_nose len ratio of ref pose
+                ref_keypoints, input_keypoints, 1, 0, 14, 16
+            )  #scaling w.r.t to neck len to ear_nose len ratio of ref pose
 
-            input_keypoints = comfy_utils.rotate(reference, input_keypoints,
-                                                15, 17)  #rotate right ear
+            input_keypoints = comfy_utils.rotate(ref_keypoints,
+                                                 input_keypoints, 15,
+                                                 17)  #rotate right ear
             input_keypoints = comfy_utils.scale(
-                reference, input_keypoints, 1, 0, 15,
-                17)  #scaling w.r.t to neck len to ear_nose len ratio of ref pose
+                ref_keypoints, input_keypoints, 1, 0, 15, 17
+            )  #scaling w.r.t to neck len to ear_nose len ratio of ref pose
 
             canvas = comfy_utils.draw_bodypose(canvas, input_keypoints)
-            canvas = comfy_utils.draw_handpose(canvas, input_keypoints[88:109])  #right hand
-            canvas = comfy_utils.draw_handpose(canvas, input_keypoints[109:])  #left hand
-            canvas = torch.from_numpy(canvas.astype(np.float32)/255.0)[None,]
-            output_posemap = {"height":input_height, "width":input_width, "keypoints":input_keypoints}
-            json.dump(output_posemap, open(output_save_path, 'w'))
+            canvas = comfy_utils.draw_handpose(
+                canvas, input_keypoints[88:109])  #right hand
+            canvas = comfy_utils.draw_handpose(
+                canvas, input_keypoints[109:])  #left hand
+            canvas = torch.from_numpy(canvas.astype(np.float32) / 255.0)[
+                None,
+            ]
             return (canvas, similar_torso)
-        
+
         if 'back' in image_angle:
 
             input_pose = json.load(open(input_pose_json_file))
@@ -1330,47 +1403,58 @@ class TRI3DPoseAdaption:
             input_keypoints = input_pose['keypoints']
 
             input_pose_type = comfy_utils.get_input_pose_type(input_keypoints)
-            canvas = np.zeros(shape=(input_height, input_width, 3), dtype=np.uint8)
+            canvas = np.zeros(shape=(input_height, input_width, 3),
+                              dtype=np.uint8)
 
             if 'back_fixed' in image_angle:
-                
-                back_pose_dir = pathlib.Path().resolve() / 'custom_nodes/tri3d-comfyui-nodes/samples/back_poses/' 
+
+                back_pose_dir = pathlib.Path().resolve(
+                ) / 'custom_nodes/tri3d-comfyui-nodes/samples/back_poses/'
                 back_pose_dictionary = {
-                    'back_fixed' : 'backpose.json',
-                    'back_fixed_left' : 'left_backpose.json',
-                    'back_fixed_right' : 'right_backpose.json'
+                    'back_fixed': 'backpose.json',
+                    'back_fixed_left': 'left_backpose.json',
+                    'back_fixed_right': 'right_backpose.json'
                 }
 
+                ref_pose_json_file = back_pose_dir / back_pose_dictionary[
+                    image_angle]
 
-                ref_pose_json_file =  back_pose_dir / back_pose_dictionary[image_angle]
-            
             # /home/ubuntu/GITHUB/comfyanonymous/ComfyUI/custom_nodes/tri3d-comfyui-nodes
             print(ref_pose_json_file)
             ref_pose = json.load(open(ref_pose_json_file))
-            
+
             ref_keypoints = ref_pose['keypoints']
             similar_torso = None
-            
+
             #check torso similarity
             if garment_category not in ["shorts", "trouser"]:
-                ls_angle_1, rs_angle_1, torso_angle_1 = comfy_utils.get_torso_angles(input_keypoints)
-                ls_angle_2, rs_angle_2, torso_angle_2 = comfy_utils.get_torso_angles(ref_keypoints)
+                ls_angle_1, rs_angle_1, torso_angle_1 = comfy_utils.get_torso_angles(
+                    input_keypoints)
+                ls_angle_2, rs_angle_2, torso_angle_2 = comfy_utils.get_torso_angles(
+                    ref_keypoints)
 
                 ls_angle_diff = abs(ls_angle_2 - ls_angle_1)
                 rs_angle_diff = abs(rs_angle_2 - rs_angle_1)
                 torso_angle_diff = abs(torso_angle_2 - torso_angle_1)
 
-                similar_torso = False if (ls_angle_diff >= 5) | (rs_angle_diff >= 5) | (torso_angle_diff >= 5) else True
+                similar_torso = False if (ls_angle_diff >= 5) | (
+                    rs_angle_diff >= 5) | (torso_angle_diff >= 5) else True
 
                 if similar_torso == False:
-                    canvas = torch.from_numpy(canvas.astype(np.float32)/255.0)[None,]
+                    canvas = torch.from_numpy(
+                        canvas.astype(np.float32) / 255.0)[
+                            None,
+                        ]
                     return (canvas, similar_torso)
 
