https://blog.csdn.net/hjimce/article/details/46550001
https://www.cnblogs.com/shushen/p/5887513.html
import numpy as np
import cv2
class trans():
def __init__(self, img, pi):
width, height = img.shape[:2]
pcth = np.repeat(np.arange(height).reshape(height, 1), [width], axis=1)
pctw = np.repeat(np.arange(width).reshape(width, 1), [height], axis=1).T
self.img_coordinate = np.swapaxes(np.array([pcth, pctw]), 1, 2).T
self.cita = compute_G(self.img_coordinate, pi, height, width)
self.pi = pi
self.W, self.A, self.Z = pre_compute_waz(self.pi, height, width, self.img_coordinate)
self.height = height
self.width = width
def deformation(self, img, qi):
qi = self.pi * 2 - qi
mapxy = np.swapaxes(
np.float32(compute_fv(qi, self.W, self.A, self.Z, self.height, self.width, self.cita, self.img_coordinate)),
0, 1)
img = cv2.remap(img, mapxy[:, :, 0], mapxy[:, :, 1], borderMode=cv2.BORDER_WRAP, interpolation=cv2.INTER_LINEAR)
return img
def pre_compute_waz(pi, height, width, img_coordinate):
'''
:param pi:
:param height:
:param width:
:param img_coordinate: 坐标信息矩阵
:return:
'''
# height*width*控制点个数
wi = np.reciprocal(
np.power(np.linalg.norm(np.subtract(pi, img_coordinate.reshape(height, width, 1, 2)) + 0.000000001, axis=3), 2))
# height*width*2
pstar = np.divide(np.matmul(wi, pi), np.sum(wi, axis=2).reshape(height, width, 1))
# height*width*控制点个数*2
phat = np.subtract(pi, pstar.reshape(height, width, 1, 2))
z1 = np.subtract(img_coordinate, pstar)
z2 = np.repeat(np.swapaxes(np.array([z1[:, :, 1], -z1[:, :, 0]]), 1, 2).T.reshape(height, width, 1, 2, 1),
[pi.shape[0]], axis=2)
# height*width*控制点个数*2*1
z1 = np.repeat(z1.reshape(height, width, 1, 2, 1), [pi.shape[0]], axis=2)
# height*width*控制点个数*1*2
s1 = phat.reshape(height, width, pi.shape[0], 1, 2)
s2 = np.concatenate((s1[:, :, :, :, 1], -s1[:, :, :, :, 0]), axis=3).reshape(height, width, pi.shape[0], 1, 2)
a = np.matmul(s1, z1)
b = np.matmul(s1, z2)
c = np.matmul(s2, z1)
d = np.matmul(s2, z2)
# 重构wi形状
ws = np.repeat(wi.reshape(height, width, pi.shape[0], 1), [4], axis=3)
# height*width*控制点个数*2*2
A = (ws * np.concatenate((a, b, c, d), axis=3).reshape(height, width, pi.shape[0], 4)).reshape(height, width,
pi.shape[0], 2, 2)
return wi, A, z1
def compute_fv(qi, W, A, Z, height, width, cita, img_coordinate):
'''
:param
qi:
:param
W:
:param
A:
:param
Z:
:param
height:
:param
width:
:param
cita: 衰减系数，减少局部变形对整体的影响
:param
img_coordinate:
:return:
'''
qstar = np.divide(np.matmul(W,qi), np.sum(W, axis=2).reshape(height,width,1))
qhat = np.subtract(qi, qstar.reshape(height, width, 1, 2)).reshape(height, width, qi.shape[0], 1, 2)
fv_ = np.sum(np.matmul(qhat, A),axis=2)
fv = np.linalg.norm(Z[:,:,0,:,:],axis=2) / (np.linalg.norm(fv_,axis=3)+0.0000000001) * fv_[:,:,0,:] + qstar
