1.瘦脸结果图

opencv+Dlib python瘦脸代码_opencv

import dlib

import cv2

import numpy as np

import math

predictor_path = ‘shape_predictor_68_face_landmarks.dat’

使用dlib自带的frontal_face_detector作为我们的特征提取器

detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(predictor_path)

def landmark_dec_dlib_fun(img_src):
    #转灰度图
    img_gray = cv2.cvtColor(img_src, cv2.COLOR_BGR2GRAY)
    cv2.imshow('grayimg',img_gray)
    land_marks = []

    rects = detector(img_gray, 0)
    print(rects)
    for i in range(len(rects)):
        land_marks_node = np.matrix([[p.x, p.y] for p in predictor(img_gray, rects[i]).parts()])
        for idx,point in enumerate(land_marks_node):
            # 68点坐标
            pos = (point[0,0],point[0,1])
            print(idx,pos)
            # 利用cv2.circle给每个特征点画一个圈，共68个
            cv2.circle(img_src, pos, 5, color=(0, 255, 0))
            # 利用cv2.putText输出1-68
            font = cv2.FONT_HERSHEY_SIMPLEX
            cv2.putText(img_src, str(idx + 1), pos, font, 0.5, (0, 0, 0), 1, cv2.LINE_AA)
        land_marks.append(land_marks_node)

    return land_marks

图像局部扭曲算法有三个：局部缩放（Local Scaling）算法、局部平移（Local Transition）算法和局部旋转（Local Rotation）算法。其中应用局部缩放算法可实现眼睛放大，局部平移算法则可用于实现瘦脸效果。

'''
方法： Interactive Image Warping 局部平移算法
'''

#startX, startY,left_landmark[0, 0], left_landmark[0, 1]
#endPt[0, 0], endPt[0, 1]
#r_left,r_right为半径
def localTranslationWarp(srcImg, startX, startY, endX, endY, radius):
    ddradius = float(radius * radius)
    copyImg = np.zeros(srcImg.shape, np.uint8)
    copyImg = srcImg.copy()

    # 计算公式中的|m-c|^2
    ddmc = (endX - startX) * (endX - startX) + (endY - startY) * (endY - startY)
    H, W, C = srcImg.shape
    for i in range(W):
        for j in range(H):
            # 计算该点是否在形变圆的范围之内
            # 优化，第一步，直接判断是会在（startX,startY)的矩阵框中
            if math.fabs(i - startX) > radius and math.fabs(j - startY) > radius:
                continue

            distance = (i - startX) * (i - startX) + (j - startY) * (j - startY)

            if (distance < ddradius):
                # 计算出（i,j）坐标的原坐标
                # 计算公式中右边平方号里的部分
                ratio = (ddradius - distance) / (ddradius - distance + ddmc)
                ratio = ratio * ratio

                # 映射原位置
                UX = i - ratio * (endX - startX)
                UY = j - ratio * (endY - startY)

                # 根据双线性插值法得到UX，UY的值
                value = BilinearInsert(srcImg, UX, UY)
                # 改变当前 i ，j的值
                copyImg[j, i] = value

    return copyImg
def BilinearInsert(src, ux, uy):
    w, h, c = src.shape
    if c == 3:
        x1 = int(ux)
        x2 = x1 + 1
        y1 = int(uy)
        y2 = y1 + 1

        part1 = src[y1, x1].astype(np.float) * (float(x2) - ux) * (float(y2) - uy)
        part2 = src[y1, x2].astype(np.float) * (ux - float(x1)) * (float(y2) - uy)
        part3 = src[y2, x1].astype(np.float) * (float(x2) - ux) * (uy - float(y1))
        part4 = src[y2, x2].astype(np.float) * (ux - float(x1)) * (uy - float(y1))

        insertValue = part1 + part2 + part3 + part4

        return insertValue.astype(np.int8)


def face_thin_auto(src):
    #68个关键点二维数组
    landmarks = landmark_dec_dlib_fun(src)

    # 如果未检测到人脸关键点，就不进行瘦脸
    if len(landmarks) == 0:
        return

    for landmarks_node in landmarks:
        #第4个点左
        left_landmark = landmarks_node[3]
        #第6个点左
        left_landmark_down = landmarks_node[5]
        #第14个点右
        right_landmark = landmarks_node[13]
        #第16个点右
        right_landmark_down = landmarks_node[15]
        #第31个点鼻尖
        endPt = landmarks_node[30]

        # 计算第4个点到第6个点的距离作为瘦脸距离
        r_left = math.sqrt(
            (left_landmark[0, 0] - left_landmark_down[0, 0])== * (left_landmark[0, 0] - left_landmark_down[0, 0]) +
            (left_landmark[0, 1] - left_landmark_down[0, 1]) * (left_landmark[0, 1] - left_landmark_down[0, 1]))

        # 计算第14个点到第16个点的距离作为瘦脸距离
        r_right = math.sqrt(
            (right_landmark[0, 0] - right_landmark_down[0, 0]) * (right_landmark[0, 0] - right_landmark_down[0, 0]) +
            (right_landmark[0, 1] - right_landmark_down[0, 1]) * (right_landmark[0, 1] - right_landmark_down[0, 1]))

        # 瘦左边脸
        thin_image = localTranslationWarp(src, left_landmark[0, 0], left_landmark[0, 1], endPt[0, 0], endPt[0, 1],
                                          r_left)
        # 瘦右边脸
        thin_image = localTranslationWarp(thin_image, right_landmark[0, 0], right_landmark[0, 1], endPt[0, 0],
                                          endPt[0, 1], r_right)

    # 显示
    cv2.imshow('thin', thin_image)
    cv2.imwrite('thin.jpg', thin_image)
src = cv2.imread('1.jpg')
cv2.imshow('src', src)
face_thin_auto(src)
# cv2.waitKey(0)

img = cv2.imread('1.jpg')
rects = detector(img, 0)
print(rects)
#绘制关键点
for i in range(len(rects)):
    land_marks_node = np.matrix([[p.x, p.y] for p in predictor(img, rects[i]).parts()])
    for idx,point in enumerate(land_marks_node):
        # 68点坐标
        pos = (point[0,0],point[0,1])
        print(idx,pos)
        # 利用cv2.circle给每个特征点画一个圈，共68个
        cv2.circle(img, pos, 5, 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, 0), 1, cv2.LINE_AA)
cv2.imshow('img', img)
cv2.waitKey(0)