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import math
import cv2
import numpy as np
from hivisionai.hycv.face_tools import face_detect_mtcnn
from hivisionai.hycv.utils import get_box_pro
from hivisionai.hycv.vision import resize_image_esp, IDphotos_cut, add_background, calTime, resize_image_by_min, \
rotate_bound_4channels
import onnxruntime
from src.error import IDError
from src.imageTransform import standard_photo_resize, hollowOutFix, get_modnet_matting, draw_picture_dots, detect_distance
from src.layoutCreate import generate_layout_photo
from src.move_image import move
testImages = []
class LinearFunction_TwoDots(object):
"""
通过两个坐标点构建线性函数
"""
def __init__(self, dot1, dot2):
self.d1 = dot1
self.d2 = dot2
self.mode = "normal"
if self.d2.x != self.d1.x:
self.k = (self.d2.y - self.d1.y) / max((self.d2.x - self.d1.x), 1)
self.b = self.d2.y - self.k * self.d2.x
else:
self.mode = "x=1"
def forward(self, input_, mode="x"):
if mode == "x":
if self.mode == "normal":
return self.k * input_ + self.b
else:
return 0
elif mode == "y":
if self.mode == "normal":
return (input_ - self.b) / self.k
else:
return self.d1.x
def forward_x(self, x):
if self.mode == "normal":
return self.k * x + self.b
else:
return 0
def forward_y(self, y):
if self.mode == "normal":
return (y - self.b) / self.k
else:
return self.d1.x
class Coordinate(object):
def __init__(self, x, y):
self.x = x
self.y = y
def __str__(self):
return "({}, {})".format(self.x, self.y)
@calTime
def face_number_and_angle_detection(input_image):
"""
本函数的功能是利用机器学习算法计算图像中人脸的数目与关键点,并通过关键点信息来计算人脸在平面上的旋转角度。
当前人脸数目!=1时,将raise一个错误信息并终止全部程序。
Args:
input_image: numpy.array(3 channels),用户上传的原图(经过了一些简单的resize)
Returns:
- dets: list,人脸定位信息(x1, y1, x2, y2)
- rotation: int,旋转角度,正数代表逆时针偏离,负数代表顺时针偏离
- landmark: list,人脸关键点信息
"""
# face++人脸检测
# input_image_bytes = CV2Bytes.cv2_byte(input_image, ".jpg")
# face_num, face_rectangle, landmarks, headpose = megvii_face_detector(input_image_bytes)
# print(face_rectangle)
faces, landmarks = face_detect_mtcnn(input_image)
face_num = len(faces)
# 排除不合人脸数目要求(必须是1)的照片
if face_num == 0 or face_num >= 2:
if face_num == 0:
status_id_ = "1101"
else:
status_id_ = "1102"
raise IDError(f"人脸检测出错!检测出了{face_num}张人脸", face_num=face_num, status_id=status_id_)
# 获得人脸定位坐标
face_rectangle = []
for iter, (x1, y1, x2, y2, _) in enumerate(faces):
x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
face_rectangle.append({'top': x1, 'left': y1, 'width': x2 - x1, 'height': y2 - y1})
# 获取人脸定位坐标与关键点信息
dets = face_rectangle[0]
# landmark = landmarks[0]
#
# # 人脸旋转角度计算
# rotation = eulerZ(landmark)
# return dets, rotation, landmark
return dets
@calTime
def image_matting(input_image, params):
"""
本函数的功能为全局人像抠图。
Args:
- input_image: numpy.array(3 channels),用户原图
