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)