             #Removing the face points if existed
-            null_indices = [i for i in range(len(ref_keypoints)) if ref_keypoints[i] == [-1,-1]]    
+            null_indices = [
+                i for i in range(len(ref_keypoints))
+                if ref_keypoints[i] == [-1, -1]
+            ]
             for i in null_indices:
-                input_keypoints[i] = [-1,-1]
-            
+                input_keypoints[i] = [-1, -1]
+
             all_x = [i[0] for i in input_keypoints if i[0] != -1]
             min_width = min(all_x)
             max_width = max(all_x)
@@ -1378,106 +1462,189 @@ class TRI3DPoseAdaption:
             if input_pose_type == "front_pose":
                 #flip horizontally
                 for i in range(len(input_keypoints)):
-                    x,y = input_keypoints[i]
-                    if input_keypoints[i] == [-1,-1]: continue
-                    input_keypoints[i] = [(max_width - x)+min_width, y]
-            
+                    x, y = input_keypoints[i]
+                    if input_keypoints[i] == [-1, -1]: continue
+                    input_keypoints[i] = [(max_width - x) + min_width, y]
+
             #Hands
-            if input_keypoints[4] == [-1,-1]: input_keypoints[4] = ref_keypoints[4]
-            if input_keypoints[7] == [-1,-1]: input_keypoints[7] = ref_keypoints[7]
-            input_keypoints[88:] = ref_keypoints[88:]     #replace hands with reference hands
-            
-            if garment_category not in ["half_sleeve_garment", "full_sleeve_garment"]:
-                input_keypoints = comfy_utils.rotate(ref_keypoints, input_keypoints, 2, 3)      # rotate left elbow
-            input_keypoints = comfy_utils.scale(ref_keypoints, input_keypoints, 2, 5, 2, 3) #scaling w.r.t to shoulder to elbow ratio of ref pose
+            if input_keypoints[4] == [-1, -1]:
+                input_keypoints[4] = ref_keypoints[4]
+            if input_keypoints[7] == [-1, -1]:
+                input_keypoints[7] = ref_keypoints[7]
+            input_keypoints[88:] = ref_keypoints[
+                88:]  #replace hands with reference hands
+
+            if garment_category not in [
+                    "half_sleeve_garment", "full_sleeve_garment"
+            ]:
+                input_keypoints = comfy_utils.rotate(ref_keypoints,
+                                                     input_keypoints, 2,
+                                                     3)  # rotate left elbow
+            input_keypoints = comfy_utils.scale(
+                ref_keypoints, input_keypoints, 2, 5, 2,
+                3)  #scaling w.r.t to shoulder to elbow ratio of ref pose
 
             prev_lw = input_keypoints[4]
             if garment_category != "full_sleeve_garment":
-                input_keypoints = comfy_utils.rotate(ref_keypoints, input_keypoints, 3, 4)      #rotate left wrist
-            input_keypoints = comfy_utils.scale(ref_keypoints, input_keypoints, 2, 3, 3, 4) #scaling w.r.t to elbow to wrist ratio of ref pose
+                input_keypoints = comfy_utils.rotate(ref_keypoints,
+                                                     input_keypoints, 3,
+                                                     4)  #rotate left wrist
+            input_keypoints = comfy_utils.scale(
+                ref_keypoints, input_keypoints, 2, 3, 3,
+                4)  #scaling w.r.t to elbow to wrist ratio of ref pose
 
-            #moving to hand ponts to wrist 
-            input_keypoints[109:] = comfy_utils.move(input_keypoints[109], input_keypoints[4], input_keypoints[109:]) 
-            input_keypoints[109:] = comfy_utils.move(ref_keypoints[4], ref_keypoints[109], input_keypoints[109:])
+            #moving to hand ponts to wrist
+            input_keypoints[109:] = comfy_utils.move(input_keypoints[109],
+                                                     input_keypoints[4],
+                                                     input_keypoints[109:])
+            input_keypoints[109:] = comfy_utils.move(ref_keypoints[4],
+                                                     ref_keypoints[109],
+                                                     input_keypoints[109:])
 
             # input_keypoints = comfy_utils.rotate_hand(ref_keypoints, input_keypoints, 109)    #rotating left hand
-            input_keypoints = comfy_utils.scale_hand(ref_keypoints, input_keypoints, 3, 4, 109)    #scaling left hand w.r.t left wrist
+            input_keypoints = comfy_utils.scale_hand(
+                ref_keypoints, input_keypoints, 3, 4,
+                109)  #scaling left hand w.r.t left wrist
 
-            if garment_category not in ["half_sleeve_garment", "full_sleeve_garment"]:
-                input_keypoints = comfy_utils.rotate(ref_keypoints, input_keypoints, 5, 6)      #rotate right elbow
-            input_keypoints = comfy_utils.scale(ref_keypoints, input_keypoints, 2, 5, 5, 6) #scaling w.r.t to shoulder to elbow ratio of ref pose
+            if garment_category not in [
+                    "half_sleeve_garment", "full_sleeve_garment"
+            ]:
+                input_keypoints = comfy_utils.rotate(ref_keypoints,
+                                                     input_keypoints, 5,
+                                                     6)  #rotate right elbow
+            input_keypoints = comfy_utils.scale(
+                ref_keypoints, input_keypoints, 2, 5, 5,
+                6)  #scaling w.r.t to shoulder to elbow ratio of ref pose
 
             prev_rw = input_keypoints[7]
             if garment_category != "full_sleeve_garment":
-                input_keypoints = comfy_utils.rotate(ref_keypoints, input_keypoints, 6, 7)      #rotate right wrist
-            input_keypoints = comfy_utils.scale(ref_keypoints, input_keypoints, 5, 6, 6, 7)    #scaling w.r.t to elbow to wrist ratio of ref pose
+                input_keypoints = comfy_utils.rotate(ref_keypoints,
+                                                     input_keypoints, 6,
+                                                     7)  #rotate right wrist
+            input_keypoints = comfy_utils.scale(
+                ref_keypoints, input_keypoints, 5, 6, 6,
+                7)  #scaling w.r.t to elbow to wrist ratio of ref pose
 
