windows python2 dlib 人脸对齐

#!/usr/bin/python

-- coding: UTF-8 --

import cv2
import dlib
import sys
import json
import os
import numpy as np
import matplotlib.pyplot as plt
import pdb
import matplotlib.pyplot as plt
SCALE_FACTOR = 1 # 图像的放缩比
index_dir = ‘E:/DisguisedFacesInTheWild/subjects’
root_dir = ‘E:/DisguisedFacesInTheWild/crop’
output_dir = ‘E:/DisguisedFacesInTheWild/alignment’
Base_path = ‘E:/DisguisedFacesInTheWild/deal/exp.jpg’
split = ‘Testing’ #Testing/Training

PREDICTOR_PATH = “E:/DisguisedFacesInTheWild/deal/shape_predictor_68_face_landmarks.dat”
ALIGN_POINTS = list(range(0,68))

detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(PREDICTOR_PATH)
images_noface = []
images_withface = []
#以numpy数组的形式获取图像特征点，并返回68x2元素矩阵，其每一行对应于输入图像中特定特征点的x，y坐标。
def get_landmarks(im):
rects = detector(im, 1)

if len(rects) > 1:
    raise TooManyFaces
if len(rects) == 0:
    return 'NoFaces'
    #raise NoFaces

return np.matrix([[p.x, p.y] for p in predictor(im, rects[0]).parts()])

#读取图片
def read_im_and_landmarks(fname):
im = cv2.imread(fname, cv2.IMREAD_COLOR)
im = cv2.resize(im, (im.shape[1] * SCALE_FACTOR,
im.shape[0] * SCALE_FACTOR))
s = get_landmarks(im)

return im, s

#人脸关键点，画图函数
def annotate_landmarks(im, landmarks):
im = im.copy()
for idx, point in enumerate(landmarks):
pos = (point[0, 0], point[0, 1])
cv2.putText(im, str(idx), pos,
fontFace=cv2.FONT_HERSHEY_SCRIPT_SIMPLEX,
fontScale=0.4,
color=(0, 0, 255))
cv2.circle(im, pos, 3, color=(0, 255, 255))
return im

#获取变换矩阵
def transformation_from_points(points1, points2):
points1 = points1.astype(np.float64)
points2 = points2.astype(np.float64)

c1 = np.mean(points1, axis=0)
c2 = np.mean(points2, axis=0)
points1 -= c1
points2 -= c2

s1 = np.std(points1)
s2 = np.std(points2)
points1 /= s1
points2 /= s2

U, S, Vt = np.linalg.svd(points1.T * points2)
R = (U * Vt).T

return np.vstack([np.hstack(((s2 / s1) * R,
                                   c2.T - (s2 / s1) * R * c1.T)),
                     np.matrix([0., 0., 1.])])

#根据变换矩阵进行变换
def warp_im(im, M, dshape):
output_im = np.zeros(dshape, dtype=im.dtype)
cv2.warpAffine(im,
M[:2],
(dshape[1], dshape[0]),
dst=output_im,
borderMode=cv2.BORDER_TRANSPARENT,
flags=cv2.WARP_INVERSE_MAP)
return output_im

人脸对齐函数

def face_Align(Base_path,cover_path):
im1, landmarks1 = read_im_and_landmarks(Base_path) # 底图
im2, landmarks2 = read_im_and_landmarks(cover_path) # 贴上来的图
if landmarks2 ==‘NoFaces’:
return ‘NoFaces’

if len(landmarks1) == 0 & len(landmarks2) == 0 :
    raise ImproperNumber("Faces detected is no face!")
if len(landmarks1) > 1 & len(landmarks2) > 1 :
    raise ImproperNumber("Faces detected is more than 1!")

M = transformation_from_points(landmarks1[ALIGN_POINTS],
                               landmarks2[ALIGN_POINTS])
warped_im2 = warp_im(im2, M, im1.shape)
return warped_im2

def text_save(filename, data):
file = open(filename,‘w’)
#data = data.replace("’", “”).replace("[", “”).replace("]", “”).replace(" “, “”).split(”,")
for i in range(len(data)):
s = str(data[i]) + ‘\n’
s = s.replace("[", “”).replace("]", “”)
file.write(s)
file.close()

if name == “main”:

#cover_path = 'E:/DisguisedFacesInTheWild/crop/Testing_data/Aaron_Eckhart/Aaron_Eckhart_a.jpg'
# warped_mask = face_Align(Base_path,cover_path)
# warped_file = os.path.join(output_dir,'Aaron_Eckhart_a.jpg')
# cv2.imwrite(warped_file, warped_mask)
# pdb.set_trace()
for i in range(2):
    num_NoFaces = 0
    if i==0: split ='Testing'
    if i==1: split ='Training'  #Testing/Training

    if split =='Testing':
        index_name = 'Testingsubjects.txt'
        image_spilt = 'Testing_data'
    elif split =='Training':
        index_name = 'Trainingsubjects.txt'
        image_spilt = 'Training_data'
    else:
        raise ImproperNumber("dataset error")
    index_file = os.path.join(index_dir,index_name)
    with open(index_file) as tr:
        annos = tr.readlines()
    num = len(annos)
    for ii,anno in enumerate(annos):
        image_files = []
        # if ii<0:
        #     continue
        parts = anno.replace("\n", "").split(" ")
        subject = parts[0]
        print subject
        image_dir = os.path.join(root_dir,image_spilt,subject)
        for root, dirs, files in os.walk(image_dir):
            image_files =files

        for image_name in image_files:
            save_name = os.path.join(image_spilt,subject,image_name)
            cover_path = os.path.join(image_dir,image_name)
            warped_mask = face_Align(Base_path,cover_path)
            if warped_mask == 'NoFaces':
                images_noface.append(save_name)
                num_NoFaces = num_NoFaces+1
                # print 'NoFaces',num_NoFaces,cover_path
                continue
            images_withface.append(save_name)
            aligned_dir = os.path.join(output_dir,image_spilt,subject)
            if not os.path.exists(aligned_dir):
                os.makedirs(aligned_dir)
            warped_file = os.path.join(aligned_dir,image_name)
            cv2.imwrite(warped_file,warped_mask)
            # print images_withface,images_noface
            # pdb.set_trace()
noface_file   = os.path.join(output_dir,'noface.txt')
withface_file = os.path.join(output_dir,'withface.txt') 
text_save(noface_file,images_noface)
text_save(withface_file,images_withface)