453 lines
15 KiB
Python
453 lines
15 KiB
Python
import torch
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from torch import Tensor
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from torchvision.transforms import functional
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from PIL import Image
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import numpy as np
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import comfy.utils
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import time
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from io import BytesIO
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from .utils import *
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class DetectFaces:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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'required': {
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'image': ('IMAGE',),
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'threshold': ('FLOAT', {'default': 0.5, 'min': 0.0, 'max': 1.0, 'step': 0.01}),
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'min_size': ('INT', {'default': 64, 'max': 512, 'step': 8}),
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'max_size': ('INT', {'default': 512, 'min': 512, 'step': 8}),
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},
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'optional': {
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'mask': ('MASK',),
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}
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}
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RETURN_TYPES = ('FACE', 'BOOLEAN')
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RETURN_NAMES = ('faces', 'has_face')
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FUNCTION = 'run'
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CATEGORY = 'sunxAI_facetools'
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def run(self, image, threshold, min_size, max_size, mask=None):
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faces = []
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masked = image
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if mask is not None:
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masked = image * tv.transforms.functional.resize(1-mask, image.shape[1:3])[..., None]
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masked = (masked * 255).type(torch.uint8)
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for i, img in enumerate(masked):
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unfiltered_faces = detect_faces(img, threshold)
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for face in unfiltered_faces:
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a, b, c, d = face.bbox
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h = abs(d-b)
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w = abs(c-a)
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if (h <= max_size or w <= max_size) and (min_size <= h or min_size <= w):
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face.image_idx = i
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face.img = image[i]
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faces.append(face)
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# 只返回面积最大的人脸，如果没有人脸则返回空列表
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if faces:
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largest_face = max(faces, key=lambda f: abs(f.bbox[2] - f.bbox[0]) * abs(f.bbox[3] - f.bbox[1]))
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faces = [largest_face]
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else:
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faces = []
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has_face = len(faces) > 0
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return (faces, has_face)
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class DetectFaceByIndex:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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'required': {
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'image': ('IMAGE',),
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'threshold': ('FLOAT', {'default': 0.5, 'min': 0.0, 'max': 1.0, 'step': 0.01, 'tooltip': '人脸检测置信度阈值，越高越严格'}),
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'min_size': ('INT', {'default': 64, 'max': 512, 'step': 8, 'tooltip': '最小人脸尺寸，过滤掉太小的检测结果'}),
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'max_size': ('INT', {'default': 512, 'min': 512, 'step': 8, 'tooltip': '最大人脸尺寸，过滤掉太大的检测结果'}),
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'face_index': ('INT', {'default': 0, 'min': 0, 'max': 10, 'step': 1, 'tooltip': '人脸索引：0=最左边第一个，1=第二个，以此类推'}),
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'gender_filter': ('INT', {'default': 0, 'min': 0, 'max': 2, 'step': 1, 'tooltip': '性别筛选：0=任意性别，1=只检测男性，2=只检测女性'}),
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},
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'optional': {
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'mask': ('MASK',),
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}
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}
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RETURN_TYPES = ('FACE', 'BOOLEAN')
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RETURN_NAMES = ('faces', 'has_face')
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FUNCTION = 'run'
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CATEGORY = 'sunxAI_facetools'
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def run(self, image, threshold, min_size, max_size, face_index, gender_filter, mask=None):
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faces = []
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masked = image
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if mask is not None:
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masked = image * tv.transforms.functional.resize(1-mask, image.shape[1:3])[..., None]
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masked = (masked * 255).type(torch.uint8)
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for i, img in enumerate(masked):
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# 只在需要性别筛选时才进行性别检测
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detect_gender = gender_filter != 0
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unfiltered_faces = detect_faces(img, threshold, detect_gender=detect_gender)
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for face in unfiltered_faces:
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a, b, c, d = face.bbox
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h = abs(d-b)
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w = abs(c-a)
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if (h <= max_size or w <= max_size) and (min_size <= h or min_size <= w):
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face.image_idx = i
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face.img = image[i]
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faces.append(face)
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# 按性别筛选人脸
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if gender_filter == 1: # 只选择男性
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faces = [face for face in faces if face.gender == "male"]
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elif gender_filter == 2: # 只选择女性
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faces = [face for face in faces if face.gender == "female"]
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# gender_filter == 0 时不进行性别筛选
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# 按从左到右的顺序排序人脸（根据bbox的x坐标）
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if faces:
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faces.sort(key=lambda f: f.bbox[0]) # 按x坐标排序
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# 根据face_index选择对应的人脸
