first-commit

align_face.py

+# -*- coding: utf-8 -*-
+'''
+* @author [v_daweihao]
+* @version [2021-08-10]
+* 〈按照眼睛关键点对齐人脸〉
+* Reference:
+    https://blog.csdn.net/weixin_35732969/article/details/83714492
+    https://blog.csdn.net/u013841196/article/details/85720897
+    https://zhuanlan.zhihu.com/p/32713815
+    https://blog.csdn.net/qq_20622615/article/details/80929746
+    https://blog.csdn.net/qq_36560894/article/details/105416273
+    https://blog.csdn.net/weixin_35732969/article/details/83714492
+'''
+
+import cv2
+import numpy as np
+import os
+import dlib
+from PIL import Image
+import shutil
+
+def get_face_mark(img_path):
+    '''
+    代码功能：
+    1. 用dlib人脸检测器检测出人脸，返回的人脸矩形框
+    2. 对检测出的人脸进行关键点检测并用圈进行标记
+    3. 将检测出的人脸关键点信息写到txt文本中
+    :param img_path:
+    :return:
+    '''
+    global landmarks
+    predictor_model = 'shape_predictor_68_face_landmarks.dat'
+    detector = dlib.get_frontal_face_detector()  # dlib人脸检测器
+    predictor = dlib.shape_predictor(predictor_model)
+
+    # cv2读取图像
+    test_img_path = img_path
+    # output_pos_info = "test_img/Messi.txt"
+    img = cv2.imread(test_img_path)
+    # file_handle = open(output_pos_info, 'a')
+    # 取灰度
+    img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+
+    # 人脸数rects
+    rects = detector(img_gray, 0)
+    print('人脸数：' + str(len(rects)))
+
+    for i in range(len(rects)):
+        landmarks = np.matrix([[p.x, p.y] for p in predictor(img, rects[i]).parts()])
+        for idx, point in enumerate(landmarks):
+            # 68点的坐标
+            pos = (point[0, 0], point[0, 1])
+            # print(idx + 1, pos)
+            # pos_info = str(point[0, 0]) + ' ' + str(point[0, 1]) + '\n'
+            # file_handle.write(pos_info)
+            # 利用cv2.circle给每个特征点画一个圈，共68个
+            cv2.circle(img, pos, 3, color=(0, 255, 0))
+            # 利用cv2.putText输出1-68
+            # font = cv2.FONT_HERSHEY_SIMPLEX
+            # cv2.putText(img, str(idx+1), pos, font, 0.5, (0, 0, 255), 1, cv2.LINE_AA)
+    # file_handle.close()
+    cv2.imwrite("face_keypoints.png", img)
+    face_landmarks = []
+    landmarks = [j for j in np.array(landmarks)]
+    for i in range(68):
+        face_landmarks.append((landmarks[i][0], landmarks[i][1]))
+
+    return face_landmarks
+
+
+def get_horizontal_offset(landmarks):
+    '''获得眼睛水平方向offset
+    :param landmarks: 68点人脸关键点
+    :return:
+    '''
+    # get list landmarks of left and right eye
+    left_eye = landmarks[37:43]
+    right_eye = landmarks[43:49]
+
+    # calculate the mean point of landmarks of left and right eye
+    left_eye_center = np.mean(left_eye, axis=0).astype("int")
+    right_eye_center = np.mean(right_eye, axis=0).astype("int")
+
+    # compute the horizontal distance between the eye centroids
+    dx = right_eye_center[0] - left_eye_center[0]
+
+    # # 计算左眼最左边关键点、右眼最右边关键点
+    # left_eye_min = np.min(left_eye, axis=0).astype("int")[1]
+    # right_eye_max = np.max(right_eye, axis=0).astype("int")[1]
+
+    # 计算两只眼睛的水平距离
+    # dx = right_eye_max[0] - left_eye_min[0]
+
+    # calculate the center of 2 eyes
+    # eye_center = ((left_eye_center[0] + right_eye_center[0]) // 2,
+    #               (left_eye_center[1] + right_eye_center[1]) // 2)
+
+    return dx
+
+
+def get_scaled_img(scale_ratio, img):
+    '''对图片进行等比例缩放
+    :param scale_ratio: dx_cartoon / dx_asian
+    :param img:
+    :return:
+    '''
+    # shape (高, 宽)
+    h = img.shape[0]  # 图像的高
+    w = img.shape[1]  # 图像的宽
+
+    ori_ratio = w / h
+    new_height = int(h * scale_ratio)
+    new_width = int(new_height * ori_ratio)
+
+    # (宽, 高)
+    img_new = cv2.resize(img, (new_width, new_height))
+
+    return img_new
+
+
+def start_crop(split_img_dir, pcg_dir, scaled_dir, crop_dir, result_path, save_path):
+    '''剪裁亚洲人脸
+        按照卡通人脸眼睛水平距离缩放图片 --> scaled image
+        对scaled image进行padding --> result --> resize (512, 512)
+        join(asian image, cartoon image)
+    :param split_img_dir:
+    :param pcg_dir:
+    :param scaled_dir:
+    :param crop_dir:
+    :param result_path:
+    :param save_path:
+    :return:
+    '''
+
+    for img in os.listdir(split_img_dir):
+        try:
+            img_path = os.path.join(split_img_dir, img)
+            asian_img = cv2.imread(img_path)
+            asian_face_landmarks = get_face_mark(img_path)
+            dx_asian = get_horizontal_offset(asian_face_landmarks)
+
+            cartoon_path = os.path.join(pcg_dir, img)
+            cartoon_face_landmarks = get_face_mark(cartoon_path)
+            dx_cartoon = get_horizontal_offset(cartoon_face_landmarks)
+
+            cartoon_chin_landmarks = cartoon_face_landmarks[0:27]
+            cartoon_chin_min = np.min(cartoon_chin_landmarks, axis=0).astype("int")[1]
+            cartoon_chin_max = np.max(cartoon_chin_landmarks, axis=0).astype("int")[1]
+            upper_offset = cartoon_chin_min
+            bottom_offset = 512 - cartoon_chin_max
+
+            # get scale ratio
+            scale_ratio = dx_cartoon / dx_asian
+            scale_img = get_scaled_img(scale_ratio, asian_img)
+            scale_path = os.path.join(scaled_dir, img)
+            cv2.imwrite(scale_path, scale_img)
+
+            scaled_face_landmarks = get_face_mark(scale_path)
+            chin_landmarks = scaled_face_landmarks[0:27]
+
+            # 计算脸的轮廓 最上边/最下边点
+            chin_min = np.min(chin_landmarks, axis=0).astype("int")[1]
+            chin_max = np.max(chin_landmarks, axis=0).astype("int")[1]
+
+            delta_upper = chin_min - upper_offset
+            delta_bottom = chin_max + bottom_offset
+            # 裁剪图片
+            crop_img = scale_img[delta_upper:delta_bottom, :]
+            crop_path = os.path.join(crop_dir, img)
+            cv2.imwrite(crop_path, crop_img)
+
+            # 先固定长边，左右两边补齐至正方形
+            h, w = crop_img.shape[0], crop_img.shape[1]
+            max_edge = max(h, w)
+            min_edge = min(h, w)
+            supple_length = max_edge - min_edge
+            # 左右
+            if h > w:
+                left = supple_length // 2
+                right = supple_length // 2
+                pad_image = cv2.copyMakeBorder(crop_img, 0, 0, left, right, cv2.BORDER_REPLICATE, value=(0, 0, 0))
+            else:
+                top = supple_length // 2
+                bottom = supple_length // 2
+                pad_image = cv2.copyMakeBorder(crop_img, top, bottom, 0, 0, cv2.BORDER_REPLICATE, value=(0, 0, 0))
+
+            result = cv2.resize(pad_image, (512, 512))
+            padding_path = os.path.join(result_path, img)
+            cv2.imwrite(padding_path, result)
+
+            img1, img2 = Image.open(padding_path), Image.open(cartoon_path)
+            size1, size2 = img1.size, img2.size
+            joint = Image.new('RGB', (size1[0] + size2[0], size1[1]))
+            loc1, loc2 = (0, 0), (size1[0], 0)
+            joint.paste(img1, loc1)
+            joint.paste(img2, loc2)
+            joint.save(save_path + str('/') + img)
+        except Exception:
+            pass
+        continue
+
+
+def remove_wrong(pcg_dir, save_path, wrong_path):
+    '''筛掉错误的数据
+        去除关键点错误的图片
+    :param pcg_dir:
+    :param save_path:
+    :param wrong_path:
+    :return:
+    '''
+
+    for img in os.listdir(pcg_dir):
+        cartoon_path = os.path.join(pcg_dir, img)
+        cartoon_img = cv2.imread(cartoon_path)
+
+        # 检测关键点是否存在
+        predictor_model = 'shape_predictor_68_face_landmarks.dat'
+        detector = dlib.get_frontal_face_detector()  # dlib人脸检测器
+        predictor = dlib.shape_predictor(predictor_model)
+
+        # 取灰度
+        img_gray = cv2.cvtColor(cartoon_img, cv2.COLOR_RGB2GRAY)
+        # 人脸数rects
+        rects = detector(img_gray, 0)
+
+        src_path = os.path.join(save_path, img)
+        dst_path = os.path.join(wrong_path, img)
+
+        # if not os.path.exists(src_path):
+        #     continue
+        # else:
+        #     if len(rects) == 0:
+        #         shutil.move(src_path, dst_path)
+        if len(rects) == 0:
+            shutil.move(src_path, dst_path)
+        else:
+            pass
+
+
+if __name__ == '__main__':
+
+    split_img_dir = '/data/data_hao/PFLD-pytorch/20200809/split_image/val'
+    pcg_dir = '/data/data_hao/PFLD-pytorch/20200809/cartoon_pcg/cartoon_pcg_noseg_0807/val'
+    scaled_dir = '/data/data_hao/PFLD-pytorch/20200809/scaled_img'
+    crop_dir = '/data/data_hao/PFLD-pytorch/20200809/crop_img'
+    result_path = '/data/data_hao/PFLD-pytorch/20200809/result'
+    save_path = '/data/data_hao/PFLD-pytorch/20200809/join/val'
+    wrong_path = '/data/data_hao/PFLD-pytorch/20200809/wrong_img'
+
+    start_crop(split_img_dir, pcg_dir, scaled_dir, crop_dir, result_path, save_path)
+
+    remove_wrong(pcg_dir, save_path, wrong_path)
+
+
+
+
+
+
+
+
+