Returns:
- origin_png_image: numpy.array(4 channels), 抠好的图
"""
print("抠图采用本地模型")
origin_png_image = get_modnet_matting(input_image, sess=params["modnet"]["human_sess"])
origin_png_image = hollowOutFix(origin_png_image) # 抠图洞洞修补
return origin_png_image
@calTime
def rotation_ajust(input_image, rotation, a, IS_DEBUG=False):
"""
本函数的功能是根据旋转角对原图进行无损旋转,并返回结果图与附带信息。
Args:
- input_image: numpy.array(3 channels), 用户上传的原图(经过了一些简单的resize、美颜)
- rotation: float, 人的五官偏离"端正"形态的旋转角
- a: numpy.array(1 channel), matting图的matte
- IS_DEBUG: DEBUG模式开关
Returns:
- result_jpg_image: numpy.array(3 channels), 原图旋转的结果图
- result_png_image: numpy.array(4 channels), matting图旋转的结果图
- L1: CLassObject, 根据旋转点连线所构造函数
- L2: ClassObject, 根据旋转点连线所构造函数
- dotL3: ClassObject, 一个特殊裁切点的坐标
- clockwise: int, 表示照片是顺时针偏离还是逆时针偏离
- drawed_dots_image: numpy.array(3 channels), 在result_jpg_image上标定了4个旋转点的结果图,用于DEBUG模式
"""
# Step1. 数据准备
rotation = -1 * rotation # rotation为正数->原图顺时针偏离,为负数->逆时针偏离
h, w = input_image.copy().shape[:2]
# Step2. 无损旋转
result_jpg_image, result_png_image, cos, sin = rotate_bound_4channels(input_image, a, rotation)
# Step3. 附带信息计算
nh, nw = result_jpg_image.shape[:2] # 旋转后的新的长宽
clockwise = -1 if rotation < 0 else 1 # clockwise代表时针,即1为顺时针,-1为逆时针
# 如果逆时针偏离:
if rotation < 0:
p1 = Coordinate(0, int(w * sin))
p2 = Coordinate(int(w * cos), 0)
p3 = Coordinate(nw, int(h * cos))
p4 = Coordinate(int(h * sin), nh)
L1 = LinearFunction_TwoDots(p1, p4)
L2 = LinearFunction_TwoDots(p4, p3)
dotL3 = Coordinate(int(0.25 * p2.x + 0.75 * p3.x), int(0.25 * p2.y + 0.75 * p3.y))
# 如果顺时针偏离:
else:
p1 = Coordinate(int(h * sin), 0)
p2 = Coordinate(nw, int(w * sin))
p3 = Coordinate(int(w * cos), nh)
p4 = Coordinate(0, int(h * cos))
L1 = LinearFunction_TwoDots(p4, p3)
L2 = LinearFunction_TwoDots(p3, p2)
dotL3 = Coordinate(int(0.75 * p4.x + 0.25 * p1.x), int(0.75 * p4.y + 0.25 * p1.y))
# Step4. 根据附带信息进行图像绘制(4个旋转点),便于DEBUG模式验证
drawed_dots_image = draw_picture_dots(result_jpg_image, [(p1.x, p1.y), (p2.x, p2.y), (p3.x, p3.y),
(p4.x, p4.y), (dotL3.x, dotL3.y)])
if IS_DEBUG:
testImages.append(["drawed_dots_image", drawed_dots_image])
return result_jpg_image, result_png_image, L1, L2, dotL3, clockwise, drawed_dots_image
@calTime
def face_number_detection_mtcnn(input_image):
"""
本函数的功能是对旋转矫正的结果图进行基于MTCNN模型的人脸检测。
Args:
- input_image: numpy.array(3 channels), 旋转矫正(rotation_adjust)的3通道结果图
Returns:
- faces: list, 人脸检测的结果,包含人脸位置信息
"""
# 如果图像的长或宽>1500px,则对图像进行1/2的resize再做MTCNN人脸检测,以加快处理速度
if max(input_image.shape[0], input_image.shape[1]) >= 1500:
input_image_resize = cv2.resize(input_image,
(input_image.shape[1] // 2, input_image.shape[0] // 2),
interpolation=cv2.INTER_AREA)
faces, _ = face_detect_mtcnn(input_image_resize, filter=True) # MTCNN人脸检测