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if face_index < len(faces):
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faces = [faces[face_index]]
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else:
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faces = [] # 下标超出范围，返回空列表
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else:
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faces = []
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has_face = len(faces) > 0
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return (faces, has_face)
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class CropFaces:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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'required': {
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'faces': ('FACE',),
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'crop_size': ('INT', {'default': 512, 'min': 512, 'max': 1024, 'step': 128}),
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'crop_factor': ('FLOAT', {'default': 1.5, 'min': 1.0, 'max': 3, 'step': 0.1}),
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'mask_type': (mask_types,)
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}
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}
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RETURN_TYPES = ('IMAGE', 'MASK', 'WARP')
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RETURN_NAMES = ('crops', 'masks', 'warps')
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FUNCTION = 'run'
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CATEGORY = 'sunxAI_facetools'
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def run(self, faces, crop_size, crop_factor, mask_type):
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if len(faces) == 0:
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empty_crop = torch.zeros((1,512,512,3))
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empty_mask = torch.zeros((1,512,512))
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empty_warp = np.array([
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[1,0,-512],
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[0,1,-512],
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], dtype=np.float32)
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return (empty_crop, empty_mask, [empty_warp])
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crops = []
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masks = []
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warps = []
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for face in faces:
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M, crop = face.crop(crop_size, crop_factor)
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mask = mask_crop(face, M, crop, mask_type)
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crops.append(np.array(crop[0]))
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masks.append(np.array(mask[0]))
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warps.append(M)
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crops = torch.from_numpy(np.array(crops)).type(torch.float32)
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masks = torch.from_numpy(np.array(masks)).type(torch.float32)
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return (crops, masks, warps)
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class WarpFaceBack:
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RETURN_TYPES = ('IMAGE',)
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FUNCTION = 'run'
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CATEGORY = 'sunxAI_facetools'
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@classmethod
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def INPUT_TYPES(cls):
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return {
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'required': {
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'images': ('IMAGE',),
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'face': ('FACE',),
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'crop': ('IMAGE',),
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'mask': ('MASK',),
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'warp': ('WARP',),
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},
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'optional': {
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'has_face': ('BOOLEAN',),
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}
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}
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def run(self, images, face, crop, mask, warp, has_face=True):
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# 如果has_face为False，直接返回原图像
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if not has_face:
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return (images,)
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# 处理单个人脸
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if len(face) == 0:
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return (images,)
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single_face = face[0]
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single_crop = crop[0]
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single_mask = mask[0]
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single_warp = warp[0]
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results = []
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for i, image in enumerate(images):
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if i != single_face.image_idx:
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result = image
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else:
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warped_mask = np.clip(cv2.warpAffine(single_mask.numpy(),
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cv2.invertAffineTransform(single_warp),
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image.shape[1::-1],
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flags=cv2.INTER_LANCZOS4), 0, 1)
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swapped = np.clip(cv2.warpAffine(single_crop.cpu().numpy(),
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cv2.invertAffineTransform(single_warp),
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image.shape[1::-1],
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flags=cv2.INTER_LANCZOS4), 0, 1)
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result = (swapped * warped_mask[..., None] +
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(1 - warped_mask[..., None]) * image.numpy())
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result = torch.from_numpy(result)
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results.append(result)
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results = torch.stack(results)
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return (results, )
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class VAEDecodeNew:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"samples": ("LATENT", {"tooltip": "The latent to be decoded."}),
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"vae": ("VAE", {"tooltip": "The VAE model used for decoding the latent."})
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},
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'optional': {
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'has_face': ('BOOLEAN',),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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OUTPUT_TOOLTIPS = ("The decoded image.",)
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FUNCTION = "decode"
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CATEGORY = "sunxAI_facetools"
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DESCRIPTION = "Decodes latent images back into pixel space images."