data_processing.py

+'''
+* @author [v_daweihao]
+* @version [2021-07-26]
+* <融合图像过程中的数据预处理>
+'''
+import cv2
+import numpy as np
+import os
+import dlib
+from PIL import Image
+import shutil
+import glob
+from blend_img_david import blend_img
+
+def start_fusion(img_dir, art_mouth_path, save_path):
+    '''
+    对数据集中的所有图片进行融合
+    :param img_dir:
+    :return:
+    '''
+    # 创建 mouth_level : mouth_path
+    # level = {'1': '01.png', '3': '02.png', '5': '03.png', '7': '04.png', '9': '05.png'}
+    level = {'0': '01.png', '2': '02.png', '4': '03.png', '6': '04.png', '8': '05.png'}
+    # 遍历男生/女生卡通人脸
+    for image in os.listdir(img_dir):
+        index = image.split('.jpg')[0].split('_')[1][-1]
+        # 按照index找到对应的美术嘴巴
+        img_list = os.listdir(art_mouth_path)
+        img_list.sort(key=lambda x: int(x[:-4]))  # 文件名按数字排序
+        # 得到待融合的两张图片路径
+        mouth_path = art_mouth_path + str('/') + level[index]
+        img_path = img_dir + str('/') + image
+        # print(mouth_path)
+        # print(img_path)
+        # 融合
+        try:
+            fusion = blend_img(img_path, mouth_path)
+            cv2.imwrite(save_path + str('/') + image.split('.jpg')[0] + '.png', fusion,
+                        [int(cv2.IMWRITE_PNG_COMPRESSION), 9])
+        except Exception:
+            delete_path = img_dir + str('/') + image
+            os.remove(delete_path)
+        continue
+
+
+
+def join(asian_dir, cartoon_dir, save_path):
+    """
+    :param png1: path
+    :param png2: path
+    :param flag: horizontal or vertical
+    :return:
+    """
+    for cartoon_img in os.listdir(cartoon_dir):
+        asian_img = asian_dir + str('/') + cartoon_img.split('.png')[0] + str('.jpg')
+        img1, img2 = Image.open(asian_img), Image.open(cartoon_dir + str('/') + cartoon_img)
+        # 统一图片尺寸，可以自定义设置（宽，高）
+        img1 = img1.resize((512, 512), Image.ANTIALIAS)
+        img2 = img2.resize((512, 512), Image.ANTIALIAS)
+        size1, size2 = img1.size, img2.size
+
+        joint = Image.new('RGB', (size1[0] + size2[0], size1[1]))
+        loc1, loc2 = (0, 0), (size1[0], 0)
+        joint.paste(img1, loc1)
+        joint.paste(img2, loc2)
+        joint.save(save_path + str('/') + cartoon_img)
+
+if __name__ == "__main__":
+    # cartoon_img_dir = '/data/data_hao/seeprettyface-face_editor/tuning_cartoon'
+    # man_cartoon = '/data/data_hao/seeprettyface-face_editor/tuning_cartoon/man_cartoon'
+    # woman_cartoon = '/data/data_hao/seeprettyface-face_editor/tuning_cartoon/woman_cartoon'
+    # with open("man_number.txt", "r") as f:
+    #     for line in f.readlines():
+    #         line = line.strip('\n')  # 去掉列表中每一个元素的换行符
+    #         # 0003_000
+    #         for i in range(10):
+    #             img_path = cartoon_img_dir + str('/') + line.split('_')[0] + str('_') + str(i).zfill(3) + '.jpg'
+    #             shutil.move(img_path, man_cartoon)
+    #             os.remove(img_path)
+
+
+    # woman_cartoon_path = glob.glob(os.path.join(cartoon_img_dir + str('/'), '*.jpg'))
+    # # print(woman_cartoon_path)
+    # for i in range(len(woman_cartoon_path)):
+    #     shutil.move(woman_cartoon_path[i], woman_cartoon)
+    #     # os.remove(woman_cartoon_path[i])
+
+    # man_img_dir = '/data/data_hao/data_base/cartoon_man'
+    # for image in os.listdir(man_img_dir):
+    #     # 0019_004.jpg
+    #     # 0493_000.jpg
+    #     try:
+    #         last_num = image.split('.jpg')[0].split('_')[1][-1]
+    #         # 选取偶数编号的卡通人脸
+    #         if (int(last_num) % 2) == 0:
+    #             even_path = man_img_dir + str('/') + image
+    #             shutil.move(even_path, '/data/data_hao/data_base/cartoon_man/even_man')
+    #             # print(delete_path)
+    #     except Exception:
+    #         print('wrong')
+    #     continue
+    #
+    # man_img_dir = '/data/data_hao/data_base/cartoon_man'
+    # for image in os.listdir(man_img_dir):
+    #     # 0019_004.jpg
+    #     # 0493_000.jpg
+    #     try:
+    #         last_num = image.split('.jpg')[0].split('_')[1][-1]
+    #         # 选取奇数编号的卡通人脸
+    #         if (int(last_num) % 2) != 0:
+    #             odd_path = man_img_dir + str('/') + image
+    #             shutil.move(odd_path, '/data/data_hao/data_base/cartoon_man/odd_man')
+    #     except Exception:
+    #         print('wrong')
+    #     continue
+    #
+    # woman_img_dir = '/data/data_hao/data_base/cartoon_woman'
+    # for image in os.listdir(woman_img_dir):
+    #     # 0019_004.jpg
+    #     # 0493_000.jpg
+    #     try:
+    #         last_num = image.split('.jpg')[0].split('_')[1][-1]
+    #         # 选取偶数编号的卡通人脸
+    #         if (int(last_num) % 2) == 0:
+    #             even_path = woman_img_dir + str('/') + image
+    #             shutil.move(even_path, '/data/data_hao/data_base/cartoon_woman/even_woman')
+    #             # print(delete_path)
+    #     except Exception:
+    #         print('wrong')
+    #     continue
+    #
+    # for image in os.listdir(woman_img_dir):
+    #     # 0019_004.jpg
+    #     # 0493_000.jpg
+    #     try:
+    #         last_num = image.split('.jpg')[0].split('_')[1][-1]
+    #         # 选取奇数编号的卡通人脸
+    #         if (int(last_num) % 2) != 0:
+    #             odd_path = woman_img_dir + str('/') + image
+    #             print(odd_path)
+    #             shutil.move(odd_path, '/data/data_hao/data_base/cartoon_woman/odd_woman')
+    #     except Exception:
+    #         print('wrong')
+    #     continue
+
+    # art_man_path = "/data/data_hao/PFLD-pytorch/man_art"
+    # save_man_path = '/data/data_hao/data_base/man_fusion'
+    # # 男 偶
+    # man_img_dir = '/data/data_hao/data_base/cartoon_man/even_man'
+    # start_fusion(man_img_dir, art_man_path, save_man_path)
+
+    # art_woman_path = "/data/data_hao/PFLD-pytorch/woman_art"
+    # woman_img_dir = '/data/data_hao/data_base/cartoon_woman/even_woman'
+    # save_woman_path = '/data/data_hao/data_base/woman_fusion'
+    # start_fusion(woman_img_dir, art_woman_path, save_woman_path)
+
+
+    # 横向拼接后图像保存路径
+    Asian_man_img_dir = '/data/data_hao/data_base/style_gan2'
+    save_man_path = '/data/data_hao/data_base/man_fusion'
+    join_man_path = '/data/data_hao/data_base/man_join'
+    join(Asian_man_img_dir, save_man_path, join_man_path)
\ No newline at end of file

