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__init__.py
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import os,sys
now_dir = os.path.dirname(os.path.abspath(__file__))
sys.path.append(now_dir)
import cv2
import shutil
import numpy as np
import torch
import torchvision
from hivision.error import FaceError
from hivision.utils import hex_to_rgb, resize_image_to_kb, add_background,add_watermark,save_image_dpi_to_bytes
from hivision import IDCreator
from hivision.creator.layout_calculator import (
generate_layout_photo,
generate_layout_image,
)
from hivision.demo.utils import csv_to_size_list,csv_to_color_list
from hivision.creator.choose_handler import choose_handler, HUMAN_MATTING_MODELS
size_list_dict_CN = csv_to_size_list(os.path.join(now_dir, "hivision/demo/assets/size_list_CN.csv"))
size_list_CN = list(size_list_dict_CN.keys())
size_list_dict_EN = csv_to_size_list(os.path.join(now_dir, "hivision/demo/assets/size_list_EN.csv"))
size_list_EN = list(size_list_dict_EN.keys())
color_list_dict_CN = csv_to_color_list(os.path.join(now_dir, "hivision/demo/assets/color_list_CN.csv"))
color_list_CN = list(color_list_dict_CN.keys())
color_list_dict_EN = csv_to_color_list(os.path.join(now_dir, "hivision/demo/assets/color_list_EN.csv"))
color_list_EN = list(color_list_dict_EN.keys())
HUMAN_MATTING_MODELS_EXIST = [
os.path.splitext(file)[0]
for file in os.listdir(os.path.join(now_dir, "hivision/creator/weights"))
if file.endswith(".onnx") or file.endswith(".mnn")
]
# 在HUMAN_MATTING_MODELS中的模型才会被加载到Gradio中显示
HUMAN_MATTING_MODELS = [
model for model in HUMAN_MATTING_MODELS if model in HUMAN_MATTING_MODELS_EXIST
]
FACE_DETECT_MODELS = ["mtcnn"]
FACE_DETECT_MODELS_EXPAND = (
["retinaface-resnet50"]
if os.path.exists(
os.path.join(
now_dir, "hivision/creator/retinaface/weights/retinaface-resnet50.onnx"
)
)
else []
)
FACE_DETECT_MODELS += FACE_DETECT_MODELS_EXPAND
class ENHivisionParamsNode:
@classmethod
def INPUT_TYPES(s):
return {
"required":{
"size":(size_list_EN,),
"bgcolor":(color_list_EN,),
"render":(["pure_color", "updown_gradient", "center_gradient"],),
"kb":("INT",{
"default": 300,
}),
"dpi":("INT",{
"default": 300,
}),
}
}
RETURN_TYPES = ("PARAMS",)
RETURN_NAMES = ("normal_params",)
FUNCTION = "get_params"
#OUTPUT_NODE = False
CATEGORY = "AIFSH_HivisionIDPhotos"
def get_params(self,size,bgcolor,render,kb,dpi):
parmas = {
"size":size_list_dict_EN[size],
"bgcolor": color_list_dict_EN[bgcolor],
"render":render,
"kb":kb,
"dpi":dpi
}
return (parmas,)
class ZHHivisionParamsNode:
@classmethod
def INPUT_TYPES(s):
return {
"required":{
"size":(size_list_CN,),
"bgcolor":(color_list_CN,),
"render":(["纯色", "上下渐变", "中心渐变"],),
"kb":("INT",{
"default": 300,
}),
"dpi":("INT",{
"default": 300,
})
}
}
RETURN_TYPES = ("PARAMS",)
RETURN_NAMES = ("normal_params",)
FUNCTION = "get_params"
#OUTPUT_NODE = False
CATEGORY = "AIFSH_HivisionIDPhotos"
def get_params(self,size,bgcolor,render,kb,dpi):
if render == "纯色":
render = "pure_color"
elif render == "上下渐变":
render = "updown_gradient"
else:
render = "center_gradient"
parmas = {
"size":size_list_dict_CN[size],
"bgcolor": color_list_dict_CN[bgcolor],
"render":render,
"kb":kb,
"dpi":dpi
}
return (parmas,)
class AddWaterMarkNode:
@classmethod
def INPUT_TYPES(s):
return {
"required":{
"input_img":("IMAGE",),
"text":("STRING",{
"default": "AIFSH",
"multiline": True,
}),
"text_color":("STRING",{
"default": "#FFFFFF"
}),
"text_size":("INT",{
"default": 20,
"min": 10,
"max": 100,
"step": 1,
"display":"slider"
}),
"text_opacity":("FLOAT",{
"min":0,
"max":1,
"default":0.15,
"step":0.01,
"round":0.001,
"display":"slider"
}),
"text_angle":("INT",{
"default": 30,
"min": 0,
