added the DeepfaceAnalyzeFaceAttributes node.

Author: zhangp365

README.md

@@ -76,3 +76,15 @@ From initial testing, the filtering effect is better than classifier models such
 
 <img src="assets/detectorForNSFW.png" width="100%"/>
 You can also adjust the confidence levels for various rules such as buttocks_exposed to be more lenient or strict. Lower confidence levels will filter out more potential NSFW images. Setting the value to 1 will stop filtering for that specific feature.
+
+## DeepfaceAnalyzeFaceAttributes
+This node integrates the [deepface](https://github.com/serengil/deepface) library to analyze face attributes (gender, race, emotion, age). It analyzes only the largest face in the image and supports processing one image at a time.
+<img src="assets/deepfaceAnalyzeFaceAttributes.png" width="100%"/>
+
+If the input image is a standard square face image, you can enable the standard_single_face_image switch. In this case, the node will skip face detection and analyze the attributes directly.
+
+Upon the first run, the node will download the [deepface](https://github.com/serengil/deepface) models, which may take some time.
+
+Note: If you encounter the following exception while running the node:
+ValueError: The layer sequential has never been called and thus has no defined input.
+please set the environment variable TF_USE_LEGACY_KERAS to 1, then restart ComfyUI.

assets/deepfaceAnalyzeFaceAttributes.png

@@ -0,0 +1,141 @@
+import os
+os.environ["TF_USE_LEGACY_KERAS"] = "1"
+
+import numpy as np
+from typing import Union, List, Dict, Any
+from .utils import tensor2np,np2tensor
+from ..r_deepface import demography
+
+import folder_paths
+import json
+import logging
+logger = logging.getLogger(__file__)
+
+
+def prepare_deepface_home():
+    deepface_path = os.path.join(folder_paths.models_dir, "deepface")
+
+    # Deepface requires a specific structure within the DEEPFACE_HOME directory
+    deepface_dot_path = os.path.join(deepface_path, ".deepface")
+    deepface_weights_path = os.path.join(deepface_dot_path, "weights")
+    if not os.path.exists(deepface_weights_path):
+        os.makedirs(deepface_weights_path)
+
+    os.environ["DEEPFACE_HOME"] = deepface_path
+
+
+def get_largest_face(faces):
+    largest_face = {}
+    largest_area = 0
+    if len(faces) == 1:
+        return faces[0]
+    
+    for face in faces:
+        if 'region' in face:
+            w = face['region']['w']
+            h = face['region']['h']
+            area = w * h
+            if area > largest_area:
+                largest_area = area
+                largest_face = face
+    return largest_face
+
+
+class DeepfaceAnalyzeFaceAttributes:
+    '''
+        - 'gender' (str): The gender in the detected face. "M" or "F"
+
+        - 'emotion' (str): The emotion in the detected face.
+            Possible values include "sad," "angry," "surprise," "fear," "happy,"
+            "disgust," and "neutral."
+
+        - 'race' (str): The race in the detected face.
+            Possible values include "indian," "asian," "latino hispanic,"
+            "black," "middle eastern," and "white."
+    '''
+
+    def __init__(self) -> None:
+        prepare_deepface_home()
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "detector_backend": ([
+                     "opencv",
+                     "ssd",
+                     "dlib",
+                     "mtcnn",
+                     "retinaface",
+                     "mediapipe",
+                     "yolov8",
+                     "yunet",
+                     "fastmtcnn",
+                ], {
+                    "default": "yolov8",
+                }),
+            },
+            "optional": {
+                "analyze_gender": ("BOOLEAN", {"default": True}),
+                "analyze_race": ("BOOLEAN", {"default": True}),
+                "analyze_emotion": ("BOOLEAN", {"default": True}),
+                "analyze_age": ("BOOLEAN", {"default": True}),
+                "standard_single_face_image": ("BOOLEAN", {"default": False}),
+            },
+        }
+
+    RETURN_TYPES = ("STRING","STRING","STRING","STRING", "STRING")
+    RETURN_NAMES = ("gender","race","emotion","age", "json_info")
+    FUNCTION = "analyze_face"
+    CATEGORY = "utils/face"
+
+    def analyze_face(self, image, detector_backend, analyze_gender=True, analyze_race=True, analyze_emotion=True, analyze_age=True, standard_single_face_image=False):
+        # 将图像转换为numpy数组
+        img_np = tensor2np(image)
+        if isinstance(img_np, List):
+            if len(img_np) > 1:
+                logger.warn(f"DeepfaceAnalyzeFaceAttributes only support for one image and only analyze the largest face.")
+            img_np = img_np[0]
+
+        # 准备actions列表
+        actions = []
+        if analyze_gender:
+            actions.append("gender")
+        if analyze_race:
+            actions.append("race")
+        if analyze_emotion:
+            actions.append("emotion")
+        if analyze_age:
+            actions.append("age")
+        
+        # 调用analyze函数
+        results = demography.analyze(img_np, actions=actions, detector_backend=detector_backend, enforce_detection=False,  is_single_face_image=standard_single_face_image)
+        
+        # 获取面积最大的脸
+        largest_face = get_largest_face(results)
+
+        if not standard_single_face_image and largest_face.get("face_confidence")==0:
+            largest_face ={}
+        
+        gender_map = {"Woman":"F","Man":"M",'':''}
+        # 提取结果
+        gender = gender_map.get(largest_face.get('dominant_gender', ''),'')if analyze_gender else ''
+        race = largest_face.get('dominant_race', '') if analyze_race else ''
+        emotion = largest_face.get('dominant_emotion', '') if analyze_emotion else ''
+        age = str(largest_face.get('age', '0')) if analyze_age else '0'
+        
+        json_info= json.dumps(largest_face)
+        return (gender, race, emotion, age, json_info)
+    
+NODE_CLASS_MAPPINGS = {
+    #image
+    "DeepfaceAnalyzeFaceAttributes": DeepfaceAnalyzeFaceAttributes,
+
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    # Image
+    "DeepfaceAnalyzeFaceAttributes": "Deepface Analyze Face Attributes",
+
+}