# 如果缩放后图像的MTCNN人脸数目检测结果等于1->两次人脸检测结果没有偏差,则对定位数据x2
if len(faces) == 1:
for item, param in enumerate(faces[0]):
faces[0][item] = param * 2
# 如果两次人脸检测结果有偏差,则默认缩放后图像的MTCNN检测存在误差,则将原图输入再做一次MTCNN(保险措施)
else:
faces, _ = face_detect_mtcnn(input_image, filter=True)
# 如果图像的长或宽<1500px,则直接进行MTCNN检测
else:
faces, _ = face_detect_mtcnn(input_image, filter=True)
return faces
@calTime
def cutting_rect_pan(x1, y1, x2, y2, width, height, L1, L2, L3, clockwise, standard_size):
"""
本函数的功能是对旋转矫正结果图的裁剪框进行修正 ———— 解决"旋转三角形"现象。
Args:
- x1: int, 裁剪框左上角的横坐标
- y1: int, 裁剪框左上角的纵坐标
- x2: int, 裁剪框右下角的横坐标
- y2: int, 裁剪框右下角的纵坐标
- width: int, 待裁剪图的宽度
- height:int, 待裁剪图的高度
- L1: CLassObject, 根据旋转点连线所构造函数
- L2: CLassObject, 根据旋转点连线所构造函数
- L3: ClassObject, 一个特殊裁切点的坐标
- clockwise: int, 旋转时针状态
- standard_size: tuple, 标准照的尺寸
Returns:
- x1: int, 新的裁剪框左上角的横坐标
- y1: int, 新的裁剪框左上角的纵坐标
- x2: int, 新的裁剪框右下角的横坐标
- y2: int, 新的裁剪框右下角的纵坐标
- x_bias: int, 裁剪框横坐标方向上的计算偏置量
- y_bias: int, 裁剪框纵坐标方向上的计算偏置量
"""
# 用于计算的裁剪框坐标x1_cal,x2_cal,y1_cal,y2_cal(如果裁剪框超出了图像范围,则缩小直至在范围内)
x1_std = x1 if x1 > 0 else 0
x2_std = x2 if x2 < width else width
# y1_std = y1 if y1 > 0 else 0
y2_std = y2 if y2 < height else height
# 初始化x和y的计算偏置项x_bias和y_bias
x_bias = 0
y_bias = 0
# 如果顺时针偏转
if clockwise == 1:
if y2 > L1.forward_x(x1_std):
y_bias = int(-(y2_std - L1.forward_x(x1_std)))
if y2 > L2.forward_x(x2_std):
x_bias = int(-(x2_std - L2.forward_y(y2_std)))
x2 = x2_std + x_bias
if x1 < L3.x:
x1 = L3.x
# 如果逆时针偏转
else:
if y2 > L1.forward_x(x1_std):
x_bias = int(L1.forward_y(y2_std) - x1_std)
if y2 > L2.forward_x(x2_std):
y_bias = int(-(y2_std - L2.forward_x(x2_std)))
x1 = x1_std + x_bias
if x2 > L3.x:
x2 = L3.x
# 计算裁剪框的y的变化
y2 = int(y2_std + y_bias)
new_cut_width = x2 - x1
new_cut_height = int(new_cut_width / standard_size[1] * standard_size[0])
y1 = y2 - new_cut_height
return x1, y1, x2, y2, x_bias, y_bias
@calTime
def idphoto_cutting(faces, head_measure_ratio, standard_size, head_height_ratio, origin_png_image, origin_png_image_pre,
rotation_params, align=False, IS_DEBUG=False, top_distance_max=0.12, top_distance_min=0.10):
"""
本函数的功能为进行证件照的自适应裁剪,自适应依据Setting.json的控制参数,以及输入图像的自身情况。
Args:
- faces: list, 人脸位置信息
- head_measure_ratio: float, 人脸面积与全图面积的期望比值
- standard_size: tuple, 标准照尺寸, 如(413, 295)
- head_height_ratio: float, 人脸中心处在全图高度的比例期望值
- origin_png_image: numpy.array(4 channels), 经过一系列转换后的用户输入图
- origin_png_image_pre:numpy.array(4 channels),经过一系列转换(但没有做旋转矫正)的用户输入图
- rotation_params:旋转参数字典
- L1: classObject, 来自rotation_ajust的L1线性函数
- L2: classObject, 来自rotation_ajust的L2线性函数
- L3: classObject, 来自rotation_ajust的dotL3点
- clockwise: int, (顺/逆)时针偏差
- drawed_image: numpy.array, 红点标定4个旋转点的图像
- align: bool, 是否图像做过旋转矫正
- IS_DEBUG: DEBUG模式开关
- top_distance_max: float, 头距离顶部的最大比例