face_cartoon_onnx.py

+'''
+* @author [v_daweihao]
+* @version [2021-07-15]
+* 〈卡通化亚洲人脸图像〉
+'''
+import cv2
+import numpy as np
+import onnxruntime
+import os
+
+class FaceCartoon:
+    sess = None
+    # MODEL_PATH = "models/FaceCartoon.pb"
+    # INPUT_SIZE = 256
+
+    MODEL_PATH = "noseg_224_20_0331.onnx"
+
+    def __init__(self):
+        self.sess = onnxruntime.InferenceSession(self.MODEL_PATH)
+
+    def infer(self, img):
+        ori_h, ori_w, _ = img.shape
+
+        inp_img = self.input_preprocess(img)
+        cartoon_face = self.sess.run(["output"], {"input": inp_img})[0]
+        cartoon_face = np.squeeze(cartoon_face)
+        cartoon_face = cartoon_face.transpose([1, 2, 0])
+        cartoon_face = ((cartoon_face + 1.) / 2) * 255.0
+
+        cartoon_face = cartoon_face.astype(np.uint8)
+        cartoon_face = cartoon_face[:, :, ::-1]
+        return cv2.resize(cartoon_face, (ori_w, ori_h))
+
+    def input_preprocess(self, img, size=224):
+        img = cv2.resize(img, (size, size))
+        nor = img / 255.0
+        inp = (nor - 0.5) / 0.5
+        inp = inp[:, :, ::-1]
+        inp = inp.transpose([2, 0, 1])
+        inp = inp.astype(np.float32)
+        return inp[np.newaxis, :, :, :]
+
+
+if __name__ == '__main__':
+    fc = FaceCartoon()
+    asiac_img = '/data/data_hao/PFLD-pytorch/00140.png'
+    mg = cv2.imread(asiac_img)
+    r = fc.infer(mg)
+    r = cv2.resize(r, (512, 512), interpolation=cv2.INTER_AREA)
+    cv2.imwrite('/data/data_hao/PFLD-pytorch/CARTOON.png', r)
+
+    # 亚洲人脸数据集路径 男
+    # man_img = '/data/data_hao/style_gan2/man'
+    # # 生成的卡通人脸存放路径 男
+    # Cartoon_img = '/data/data_hao/data_base/cartoon_man'
+    # for image in os.listdir(man_img):
+    #     image_path = os.path.join(man_img, image)
+    #     img = cv2.imread(image_path)
+    #     r = fc.infer(img)
+    #     cv2.imwrite(Cartoon_img + str('/') + image.split('.jpg')[0] + '.jpg', r)
+
+    # # 亚洲人脸数据集路径 男
+    # woman_img = '/data/data_hao/style_gan2/woman'
+    # # 生成的卡通人脸存放路径 男
+    # Cartoon_img = '/data/data_hao/data_base/cartoon_woman'
+    # for image in os.listdir(woman_img):
+    #     image_path = os.path.join(woman_img, image)
+    #     img = cv2.imread(image_path)
+    #     r = fc.infer(img)
+    #     cv2.imwrite(Cartoon_img + str('/') + image.split('.jpg')[0] + '.jpg', r)
+
+
+