py/node_face_attributes.py

@@ -8,21 +8,12 @@
 from typing import Union, List
 import json
 import logging
+from .utils import tensor2np,np2tensor
+
 logger = logging.getLogger(__file__)
 
 comfy_paths.folder_names_and_paths["nsfw"] = ([os.path.join(models_dir, "nsfw")], {".pt",".onnx"})
 
-def tensor2np(tensor: torch.Tensor):
-    if len(tensor.shape) == 3:  # Single image
-        return np.clip(255.0 * tensor.cpu().numpy(), 0, 255).astype(np.uint8)
-    else:  # Batch of images
-        return [np.clip(255.0 * t.cpu().numpy(), 0, 255).astype(np.uint8) for t in tensor]
-
-def np2tensor(img_np: Union[np.ndarray, List[np.ndarray]]) -> torch.Tensor:
-    if isinstance(img_np, list):
-        return torch.cat([np2tensor(img) for img in img_np], dim=0)
-    return torch.from_numpy(img_np.astype(np.float32) / 255.0).unsqueeze(0)
-
 
 class DetectorForNSFW:
 

py/node_nsfw.py

@@ -0,0 +1,16 @@
+import cv2
+import numpy as np
+from typing import Union, List
+import torch
+
+
+def tensor2np(tensor: torch.Tensor):
+    if len(tensor.shape) == 3:  # Single image
+        return np.clip(255.0 * tensor.cpu().numpy(), 0, 255).astype(np.uint8)
+    else:  # Batch of images
+        return [np.clip(255.0 * t.cpu().numpy(), 0, 255).astype(np.uint8) for t in tensor]
+
+def np2tensor(img_np: Union[np.ndarray, List[np.ndarray]]) -> torch.Tensor:
+    if isinstance(img_np, list):
+        return torch.cat([np2tensor(img) for img in img_np], dim=0)
+    return torch.from_numpy(img_np.astype(np.float32) / 255.0).unsqueeze(0)

py/utils.py

@@ -1,7 +1,7 @@
 [project]
 name = "comfyui-utils-nodes"
-description = "Nodes:LoadImageWithSwitch, ImageBatchOneOrMore, ModifyTextGender, GenderControlOutput, ImageCompositeMaskedWithSwitch, ColorCorrectOfUtils, SplitMask, MaskFastGrow, CheckpointLoaderSimpleWithSwitch, ImageResizeTo8x, MatchImageRatioToPreset, UpscaleImageWithModelIfNeed, MaskFromFaceModel, MaskCoverFourCorners, DetectorForNSFW etc."
-version = "1.1.5"
+description = "Nodes:LoadImageWithSwitch, ImageBatchOneOrMore, ModifyTextGender, GenderControlOutput, ImageCompositeMaskedWithSwitch, ColorCorrectOfUtils, SplitMask, MaskFastGrow, CheckpointLoaderSimpleWithSwitch, ImageResizeTo8x, MatchImageRatioToPreset, UpscaleImageWithModelIfNeed, MaskFromFaceModel, MaskCoverFourCorners, DetectorForNSFW, DeepfaceAnalyzeFaceAttributes etc."
+version = "1.1.6"
 license = { file = "LICENSE" }
 dependencies = []