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def decode(self, vae, samples, has_face=True):
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# 如果has_face为False，返回空白画布，节省VAE解码时间
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if not has_face:
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# 创建512x512的黑色空白画布
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blank_canvas = torch.zeros((1, 512, 512, 3), dtype=torch.float32)
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return (blank_canvas,)
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images = vae.decode(samples["samples"])
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if len(images.shape) == 5: #Combine batches
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images = images.reshape(-1, images.shape[-3], images.shape[-2], images.shape[-1])
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return (images, )
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class VAEEncodeNew:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": { "pixels": ("IMAGE", ), "vae": ("VAE", )},
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'optional': {
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'has_face': ('BOOLEAN',),
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}
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}
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RETURN_TYPES = ("LATENT",)
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FUNCTION = "encode"
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CATEGORY = "sunxAI_facetools"
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def encode(self, vae, pixels, has_face=True):
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# 如果has_face为False，返回空白latent，节省VAE编码时间
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if not has_face:
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# 创建512x512对应的空白latent (512//8 = 64)
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blank_latent = torch.zeros([1, 4, 64, 64])
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return ({"samples": blank_latent},)
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t = vae.encode(pixels[:,:,:,:3])
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return ({"samples":t}, )
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class SelectFloatByBool:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"cond": ("BOOLEAN",),
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"true_value": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
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"false_value": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
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}
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}
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RETURN_TYPES = ("FLOAT",)
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RETURN_NAMES = ("value",)
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FUNCTION = "run"
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CATEGORY = "sunxAI_facetools"
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def run(self, cond, true_value, false_value):
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return (true_value if cond else false_value,)
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class ColorAdjust:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"image": ("IMAGE",),
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"contrast": ("FLOAT", {
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"default": 1.0,
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"min": 0,
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"max": 255,
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"step": 0.01,
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"round": 0.001,
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"display": "number"
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}),
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"brightness": ("FLOAT", {
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"default": 1.0,
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"min": -255,
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"max": 255,
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"step": 0.01,
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"round": 0.001, #The value represeting the precision to round to, will be set to the step value by default. Can be set to False to disable rounding.
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"display": "number"
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}),
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"saturation": ("FLOAT", {
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"default": 1.0,
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"min": 0,
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"max": 255,
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"step": 0.01,
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"round": 0.001,
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"display": "number"
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}),
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"hue": ("FLOAT", {
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"default": 0,
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"min": -0.5,
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"max": 0.5,
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"step": 0.001,
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"round": 0.001,
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"display": "number"
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}),
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"gamma": ("FLOAT", {
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"default": 1.0,
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"min": 0,
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"max": 255,
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"step": 0.01,
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"round": 0.001,
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"display": "number"
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}),
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},
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'optional': {
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'has_face': ('BOOLEAN',),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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#RETURN_NAMES = ("image_output_name",)
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FUNCTION = "main"
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#OUTPUT_NODE = False
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CATEGORY = "sunxAI_facetools"
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def main(self,
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image: Tensor,
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contrast: float = 1,
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brightness: float = 1,
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saturation: float = 1,
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hue: float = 0,
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gamma: float = 1,
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has_face: bool = True):
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if not has_face:
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return (image,)
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permutedImage = image.permute(0, 3, 1, 2)
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if (contrast != 1):
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permutedImage = functional.adjust_contrast(permutedImage, contrast)
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if (brightness != 1):
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permutedImage = functional.adjust_brightness(permutedImage, brightness)
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if (saturation != 1):
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permutedImage = functional.adjust_saturation(permutedImage, saturation)
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if (hue != 0):
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permutedImage = functional.adjust_hue(permutedImage, hue)
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if (gamma != 1):
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permutedImage = functional.adjust_gamma(permutedImage, gamma)
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result = permutedImage.permute(0, 2, 3, 1)
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return (result,)
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class SaveImageWebsocket:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"images": ("IMAGE",),
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"jpeg_quality": (
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"INT",
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{
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"default": 95,
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"min": 60,
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"max": 100,
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"step": 1,
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"tooltip": "JPEG压缩质量（60=低质量，100=高质量）"
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},
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),
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}
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}
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RETURN_TYPES = ()
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FUNCTION = "save_images"
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OUTPUT_NODE = True
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CATEGORY = "sunxAI_facetools"
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def save_images(self, images, jpeg_quality):
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pbar = comfy.utils.ProgressBar(images.shape[0])
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for idx, image in enumerate(images):
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try:
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i = 255. * image.cpu().numpy()
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img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
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# 直接在内存中进行JPEG压缩
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buffer = BytesIO()
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img.save(buffer, format="JPEG", quality=jpeg_quality)
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buffer.seek(0)
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jpg_img = Image.open(buffer).convert("RGB").copy()
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# 发送JPEG格式图像
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pbar.update_absolute(idx, images.shape[0], ("JPEG", jpg_img, None))
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except Exception as e:
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print(f"[SaveImageWebsocket] ❌ Skipped idx={idx} due to error: {e}")
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continue
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return {}
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@classmethod
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def IS_CHANGED(s, images, jpeg_quality):
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return time.time()
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