"max": 360,
"step": 1,
"display":"slider"
}),
"text_space":("INT",{
"default": 25,
"min": 10,
"max": 200,
"step": 1,
"display":"slider"
}),
}
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "gen_img"
#OUTPUT_NODE = False
CATEGORY = "AIFSH_HivisionIDPhotos"
def gen_img(self,input_img,text,text_color,text_size,
text_opacity,text_angle,text_space):
img_np = input_img.numpy()[0] * 255
img_np = img_np.astype(np.uint8)
input_image_standard = cv2.cvtColor(img_np,cv2.COLOR_BGR2RGB)
result_image = add_watermark(image=input_image_standard,
text=text,size=text_size,opacity=text_opacity,
angle=text_angle,space=text_space,color=text_color)
standard_cv2 = cv2.cvtColor(result_image,cv2.COLOR_BGR2RGB)
# hd_cv2 = cv2.cvtColor(result_image,cv2.COLOR_BGR2RGBA)
standard_img = torchvision.transforms.ToTensor()(standard_cv2)
standard_img = standard_img.permute(1,2,0).unsqueeze(0)
# hd_img = torchvision.transforms.ToTensor()(hd_cv2).permute(1,2,0).unsqueeze(0)
return (standard_img,)
class AddBackgroundNode:
@classmethod
def INPUT_TYPES(s):
return {
"required":{
"input_img":("IMAGE",),
"normal_params":("PARAMS",),
}
}
RETURN_TYPES = ("IMAGE",)
# RETURN_NAMES = ("3ch_standard_img","4ch_hd_img",)
FUNCTION = "gen_img"
#OUTPUT_NODE = False
CATEGORY = "AIFSH_HivisionIDPhotos"
def gen_img(self,input_img,normal_params):
img_np = input_img.numpy()[0] * 255
img_np = img_np.astype(np.uint8)
input_image = cv2.cvtColor(img_np,cv2.COLOR_BGR2RGBA)
color = normal_params["bgcolor"]
render = normal_params["render"]
# print(color)
color = hex_to_rgb(color)
# print(color)
# 将元祖的 0 和 2 号数字交换
color = (color[2], color[1], color[0])
result_image = add_background(
input_image, bgr=color, mode=render
)
result_image = result_image.astype(np.uint8)
standard_cv2 = cv2.cvtColor(result_image,cv2.COLOR_BGR2RGB)
standard_img = torchvision.transforms.ToTensor()(standard_cv2).permute(1,2,0).unsqueeze(0)
# hd_cv2 = cv2.cvtColor(result_image,cv2.COLOR_BGR2RGBA)
# hd_img = torchvision.transforms.ToTensor()(hd_cv2).permute(1,2,0).unsqueeze(0)
#print(result_image.shape)
return (standard_img,)
class HivisionLayOutNode:
@classmethod
def INPUT_TYPES(s):
return {
"required":{
"input_img":("IMAGE",),
"normal_params":("PARAMS",),
}
}
RETURN_TYPES = ("IMAGE",)
# RETURN_NAMES = ("3ch_standard_img","4ch_hd_img",)
FUNCTION = "gen_img"
#OUTPUT_NODE = False
CATEGORY = "AIFSH_HivisionIDPhotos"
def gen_img(self,input_img,normal_params):
img_np = input_img.numpy()[0] * 255
img_np = img_np.astype(np.uint8)
input_image = cv2.cvtColor(img_np,cv2.COLOR_BGR2RGB)
size = normal_params["size"]
typography_arr, typography_rotate = generate_layout_photo(
input_height=size[0], input_width=size[1]
)
result_layout_image = generate_layout_image(
input_image,
typography_arr,
typography_rotate,
height=size[0],
width=size[1],
)
# result_layout_image = cv2.cvtColor(result_layout_image, cv2.COLOR_RGB2BGR)
standard_cv2 = cv2.cvtColor(result_layout_image,cv2.COLOR_BGR2RGB)
standard_img = torchvision.transforms.ToTensor()(standard_cv2).permute(1,2,0).unsqueeze(0)
# hd_cv2 = cv2.cvtColor(result_layout_image,cv2.COLOR_BGR2RGBA)
# hd_img = torchvision.transforms.ToTensor()(hd_cv2).permute(1,2,0).unsqueeze(0)
return (standard_img,)
class LaterProcessNode:
@classmethod
def INPUT_TYPES(s):
return {
"required":{
"input_img":("IMAGE",),
"normal_params":("PARAMS",),
}
}
RETURN_TYPES = ("IMAGE",)
# RETURN_NAMES = ("standard_img","hd_img",)
FUNCTION = "gen_img"
#OUTPUT_NODE = False
CATEGORY = "AIFSH_HivisionIDPhotos"
def gen_img(self,input_img,normal_params):
img_np = input_img.numpy()[0] * 255
img_np = img_np.astype(np.uint8)
input_image = cv2.cvtColor(img_np,cv2.COLOR_BGR2RGB)
tmp_img_path = "tmp.png"
