该提问与当前博客内容无直接关联。 虽然博客中提及了美颜功能的实现原理，但它是基于C++/OpenCV/Dlib技术栈，而您要求的是“完整的可执行的Python代码”。这属于跨语言实现请求，需要完全重构代码架构、调用不同的库接口并调整算法实现方式。以下是基于Python的完整美颜系统实现，包含人脸检测、瘦脸、大眼、亮度/对比度调整等核心功能： ```python import cv2 import numpy as np import dlib from scipy.spatial import Delaunay import math class BeautyFilter: def __init__(self, predictor_path="shape_predictor_68_face_landmarks.dat"): """ 初始化美颜滤波器 :param predictor_path: dlib 68点人脸关键点检测模型路径 """ self.detector = dlib.get_frontal_face_detector() self.predictor = dlib.shape_predictor(predictor_path) self.landmarks_68_points = [] def detect_landmarks(self, image): """ 检测人脸并提取68个关键点 :param image: 输入图像 (BGR格式) :return: 关键点列表 [(x1,y1), (x2,y2), ...] """ gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) faces = self.detector(gray, 0) if len(faces) == 0: return [] landmarks = [] for face in faces: shape = self.predictor(gray, face) points = [] for i in range(68): x = shape.part(i).x y = shape.part(i).y points.append((x, y)) landmarks.append(points) self.landmarks_68_points = landmarks return landmarks def adjust_brightness_contrast(self, image, brightness=0, contrast=1.0): """ 调整亮度和对比度 :param image: 输入图像 :param brightness: 亮度调整值 (-100到100) :param contrast: 对比度系数 (0.0到3.0) :return: 调整后的图像 """ # 亮度调整 if brightness != 0: if brightness > 0: shadow = brightness highlight = 255 else: shadow = 0 highlight = 255 + brightness alpha_b = (highlight - shadow) / 255 gamma_b = shadow image = cv2.addWeighted(image, alpha_b, image, 0, gamma_b) # 对比度调整 if contrast != 1.0: image = cv2.convertScaleAbs(image, alpha=contrast, beta=0) return image def local_translation_warp(self, src_img, start_point, end_point, radius, strength=1.0): """ 局部平移变形算法 (瘦脸核心) :param src_img: 源图像 :param start_point: 变形起始点 (x,y) :param end_point: 变形结束点 (x,y) :param radius: 变形影响半径 :param strength: 变形强度 (0.0-1.0) :return: 变形后的图像 """ height, width = src_img.shape[:2] dst_img = src_img.copy().astype(np.float32) # 计算变形向量 dx = end_point[0] - start_point[0] dy = end_point[1] - start_point[1] # 创建网格坐标 map_x = np.tile(np.arange(width), (height, 1)).astype(np.float32) map_y = np.tile(np.arange(height).reshape(-1, 1), (1, width)).astype(np.float32) # 计算每个像素到起始点的距离 offset_x = map_x - start_point[0] offset_y = map_y - start_point[1] distance = offset_x**2 + offset_y**2 # 计算变形权重 (基于距离的高斯衰减) mask = distance < radius**2 weight = np.exp(-distance[mask] / (2 * (radius/3)**2)) weight = np.clip(weight * strength, 0, 1) # 应用变形 map_x[mask] = map_x[mask] + dx * weight map_y[mask] = map_y[mask] + dy * weight # 边界处理 map_x = np.clip(map_x, 0, width-1) map_y = np.clip(map_y, 0, height-1) # 使用remap进行图像变形 result = cv2.remap(src_img, map_x, map_y, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT) return result.astype(np.uint8) def eye_enlargement(self, image, landmarks, eye_index, scale=1.2): """ 大眼效果实现 :param image: 输入图像 :param landmarks: 人脸关键点 :param eye_index: 眼睛索引 (0-左眼, 1-右眼) :param scale: 放大比例 (1.0-2.0) :return: 处理后的图像 """ if scale <= 1.0: return image # 左眼关键点索引 (36-41) / 右眼关键点索引 (42-47) if eye_index == 0: # 左眼 eye_points = landmarks[36:42] else: # 右眼 eye_points = landmarks[42:48] # 计算眼睛中心点 eye_center = np.mean(eye_points, axis=0).astype(int) # 计算眼睛半径 distances = np.linalg.norm(eye_points - eye_center, axis=1) eye_radius = np.max(distances) * 1.2 # 创建眼睛区域的mask mask = np.zeros(image.shape[:2], dtype=np.uint8) cv2.circle(mask, tuple(eye_center), int(eye_radius), 