gaussian_blend.py

+'''
+* @author [v_daweihao]
+* @version [2021-08-03]
+* <将美术嘴巴融合到卡通人脸中，卡通化人脸数据集用美术提供的嘴巴进行替换>
+* <高斯融合>
+'''
+
+import cv2
+import numpy as np
+import os
+import dlib
+from PIL import Image
+
+
+def get_gaussian_filter(w, h, c_x, c_y, variance):
+    '''
+    高斯滤波
+    :param w:
+    :param h:
+    :param c_x:
+    :param c_y:
+    :param variance:
+    :return:
+    '''
+    # h, w = 1280, 720
+    heatmap = np.zeros((h, w))
+    # variance = 60
+    mul = 1.5
+    # c_x, c_y = (360, 640)
+    for x_p in range(0, w):
+        for y_p in range(0, h):
+            dist_sq = (x_p - c_x) * (x_p - c_x) + \
+                      (y_p - c_y) * (y_p - c_y)
+
+            exponent = dist_sq / 2.0 / variance / variance
+            new_val = np.exp(-exponent) * mul
+            new_val = min(1, max(0, new_val))
+
+            heatmap[y_p, x_p] = new_val
+    return heatmap
+
+
+class FaceLandmark:
+    '''
+    获取嘴巴关键点，生成色块颜色
+    '''
+    def __init__(self):
+        predictor_path = "shape_predictor_68_face_landmarks.dat"
+        # faces_path = "amazing_cartoon/0001_004.jpg"
+        # faces_path = "sample_imgs/0144_006.jpg"
+        # 加载dlib自带的人脸检测器
+        self.detector = dlib.get_frontal_face_detector()
+        # 加载模型
+        self.predictor = dlib.shape_predictor(predictor_path)
+
+    def __call__(self, img):
+        # 参数1表示对图片进行上采样一次，有利于检测到更多的人脸
+        dets = self.detector(img, 1)
+        shape = self.predictor(img, dets[0])
+        landmark = np.array([[p.x, p.y] for p in shape.parts()])
+        # 48 -> 60 是嘴巴的关键点
+        xmin = np.min(landmark[48:60, 0])
+        xmax = np.max(landmark[48:60, 0])
+        ymin = np.min(landmark[48:60, 1])
+        ymax = np.max(landmark[48:60, 1])
+
+        # 获取色块颜色，8 是下巴的点
+        color_bgr = [0, 0, 0]
+        for y in range(ymax + 5, landmark[8, 1]):
+            color_bgr[0] += img[int(y), (xmax + xmin) // 2, 0]
+            color_bgr[1] += img[int(y), (xmax + xmin) // 2, 1]
+            color_bgr[2] += img[int(y), (xmax + xmin) // 2, 2]
+
+        div = landmark[8, 1] - (ymax + 5)
+        color_bgr = list(map(lambda _: _ // div, color_bgr))
+
+        return [xmin, xmax, ymin, ymax], color_bgr
+
+
+def blend_img(img_path, art_mouth):
+    '''
+    图像融合
+    :param img_path: 卡通人脸图像
+    :param art_mouth: 美术嘴巴图像
+    :return:
+    '''
+    # img_path .jpg文件
+    # art_mouth .png文件
+    fl = FaceLandmark()
+    img = cv2.imread(img_path)
+    box, color = fl(img)
+    h, w, c = img.shape
+
+    c_x = (box[1] + box[0]) // 2
+    c_y = (box[2] + box[3]) // 2
+
+    var = min(box[1] - box[0], box[3] - box[2])
+    mask = get_gaussian_filter(w, h, c_x, c_y, var)
+    cv2.imwrite('mask.jpg', mask)
+    # 色块结合高斯 mask 融合
+    for ri in range(h):
+        for ci in range(w):
+            img_color = img[ri, ci, :]
+            mask_val = mask[ri, ci]
+            blend_color = np.array([
+                mask_val * color[0] + (1 - mask_val) * img_color[0],
+                mask_val * color[1] + (1 - mask_val) * img_color[1],
+                mask_val * color[2] + (1 - mask_val) * img_color[2],
+            ])
+            img[ri, ci, :] = blend_color.astype(np.uint8)
+    cv2.imwrite('mask_img.jpg', img)
+    # 把嘴巴贴上去，因为嘴巴的 box 和美术的 box 在尺寸上不一致
+    # 读取alpha通道
+    mouth_img = cv2.imread(art_mouth, cv2.IMREAD_UNCHANGED)
+    mh, mw, _ = mouth_img.shape
+    # 取嘴巴矩阵框最大那条边为基准，对应美术 05 的 w，需要根据不同情况来设置
+    # 但有一个最简单的方法是生成多个 slide
+    # [max_slide * 0.7, max_slide * 1.0, max_slide * 1.3, max_slide * 1.5]
+    max_slide = max(box[1] - box[0], box[3] - box[2])
+    factor = max_slide / mw
+    new_w, new_h = int(max_slide), int(mh * factor)
+    mouth_img = cv2.resize(mouth_img, (new_w, new_h), cv2.INTER_CUBIC)
+    # 计算出嘴巴矩阵的中心点，来确定美术嘴巴放置的位置
+    xmin = max(0, int(c_x - new_w / 2))
+    xmax = min(w, int(c_x + new_w / 2))
+    ymin = max(0, int(c_y - new_h / 2))
+    ymax = min(h, int(c_y + new_h / 2))
+
+    for ri in range(ymin, ymax):
+        for ci in range(xmin, xmax):
+            img_color = img[ri, ci, :]
+            mask_val = mouth_img[ri - ymin, ci - xmin, 3].astype(np.float32) / 255.0
+            mouth_color = mouth_img[ri - ymin, ci - xmin, :3]
+            blend_color = np.array([
+                mask_val * mouth_color[0] + (1 - mask_val) * img_color[0],
+                mask_val * mouth_color[1] + (1 - mask_val) * img_color[1],
+                mask_val * mouth_color[2] + (1 - mask_val) * img_color[2],
+            ])
+            img[ri, ci, :] = blend_color.astype(np.uint8)
+
+    return img
+
+
+def select_image(img_dir):
+    '''
+    从数据集中选取图片，按照奇数偶数分成两类(分别对应美术提供的嘴巴五种类型)
+    :param img_dir: 卡通人脸图像路径
+    '''
+    for image in os.listdir(img_dir):
+        # 0019_004.jpg
+        # 0493_000.jpg
+        last_num = image.split('.jpg')[0].split('_')[1][-1]
+        # 选取奇数编号的卡通人脸，偶数编号的卡通人脸移除
+        if (int(last_num) % 2) == 0:
+            delete_path = img_dir + str('/') + image
+            os.remove(delete_path)
+            # print(delete_path)
+        else:
+            continue
+
+
+def start_fusion(img_dir, art_mouth_path, save_path):
+    '''
+    对数据集中的所有图片进行融合
+    :param img_dir: 卡通人脸图像
+    :param art_mouth_path: 美术提供的嘴巴图像
+    :param save_path: 融合后的保存路径
+    '''
+    # 创建 mouth_level : mouth_path
+    # 根据选取的卡通路径是属于奇数还是偶数，选择打卡
+    level = {'1': '01.png', '3': '02.png', '5': '03.png', '7': '04.png', '9': '05.png'}
+    # level = {'0': '01.png', '2': '02.png', '4': '03.png', '6': '04.png', '8': '05.png'}
+    # 遍历男生/女生卡通人脸
+    for image in os.listdir(img_dir):
+        index = image.split('.jpg')[0].split('_')[1][-1]
+        # 按照index找到对应的美术嘴巴
+        img_list = os.listdir(art_mouth_path)
+        img_list.sort(key=lambda x: int(x[:-4]))  # 文件名按数字排序
+        # 得到待融合的两张图片路径
+        mouth_path = art_mouth_path + str('/') + level[index]
+        img_path = img_dir + str('/') + image
+        # print(mouth_path)
+        # print(img_path)
+        # 融合
+        fusion = blend_img(img_path, mouth_path)
+        cv2.imwrite(save_path + str('/') + image.split('.jpg')[0] + '.png', fusion,
+                    [int(cv2.IMWRITE_PNG_COMPRESSION), 9])
+            # print(fusion)
+            # print(save_path + str('/') + image.split('.jpg')[0] + '.png')
+        # except Exception:
+        #     delete_path = img_dir + str('/') + image
+        #     os.remove(delete_path)
+        # continue
+
+
+def join(asian_dir, cartoon_dir, save_path):
+    '''
+    将亚洲人脸与对应的融合后的卡通人脸进行拼接
+    :param asian_dir: 亚洲人脸路径
+    :param cartoon_dir: 融合后的卡通人脸路径
+    :param save_path: 拼接图像保存路径
+    '''
+    for cartoon_img in os.listdir(cartoon_dir):
+        asian_img = asian_dir + str('/') + cartoon_img.split('.png')[0] + str('.jpg')
+        img1, img2 = Image.open(asian_img), Image.open(cartoon_dir + str('/') + cartoon_img)
+        # 统一图片尺寸，可以自定义设置（宽，高）
+        img1 = img1.resize((512, 512), Image.ANTIALIAS)
+        img2 = img2.resize((512, 512), Image.ANTIALIAS)
+        size1, size2 = img1.size, img2.size
+
+        joint = Image.new('RGB', (size1[0] + size2[0], size1[1]))
+        loc1, loc2 = (0, 0), (size1[0], 0)
+        joint.paste(img1, loc1)
+        joint.paste(img2, loc2)
+        joint.save(save_path + str('/') + cartoon_img)
+
+
+if __name__ == '__main__':
+    img_path = '/data/data_hao/PFLD-pytorch/0007_005.jpg'
+    art_mouth= '/data/data_hao/PFLD-pytorch/mouth03.png'
+    img = blend_img(img_path, art_mouth)
+    cv2.imwrite('/data/data_hao/PFLD-pytorch/fusion.jpg', img)
+
+    # # 处理图片
+    # Asian_woman_img_dir = '/data/data_hao/seeprettyface-face_editor/amazing_tuning/Asian_woman'
+    # # woamn_img_dir = '/data/data_hao/PFLD-pytorch/cartoon_face_database/woman'
+    # # # select_image(woamn_img_dir)
+    # Asian_man_img_dir = '/data/data_hao/seeprettyface-face_editor/amazing_tuning/Asian_man'
+
+    # select_image(man_img_dir)
+
+    # 对奇数编号男生进行融合
+    # art_man_path = "/data/data_hao/PFLD-pytorch/man_art"
+    # save_man_path = '/data/data_hao/data_base/man_fusion'
+    # man_img_dir = '/data/data_hao/data_base/cartoon_man/odd_man'
+    # start_fusion(man_img_dir, art_man_path, save_man_path)
+
+    # 对偶数编号男生进行融合 需要调整start_fusion代码level字典
+    # man_img_dir = '/data/data_hao/seeprettyface-face_editor/tuning_cartoon/man_cartoon/even_man'
+    # start_fusion(man_img_dir, art_man_path, save_man_path)
+
+    # # 横向拼接后图像保存路径
+    # join_man_path = '/data/data_hao/PFLD-pytorch/save_path/man_join'
+    # # 横向拼接 男生
+    # join(Asian_man_img_dir, save_man_path, join_man_path)
+
+    # art_woman_path = "/data/data_hao/PFLD-pytorch/woman_art"
+    # woman_img_dir = '/data/data_hao/data_base/cartoon_woman/odd_woman'
+    # save_woman_path = '/data/data_hao/data_base/woman_fusion'
+    # start_fusion(woman_img_dir, art_woman_path, save_woman_path)
+
+    # woamn_img_dir = '/data/data_hao/seeprettyface-face_editor/tuning_cartoon/man_cartoon/even_woman'
+    # start_fusion(woamn_img_dir, art_woman_path, save_woman_path)
+
+    # # # 横向拼接后图像保存路径
+    # join_woman_path = '/data/data_hao/PFLD-pytorch/save_path/woman_join'
+    # # # 横向拼接 男生
+
+    # join(Asian_man_img_dir, save_woman_path, join_woman_path)
+    # Asian_woman_img_dir = '/data/data_hao/data_base/style_gan2'
+    # save_woman_path = '/data/data_hao/data_base/woman_fusion'
+    # join_woman_path = '/data/data_hao/data_base/woman_join'
+    # join(Asian_woman_img_dir, save_woman_path, join_woman_path)
+