resize_image_to_kb(
input_image, tmp_img_path, normal_params["kb"]
)
result_layout_image = cv2.imread(tmp_img_path)
save_image_dpi_to_bytes(result_layout_image,tmp_img_path,normal_params['dpi'])
result_layout_image = cv2.imread(tmp_img_path)
os.remove(tmp_img_path)
# result_layout_image = cv2.cvtColor(result_layout_image, cv2.COLOR_RGB2BGR)
standard_cv2 = cv2.cvtColor(result_layout_image,cv2.COLOR_BGR2RGB)
standard_img = torchvision.transforms.ToTensor()(standard_cv2).permute(1,2,0).unsqueeze(0)
# hd_cv2 = cv2.cvtColor(result_layout_image,cv2.COLOR_BGR2RGBA)
# hd_img = torchvision.transforms.ToTensor()(hd_cv2).permute(1,2,0).unsqueeze(0)
return (standard_img,)
class HivisionNode:
@classmethod
def INPUT_TYPES(s):
return {
"required":{
"input_img":("IMAGE",),
"normal_params":("PARAMS",),
"face_alignment":("BOOLEAN",{
"default": True
}),
"change_bg_only":("BOOLEAN",{
"default": False
}),
"crop_only":("BOOLEAN",{
"default": False
}),
"matting_model":(HUMAN_MATTING_MODELS,),
"face_detect_model":(FACE_DETECT_MODELS,),
"head_measure_ratio":("FLOAT",{
"default": 0.2,
"min":0.1,
"max":0.5,
"step":0.01,
"round": 0.001,
"display":"slider"
}),
"top_distance":("FLOAT",{
"default": 0.12,
"min":0.02,
"max":0.5,
"step":0.01,
"round": 0.001,
"display":"slider"
}),
"whitening_strength":("INT",{
"default": 2,
"min":0,
"max":15,
"step":1,
"display":"slider"
}),
"brightness_strength":("INT",{
"default": 0,
"min":-5,
"max":25,
"step":1,
"display":"slider"
}),
"contrast_strength":("INT",{
"default": 0,
"min":-10,
"max":50,
"step":1,
"display":"slider"
}),
"saturation_strength":("INT",{
"default": 0,
"min":-10,
"max":50,
"step":1,
"display":"slider"
}),
"sharpen_strength":("INT",{
"default": 0,
"min":0,
"max":5,
"step":1,
"display":"slider"
}),
}
}
RETURN_TYPES = ("IMAGE","IMAGE",)
RETURN_NAMES = ("standard_img","hd_img",)
FUNCTION = "gen_img"
#OUTPUT_NODE = False
CATEGORY = "AIFSH_HivisionIDPhotos"
def gen_img(self,input_img,normal_params,face_alignment,change_bg_only,crop_only,matting_model,
face_detect_model,head_measure_ratio,top_distance,whitening_strength,
brightness_strength,contrast_strength,saturation_strength,sharpen_strength):
creator = IDCreator()
# ------------------- 人像抠图模型选择 -------------------
choose_handler(creator,matting_model_option=matting_model,
face_detect_option=face_detect_model)
img_np = input_img.numpy()[0] * 255
img_np = img_np.astype(np.uint8)
input_image = cv2.cvtColor(img_np,cv2.COLOR_BGR2RGB)
size = normal_params["size"]
try:
result = creator(input_image, size=size,
head_measure_ratio=head_measure_ratio,
head_top_range=(top_distance, top_distance-0.02),
change_bg_only=change_bg_only,
crop_only=crop_only,
face_alignment=face_alignment,
whitening_strength=whitening_strength,
brightness_strength=brightness_strength,
contrast_strength = contrast_strength,
sharpen_strength=sharpen_strength,
saturation_strength=saturation_strength,)
except FaceError:
print("人脸数量不等于 1，请上传单张人脸的图像。")
else:
standard_cv2 = cv2.cvtColor(result.standard,cv2.COLOR_BGRA2RGBA)
hd_cv2 = cv2.cvtColor(result.hd,cv2.COLOR_BGRA2RGBA)
standard_img = torchvision.transforms.ToTensor()(standard_cv2)
standard_img = standard_img.permute(1,2,0).unsqueeze(0)
hd_img = torchvision.transforms.ToTensor()(hd_cv2).permute(1,2,0).unsqueeze(0)
# out_put = torch.cat((standard_img,hd_img))
# print(hd_img.shape)
return (standard_img, hd_img)
NODE_CLASS_MAPPINGS = {
"LaterProcessNode":LaterProcessNode,
"HivisionNode": HivisionNode,
"ZHHivisionParamsNode":ZHHivisionParamsNode,
"ENHivisionParamsNode":ENHivisionParamsNode,
"AddWaterMarkNode":AddWaterMarkNode,
"AddBackgroundNode":AddBackgroundNode,
"HivisionLayOutNode":HivisionLayOutNode,
}