- top_distance_min: float, 头距离顶部的最小比例
Returns:
- result_image_hd: numpy.array(4 channels), 高清照
- result_image_standard: numpy.array(4 channels), 标准照
- clothing_params: json, 换装配置参数,便于后续换装功能的使用
"""
# Step0. 旋转参数准备
L1 = rotation_params["L1"]
L2 = rotation_params["L2"]
L3 = rotation_params["L3"]
clockwise = rotation_params["clockwise"]
drawed_image = rotation_params["drawed_image"]
# Step1. 准备人脸参数
face_rect = faces[0]
x, y = face_rect[0], face_rect[1]
w, h = face_rect[2] - x + 1, face_rect[3] - y + 1
height, width = origin_png_image.shape[:2]
width_height_ratio = standard_size[0] / standard_size[1] # 高宽比
# Step2. 计算高级参数
face_center = (x + w / 2, y + h / 2) # 面部中心坐标
face_measure = w * h # 面部面积
crop_measure = face_measure / head_measure_ratio # 裁剪框面积:为面部面积的5倍
resize_ratio = crop_measure / (standard_size[0] * standard_size[1]) # 裁剪框缩放率
resize_ratio_single = math.sqrt(resize_ratio) # 长和宽的缩放率(resize_ratio的开方)
crop_size = (int(standard_size[0] * resize_ratio_single),
int(standard_size[1] * resize_ratio_single)) # 裁剪框大小
# 裁剪框的定位信息
x1 = int(face_center[0] - crop_size[1] / 2)
y1 = int(face_center[1] - crop_size[0] * head_height_ratio)
y2 = y1 + crop_size[0]
x2 = x1 + crop_size[1]
# Step3. 对于旋转矫正图片的裁切处理
# if align:
# y_top_pre, _, _, _ = get_box_pro(origin_png_image.astype(np.uint8), model=2,
# correction_factor=0) # 获取matting结果图的顶距
# # 裁剪参数重新计算,目标是以最小的图像损失来消除"旋转三角形"
# x1, y1, x2, y2, x_bias, y_bias = cutting_rect_pan(x1, y1, x2, y2, width, height, L1, L2, L3, clockwise,
# standard_size)
# # 这里设定一个拒绝判定条件,如果裁剪框切进了人脸检测框的话,就不进行旋转
# if y1 > y_top_pre:
# y2 = y2 - (y1 - y_top_pre)
# y1 = y_top_pre
# # 如何遇到裁剪到人脸的情况,则转为不旋转裁切
# if x1 > x or x2 < (x + w) or y1 > y or y2 < (y + h):
# return idphoto_cutting(faces, head_measure_ratio, standard_size, head_height_ratio, origin_png_image_pre,
# origin_png_image_pre, rotation_params, align=False, IS_DEBUG=False)
#
# if y_bias != 0:
# origin_png_image = origin_png_image[:y2, :]
# if x_bias > 0: # 逆时针
# origin_png_image = origin_png_image[:, x1:]
# if drawed_image is not None and IS_DEBUG:
# drawed_x = x1
# x = x - x1
# x2 = x2 - x1
# x1 = 0
# else: # 顺时针
# origin_png_image = origin_png_image[:, :x2]
#
# if drawed_image is not None and IS_DEBUG:
# drawed_x = drawed_x if x_bias > 0 else 0
# drawed_image = draw_picture_dots(drawed_image, [(x1 + drawed_x, y1), (x1 + drawed_x, y2),
# (x2 + drawed_x, y1), (x2 + drawed_x, y2)],
# pen_color=(255, 0, 0))
# testImages.append(["drawed_image", drawed_image])
# Step4. 对照片的第一轮裁剪
cut_image = IDphotos_cut(x1, y1, x2, y2, origin_png_image)
cut_image = cv2.resize(cut_image, (crop_size[1], crop_size[0]))
y_top, y_bottom, x_left, x_right = get_box_pro(cut_image.astype(np.uint8), model=2,
correction_factor=0) # 得到cut_image中人像的上下左右距离信息