             #moving hand points to wrist
-            input_keypoints[88:109] = comfy_utils.move(input_keypoints[88], input_keypoints[7], input_keypoints[88:109])
-            input_keypoints[88:109] = comfy_utils.move(ref_keypoints[7], ref_keypoints[88], input_keypoints[88:109])
+            input_keypoints[88:109] = comfy_utils.move(input_keypoints[88],
+                                                       input_keypoints[7],
+                                                       input_keypoints[88:109])
+            input_keypoints[88:109] = comfy_utils.move(ref_keypoints[7],
+                                                       ref_keypoints[88],
+                                                       input_keypoints[88:109])
 
             # input_keypoints = comfy_utils.rotate_hand(ref_keypoints, input_keypoints, 88)    #rotating right hand
-            input_keypoints = comfy_utils.scale_hand(ref_keypoints, input_keypoints, 6, 7, 88)    #scaling right hand w.r.t right wrist
+            input_keypoints = comfy_utils.scale_hand(
+                ref_keypoints, input_keypoints, 6, 7,
+                88)  #scaling right hand w.r.t right wrist
 
             #legs
             if garment_category not in ["trouser", "shorts"]:
-                input_keypoints = comfy_utils.rotate(ref_keypoints, input_keypoints, 8, 9)      #rotate left knee
-            input_keypoints = comfy_utils.scale(ref_keypoints, input_keypoints, 1, 8, 8, 9)      #scale left knee
+                input_keypoints = comfy_utils.rotate(ref_keypoints,
+                                                     input_keypoints, 8,
+                                                     9)  #rotate left knee
+            input_keypoints = comfy_utils.scale(ref_keypoints, input_keypoints,
+                                                1, 8, 8, 9)  #scale left knee
 
             if garment_category != "trouser":
-                input_keypoints = comfy_utils.rotate(ref_keypoints, input_keypoints, 9, 10)     #rotate left foot
-            input_keypoints = comfy_utils.scale(ref_keypoints, input_keypoints, 8, 9, 9, 10)    #scaling w.r.t to knee to foot ratio of ref pose
+                input_keypoints = comfy_utils.rotate(ref_keypoints,
+                                                     input_keypoints, 9,
+                                                     10)  #rotate left foot
+            input_keypoints = comfy_utils.scale(
+                ref_keypoints, input_keypoints, 8, 9, 9,
+                10)  #scaling w.r.t to knee to foot ratio of ref pose
 
-            if garment_category not in ["half_sleeve_garment", "full_sleeve_garment"]:
-                input_keypoints = comfy_utils.rotate(ref_keypoints, input_keypoints, 11, 12)     #rotate right knee
-            input_keypoints = comfy_utils.scale(ref_keypoints, input_keypoints, 1, 11, 11, 12)      #scale right knee
+            if garment_category not in [
+                    "half_sleeve_garment", "full_sleeve_garment"
+            ]:
+                input_keypoints = comfy_utils.rotate(ref_keypoints,
+                                                     input_keypoints, 11,
+                                                     12)  #rotate right knee
+            input_keypoints = comfy_utils.scale(ref_keypoints, input_keypoints,
+                                                1, 11, 11,
+                                                12)  #scale right knee
 
             if garment_category != "trouser":
-                input_keypoints = comfy_utils.rotate(ref_keypoints, input_keypoints, 12, 13)    #rotate right foot
-            input_keypoints = comfy_utils.scale(ref_keypoints, input_keypoints, 11, 12, 12, 13)    #scaling w.r.t to knee to foot ratio of ref pose
+                input_keypoints = comfy_utils.rotate(ref_keypoints,
+                                                     input_keypoints, 12,
+                                                     13)  #rotate right foot
+            input_keypoints = comfy_utils.scale(
+                ref_keypoints, input_keypoints, 11, 12, 12,
+                13)  #scaling w.r.t to knee to foot ratio of ref pose
 
             #face
             input_keypoints[14:18] = ref_keypoints[14:18]
             input_keypoints[0] = ref_keypoints[0]
 
+            if input_keypoints[0] == [-1, -1] and input_keypoints[14] == [
+                    -1, -1
+            ] and input_keypoints[15] == [-1, -1]:
+                input_keypoints = comfy_utils.rotate(ref_keypoints,
+                                                     input_keypoints, 1,
+                                                     16)  #rotate left ear
+                input_keypoints = comfy_utils.scale(
+                    ref_keypoints, input_keypoints, 2, 5, 1, 16
+                )  #scaling w.r.t to neck len to ear_nose len ratio of ref pose
 