gender_detect.py

+'''
+* @author [v_daweihao]
+* @version [2021-08-03]
+* 〈亚洲人脸数据集按照男女分类〉
+'''
+import cv2
+import os
+import shutil
+
+def getFaceBox(net, frame, conf_threshold=0.7):
+    frameOpencvDnn = frame.copy()
+    frameHeight = frameOpencvDnn.shape[0]
+    frameWidth = frameOpencvDnn.shape[1]
+    blob = cv2.dnn.blobFromImage(frameOpencvDnn, 1.0, (300, 300), [104, 117, 123], True, False)
+    net.setInput(blob)
+    detections = net.forward()
+    bboxes = []
+    for i in range(detections.shape[2]):
+        confidence = detections[0, 0, i, 2]
+        # print(confidence)
+        if confidence > conf_threshold:
+            x1 = int(detections[0, 0, i, 3] * frameWidth)
+            y1 = int(detections[0, 0, i, 4] * frameHeight)
+            x2 = int(detections[0, 0, i, 5] * frameWidth)
+            y2 = int(detections[0, 0, i, 6] * frameHeight)
+            bboxes.append([x1, y1, x2, y2])
+            cv2.rectangle(frameOpencvDnn, (x1, y1), (x2, y2), (0, 255, 0), int(round(frameHeight / 150)), 8)
+    return frameOpencvDnn, bboxes
+
+
+def genderDetect(img_path):
+    '''
+    性别检测
+    :param img_path:
+    :return:
+    '''
+    global gender
+    faceProto = "opencv_face_detector.pbtxt"
+    faceModel = "opencv_face_detector_uint8.pb"
+
+    genderProto = "gender_deploy.prototxt"
+    genderModel = "gender_net.caffemodel"
+
+    MODEL_MEAN_VALUES = (78.4263377603, 87.7689143744, 114.895847746)
+    genderList = ['Male', 'Female']
+
+    # Load network
+    genderNet = cv2.dnn.readNet(genderModel, genderProto)
+    faceNet = cv2.dnn.readNet(faceModel, faceProto)
+
+    # Open a video file or an image file or a camera stream
+    padding = 20
+    # Read frame
+    frame = cv2.imread(img_path)
+    frameFace, bboxes = getFaceBox(faceNet, frame)
+    # print(bboxes)
+    bbox = bboxes[0]
+    # for bbox in bboxes:
+        # print(bbox)
+    face = frame[max(0, bbox[1] - padding):min(bbox[3] + padding, frame.shape[0] - 1),
+           max(0, bbox[0] - padding):min(bbox[2] + padding, frame.shape[1] - 1)]
+
+    blob = cv2.dnn.blobFromImage(face, 1.0, (227, 227), MODEL_MEAN_VALUES, swapRB=False)
+    genderNet.setInput(blob)
+    genderPreds = genderNet.forward()
+    gender = genderList[genderPreds[0].argmax()]
+        # print("Gender Output : {}".format(genderPreds))
+        # print("Gender : {}, conf = {:.3f}".format(gender, genderPreds[0].max()))
+        # cv2.imshow("Age Gender Demo", frameFace)
+
+    return gender
+
+
+if __name__ == '__main__':
+    asian_dir = '/data/data_hao/style_gan2'
+    man_dir = '/data/data_hao/style_gan2/man/'
+    woman_dir = '/data/data_hao/style_gan2/woman/'
+    for img_name in os.listdir(asian_dir):
+        img_path = asian_dir + str('/') + img_name
+        try:
+            gender = genderDetect(img_path)
+
+            if gender == 'Male':
+                shutil.move(img_path, man_dir)
+            elif gender == 'Female':
+                shutil.move(img_path, woman_dir)
+        except Exception:
+            pass
+        continue
+
+    # # 亚洲人脸 男生
+    # man_dir = '/data/data_hao/style_gan2/man/'
+    # asian_dir = '/data/data_hao/style_gan2'
+    # for img in os.listdir(man_dir):
+    #     index = img.split('.jpg')[0].split('_')[0]
+    #     for as_img in os.listdir(asian_dir):
+    #         img_path = asian_dir + str('/') + as_img
+    #         as_index = as_img.split('.jpg')[0].split('_')[0]
+    #         if index == as_index:
+    #             shutil.move(img_path, man_dir)
+    #         else:
+    #             pass
+    #
+    # # 亚洲人脸 女生
+    # woman_dir = '/data/data_hao/style_gan2/woman/'
+    # asian_dir = '/data/data_hao/style_gan2'
+    # for img in os.listdir(woman_dir):
+    #     index = img.split('.jpg')[0].split('_')[0]
+    #     for as_img in os.listdir(asian_dir):
+    #         img_path = asian_dir + str('/') + as_img
+    #         as_index = as_img.split('.jpg')[0].split('_')[0]
+    #         if index == as_index:
+    #             shutil.move(img_path, woman_dir)
+    #         else:
+    #             pass
\ No newline at end of file