fv = (fv - img_coordinate) * cita.reshape(height, width, 1) + img_coordinate
return fv
def compute_G(img_coordinate, pi, height, width, thre = 0.7):
'''
衰减系数计算
:param img_coordinate:
:param pi:
:param height:
:param width:
:param thre: 影响系数，数值越大对控制区域外影响越大，反之亦然，取值范围0到无穷大
:return:
'''
max = np.max(pi, 0)
min = np.min(pi, 0)
length = np.max(max - min)
# 计算控制区域中心
# p_ = (max + min) // 2
p_ = np.sum(pi,axis=0) // pi.shape[0]
# 计算控制区域
minx, miny = min - length
maxx, maxy = max + length
minx = minx if minx > 0 else 0
miny = miny if miny > 0 else 0
maxx = maxx if maxx < height else height
maxy = maxy if maxy < width else width
k1 =(p_ - [0,0])[1] / (p_ - [0,0])[0]
k2 =(p_ - [height,0])[1] / (p_ - [height,0])[0]
k4 =(p_ - [0,width])[1] / (p_ - [0,width])[0]
k3 =(p_ - [height, width])[1] / (p_ - [height, width])[0]
k = (np.subtract(p_, img_coordinate)[:, :, 1] / (np.subtract(p_, img_coordinate)[:, :, 0] + 0.000000000001)).reshape(height, width, 1)
k = np.concatenate((img_coordinate, k), axis=2)
k[:,:p_[1],0][(k[:,:p_[1],2] > k1) | (k[:,:p_[1],2] < k2)] = (np.subtract(p_[1], k[:,:,1]) / p_[1]).reshape(height, width, 1)[:,:p_[1],0][(k[:,:p_[1],2] > k1) | (k[:,:p_[1],2] < k2)]
k[:,p_[1]:,0][(k[:,p_[1]:,2] > k3) | (k[:,p_[1]:,2] < k4)] = (np.subtract(k[:,:,1], p_[1]) / (width - p_[1])).reshape(height, width, 1)[:,p_[1]:,0][(k[:,p_[1]:,2] > k3) | (k[:,p_[1]:,2] < k4)]
k[:p_[0],:,0][(k1 >= k[:p_[0],:,2]) & (k[:p_[0],:,2] >= k4)] = (np.subtract(p_[0], k[:,:,0]) / p_[0]).reshape(height, width, 1)[:p_[0],:,0][(k1 >= k[:p_[0],:,2]) & (k[:p_[0],:,2] >= k4)]
k[p_[0]:,:,0][(k3 >= k[p_[0]:,:,2]) & (k[p_[0]:,:,2] >= k2)] = (np.subtract(k[:,:,0], p_[0]) / (height - p_[0])).reshape(height, width, 1)[p_[0]:,:,0][(k3 >= k[p_[0]:,:,2]) & (k[p_[0]:,:,2] >= k2)]
cita = np.exp(-np.power(k[:,:,0] / thre,2))
cita[minx:maxx,miny:maxy] = 1
# 如果不需要局部变形，可以把cita的值全置为1
# cita = 1
return cita
if __name__=="__main__":
img=cv2.imread('20200628112442460.jpg')
img0=img
"""
pi:初始点位，这里定位双眼，鼻尖，双腮边缘共5个点
qi:目标点位，保持双眼，鼻尖不动，双腮边缘向内侧移动20个像素点，呈现效果为瘦脸
"""
pi = np.array([200, 200, 315, 200, 260, 260, 160, 326, 356, 326]).reshape(-1, 2) # -1代表不设限制，由程序自己计算是多少
qi = np.array([200, 200, 315, 200, 260, 260, 180, 326, 336, 326]).reshape(-1, 2)
# 用绿点标出出初始点，观察是否定位准确
# for i in range(len(pi)):
# cv2.circle(img0, (pi[i][0], pi[i][1]), 2, (0,255,0))
# cv2.namedWindow("sour", 0)
# cv2.resizeWindow("sour", 640, 480)
# cv2.imshow('sour', img0)
# cv2.waitKey(0)
# 开始图像变形
ddd = trans(img, pi)
img = ddd.deformation(img, qi)
# 展示源图
cv2.namedWindow("sour", 0) # 显示原图
cv2.resizeWindow("sour", 640, 480)
cv2.imshow('sour', img0)
# 展示效果图
cv2.namedWindow("bianxing", 0) # 显示效果图
cv2.resizeWindow("bianxing", 640, 480)
cv2.imshow('bianxing', img)
cv2.waitKey(0)