if IS_DEBUG:
testImages.append(["firstCut", cut_image])
# Step5. 判定cut_image中的人像是否处于合理的位置,若不合理,则处理数据以便之后调整位置
# 检测人像与裁剪框左边或右边是否存在空隙
if x_left > 0 or x_right > 0:
status_left_right = 1
cut_value_top = int(((x_left + x_right) * width_height_ratio) / 2) # 减去左右,为了保持比例,上下也要相应减少cut_value_top
else:
status_left_right = 0
cut_value_top = 0
"""
检测人头顶与照片的顶部是否在合适的距离内:
- status==0: 距离合适, 无需移动
- status=1: 距离过大, 人像应向上移动
- status=2: 距离过小, 人像应向下移动
"""
status_top, move_value = detect_distance(y_top - cut_value_top, crop_size[0], max=top_distance_max,
min=top_distance_min)
# Step6. 对照片的第二轮裁剪
if status_left_right == 0 and status_top == 0:
result_image = cut_image
else:
result_image = IDphotos_cut(x1 + x_left,
y1 + cut_value_top + status_top * move_value,
x2 - x_right,
y2 - cut_value_top + status_top * move_value,
origin_png_image)
if IS_DEBUG:
testImages.append(["result_image_pre", result_image])
# 换装参数准备
relative_x = x - (x1 + x_left)
relative_y = y - (y1 + cut_value_top + status_top * move_value)
# Step7. 当照片底部存在空隙时,下拉至底部
result_image, y_high = move(result_image.astype(np.uint8))
relative_y = relative_y + y_high # 更新换装参数
# cv2.imwrite("./temp_image.png", result_image)
# Step8. 标准照与高清照转换
result_image_standard = standard_photo_resize(result_image, standard_size)
result_image_hd, resize_ratio_max = resize_image_by_min(result_image, esp=max(600, standard_size[1]))
# Step9. 参数准备-为换装服务
clothing_params = {
"relative_x": relative_x * resize_ratio_max,
"relative_y": relative_y * resize_ratio_max,
"w": w * resize_ratio_max,
"h": h * resize_ratio_max
}
return result_image_hd, result_image_standard, clothing_params
@calTime
def debug_mode_process(testImages):
for item, (text, imageItem) in enumerate(testImages):
channel = imageItem.shape[2]
(height, width) = imageItem.shape[:2]
if channel == 4:
imageItem = add_background(imageItem, bgr=(255, 255, 255))
imageItem = np.uint8(imageItem)
if item == 0:
testHeight = height
result_image_test = imageItem
result_image_test = cv2.putText(result_image_test, text, (50, 50), cv2.FONT_HERSHEY_COMPLEX, 1.0,
(200, 100, 100), 3)
else:
imageItem = cv2.resize(imageItem, (int(width * testHeight / height), testHeight))
imageItem = cv2.putText(imageItem, text, (50, 50), cv2.FONT_HERSHEY_COMPLEX, 1.0, (200, 100, 100),
3)
result_image_test = cv2.hconcat([result_image_test, imageItem])
if item == len(testImages) - 1:
return result_image_test
@calTime("主函数")
def IDphotos_create(input_image,
mode="ID",
size=(413, 295),
head_measure_ratio=0.2,
head_height_ratio=0.45,
align=False,
beauty=True,
fd68=None,
human_sess=None,
IS_DEBUG=False,
top_distance_max=0.12,
top_distance_min=0.10):
"""
证件照制作主函数
Args:
input_image: 输入图像矩阵
size: (h, w)
head_measure_ratio: 头部占比?
head_height_ratio: 头部高度占比?