-        
-            if input_keypoints[0] == [-1,-1] and input_keypoints[14] == [-1,-1] and input_keypoints[15] == [-1,-1]:
-                input_keypoints = comfy_utils.rotate(ref_keypoints, input_keypoints, 1, 16)        #rotate left ear
-                input_keypoints = comfy_utils.scale(ref_keypoints, input_keypoints, 2, 5, 1, 16)        #scaling w.r.t to neck len to ear_nose len ratio of ref pose
-
-                input_keypoints = comfy_utils.rotate(ref_keypoints, input_keypoints, 1,17)        #rotate right ear
-                input_keypoints = comfy_utils.scale(ref_keypoints, input_keypoints, 2, 5, 1, 17)        #scaling w.r.t to neck len to ear_nose len ratio of ref pose
+                input_keypoints = comfy_utils.rotate(ref_keypoints,
+                                                     input_keypoints, 1,
+                                                     17)  #rotate right ear
+                input_keypoints = comfy_utils.scale(
+                    ref_keypoints, input_keypoints, 2, 5, 1, 17
+                )  #scaling w.r.t to neck len to ear_nose len ratio of ref pose
             else:
                 prev_nose = input_keypoints[0]
-                input_keypoints = comfy_utils.rotate(ref_keypoints, input_keypoints, 1, 0)      #rotate nose
-                input_keypoints = comfy_utils.scale(ref_keypoints, input_keypoints, 2, 5, 1, 0)      #scale neck to nose w.r.t to shoulder neck-nose len ratio of ref pose
-                
+                input_keypoints = comfy_utils.rotate(ref_keypoints,
+                                                     input_keypoints, 1,
+                                                     0)  #rotate nose
+                input_keypoints = comfy_utils.scale(
+                    ref_keypoints, input_keypoints, 2, 5, 1, 0
+                )  #scale neck to nose w.r.t to shoulder neck-nose len ratio of ref pose
+
                 #changing face points to w.r.t to new nose point after rotation
-                input_keypoints[14:18] = comfy_utils.move(prev_nose, input_keypoints[0], input_keypoints[14:18])
+                input_keypoints[14:18] = comfy_utils.move(
+                    prev_nose, input_keypoints[0], input_keypoints[14:18])
 
-                if input_keypoints[14] != [-1,-1]: 
-                    input_keypoints = comfy_utils.rotate(ref_keypoints, input_keypoints, 0, 14)      #rotate left eye
-                    input_keypoints = comfy_utils.scale(ref_keypoints, input_keypoints, 1, 0, 0, 14)        #scaling w.r.t to neck len to eye_nose len ratio of ref pose
+                if input_keypoints[14] != [-1, -1]:
+                    input_keypoints = comfy_utils.rotate(
+                        ref_keypoints, input_keypoints, 0,
+                        14)  #rotate left eye
+                    input_keypoints = comfy_utils.scale(
+                        ref_keypoints, input_keypoints, 1, 0, 0, 14
+                    )  #scaling w.r.t to neck len to eye_nose len ratio of ref pose
 
-                if input_keypoints[15] != [-1,-1]:
-                    input_keypoints = comfy_utils.rotate(ref_keypoints, input_keypoints, 0, 15)      #rotate right eye
-                    input_keypoints = comfy_utils.scale(ref_keypoints, input_keypoints, 1, 0, 0, 15)        #scaling w.r.t to neck len to eye_nose len ratio of ref pose
+                if input_keypoints[15] != [-1, -1]:
+                    input_keypoints = comfy_utils.rotate(
+                        ref_keypoints, input_keypoints, 0,
+                        15)  #rotate right eye
+                    input_keypoints = comfy_utils.scale(
+                        ref_keypoints, input_keypoints, 1, 0, 0, 15
+                    )  #scaling w.r.t to neck len to eye_nose len ratio of ref pose
 
-                if input_keypoints[16] != [-1,-1]:
-                    input_keypoints = comfy_utils.rotate(ref_keypoints, input_keypoints, 14, 16)        #rotate left ear
-                    input_keypoints = comfy_utils.scale(ref_keypoints, input_keypoints, 1, 0, 14, 16)        #scaling w.r.t to neck len to ear_nose len ratio of ref pose
+                if input_keypoints[16] != [-1, -1]:
+                    input_keypoints = comfy_utils.rotate(
+                        ref_keypoints, input_keypoints, 14,
+                        16)  #rotate left ear
+                    input_keypoints = comfy_utils.scale(
+                        ref_keypoints, input_keypoints, 1, 0, 14, 16
+                    )  #scaling w.r.t to neck len to ear_nose len ratio of ref pose
 
-                if input_keypoints[17] != [-1,-1]:
-                    input_keypoints = comfy_utils.rotate(ref_keypoints, input_keypoints, 15,17)        #rotate right ear
-                    input_keypoints = comfy_utils.scale(ref_keypoints, input_keypoints, 1, 0, 15, 17)        #scaling w.r.t to neck len to ear_nose len ratio of ref pose
+                if input_keypoints[17] != [-1, -1]:
+                    input_keypoints = comfy_utils.rotate(
+                        ref_keypoints, input_keypoints, 15,
+                        17)  #rotate right ear
+                    input_keypoints = comfy_utils.scale(
+                        ref_keypoints, input_keypoints, 1, 0, 15, 17
+                    )  #scaling w.r.t to neck len to ear_nose len ratio of ref pose
 
             canvas = comfy_utils.draw_bodypose(canvas, input_keypoints)
-            canvas = comfy_utils.draw_handpose(canvas, input_keypoints[88:109])  #right hand
-            canvas = comfy_utils.draw_handpose(canvas, input_keypoints[109:])  #left hand
-            canvas = torch.from_numpy(canvas.astype(np.float32)/255.0)[None,]
-            output_posemap = {"height":input_height, "width":input_width, "keypoints":input_keypoints}
-            json.dump(output_posemap, open(output_save_path, 'w'))
+            canvas = comfy_utils.draw_handpose(
+                canvas, input_keypoints[88:109])  #right hand
+            canvas = comfy_utils.draw_handpose(
+                canvas, input_keypoints[109:])  #left hand
+            canvas = torch.from_numpy(canvas.astype(np.float32) / 255.0)[
+                None,
+            ]
             return (canvas, similar_torso)
 