generate_video.py

+'''
+* @author [v_daweihao]
+* @version [2021-08-04]
+* 〈生成视频图像数据处理〉
+'''
+import cv2
+import numpy as np
+import onnxruntime
+import os
+from PIL import Image
+
+
+class FaceCartoonTest:
+    MODEL_PATH = "cartoon_gan_0804.onnx"
+
+    def __init__(self, model_path=None):
+        if model_path is not None:
+            self.cartoon = onnxruntime.InferenceSession(model_path)
+        else:
+            self.cartoon = onnxruntime.InferenceSession(self.MODEL_PATH)
+
+        _, _1, self.inp_h, self.inp_w = self.cartoon._sess.inputs_meta[0].shape
+
+    def infer(self, frame):
+        ori_h, ori_w, _ = frame.shape
+
+        inp_img = self.input_preprocess(frame)
+        cartoon_face = self.cartoon.run(["310"], {"input.1": inp_img})[0]
+        cartoon_face = np.squeeze(cartoon_face)
+        cartoon_face = cartoon_face.transpose([1, 2, 0])
+        cartoon_face = ((cartoon_face + 1.) / 2) * 255.0
+
+        cartoon_face = cartoon_face.astype(np.uint8)
+        cartoon_face = cartoon_face[:, :, ::-1]
+        return cv2.resize(cartoon_face, (ori_w, ori_h))
+
+    def input_preprocess(self, img):
+        img = cv2.resize(img, (self.inp_w, self.inp_h))
+        nor = img / 255.0
+        inp = (nor - 0.5) / 0.5
+        inp = inp[:, :, ::-1]
+        inp = inp.transpose([2, 0, 1])
+        inp = inp.astype(np.float32)
+        return inp[np.newaxis, :, :, :]
+
+
+if __name__ == '__main__':
+    # 遍历含有21种属性的亚洲人脸
+    img_list = []
+    file_path = 'D:\\20210712\\Test_demo\\results\\results'
+    for dir in os.listdir(file_path):
+        sub_dir_path = os.path.join(file_path, dir)
+        for sub_file in os.listdir(sub_dir_path):
+            img_path = os.path.join(sub_dir_path, sub_file)
+            img_list.append(img_path)
+    # print(img_list[1])
+    # print(len(img_list))
+
+    # 亚洲人脸卡通化
+    car_list = []
+    name_list = []
+    fc = FaceCartoonTest()
+    for i in range(len(img_list)):
+        img_dir = img_list[i]
+        path, file = os.path.split(img_dir)
+        img_name = file[:-4]
+        img = cv2.imread(img_dir)
+        cartoon_img = fc.infer(img)
+        cartoon_img_dir = 'D:\\20210712\\Test_demo\\results\\cartoon\\{0}.jpg'.format(str(img_name))
+        cv2.imwrite(cartoon_img_dir, cartoon_img)
+        car_list.append(cartoon_img_dir)
+        name_list.append(img_name)
+
+    # 拼接所有图片
+    for i in range(len(img_list)):
+        asian_img_path = img_list[i]
+        cartoon_img_path = car_list[i]
+        save_path = 'D:\\20210712\\Test_demo\\results\\join\\{0}.jpg'.format(str(name_list[i]))
+        img1, img2 = Image.open(asian_img_path), Image.open(cartoon_img_path)
+
+        # 统一图片尺寸，可以自定义设置（宽，高）
+        img1 = img1.resize((512, 512), Image.ANTIALIAS)
+        img2 = img2.resize((512, 512), Image.ANTIALIAS)
+        size1, size2 = img1.size, img2.size
+
+        joint = Image.new('RGB', (size1[0] + size2[0], size1[1]))
+        loc1, loc2 = (0, 0), (size1[0], 0)
+        joint.paste(img1, loc1)
+        joint.paste(img2, loc2)
+        joint.save(save_path)
+
+    # 控制属性创建10FPS视频
+    size = (1024, 512)
+    # 创建视频对象
+    object = ['0000', '0008', '0010', '0013', '0016']
+    for i in range(5):
+        videowrite = cv2.VideoWriter(object[i] + '.mp4', cv2.VideoWriter_fourcc(*"mp4v"), 10, size)
+
+        for filename in [r'D:\20210712\Test_demo\results\join\{0}.jpg'.format(object[i] + '_' + str(j).zfill(3)) for j
+                         in range(420)]:
+            videowrite.write(cv2.imread(filename))
+
+        videowrite.release()
+    print('end!')
+

hand_landmouth.py

+# -*- coding:utf-8 -*-
+import cv2
+
+# 读入美术图像
+img = cv2.imread("D:\\20210712\\PFLD-pytorch\\woman04.png")
+img = cv2.resize(img, (512, 512), interpolation=cv2.INTER_AREA)
+
+def on_EVENT_LBUTTONDOWN(event, x, y, flags, param):
+    if event == cv2.EVENT_LBUTTONDOWN:
+        # xy = "%d,%d" % (x, y)
+        # 人工打标注
+        cv2.circle(img, (x, y), 1, (255, 0, 0), thickness=-1)
+        # 将嘴巴关键点写入txt文件中
+        txt_path = "D:\\20210712\\PFLD-pytorch\\mouth04.txt"
+        file_handle = open(txt_path, 'a')
+        file_handle.write(str((x, y)[0]) + ' ' + str((x, y)[1]) + '\n')
+
+        # cv2.putText(img, xy, (x, y), cv2.FONT_HERSHEY_PLAIN,
+        #             1.0, (0, 0, 0), thickness=1)
+        print((x, y))
+        cv2.imshow("image", img)
+        file_handle.close()
+
+cv2.namedWindow("image", cv2.WINDOW_NORMAL)
+cv2.moveWindow("image", 512, 512)
+cv2.setMouseCallback("image", on_EVENT_LBUTTONDOWN)
+
+while (1):
+    cv2.imshow("image", img)
+    if cv2.waitKey(0) & 0xFF == 27:
+        break
+cv2.destroyAllWindows()
\ No newline at end of file

mouth_open.py

+'''
+* @author [v_daweihao]
+* @version [2021-08-03]
+* <检测嘴巴是否张开>
+'''
+import math
+from PIL import Image, ImageDraw
+import face_recognition
+import os
+import shutil
+import cv2
+
+
+def get_lip_height(lip):
+    sum = 0
+    for i in [2, 3, 4]:
+        # distance between two near points up and down
+        distance = math.sqrt((lip[i][0] - lip[12 - i][0]) ** 2 +
+                             (lip[i][1] - lip[12 - i][1]) ** 2)
+        sum += distance
+    return sum / 3
+
+
+def get_mouth_height(top_lip, bottom_lip):
+    sum = 0
+    for i in [8, 9, 10]:
+        # distance between two near points up and down
+        distance = math.sqrt((top_lip[i][0] - bottom_lip[18 - i][0]) ** 2 +
+                             (top_lip[i][1] - bottom_lip[18 - i][1]) ** 2)
+        sum += distance
+    return sum / 3
+
+
+def check_mouth_open(top_lip, bottom_lip):
+    top_lip_height = get_lip_height(top_lip)
+    bottom_lip_height = get_lip_height(bottom_lip)
+    mouth_height = get_mouth_height(top_lip, bottom_lip)
+
+    # if mouth is open more than lip height * ratio, return true.
+    ratio = 0.5
+    if mouth_height > min(top_lip_height, bottom_lip_height) * ratio:
+        return True
+    else:
+        return False
+
+
+if __name__ == "__main__":
+
+    # ffhq_path = '/data/data_hao/detect_mouth_open/images1024x1024'
+    # for image_file in os.listdir(ffhq_path):
+    #     try:
+    #         image = face_recognition.load_image_file(ffhq_path + str('/') + image_file)
+    #         face_landmarks_list = face_recognition.face_landmarks(image)
+    #         pil_image = Image.fromarray(image)
+    #         d = ImageDraw.Draw(pil_image)
+    #
+    #         top_lip = face_landmarks_list[0]['top_lip']
+    #         bottom_lip = face_landmarks_list[0]['bottom_lip']
+    #         print(top_lip)
+    #         print(bottom_lip)
+    #
+    #
+    #
+    #         top_lip_height = get_lip_height(top_lip)
+    #         bottom_lip_height = get_lip_height(bottom_lip)
+    #         mouth_height = get_mouth_height(top_lip, bottom_lip)
+    #
+    #         # if mouth is open more than lip height * ratio, return true.
+    #         ratio = 0.5
+    #         if mouth_height > min(top_lip_height, bottom_lip_height) * ratio:
+    #             # print("open")
+    #             shutil.move(ffhq_path + str('/') + image_file, '/data/data_hao/detect_mouth_open/ffhq_open_mouth')
+    #         else:
+    #             pass
+    #     except Exception:
+    #         print('IndexError: list index out of range')
+    #     continue
+    # /data/data_hao/PFLD-pytorch/03.png
+    # img = cv2.imread('/data/data_hao/PFLD-pytorch/woman_face/woman04.png')
+    # img = cv2.resize(img, (512, 512))
+    # cv2.imwrite('/data/data_hao/PFLD-pytorch/woman_crop04.jpg', img)
+    # image = face_recognition.load_image_file('/data/data_hao/PFLD-pytorch/0000_006.jpg')
+    image = face_recognition.load_image_file('/data/data_hao/PFLD-pytorch/woman_face/woman04.png')
+    face_landmarks_list = face_recognition.face_landmarks(image)
+    pil_image = Image.fromarray(image)
+    d = ImageDraw.Draw(pil_image)
+    print(face_landmarks_list)
+    top_lip = face_landmarks_list[0]['top_lip']
+    bottom_lip = face_landmarks_list[0]['bottom_lip']
+    print(top_lip)
+    print(bottom_lip)
+
+    for tup in top_lip:
+        bottom_lip.append(tup)
+    # print(set(bottom_lip))  # 去重
+    # landmark = list(set(bottom_lip))
+    # print(landmark)
+    # 将得到的关键点位置信息按行写进txt文件中，并将文件名以图像名字命名
+    fw = open('04.txt', 'w')
+    for line in list(set(bottom_lip)):
+        for a in line:
+            a = str(a)
+            fw.write(a)
+            fw.write('\t')
+        fw.write('\n')
+    fw.close()
+
+    # fw.writelines(["%s\n" % item for item in list(set(bottom_lip))])
\ No newline at end of file

