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#!/usr/bin/env python
# -*- coding: utf-8 -*-
r"""
@DATE: 2024/9/5 16:45
@File: __init__.py
@IDE: pycharm
@Description:
创建证件照
"""
import numpy as np
from typing import Tuple
import hivision.creator.utils as U
from .context import Context, ContextHandler, Params, Result
from .human_matting import extract_human
from .face_detector import detect_face_mtcnn
from hivision.plugin.beauty.whitening import make_whitening
from .photo_adjuster import adjust_photo
class IDCreator:
"""
证件照创建类，包含完整的证件照流程
"""
def __init__(self):
# 回调时机
self.before_all: ContextHandler = None
"""
在所有处理之前，此时图像已经被 resize 到最大边长为 2000
"""
self.after_matting: ContextHandler = None
"""
在抠图之后，ctx.matting_image 被赋值
"""
self.after_detect: ContextHandler = None
"""
在人脸检测之后，ctx.face 被赋值，如果为仅换底，则不会执行此回调
"""
self.after_all: ContextHandler = None
"""
在所有处理之后，此时 ctx.result 被赋值
"""
# 处理者
self.matting_handler: ContextHandler = extract_human
self.detection_handler: ContextHandler = detect_face_mtcnn
# 上下文
self.ctx = None
def __call__(
self,
image: np.ndarray,
size: Tuple[int, int] = (413, 295),
change_bg_only: bool = False,
crop_only: bool = False,
head_measure_ratio: float = 0.2,
head_height_ratio: float = 0.45,
head_top_range: float = (0.12, 0.1),
face: Tuple[int, int, int, int] = None,
whitening_strength: int = 0,
) -> Result:
"""
证件照处理函数
:param image: 输入图像
:param change_bg_only: 是否只需要抠图
:param crop_only: 是否只需要裁剪
:param size: 输出的图像大小（h,w)
:param head_measure_ratio: 人脸面积与全图面积的期望比值
:param head_height_ratio: 人脸中心处在全图高度的比例期望值
:param head_top_range: 头距离顶部的比例（max,min)
:param face: 人脸坐标
:param whitening_strength: 美白强度
:return: 返回处理后的证件照和一系列参数
"""
# 0.初始化上下文
params = Params(
size=size,
change_bg_only=change_bg_only,
head_measure_ratio=head_measure_ratio,
head_height_ratio=head_height_ratio,
head_top_range=head_top_range,
crop_only=crop_only,
face=face,
whitening_strength=whitening_strength,
)
self.ctx = Context(params)
ctx = self.ctx
ctx.processing_image = image
ctx.processing_image = U.resize_image_esp(
ctx.processing_image, 2000
) # 将输入图片 resize 到最大边长为 2000
ctx.origin_image = ctx.processing_image.copy()
self.before_all and self.before_all(ctx)
# 美白
if ctx.params.whitening_strength > 0:
ctx.processing_image = make_whitening(
ctx.processing_image, ctx.params.whitening_strength
)
# 1. 人像抠图
if not ctx.params.crop_only:
# 调用抠图工作流
self.matting_handler(ctx)
self.after_matting and self.after_matting(ctx)
else:
ctx.matting_image = ctx.processing_image
# 如果仅换底，则直接返回抠图结果
if ctx.params.change_bg_only:
ctx.result = Result(
standard=ctx.matting_image,
hd=ctx.matting_image,
matting=ctx.matting_image,
clothing_params=None,
typography_params=None,
face=None,
)
self.after_all and self.after_all(ctx)
return ctx.result
# 2. 人脸检测
self.detection_handler(ctx)
self.after_detect and self.after_detect(ctx)
# 3. 图像调整
result_image_hd, result_image_standard, clothing_params, typography_params = (
adjust_photo(ctx)
)
# 4. 返回结果
ctx.result = Result(
standard=result_image_standard,
hd=result_image_hd,
matting=ctx.matting_image,
clothing_params=clothing_params,
typography_params=typography_params,
face=ctx.face,
)
self.after_all and self.after_all(ctx)
return ctx.result
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