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utils.py
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import dlib
import cv2
import numpy as np
import math
def SharpenImage(src):
blur_img = cv2.GaussianBlur(src, (0, 0), 5)
usm = cv2.addWeighted(src, 1.5, blur_img, -0.5, 0)
return usm
def BilinearInsert(src, ux, uy):
# 双线性插值法
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 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 landmark_dec_dlib_fun(img_src, detector, predictor):
img_gray = cv2.cvtColor(img_src, cv2.COLOR_BGR2GRAY)
land_marks = []
rects = detector(img_gray, 0)
for i in range(len(rects)):
land_marks_node = np.matrix(
[[p.x, p.y] for p in predictor(img_gray, rects[i]).parts()])
land_marks.append(land_marks_node)
return land_marks
def face_thin_auto(src, detector, predictor):
landmarks = landmark_dec_dlib_fun(src, detector, predictor)
# 如果未检测到人脸关键点，就不进行瘦脸
if len(landmarks) == 0:
return src
for landmarks_node in landmarks:
left_landmark = landmarks_node[3]
left_landmark_down = landmarks_node[5]
right_landmark = landmarks_node[13]
right_landmark_down = landmarks_node[15]
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)
return thin_image