mouth_swap.py

+#! /usr/bin/env python
+'''
+* @author [v_daweihao]
+* @version [2021-08-03]
+* 〈将卡通人脸嘴巴与美术提供的五官中的嘴巴进行置换〉
+'''
+import numpy as np
+import os
+import cv2
+import dlib
+
+
+def readPoints(path):
+    '''
+    Read points from text file
+    :param path: 嘴巴关键点txt文件路径
+    :return: 获取嘴巴关键点坐标列表
+    '''
+    # Create an array of points.
+    points = []
+
+    # Read points
+    with open(path) as file:
+        for line in file:
+            x, y = line.split()
+            points.append((int(x), int(y)))
+
+    return points
+
+
+
+def applyAffineTransform(src, srcTri, dstTri, size):
+    '''
+    Apply affine transform calculated using srcTri and dstTri to src and output an image of size.
+    :param src:
+    :param srcTri:
+    :param dstTri:
+    :param size:
+    :return:
+    '''
+    # Given a pair of triangles, find the affine transform.
+    warpMat = cv2.getAffineTransform(np.float32(srcTri), np.float32(dstTri))
+
+    # Apply the Affine Transform just found to the src image
+    dst = cv2.warpAffine(src, warpMat, (size[0], size[1]), None, flags=cv2.INTER_LINEAR,
+                         borderMode=cv2.BORDER_REFLECT_101)
+
+    return dst
+
+
+# Check if a point is inside a rectangle
+def rectContains(rect, point):
+    if point[0] < rect[0]:
+        return False
+    elif point[1] < rect[1]:
+        return False
+    elif point[0] > rect[0] + rect[2]:
+        return False
+    elif point[1] > rect[1] + rect[3]:
+        return False
+    return True
+
+
+# calculate delanauy triangle
+def calculateDelaunayTriangles(rect, points):
+    '''
+    根据美术嘴巴的convex hull的点得到
+    The next step in alignment is to do a Delaunay triangulation of the points on the convex hull.
+    This allows us to divide the face into smaller parts.
+    :param rect: 待覆盖卡通人脸的嘴巴区域
+    :param points: 待覆盖卡通人脸的嘴巴convex hull中的点
+    :return: a Delaunay triangulation of the points on the convex hull
+    '''
+    # create subdiv
+    subdiv = cv2.Subdiv2D(rect)
+
+    # Insert points into subdiv
+    for p in points:
+        subdiv.insert(p)
+
+    triangleList = subdiv.getTriangleList()
+
+    delaunayTri = []
+
+    pt = []
+
+    for t in triangleList:
+        pt.append((t[0], t[1]))
+        pt.append((t[2], t[3]))
+        pt.append((t[4], t[5]))
+
+        pt1 = (t[0], t[1])
+        pt2 = (t[2], t[3])
+        pt3 = (t[4], t[5])
+
+        if rectContains(rect, pt1) and rectContains(rect, pt2) and rectContains(rect, pt3):
+            ind = []
+            # Get face-points (from 68 face detector) by coordinates
+            for j in range(0, 3):
+                for k in range(0, len(points)):
+                    if (abs(pt[j][0] - points[k][0]) < 1.0 and abs(pt[j][1] - points[k][1]) < 1.0):
+                        ind.append(k)
+                        # Three points form a triangle. Triangle array corresponds to the file tri.txt in FaceMorph
+            if len(ind) == 3:
+                delaunayTri.append((ind[0], ind[1], ind[2]))
+
+        pt = []
+
+    return delaunayTri
+
+
+def warpTriangle(img1, img2, t1, t2):
+    '''
+    Warps and alpha blends triangular regions from img1 and img2 to img
+    对美术嘴巴进行仿射变换，覆盖卡通人脸中的嘴巴，生成融合后的卡通人脸
+    :param img1: 美术提供的卡通人脸图像
+    :param img2: 待覆盖的卡通人脸图像
+    :param t1: 美术嘴巴的三角顶点
+    :param t2: 卡通人脸嘴巴的三角顶点
+    :return: 融合后的卡通人脸
+    '''
+    # Find bounding rectangle for each triangle
+    r1 = cv2.boundingRect(np.float32([t1]))
+    r2 = cv2.boundingRect(np.float32([t2]))
+
+    # Offset points by left top corner of the respective rectangles
+    t1Rect = []
+    t2Rect = []
+    t2RectInt = []
+
+    for i in range(0, 3):
+        t1Rect.append(((t1[i][0] - r1[0]), (t1[i][1] - r1[1])))
+        t2Rect.append(((t2[i][0] - r2[0]), (t2[i][1] - r2[1])))
+        t2RectInt.append(((t2[i][0] - r2[0]), (t2[i][1] - r2[1])))
+
+    # Get mask by filling triangle
+    mask = np.zeros((r2[3], r2[2], 3), dtype=np.float32)
+    cv2.fillConvexPoly(mask, np.int32(t2RectInt), (1.0, 1.0, 1.0), 16, 0)
+
+    # Apply warpImage to small rectangular patches
+    img1Rect = img1[r1[1]:r1[1] + r1[3], r1[0]:r1[0] + r1[2]]
+    # img2Rect = np.zeros((r2[3], r2[2]), dtype = img1Rect.dtype)
+
+    size = (r2[2], r2[3])
+
+    img2Rect = applyAffineTransform(img1Rect, t1Rect, t2Rect, size)
+
+    img2Rect = img2Rect * mask
+
+    # Copy triangular region of the rectangular patch to the output image
+    img2[r2[1]:r2[1] + r2[3], r2[0]:r2[0] + r2[2]] = img2[r2[1]:r2[1] + r2[3], r2[0]:r2[0] + r2[2]] * (
+                (1.0, 1.0, 1.0) - mask)
+
+    img2[r2[1]:r2[1] + r2[3], r2[0]:r2[0] + r2[2]] = img2[r2[1]:r2[1] + r2[3], r2[0]:r2[0] + r2[2]] + img2Rect
+
+
+def getMouthLandmarks(img_path):
+    '''
+    得到卡通人脸的嘴巴轮廓关键点txt文件
+    :param img_path: 人脸图像的路径
+    :return: 嘴巴关键点txt文件路径
+    '''
+    # image = face_recognition.load_image_file(img_path)
+    # face_landmarks_list = face_recognition.face_landmarks(image)
+    # pil_image = Image.fromarray(image)
+    # d = ImageDraw.Draw(pil_image)
+    #
+    # top_lip = face_landmarks_list[0]['top_lip']
+    # bottom_lip = face_landmarks_list[0]['bottom_lip']
+    #
+    # for tup in top_lip:
+    #     bottom_lip.append(tup)
+
+    # 嘴巴关键点检测
+    global mouth_landmark
+    img = cv2.imread(img_path)
+
+    predictor_model = 'shape_predictor_68_face_landmarks.dat'
+    detector = dlib.get_frontal_face_detector()  # dlib人脸检测器
+    predictor = dlib.shape_predictor(predictor_model)
+
+    # cv2读取图像
+    img = cv2.imread(img_path)
+
+    # 取灰度
+    img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+
+    # 人脸数rects（rectangles）
+    rects = detector(img_gray, 0)
+
+    (path, file) = os.path.split(img_path)
+    txt_path = path + str('/') + file[:-4] + '.txt'
+    # print(txt_path)
+    file_handle = open(txt_path, 'a')
+
+    for i in range(len(rects)):
+        landmarks = np.matrix([[p.x, p.y] for p in predictor(img, rects[i]).parts()])
+
+        for idx, point in enumerate(landmarks[49:68], 48):
+            # 68点的坐标
+            pos = (point[0, 0], point[0, 1])
+            print(idx + 1, pos)
+            pos_info = str(point[0, 0]) + ' ' + str(point[0, 1]) + '\n'
+            file_handle.write(pos_info)
+            # 利用cv2.circle给每个特征点画一个圈，共68个
+            cv2.circle(img, pos, 3, color=(0, 255, 0))
+
+        mouth_landmark = landmarks[49:68]
+
+    file_handle.close()
+
+    # 将得到的关键点位置信息按行写进txt文件中，并将文件名以图像名字命名
+    # 获取卡通人脸图片的filename
+    # if not os.path.exists(txt_path):
+    #     with open(txt_path, mode='w', encoding='utf-8') as ff:
+    #         for line in mouth_landmark:
+    #             for a in line:
+    #                 a = str(a)
+    #                 ff.write(a)
+    #                 ff.write('\t')
+    #             ff.write('\n')
+
+    cv2.imwrite(file[:-4] + str('_') + "draw.png", img, [int(cv2.IMWRITE_PNG_COMPRESSION), 9])
+
+    return txt_path
+
+
+def startSwap(img1_path, img2_path):
+    '''
+    生成置换后带有美术嘴巴的卡通人脸
+    :param img1_path: 形变对象 .png
+    :param img2_path: 待覆盖对象 .jpg
+    :return:
+    '''
+    # Read images
+    # 对美术嘴巴图片进行预处理
+    ori_img1 = cv2.imread(img1_path)
+    img1 = cv2.resize(ori_img1, (512, 512), interpolation=cv2.INTER_AREA)
+    path, file = os.path.split(img1_path)
+    # print(path) # /data/data_hao/PFLD-pytorch/woman_face
+    # print(file) # woman05.png
+    filename = file.split('.png')[0]
+    img1_crop_path = path + str('/') + filename + '.png'
+    cv2.imwrite(img1_crop_path, img1, [int(cv2.IMWRITE_PNG_COMPRESSION), 9])
+
+    img2 = cv2.imread(img2_path)
+    img1Warped = np.copy(img2)
+
+    # get txt_file of corresponding points
+    txt_path1 = getMouthLandmarks(img1_crop_path)
+    # 如果美术人脸得到关键点有误，人工打标注得到嘴巴区域的关键点保存为txt文件
+    # txt_path2 = '/data/data_hao/PFLD-pytorch/woman_face/woman04.txt'
+    txt_path2 = getMouthLandmarks(img2_path)
+
+    # Read array of corresponding points
+    points1 = readPoints(txt_path1)
+    points2 = readPoints(txt_path2)
+
+    # Find convex hull
+    # 寻找嘴巴凸包(将边界点顺序连接成多边形)
+    hull1 = []
+    hull2 = []
+
+    #  In Computer Vision and Math jargon, the boundary of a collection of points or shape is called a “hull”.
+    #  A boundary that does not have any concavities is called a “Convex Hull”.
+    #  The convex hull of a set of points can be calculated using OpenCV’s convexHull function.
+    hullIndex = cv2.convexHull(np.array(points2), returnPoints=False)
+    print(len(hullIndex))
+    for i in range(0, len(hullIndex)):
+        hull1.append(points1[int(hullIndex[i])])
+        hull2.append(points2[int(hullIndex[i])])
+
+    # Find delanauy traingulation for convex hull points
+    # 三角剖分，将嘴巴区域进行三角剖分，划分成多个微小区域
+    sizeImg2 = img2.shape
+    rect = (0, 0, sizeImg2[1], sizeImg2[0])
+
+    # do a Delaunay triangulation of the points on the convex hull
+    dt = calculateDelaunayTriangles(rect, hull2)
+
+    if len(dt) == 0:
+        quit()
+
+    # Apply affine transformation to Delaunay triangles
+    # The final steps of face alignment to to consider corresponding triangles between the source face
+    # and the target face, and affine warp the source face triangle onto the target face.
+    for i in range(0, len(dt)):
+        t1 = []
+        t2 = []
+
+        # get points for img1, img2 corresponding to the triangles
+        for j in range(0, 3):
+            t1.append(hull1[dt[i][j]])
+            t2.append(hull2[dt[i][j]])
+        # 仿射变化对齐
+        warpTriangle(img1, img1Warped, t1, t2)
+
+    # How to seamlessly combine the two images?
+    # Clone seamlessly
+
+    # Calculate Mask
+    hull8U = []
+    for i in range(0, len(hull2)):
+        hull8U.append((hull2[i][0], hull2[i][1]))
+
+    mask = np.zeros(img2.shape, dtype=img2.dtype)
+
+    cv2.fillConvexPoly(mask, np.int32(hull8U), (255, 255, 255))
+
+    r = cv2.boundingRect(np.float32([hull2]))
+
+    center = ((r[0] + int(r[2] / 2), r[1] + int(r[3] / 2)))
+
+    # Clone seamlessly.
+    output = cv2.seamlessClone(np.uint8(img1Warped), img2, mask, center, cv2.NORMAL_CLONE)
+
+    return output
+
+
+if __name__ == '__main__':
+    # img1_path表示形变卡通人脸
+    # img2_path表示待覆盖卡通人脸
+    img1_path = '/data/data_hao/PFLD-pytorch/woman_face/woman04.png'
+    img2_path = '/data/data_hao/PFLD-pytorch/0000_007.jpg'
+    output = startSwap(img1_path, img2_path)
+    cv2.imwrite('swap_mouth_new.jpg', output, [int(cv2.IMWRITE_PNG_COMPRESSION), 9])
+    # cv2.imshow("Face Swapped", output)
+    # cv2.waitKey(0)
+    # cv2.destroyAllWindows()
+
+    # # Make sure OpenCV is version 3.0 or above
+    # (major_ver, minor_ver, subminor_ver) = (cv2.__version__).split('.')
+    #
+    # if int(major_ver) < 3 :
+    #     print >>sys.stderr, 'ERROR: Script needs OpenCV 3.0 or higher'
+    #     sys.exit(1)
+
+