 
@@ -1517,9 +1684,13 @@ class TRI3DLoadPoseJson:
 
             canvas = np.zeros(shape=(height, width, 3), dtype=np.uint8)
             canvas = comfy_utils.draw_bodypose(canvas, keypoints)
-            canvas = comfy_utils.draw_handpose(canvas, keypoints[88:109])  #right hand
-            canvas = comfy_utils.draw_handpose(canvas, keypoints[109:])  #left hand
-            canvas = torch.from_numpy(canvas.astype(np.float32)/255.0)[None,]
+            canvas = comfy_utils.draw_handpose(canvas,
+                                               keypoints[88:109])  #right hand
+            canvas = comfy_utils.draw_handpose(canvas,
+                                               keypoints[109:])  #left hand
+            canvas = torch.from_numpy(canvas.astype(np.float32) / 255.0)[
+                None,
+            ]
         else:
             canvas = np.zeros(shape=(height, width, 3), dtype=np.uint8)
         # if 'A' in i.getbands():
@@ -1637,6 +1808,7 @@ class FloatToImage:
 
         return image
 
+
 class TRI3D_recolor_LAB:
 
     @classmethod
@@ -2007,8 +2179,6 @@ class TRI3D_recolor:
         return image_output
 
 
-
-
 class TRI3D_image_mask_2_box:
 
     def __init__(self):
@@ -2062,6 +2232,7 @@ class TRI3D_image_mask_box_2_image:
         image = to_torch_image(image)
         return image
 
+
 class TRI3D_clipdrop_bgremove_api:
 
     def __init__(self):
@@ -2088,13 +2259,14 @@ class TRI3D_clipdrop_bgremove_api:
         CLIPDROP_API_KEY = os.getenv('CLIPDROP_API_KEY')
 
         # CLIPDROP_API_KEY = os.environ.get('CLIPDROP_API_KEY')
-        
+
         r = requests.post('https://clipdrop-api.co/remove-background/v1',
-        files = {
-            'image_file': ("mannequin.jpg", enc_image.tobytes(), 'image/jpeg'),
-            },
-        headers = { 'x-api-key': CLIPDROP_API_KEY}
-        )
+                          files={
+                              'image_file':
+                              ("mannequin.jpg", enc_image.tobytes(),
+                               'image/jpeg'),
+                          },
+                          headers={'x-api-key': CLIPDROP_API_KEY})
         if (r.ok):
             pass
         else:
@@ -2104,7 +2276,9 @@ class TRI3D_clipdrop_bgremove_api:
         # print("decoded output",output.shape)
         # mask = output[:,:,3]
         # output = output[:,:,0:3]
-        output = torch.from_numpy(output.astype(np.float32)/255.0)[None,]
+        output = torch.from_numpy(output.astype(np.float32) / 255.0)[
+            None,
+        ]
         # print("converted image to torch")
         # print(output.shape)
         # mask = torch.from_numpy(mask.astype(np.float32)/255.0)[None,]
@@ -2112,99 +2286,36 @@ class TRI3D_clipdrop_bgremove_api:
         # print(mask.shape)
         return output,
 
-class TRI3DPoseProportion:
-    
+
+class TRI3D_oneformer_person_mask:
+
     def __init__(self):
         pass
 
     @classmethod
     def INPUT_TYPES(s):
-        return {"required":
-                    {"pose1_json_file":("STRING",{"default":""}), "pose2_json_file":("STRING",{"default":""}), \
-                     "body_part":(["face", "left_elbow", "right_elbow", "left_wrist", "right_wrist", \
-                                   "left_knee", "right_knee", "left_ankle", "right_ankle"],{"default":"face"})
-                     }
-                }
+        return {
+            "required": {
+                "image": ("IMAGE", ),
+            },
+        }
 
     FUNCTION = "run"
-    RETURN_TYPES = ("FLOAT","FLOAT")
-    RETURN_NAMES = ("pose1_proportion", "pose2_proportion")
-    CATEGORY = "TRI3D"
+    RETURN_TYPES = ("IMAGE", )
+    CATEGORY = "tri3d"
 
-    def run(self, pose1_json_file, pose2_json_file, body_part):
-        from .dwpose import comfy_utils
+    def run(self, image):
+        image = from_torch_image(image)
 
-        pose1 = json.load(open(pose1_json_file,'r'))
-        pose2 = json.load(open(pose2_json_file,'r'))
-        
-        pose1_keypoints = pose1['keypoints']
-        pose2_keypoints = pose2['keypoints']
+        image_final = np.zeros((image.shape[0], image.shape[1], 4),
+                               dtype=np.uint8)
 
-        #Calculate torso 2nd coord
-        pose1_ref_torso_x = int((pose1_keypoints[8][0] + pose1_keypoints[11][0]) / 2)
-        pose1_ref_torso_y = int((pose1_keypoints[8][1] + pose1_keypoints[11][1]) / 2)
-        pose2_ref_torso_x = int((pose2_keypoints[8][0] + pose2_keypoints[11][0]) / 2)
-        pose2_ref_torso_y = int((pose2_keypoints[8][1] + pose2_keypoints[11][1]) / 2)
+        image_final[:, :, 0:3] = image
+        image = get_segmentation_of_person(image)
+        image_final[:, :, 3] = image[:, :]
+        image = to_torch_image(image_final)
+        return image
 
