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preprocess_img.py
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preprocess_img.py
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import numpy as np
from scipy.io import loadmat,savemat
from PIL import Image
from skin import skinmask
import argparse
from utils import *
import os
import glob
import tensorflow as tf
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
#calculating least square problem
def POS(xp,x):
npts = xp.shape[1]
A = np.zeros([2*npts,8])
A[0:2*npts-1:2,0:3] = x.transpose()
A[0:2*npts-1:2,3] = 1
A[1:2*npts:2,4:7] = x.transpose()
A[1:2*npts:2,7] = 1;
b = np.reshape(xp.transpose(),[2*npts,1])
k,_,_,_ = np.linalg.lstsq(A,b)
R1 = k[0:3]
R2 = k[4:7]
sTx = k[3]
sTy = k[7]
s = (np.linalg.norm(R1) + np.linalg.norm(R2))/2
t = np.stack([sTx,sTy],axis = 0)
return t,s
# resize and crop images
def resize_n_crop_img(img,lm,t,s,target_size = 224.):
w0,h0 = img.size
w = (w0/s*102).astype(np.int32)
h = (h0/s*102).astype(np.int32)
img = img.resize((w,h),resample = Image.BICUBIC)
left = (w/2 - target_size/2 + float((t[0] - w0/2)*102/s)).astype(np.int32)
right = left + target_size
up = (h/2 - target_size/2 + float((h0/2 - t[1])*102/s)).astype(np.int32)
below = up + target_size
img = img.crop((left,up,right,below))
img = np.array(img)
img = img[:,:,::-1] #RGBtoBGR
img = np.expand_dims(img,0)
lm = np.stack([lm[:,0] - t[0] + w0/2,lm[:,1] - t[1] + h0/2],axis = 1)/s*102
lm = lm - np.reshape(np.array([(w/2 - target_size/2),(h/2-target_size/2)]),[1,2])
return img,lm
# resize and crop input images before sending to the R-Net
def align_img(img,lm,lm3D):
w0,h0 = img.size
# change from image plane coordinates to 3D sapce coordinates(X-Y plane)
lm = np.stack([lm[:,0],h0 - 1 - lm[:,1]], axis = 1)
# calculate translation and scale factors using 5 facial landmarks and standard landmarks of a 3D face
t,s = POS(lm.transpose(),lm3D.transpose())
# processing the image
img_new,lm_new = resize_n_crop_img(img,lm,t,s)
lm_new = np.stack([lm_new[:,0],223 - lm_new[:,1]], axis = 1)
trans_params = np.array([w0,h0,102.0/s,t[0],t[1]])
return img_new,lm_new,trans_params
# detect 68 face landmarks for aligned images
def get_68landmark(img,detector,sess):
input_img = detector.get_tensor_by_name('input_imgs:0')
lm = detector.get_tensor_by_name('landmark:0')
landmark = sess.run(lm,feed_dict={input_img:img})
landmark = np.reshape(landmark,[68,2])
landmark = np.stack([landmark[:,1],223-landmark[:,0]],axis=1)
return landmark
# get skin attention mask for aligned images
def get_skinmask(img):
img = np.squeeze(img,0)
skin_img = skinmask(img)
return skin_img
def parse_args():
desc = "Data preprocessing for Deep3DRecon."
parser = argparse.ArgumentParser(description=desc)
parser.add_argument('--img_path', type=str, default='./input', help='original images folder')
parser.add_argument('--save_path', type=str, default='./processed_data', help='custom path to save proccessed images and labels')
return parser.parse_args()
# training data pre-processing
def preprocessing():
args = parse_args()
image_path = args.img_path
save_path = args.save_path
if not os.path.isdir(save_path):
os.makedirs(save_path)
if not os.path.isdir(os.path.join(save_path,'lm')):
os.makedirs(os.path.join(save_path,'lm'))
if not os.path.isdir(os.path.join(save_path,'lm_bin')):
os.makedirs(os.path.join(save_path,'lm_bin'))
if not os.path.isdir(os.path.join(save_path,'mask')):
os.makedirs(os.path.join(save_path,'mask'))
img_list = sorted(glob.glob(image_path + '/' + '*.png'))
img_list += sorted(glob.glob(image_path + '/' + '*.jpg'))
lm3D = load_lm3d()
with tf.Graph().as_default() as graph, tf.device('/gpu:0'):
lm_detector = load_graph(os.path.join('network','landmark68_detector.pb'))
tf.import_graph_def(lm_detector,name='')
sess = tf.InteractiveSession()
for file in img_list:
print(file)
name = file.split('/')[-1].replace('.png','').replace('.jpg','')
img,lm5p = load_img(file,file.replace('png','txt').replace('jpg','txt'))
img_align,_,_ = align_img(img,lm5p,lm3D) # [1,224,224,3] BGR image
lm68p = get_68landmark(img_align,graph,sess)
lm68p = lm68p.astype(np.float64)
skin_mask = get_skinmask(img_align)
Image.fromarray(img_align.squeeze(0)[:,:,::-1].astype(np.uint8),'RGB').save(os.path.join(save_path,name+'.png'))
Image.fromarray(skin_mask.astype(np.uint8)).save(os.path.join(save_path,'mask',name+'.png'))
np.savetxt(os.path.join(save_path,'lm',name+'.txt'),lm68p)
lm_bin = np.reshape(lm68p,[-1])
lm_bin.tofile(os.path.join(save_path,'lm_bin',name+'.bin'))
if __name__ == '__main__':
preprocessing()