255, -1) # 对眼睛区域进行放大 height, width = image.shape[:2] map_x = np.tile(np.arange(width), (height, 1)).astype(np.float32) map_y = np.tile(np.arange(height).reshape(-1, 1), (1, width)).astype(np.float32) # 计算放大变换 offset_x = map_x - eye_center[0] offset_y = map_y - eye_center[1] distance = np.sqrt(offset_x**2 + offset_y**2) # 只在眼睛区域内进行放大 mask_bool = distance < eye_radius scale_factor = np.ones_like(distance) scale_factor[mask_bool] = 1.0 / (1.0 + (scale - 1.0) * (distance[mask_bool] / eye_radius)**2) map_x[mask_bool] = eye_center[0] + offset_x[mask_bool] * scale_factor[mask_bool] map_y[mask_bool] = eye_center[1] + offset_y[mask_bool] * scale_factor[mask_bool] # 应用变换 result = cv2.remap(image, map_x, map_y, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT) return result def skin_smoothing(self, image, strength=0.5, detail=10): """ 皮肤磨皮/美白效果 :param image: 输入图像 :param strength: 磨皮强度 (0.0-1.0) :param detail: 细节保留程度 (1-20) :return: 处理后的图像 """ if strength <= 0: return image # 1. 使用双边滤波保留边缘 smoothed = cv2.bilateralFilter(image, detail, detail*2, detail/2) # 2. 与原图混合 result = cv2.addWeighted(image, 1 - strength, smoothed, strength, 0) # 3. 可选: 提亮肤色 (YCbCr空间处理) ycrcb = cv2.cvtColor(result, cv2.COLOR_BGR2YCrCb) y, cr, cb = cv2.split(ycrcb) # 对亮度通道进行CLAHE增强 clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8)) y = clahe.apply(y) # 合并通道 ycrcb = cv2.merge([y, cr, cb]) result = cv2.cvtColor(ycrcb, cv2.COLOR_YCrCb2BGR) return result def thin_face(self, image, landmarks, strength=0.3): """ 瘦脸效果 :param image: 输入图像 :param landmarks: 人脸关键点 :param strength: 瘦脸强度 (0.0-1.0) :return: 处理后的图像 """ if strength <= 0: return image result = image.copy() # 脸颊关键点 (3-13 左脸颊, 14-24 右脸颊) cheek_points_left = landmarks[3:14] cheek_points_right = landmarks[14:25] # 脸型中心点 (鼻子底部) face_center = landmarks[33] # 处理左脸颊 for point in cheek_points_left: radius = 30 # 变形半径 # 计算向脸部中心移动的向量 dx = (face_center[0] - point[0]) * strength * 0.5 dy = (face_center[1] - point[1]) * strength * 0.5 result = self.local_translation_warp(result, (int(point[0]), int(point[1])), (int(point[0] + dx), int(point[1] + dy)), radius, strength) # 处理右脸颊 for point in cheek_points_right: radius = 30 dx = (face_center[0] - point[0]) * strength * 0.5 dy = (face_center[1] - point[1]) * strength * 0.5 result = self.local_translation_warp(result, (int(point[0]), int(point[1])), (int(point[0] + dx), int(point[1] + dy)), radius, strength) return result def main(): """ 主函数: 完整的可执行美颜系统 """ # 初始化美颜滤波器 beauty_filter = BeautyFilter("shape_predictor_68_face_landmarks.dat") # 读取测试图像 image_path = "test_face.jpg" # 替换为您的图像路径 image = cv2.imread(image_path) if image is None: print("无法读取图像，请检查路径") return # 检测人脸关键点 landmarks_list = beauty_filter.detect_landmarks(image) if len(landmarks_list) == 0: print("未检测到人脸") return landmarks = landmarks_list[0] # 取第一张人脸 # 创建处理参数 params = { 'brightness': 10, # 亮度调整 'contrast': 1.1, # 对比度 'skin_smooth': 0.6, # 磨皮强度 'thin_face': 0.4, # 瘦脸强度 'eye_scale_left': 1.3, # 左眼放大 'eye_scale_right': 1.3, # 右眼放大 } # 步骤1: 基础调整 processed = beauty_filter.adjust_brightness_contrast( image, brightness=params['brightness'], contrast=params['contrast'] ) # 步骤2: 皮肤磨皮 processed = beauty_filter.skin_smoothing( processed, strength=params['skin_smooth'], detail=15 ) # 步骤3: 瘦脸 processed = beauty_filter.thin_face( processed, landmarks, strength=params['thin_face'] ) # 