-        if body_part == "face":
-            pose1_ratio = comfy_utils.get_ratio(pose1_keypoints[2][0], pose1_keypoints[2][1], pose1_keypoints[5][0], pose1_keypoints[5][1],                       
-                            pose1_keypoints[16][0], pose1_keypoints[16][1], pose1_keypoints[17][0], pose1_keypoints[17][1])
-
-            pose2_ratio = comfy_utils.get_ratio(pose2_keypoints[2][0], pose2_keypoints[2][1], pose2_keypoints[5][0], pose2_keypoints[5][1],                       
-                            pose2_keypoints[16][0], pose2_keypoints[16][1], pose2_keypoints[17][0], pose2_keypoints[17][1])
-        elif body_part == "left_elbow":
-            pose1_ratio = comfy_utils.get_ratio(pose1_keypoints[2][0], pose1_keypoints[2][1], pose1_keypoints[5][0], pose1_keypoints[5][1],                       
-                            pose1_keypoints[2][0], pose1_keypoints[2][1], pose1_keypoints[3][0], pose1_keypoints[3][1])
-
-            pose2_ratio = comfy_utils.get_ratio(pose2_keypoints[2][0], pose2_keypoints[2][1], pose2_keypoints[5][0], pose2_keypoints[5][1],                       
-                            pose2_keypoints[2][0], pose2_keypoints[2][1], pose2_keypoints[3][0], pose2_keypoints[3][1])
-        elif body_part == "right_elbow":
-            pose1_ratio = comfy_utils.get_ratio(pose1_keypoints[2][0], pose1_keypoints[2][1], pose1_keypoints[5][0], pose1_keypoints[5][1],                       
-                            pose1_keypoints[5][0], pose1_keypoints[5][1], pose1_keypoints[6][0], pose1_keypoints[6][1])
-
-            pose2_ratio = comfy_utils.get_ratio(pose2_keypoints[2][0], pose2_keypoints[2][1], pose2_keypoints[5][0], pose2_keypoints[5][1],                       
-                            pose2_keypoints[5][0], pose2_keypoints[5][1], pose2_keypoints[6][0], pose2_keypoints[6][1])
-        elif body_part == "left_wrist":
-            pose1_ratio = comfy_utils.get_ratio(pose1_keypoints[2][0], pose1_keypoints[2][1], pose1_keypoints[3][0], pose1_keypoints[3][1],                       
-                            pose1_keypoints[3][0], pose1_keypoints[3][1], pose1_keypoints[4][0], pose1_keypoints[4][1])
-
-            pose2_ratio = comfy_utils.get_ratio(pose2_keypoints[2][0], pose2_keypoints[2][1], pose2_keypoints[3][0], pose2_keypoints[3][1],                       
-                            pose2_keypoints[3][0], pose2_keypoints[3][1], pose2_keypoints[4][0], pose2_keypoints[4][1])
-        elif body_part == "right_wrist":
-            pose1_ratio = comfy_utils.get_ratio(pose1_keypoints[5][0], pose1_keypoints[5][1], pose1_keypoints[6][0], pose1_keypoints[6][1],                       
-                            pose1_keypoints[6][0], pose1_keypoints[6][1], pose1_keypoints[7][0], pose1_keypoints[7][1])
-
-            pose2_ratio = comfy_utils.get_ratio(pose2_keypoints[5][0], pose2_keypoints[5][1], pose2_keypoints[6][0], pose2_keypoints[6][1],                       
-                            pose2_keypoints[6][0], pose2_keypoints[6][1], pose2_keypoints[7][0], pose2_keypoints[7][1])
-        elif body_part == "left_knee":
-            pose1_ratio = comfy_utils.get_ratio(pose1_keypoints[1][0], pose1_keypoints[1][1], pose1_ref_torso_x, pose1_ref_torso_y,                       
-                            pose1_keypoints[8][0], pose1_keypoints[8][1], pose1_keypoints[9][0], pose1_keypoints[9][1])
-
-            pose2_ratio = comfy_utils.get_ratio(pose2_keypoints[1][0], pose2_keypoints[1][1], pose2_ref_torso_x, pose2_ref_torso_y,                       
-                            pose2_keypoints[8][0], pose2_keypoints[8][1], pose2_keypoints[9][0], pose2_keypoints[9][1])
-
-        elif body_part == "right_knee":
-            pose1_ratio = comfy_utils.get_ratio(pose1_keypoints[1][0], pose1_keypoints[1][1], pose1_ref_torso_x, pose1_ref_torso_y,                       
-                            pose1_keypoints[11][0], pose1_keypoints[11][1], pose1_keypoints[12][0], pose1_keypoints[12][1])
-
-            pose2_ratio = comfy_utils.get_ratio(pose2_keypoints[1][0], pose2_keypoints[1][1], pose2_ref_torso_x, pose2_ref_torso_y,                       
-                            pose2_keypoints[11][0], pose2_keypoints[11][1], pose2_keypoints[12][0], pose2_keypoints[12][1])
-        
-        elif body_part == "left_ankle":
-            pose1_ratio = comfy_utils.get_ratio(pose1_keypoints[8][0], pose1_keypoints[8][1], pose1_keypoints[9][0], pose1_keypoints[9][1],                       
-                            pose1_keypoints[9][0], pose1_keypoints[9][1], pose1_keypoints[10][0], pose1_keypoints[10][1])
-
-            pose2_ratio = comfy_utils.get_ratio(pose2_keypoints[8][0], pose2_keypoints[8][1], pose2_keypoints[9][0], pose2_keypoints[9][1],                       
-                            pose2_keypoints[9][0], pose2_keypoints[9][1], pose2_keypoints[10][0], pose2_keypoints[10][1])
-
-        elif body_part == "right_ankle":
-            pose1_ratio = comfy_utils.get_ratio(pose1_keypoints[11][0], pose1_keypoints[11][1], pose1_keypoints[12][0], pose1_keypoints[12][1],                       
-                            pose1_keypoints[12][0], pose1_keypoints[12][1], pose1_keypoints[13][0], pose1_keypoints[13][1])
-
-            pose2_ratio = comfy_utils.get_ratio(pose2_keypoints[11][0], pose2_keypoints[11][1], pose2_keypoints[12][0], pose2_keypoints[12][1],                       
-                            pose2_keypoints[12][0], pose2_keypoints[12][1], pose2_keypoints[13][0], pose2_keypoints[13][1])
-        
-        return pose1_ratio, pose2_ratio
 
