import cv2 import math from ..utils import get_box_pro from ..face_tools import face_detect_mtcnn from ..vision import IDphotos_cut, detect_distance, resize_image_esp, draw_picture_dots from ..matting_tools import get_modnet_matting from .move_image import move from src.hivisionai.hyTrain.APIs import aliyun_face_detect_api import numpy as np import json def get_max(height, width, d1, d2, d3, d4, rotation_flag): if rotation_flag: height1 = height height2 = height - int(d1.y) # d2 height3 = int(d4.y) # d3 height4 = int(d4.y) - int(d1.x) width1 = width width2 = width - int(d3.x) width3 = int(d2.x) width4 = int(d2.x) - int(d3.x) else: height1 = height height2 = height - int(d2.y) height3 = int(d3.y) height4 = int(d3.y) - int(d2.y) width1 = width width2 = width - int(d1.x) width3 = int(d4.x) width4 = int(d4.x) - int(d1.x) height_list = [height1, height2, height3, height4] width_list = [width1, width2, width3, width4] background_height = max(height_list) status_height = height_list.index(background_height) background_width = max(width_list) status_width = width_list.index(background_width) height_change = 0 width_change = 0 height_change2 = 0 width_change2 = 0 if status_height == 1 or status_height == 3: if rotation_flag: height_change = abs(d1.y) height_change2 = d1.y else: height_change = abs(d2.y) height_change2 = d2.y if status_width == 1 or status_width == 3: if rotation_flag: width_change = abs(d3.x) width_change2 = d3.x else: width_change = abs(d1.x) width_change2 = d1.x return background_height, status_height, background_width, status_width, height_change, width_change,\ height_change2, width_change2 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.k = (self.d2.y - self.d1.y) / (self.d2.x - self.d1.x) self.b = self.d2.y - self.k * self.d2.x def forward(self, input, mode="x"): if mode == "x": return self.k * input + self.b elif mode == "y": return (input - self.b) / self.k def forward_x(self, x): return self.k * x + self.b def forward_y(self, y): return (y - self.b) / self.k class Coordinate(object): def __init__(self, x, y): self.x = x self.y = y def __str__(self): return "({}, {})".format(self.x, self.y) def IDphotos_create(input_image, size=(413, 295), head_measure_ratio=0.2, head_height_ratio=0.45, checkpoint_path="checkpoint/ModNet1.0.onnx", align=True): """ input_path: 输入图像路径 output_path: 输出图像路径 size: 裁剪尺寸,格式应该如(413,295),竖直距离在前,水平距离在后 head_measure_ratio: 人头面积占照片面积的head_ratio head_height_ratio: 人头中心处于照片从上到下的head_height align: 是否进行人脸矫正 """ input_image = resize_image_esp(input_image, 2000) # 将输入图片压缩到最大边长为2000 # cv2.imwrite("./temp_input_image.jpg", input_image) origin_png_image = get_modnet_matting(input_image, checkpoint_path) # cv2.imwrite("./test_image/origin_png_image.png", origin_png_image) _, _, _, a = cv2.split(origin_png_image) width_length_ratio = size[0]/size[1] # 长宽比 rotation = aliyun_face_detect_api("./temp_input_image.jpg") # 如果旋转角过小，则不进行矫正 if abs(rotation) < 0.025: align=False if align: print("开始align") if rotation > 0: rotation_flag = 0 # 逆时针旋转 else: rotation_flag = 1 # 顺时针旋转 width, height, channels = input_image.shape p_list = [(0, 0), (0, height), (width, 0), (width, height)] rotate_list = [] rotate = cv2.getRotationMatrix2D((height * 0.5, width * 0.5), rotation, 0.75) for p in p_list: p_m = np.array([[p[1]], [p[0]], [1]]) rotate_list.append(np.dot(rotate[:2], p_m)) # print("旋转角的四个顶点", rotate_list) input_image = cv2.warpAffine(input_image, rotate, (height, width), flags=cv2.INTER_AREA) new_a = cv2.warpAffine(a, rotate, (height, width), flags=cv2.INTER_AREA) # cv2.imwrite("./test_image/rotation.jpg", input_image) # ===================== 开始人脸检测 ===================== # faces, _ = face_detect_mtcnn(input_image, filter=True) face_num = len(faces) print("检测到的人脸数目为:", len(faces)) # ===================== 