align: 是否进行人脸矫正(roll),默认为True(是)
fd68: 人脸68关键点检测类,详情参见hycv.FaceDetection68.faceDetection68
human_sess: 人像抠图模型类,由onnx载入(不与下面两个参数连用)
oss_image_name: 阿里云api需要的参数,实际上是上传到oss的路径
user: 阿里云api的accessKey配置对象
top_distance_max: float, 头距离顶部的最大比例
top_distance_min: float, 头距离顶部的最小比例
Returns:
result_image(高清版), result_image(普清版), api请求日志,
排版照参数(list),排版照是否旋转参数,照片尺寸(x, y)
在函数不出错的情况下,函数会因为一些原因主动抛出异常:
1. 无人脸(或者只有半张,dlib无法检测出来),抛出IDError异常,内部参数face_num为0
2. 人脸数量超过1,抛出IDError异常,内部参数face_num为2
3. 抠图api请求失败,抛出IDError异常,内部参数face_num为-1
"""
# Step0. 数据准备/图像预处理
matting_params = {"modnet": {"human_sess": human_sess}}
rotation_params = {"L1": None, "L2": None, "L3": None, "clockwise": None, "drawed_image": None}
input_image = resize_image_esp(input_image, 2000) # 将输入图片resize到最大边长为2000
# Step1. 人脸检测
# dets, rotation, landmark = face_number_and_angle_detection(input_image)
# dets = face_number_and_angle_detection(input_image)
# Step2. 美颜
# if beauty:
# input_image = makeBeautiful(input_image, landmark, 2, 2, 5, 4)
# Step3. 抠图
origin_png_image = image_matting(input_image, matting_params)
if mode == "只换底":
return origin_png_image, origin_png_image, None, None, None, None, None, None, 1
origin_png_image_pre = origin_png_image.copy() # 备份一下现在抠图结果图,之后在iphoto_cutting函数有用
# Step4. 旋转矫正
# 如果旋转角不大于2, 则不做旋转
# if abs(rotation) <= 2:
# align = False
# # 否则,进行旋转矫正
# if align:
# input_image_candidate, origin_png_image_candidate, L1, L2, L3, clockwise, drawed_image \
# = rotation_ajust(input_image, rotation, cv2.split(origin_png_image)[-1], IS_DEBUG=IS_DEBUG) # 图像旋转
#
# origin_png_image_pre = origin_png_image.copy()
# input_image = input_image_candidate.copy()
# origin_png_image = origin_png_image_candidate.copy()
#
# rotation_params["L1"] = L1
# rotation_params["L2"] = L2
# rotation_params["L3"] = L3
# rotation_params["clockwise"] = clockwise
# rotation_params["drawed_image"] = drawed_image
# Step5. MTCNN人脸检测
faces = face_number_detection_mtcnn(input_image)
# Step6. 证件照自适应裁剪
face_num = len(faces)
# 报错MTCNN检测结果不等于1的图片
if face_num != 1:
return None, None, None, None, None, None, None, None, 0
# 符合条件的进入下一环
else:
result_image_hd, result_image_standard, clothing_params = \
idphoto_cutting(faces, head_measure_ratio, size, head_height_ratio, origin_png_image,
origin_png_image_pre, rotation_params, align=align, IS_DEBUG=IS_DEBUG,
top_distance_max=top_distance_max, top_distance_min=top_distance_min)
# Step7. 排版照参数获取
typography_arr, typography_rotate = generate_layout_photo(input_height=size[0], input_width=size[1])
return result_image_hd, result_image_standard, typography_arr, typography_rotate, \
clothing_params["relative_x"], clothing_params["relative_y"], clothing_params["w"], clothing_params["h"], 1
if __name__ == "__main__":
HY_HUMAN_MATTING_WEIGHTS_PATH = "./hivision_modnet.onnx"
sess = onnxruntime.InferenceSession(HY_HUMAN_MATTING_WEIGHTS_PATH)
input_image = cv2.imread("test.jpg")
result_image_hd, result_image_standard, typography_arr, typography_rotate, \
_, _, _, _, _ = IDphotos_create(input_image,
size=(413, 295),
head_measure_ratio=0.2,
head_height_ratio=0.45,
align=True,
beauty=True,
fd68=None,
human_sess=sess,
oss_image_name="test_tmping.jpg",
user=None,
IS_DEBUG=False,
top_distance_max=0.12,
top_distance_min=0.10)
cv2.imwrite("result_image_hd.png", result_image_hd)
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