 # A dictionary that contains all nodes you want to export with their names
 # NOTE: names should be globally unique
@@ -2228,32 +2339,50 @@ NODE_CLASS_MAPPINGS = {
     "tri3d-image-mask-2-box": TRI3D_image_mask_2_box,
     "tri3d-image-mask-box-2-image": TRI3D_image_mask_box_2_image,
     "tri3d-clipdrop-bgremove-api": TRI3D_clipdrop_bgremove_api,
-    "tri3d-pose-proportion":TRI3DPoseProportion
+    "tri3d-oneformer-person-mask": TRI3D_oneformer_person_mask,
 }
 
 VERSION = "2.3"
 # A dictionary that contains the friendly/humanly readable titles for the nodes
 NODE_DISPLAY_NAME_MAPPINGS = {
-    "tri3d-atr-parse-batch": "ATR Parse Batch" + " v" + VERSION,
-    'tri3d-extract-parts-batch': 'Extract Parts Batch' + " v" + VERSION,
-    'tri3d-extract-parts-batch2': 'Extract Parts Batch 2' + " v" + VERSION,
-    "tri3d-position-parts-batch": "Position Parts Batch" + " v" + VERSION,
-    "tri3d-swap-pixels": "Swap Pixels by Mask" + " v" + VERSION,
+    "tri3d-atr-parse-batch":
+    "ATR Parse Batch" + " v" + VERSION,
+    'tri3d-extract-parts-batch':
+    'Extract Parts Batch' + " v" + VERSION,
+    'tri3d-extract-parts-batch2':
+    'Extract Parts Batch 2' + " v" + VERSION,
+    "tri3d-position-parts-batch":
+    "Position Parts Batch" + " v" + VERSION,
+    "tri3d-swap-pixels":
+    "Swap Pixels by Mask" + " v" + VERSION,
     "tri3d-skin-feathered-padded-mask":
     "Skin Feathered Padded Mask" + " v" + VERSION,
     "tri3d-interaction-canny":
     "Garment Skin Interaction Canny" + " v" + VERSION,
-    "tri3d-dwpose": "DWPose" + " v" + VERSION,
-    "tri3d-pose-to-image": "Pose to Image" + " v" + VERSION,
-    "tri3d-pose-adaption": "Pose Adaption" + " v" + VERSION,
-    "tri3d-load-pose-json": "Load Pose Json" + " v" + VERSION,
-    "tri3d-face-recognise": "Recognise face" + " v" + VERSION,
-    "tri3d-float-to-image": "Render float" + " v" + VERSION,
-    "tri3d-recolor-mask": "Recolor mask HSV space" + " v" + VERSION,
-    "tri3d-recolor-mask-LAB_space": "Recolor mask LAB space" + " v" + VERSION,
-    "tri3d-recolor-mask-RGB_space": "Recolor mask RGB space" + " v" + VERSION,
-    "tri3d--image-mask-2-box": "Extract box from image" + " v" + VERSION,
-    "tri3d-image-mask-box-2-image": "Stitch box to image" + " v" + VERSION,
-    "tri3d-clipdrop-bgremove-api": "RemBG ClipDrop" + " v" + VERSION,
-    "tri3d-pose-proportion":"Get pose proportion" + "v" + VERSION
+    "tri3d-dwpose":
+    "DWPose" + " v" + VERSION,
+    "tri3d-pose-to-image":
+    "Pose to Image" + " v" + VERSION,
+    "tri3d-pose-adaption":
+    "Pose Adaption" + " v" + VERSION,
+    "tri3d-load-pose-json":
+    "Load Pose Json" + " v" + VERSION,
+    "tri3d-face-recognise":
+    "Recognise face" + " v" + VERSION,
+    "tri3d-float-to-image":
+    "Render float" + " v" + VERSION,
+    "tri3d-recolor-mask":
+    "Recolor mask HSV space" + " v" + VERSION,
+    "tri3d-recolor-mask-LAB_space":
+    "Recolor mask LAB space" + " v" + VERSION,
+    "tri3d-recolor-mask-RGB_space":
+    "Recolor mask RGB space" + " v" + VERSION,
+    "tri3d--image-mask-2-box":
+    "Extract box from image" + " v" + VERSION,
+    "tri3d-image-mask-box-2-image":
+    "Stitch box to image" + " v" + VERSION,
+    "tri3d-clipdrop-bgremove-api":
+    "RemBG ClipDrop" + " v" + VERSION,
+    "tri3d-oneformer-person-mask":
+    "Get person mask using oneformer" + " v" + VERSION,
 }

dwpose/comfy_utils.py

@@ -35,7 +35,7 @@ def draw_bodypose(canvas: np.ndarray, keypoints: list) -> np.ndarray:
         keypoint1 = keypoints[k1_index - 1]
         keypoint2 = keypoints[k2_index - 1]
 
-        if any(i < 0 for i in keypoint1) or any(i < 0 for i in keypoint2):
+        if -1 in keypoint1 or -1 in keypoint2:
             continue
 