split_image.py

+# -*- coding: utf-8 -*-
+'''
+* @author [v_daweihao]
+* @version [2021-08-09]
+* <image size(1024, 512)-->(512, 512)>
+'''
+
+import cv2
+import os
+from PIL import Image
+
+def split_img(img_dir, save_dir):
+    """分离图片
+        Args:
+            img_dir:
+            save_dir:
+    """
+
+    for img_file in os.listdir(img_dir):
+        img_path = os.path.join(img_dir, img_file)
+        img = cv2.imread(img_path)
+        size = img.shape
+        new_height = size[0]
+        new_width = size[1] // 2
+        split_img = img[0:new_height, 0:new_width]
+        split_img_path = os.path.join(save_dir, img_file)
+        cv2.imwrite(split_img_path, split_img)
+
+
+if __name__ == '__main__':
+    # img_dir = r'D:\20210809\cartoon_noseg_0807\cartoon_noseg_0727\train'
+    # save_dir = r'D:\20210809\split_image\train'
+    # split_img(img_dir, save_dir)
+
+    # img_dir = r'D:\20210809\cartoon_noseg_0807\cartoon_noseg_0727\test'
+    # save_dir = r'D:\20210809\split_image\test'
+    # split_img(img_dir, save_dir)
+    #
+    # img_dir = r'D:\20210809\cartoon_noseg_0807\cartoon_noseg_0727\val'
+    # save_dir = r'D:\20210809\split_image\val'
+    # split_img(img_dir, save_dir)
+    img = cv2.imread(r'D:\20210809\split_image\train\MX2mzRjuQB.jpg')
+    crop_img = cv2.resize(img, (512, 512), interpolation=cv2.INTER_AREA)
+
+    cv2.imwrite('crop_asian.png', crop_img)
+
+
+
+
+