人脸检测结束 ===================== # if face_num == 1: face_rect = faces[0] x, y = face_rect[0], face_rect[1] w, h = face_rect[2] - x + 1, face_rect[3] - y + 1 elif face_num == 0: print("无人脸，返回0!!!") return 0 else: print("太多人脸，返回2!!!") return 2 d1, d2, d3, d4 = rotate_list[0], rotate_list[1], rotate_list[2], rotate_list[3] d1 = Coordinate(int(d1[0]), int(d1[1])) d2 = Coordinate(int(d2[0]), int(d2[1])) d3 = Coordinate(int(d3[0]), int(d3[1])) d4 = Coordinate(int(d4[0]), int(d4[1])) print("d1:", d1) print("d2:", d2) print("d3:", d3) print("d4:", d4) background_height, status_height, background_width, status_width,\ height_change, width_change, height_change2, width_change2 = get_max(width, height, d1, d2, d3, d4, rotation_flag) print("background_height:", background_height) print("background_width:", background_width) print("status_height:", status_height) print("status_width:", status_width) print("height_change:", height_change) print("width_change:", width_change) background = np.zeros([background_height, background_width, 3]) background_a = np.zeros([background_height, background_width]) background[height_change:height_change+width, width_change:width_change+height] = input_image background_a[height_change:height_change+width, width_change:width_change+height] = new_a d1 = Coordinate(int(d1.x)-width_change2, int(d1.y)-height_change2) d2 = Coordinate(int(d2.x)-width_change2, int(d2.y)-height_change2) d3 = Coordinate(int(d3.x)-width_change2, int(d3.y)-height_change2) d4 = Coordinate(int(d4.x)-width_change2, int(d4.y)-height_change2) print("d1:", d1) print("d2:", d2) print("d3:", d3) print("d4:", d4) if rotation_flag: f13 = LinearFunction_TwoDots(d1, d3) d5 = Coordinate(max(0, d3.x), f13.forward_x(max(0, d3.x))) print("d5:", d5) f42 = LinearFunction_TwoDots(d4, d2) d7 = Coordinate(f42.forward_y(d5.y), d5.y) print("d7", d7) background_draw = draw_picture_dots(background, dots=[(d1.x, d1.y), (d2.x, d2.y), (d3.x, d3.y), (d4.x, d4.y), (d5.x, d5.y), (d7.x, d7.y)]) # cv2.imwrite("./test_image/rotation_background.jpg", background_draw) if xd7.x: print("return 6") return 6 background_output = background[:int(d5.y), int(d5.x):int(d7.x)] background_a_output = background_a[:int(d5.y), int(d5.x):int(d7.x)] # cv2.imwrite("./test_image/rotation_background_cut.jpg", background_output) else: f34 = LinearFunction_TwoDots(d3, d4) d5 = Coordinate(min(width_change+height, d4.x), f34.forward_x(min(width_change+height, d4.x))) print("d5:", d5) f13 = LinearFunction_TwoDots(d1, d3) d7 = Coordinate(f13.forward_y(d5.y), d5.y) print("d7", d7) if xd5.x: print("return 6") return 6 background_draw = draw_picture_dots(background, dots=[(d1.x, d1.y), (d2.x, d2.y), (d3.x, d3.y), (d4.x, d4.y), (d5.x, d5.y), (d7.x, d7.y)]) # cv2.imwrite("./test_image/rotation_background.jpg", background_draw) background_output = background[:int(d5.y), int(d7.x):int(d5.x)] background_a_output = background_a[:int(d5.y), int(d7.x):int(d5.x)] # cv2.imwrite("./test_image/rotation_background_cut.jpg", background_output) input_image = np.uint8(background_output) b, g, r = cv2.split(input_image) origin_png_image = cv2.merge((b, g, r, np.uint8(background_a_output))) # ===================== 开始人脸检测 ===================== # width, length = input_image.shape[0], input_image.shape[1] faces, _ = face_detect_mtcnn(input_image, filter=True) face_num = len(faces) print("检测到的人脸数目为:", len(faces)) # ===================== 人脸检测结束 ===================== # if face_num == 1: face_rect = faces[0] x, y = face_rect[0], face_rect[1] w, h = face_rect[2] - x + 1, face_rect[3] - y + 1 # x,y,w,h代表人脸框的左上角坐标和宽高 # 检测头顶下方空隙,如果头顶下方空隙过小,则拒绝 if y+h >= 0.85*width: # print("face bottom too short! y+h={} width={}".format(y+h, width)) print("在人脸下方的空间太少，返回值3!!!") return 3 # 第一次裁剪 # 确定裁剪的基本参数 