         Y = np.array([keypoint1[0], keypoint2[0]]) 
@@ -46,9 +46,8 @@ def draw_bodypose(canvas: np.ndarray, keypoints: list) -> np.ndarray:
         angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1]))
         polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), stickwidth), int(angle), 0, 360, 1)
         cv2.fillConvexPoly(canvas, polygon, [int(float(c) * 0.6) for c in color])
-    
     for i, (keypoint, color) in enumerate(zip(keypoints, colors)):
-        if any(i < 0 for i in keypoint1):
+        if -1 in keypoint:
             continue
         
         x, y = keypoint[0], keypoint[1]
@@ -107,38 +106,25 @@ def rotate(src_keypoints, dest_keypoints, point1_idx, point2_idx):
     
     return dest_keypoints
 
-def scale(src_keypoints, dest_keypoints, ref_point1_idx, ref_point2_idx, point1_idx, point2_idx, ref_torso=False):
-
-    if ref_torso == True:
-        ref_x1,ref_y1 = src_keypoints[1]
-        ref_x3,ref_y3 = dest_keypoints[1]
-
-        ref_x2 = int((src_keypoints[8][0] + src_keypoints[11][0]) / 2)
-        ref_y2 = int((src_keypoints[8][1] + src_keypoints[11][1]) / 2)
-        ref_x4 = int((dest_keypoints[8][0] + dest_keypoints[11][0]) / 2)
-        ref_y4 = int((dest_keypoints[8][1] + dest_keypoints[11][1]) / 2)
-    else:
-        ref_x1,ref_y1 = src_keypoints[ref_point1_idx]
-        ref_x2,ref_y2 = src_keypoints[ref_point2_idx]
-        ref_x3,ref_y3 = dest_keypoints[ref_point1_idx]
-        ref_x4,ref_y4 = dest_keypoints[ref_point2_idx]
+def scale(src_keypoints, dest_keypoints, ref_point1_idx, ref_point2_idx, point1_idx, point2_idx):
+    ref_x1,ref_y1 = src_keypoints[ref_point1_idx]
+    ref_x2,ref_y2 = src_keypoints[ref_point2_idx]
+    ref_x3,ref_y3 = dest_keypoints[ref_point1_idx]
+    ref_x4,ref_y4 = dest_keypoints[ref_point2_idx]
     
     x1,y1 = src_keypoints[point1_idx]
     x2,y2 = src_keypoints[point2_idx]
     x3,y3 = dest_keypoints[point1_idx]
     x4,y4 = dest_keypoints[point2_idx]
     
-    # src_ref_len = np.linalg.norm(np.array([ref_x1, ref_y1]) - np.array([ref_x2, ref_y2]))             #src ref part distance
-    # dest_ref_len = np.linalg.norm(np.array([ref_x3, ref_y3]) - np.array([ref_x4, ref_y4]))          #dest ref part distance
+    src_ref_len = np.linalg.norm(np.array([ref_x1, ref_y1]) - np.array([ref_x2, ref_y2]))             #src ref part distance
+    dest_ref_len = np.linalg.norm(np.array([ref_x3, ref_y3]) - np.array([ref_x4, ref_y4]))          #dest ref part distance
 
-    # src_targ_len = np.linalg.norm(np.array([x1, y1]) - np.array([x2, y2]))             #src targ part distance
-    # dest_targ_len = np.linalg.norm(np.array([x3, y3]) - np.array([x4,y4]))          #dest targ part distance
+    src_targ_len = np.linalg.norm(np.array([x1, y1]) - np.array([x2, y2]))             #src targ part distance
+    dest_targ_len = np.linalg.norm(np.array([x3, y3]) - np.array([x4,y4]))          #dest targ part distance
 
-    # src_targ_ref_ratio = src_targ_len / src_ref_len                  #src targ to ref ratio
-    # dest_targ_ref_ratio = dest_targ_len / dest_ref_len            #dest targ to ref ratio
-
-    src_targ_ref_ratio = get_ratio(ref_x1, ref_y1, ref_x2, ref_y2, x1, y1, x2, y2)                #src targ to ref ratio
-    dest_targ_ref_ratio = get_ratio(ref_x3, ref_y3, ref_x4, ref_y4, x3, y3, x4, y4)            #dest targ to ref ratio
+    src_targ_ref_ratio = src_targ_len / src_ref_len                  #src targ to ref ratio
+    dest_targ_ref_ratio = dest_targ_len / dest_ref_len            #dest targ to ref ratio
 
     scale = src_targ_ref_ratio / dest_targ_ref_ratio
 
@@ -149,11 +135,6 @@ def scale(src_keypoints, dest_keypoints, ref_point1_idx, ref_point2_idx, point1_
     
     return dest_keypoints
 
-def get_ratio(ref_x1, ref_y1, ref_x2, ref_y2, x1, y1, x2, y2):
-    ref_len = np.linalg.norm(np.array([ref_x1, ref_y1]) - np.array([ref_x2, ref_y2]))             #ref body part length
-    targ_len = np.linalg.norm(np.array([x1, y1]) - np.array([x2, y2]))             #targ body part length
-    return targ_len / ref_len
-
 def scale_hand(src_keypoints, dest_keypoints, ref_point1_idx, ref_point2_idx, starting_idx):
     
     """
@@ -258,7 +239,7 @@ def get_torso_angles(keypoints):
     a,b = keypoints[5],keypoints[1]
     angle_radians = math.tan((a[1]-b[1])/(a[0]-b[0]))
     rs_angle = abs(math.degrees(angle_radians))
-
+    
     #getting bisector of torso and getting angle with y_axis
     a,b,c = keypoints[8], keypoints[1], keypoints[11]