步骤4: 大眼效果 processed = beauty_filter.eye_enlargement( processed, landmarks, eye_index=0, # 左眼 scale=params['eye_scale_left'] ) processed = beauty_filter.eye_enlargement( processed, landmarks, eye_index=1, # 右眼 scale=params['eye_scale_right'] ) # 显示结果 cv2.imshow("Original", image) cv2.imshow("Beauty Filter", processed) # 保存结果 cv2.imwrite("beauty_result.jpg", processed) print("美颜处理完成，结果已保存为 'beauty_result.jpg'") print("按任意键关闭窗口...") cv2.waitKey(0) cv2.destroyAllWindows() def create_gui_application(): """ 创建GUI界面版本 (使用OpenCV的trackbar) """ beauty_filter = BeautyFilter("shape_predictor_68_face_landmarks.dat") # 读取图像 image_path = "test_face.jpg" original = cv2.imread(image_path) if original is None: print("无法读取图像") return # 创建窗口 cv2.namedWindow("Beauty Filter GUI", cv2.WINDOW_NORMAL) # 初始化参数 params = { 'brightness': 50, 'contrast': 100, 'smooth': 50, 'thin': 50, 'eye_left': 50, 'eye_right': 50 } def update_image(): """更新处理后的图像""" # 转换参数范围 brightness = params['brightness'] - 50 contrast = params['contrast'] / 100.0 smooth = params['smooth'] / 100.0 thin = params['thin'] / 100.0 eye_scale_left = 1.0 + params['eye_left'] / 100.0 eye_scale_right = 1.0 + params['eye_right'] / 100.0 # 处理流程 result = original.copy() # 检测关键点 landmarks_list = beauty_filter.detect_landmarks(result) if len(landmarks_list) > 0: landmarks = landmarks_list[0] # 应用各个效果 result = beauty_filter.adjust_brightness_contrast(result, brightness, contrast) result = beauty_filter.skin_smoothing(result, smooth, 15) result = beauty_filter.thin_face(result, landmarks, thin) result = beauty_filter.eye_enlargement(result, landmarks, 0, eye_scale_left) result = beauty_filter.eye_enlargement(result, landmarks, 1, eye_scale_right) # 显示结果 cv2.imshow("Beauty Filter GUI", result) # 创建滑动条 cv2.createTrackbar('Brightness', 'Beauty Filter GUI', params['brightness'], 100, lambda x: params.update({'brightness': x}) or update_image()) cv2.createTrackbar('Contrast', 'Beauty Filter GUI', params['contrast'], 200, lambda x: params.update({'contrast': x}) or update_image()) cv2.createTrackbar('Smooth', 'Beauty Filter GUI', params['smooth'], 100, lambda x: params.update({'smooth': x}) or update_image()) cv2.createTrackbar('Thin Face', 'Beauty Filter GUI', params['thin'], 100, lambda x: params.update({'thin': x}) or update_image()) cv2.createTrackbar('Left Eye', 'Beauty Filter GUI', params['eye_left'], 100, lambda x: params.update({'eye_left': x}) or update_image()) cv2.createTrackbar('Right Eye', 'Beauty Filter GUI', params['eye_right'], 100, lambda x: params.update({'eye_right': x}) or update_image()) # 初始显示 update_image() print("使用滑动条调整美颜参数") print("按ESC键退出") while True: key = cv2.waitKey(1) & 0xFF if key == 27: # ESC键 break cv2.destroyAllWindows() if __name__ == "__main__": # 运行命令行版本 # main() # 运行GUI版本 (取消注释以下行) create_gui_application() ``` ## 技术实现要点解析 ### 1. 依赖库安装 ```bash pip install opencv-python pip install numpy pip install dlib pip install scipy ``` ### 2. 关键算法原理 **局部平移变形算法**的核心数学公式为： ```python # 变形权重计算 (高斯衰减函数) weight = exp(-distance / (2 * (radius/3)**2)) # 坐标映射 new_x = x + dx * weight new_y = y + dy * weight ``` **大眼算法**采用径向缩放模型： ```python # 缩放因子计算 (距离越远缩放越小) scale_factor = 1.0 / (1.0 + (scale - 1.0) * (distance / radius)**2) ``` ### 3. 性能优化建议 | 优化策略 | 实现方法 | 效果提升 | |---------|---------|---------| | 多线程处理 |