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/(size[0]*size[1]) # 裁剪框缩放率(以输入尺寸为标准) resize_ratio_single = math.sqrt(resize_ratio) crop_size = (int(size[0]*resize_ratio_single), int(size[1]*resize_ratio_single)) # 裁剪框大小 print("crop_size:", crop_size) # 裁剪规则：x1和y1为裁剪的起始坐标,x2和y2为裁剪的最终坐标 # y的确定由人脸中心在照片的45%位置决定 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] # 对原图进行抠图,得到透明图img print("开始进行抠图") # origin_png_image => 对原图的抠图结果 # cut_image => 第一次裁剪后的图片 # result_image => 第二次裁剪后的图片/输出图片 # origin_png_image = get_human_matting(input_image, get_file_dir(checkpoint_path)) cut_image = IDphotos_cut(x1, y1, x2, y2, origin_png_image) # cv2.imwrite("./temp.png", cut_image) # 对裁剪得到的图片temp_path,我们将image=temp_path resize为裁剪框大小,这样方便进行后续计算 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, model=2) # 得到透明图中人像的上下左右距离信息 print("y_top:{}, y_bottom:{}, x_left:{}, x_right:{}".format(y_top, y_bottom, x_left, x_right)) # 判断左右是否有间隙 if x_left > 0 or x_right > 0: # 左右有空隙, 我们需要减掉它 print("左右有空隙!") status_left_right = 1 cut_value_top = int(((x_left + x_right) * width_length_ratio) / 2) # 减去左右,为了保持比例,上下也要相应减少cut_value_top print("cut_value_top:", cut_value_top) else: # 左右没有空隙, 则不管 status_left_right = 0 cut_value_top = 0 print("cut_value_top:", cut_value_top) # 检测人头顶与照片的顶部是否在合适的距离内 print("y_top:", y_top) status_top, move_value = detect_distance(y_top-int((x_left+x_right)*width_length_ratio/2), crop_size[0]) # 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 # move_value => 上下移动的距离 print("status_top:", status_top) print("move_value:", move_value) # 开始第二次裁剪 if status_top == 0: # 如果上下距离合适,则无需移动 if status_left_right: # 如果左右有空隙,则需要用到cut_value_top result_image = IDphotos_cut(x1 + x_left, y1 + cut_value_top, x2 - x_right, y2 - cut_value_top, origin_png_image) else: # 如果左右没有空隙,那么则无需改动 result_image = cut_image elif status_top == 1: # 如果头顶离照片顶部距离过大,需要人像向上移动,则需要用到move_value if status_left_right: # 左右存在距离,则需要cut_value_top result_image = IDphotos_cut(x1 + x_left, y1 + cut_value_top + move_value, x2 - x_right, y2 - cut_value_top + move_value, origin_png_image) else: # 左右不存在距离 result_image = IDphotos_cut(x1 + x_left, y1 + move_value, x2 - x_right, y2 + move_value, origin_png_image) else: # 如果头顶离照片顶部距离过小,则需要人像向下移动,则需要用到move_value if status_left_right: # 左右存在距离,则需要cut_value_top result_image = IDphotos_cut(x1 + x_left, y1 + cut_value_top - move_value, x2 - x_right, y2 - cut_value_top - move_value, origin_png_image) else: # 左右不存在距离 result_image = IDphotos_cut(x1 + x_left, y1 - move_value, x2 - x_right, y2 - move_value, origin_png_image) # 调节头顶位置————防止底部空一块儿 result_image = move(result_image) # 高清保存 # cv2.imwrite(output_path.replace(".png", "_HD.png"), result_image) # 普清保存 result_image2 = cv2.resize(result_image, (size[1], size[0]), interpolation=cv2.INTER_AREA) # cv2.imwrite("./output_image.png", result_image) print("完成.返回1") return 1, result_image, result_image2 elif face_num == 0: print("无人脸，返回0!!!") return 0 else: print("太多人脸，返回2!!!") return 2 if __name__ == "__main__": with open("./Setting.json") as json_file: # file_list = get_filedir_filelist("./input_image") setting = json.load(json_file) filedir = "../IDPhotos/input_image/linzeyi.jpg" file_list = [filedir] for filedir in file_list: print(filedir) # try: status_id, result_image, result_image2 = IDphotos_create(cv2.imread(filedir), size=(setting["size_height"], setting["size_width"]), head_height_ratio=setting["head_height_ratio"], head_measure_ratio=setting["head_measure_ratio"], checkpoint_path=setting["checkpoint_path"], align=True) # cv2.imwrite("./result_image.png", result_image) if status_id == 1: print("处理完毕!") elif status_id == 0: print("没有人脸！请重新上传有人脸的照片.") elif status_id == 2: print("人脸不只一张！请重新上传单独人脸的照片.") elif status_id == 3: print("人头下方空隙不足！") elif status_id == 4: print("此照片不能制作该规格！") # except Exception as e: # print(e)