Tensorflow-implementation-of-LCNN
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yxu0611
Welcome to raise issues and your requests here
is there any code to try the implementation to verify either it's an image or not? your help is really appreciated
Hi, I assume that you have several different format files, e.g jpg, png, txt, doc, there may be two possible ways to verify the file is a image format or not. Solution 1:
from os import listdir
import os
img_format = ['.jpg', '.jpeg', '.png', '.tif']
list_img = listdir(path_img_folder)
for img_name in list_img:
extension = os.path.splitext(img_name)[1]
if extension in img_format:
print("It's image")
else:
print("It's not image")
Solution 2: You can probably use opencv function. e.g. cv2.imread(img_path), if the output returns NULL, it means your input file is not basically image format
Sorry I meant a code to verify if this is the person or not to verify the face not to recognize ,any tips ? thank you so much
@AdhamGouda the input_size of model is 1281283,but why you need to resize to 144144?
what half means in the function MFMfc?
def MFMfc(x,half,name): with tf.variable_scope(name): shape = x.get_shape().as_list() res = tf.reduce_max(tf.reshape(x,[-1,2,shape[-1]//2]),reduction_indices=[1]) return res
@tensorflowt
MFMfc is represented as an active function( similar to maxout), meanwhile, it also can perform feature dimension reduction. See the explanation from paper[1]:
[1] Wu, Xiang, et al. "A light CNN for deep face representation with noisy labels." arXiv preprint arXiv:1511.02683 (2015).
@yxu0611 How is image pre-processing performed? The paper states that images are transformed to greyscale and rotated based upon facial features; there doesn't seem to be any function in the code to perform this.
@fraserprice These pre-processing codes can be found as below. We did the rotation and transformation based on 3 facial points (left eye, right eye, and mouth)
# get grey scale image
img = cv2.imread(img_path, 0)
# get the left/right center eye position
leye = np.float32([points[0], points[1]])
reye = np.float32([points[2], points[3]])
# get the center mouth position
lmouth = np.float32([points[6], points[7]])
rmouth = np.float32([points[8], points[9]])
mouth = [(lmouth[0]+rmouth[0])/2., (lmouth[1]+rmouth[1])/2.]
# set eye to eye pixel distance 90, eye to mouth pixel distance 87 (template image size: 256x300)
src = np.float32([leye, reye, mouth])
dst = np.float32([[88, 125], [178, 125], [133, 212]])
M = cv2.getAffineTransform(src, dst)
crop_image = cv2.warpAffine(img, M, (256, 300))
# resize cropped image from 256x300 to 122x144
crop_image2 = cv2.resize(crop_image , (122, 144))
def sum(self, layerin):
layer1_size = self.res_size
layer2_size = layerin[1]
print (layer1_size[2], layer1_size[3])
print (layer2_size[2], layer2_size[3])
assert layer1_size[1] == layer2_size[1]
assert layer1_size[2] == layer2_size[2] and layer1_size[3] == layer2_size[3]
inp_size = self.res_size
with tf.name_scope('sum_' + str(self.layer_num)):
self.res = self.res + layerin[0]
self.res_size = self.res.get_shape()
print ('sum_'+str(self.layer_num), inp_size, self.res_size)
return [self.res, self.res_size]
mod = M.Model(img_holder, [None, 128, 128, 3])
mod.conv_layer(5, 96, activation=1)
mod.maxpooling_layer(2, 2) #pool1
#
a = mod.get_current_layer()
mod.conv_layer(3, 96, activation=1)
mod.conv_layer(3, 96, activation=1)
# print (mod.get_shape())
mod.sum(a)
hello，I am sorry to disturb you，but I encountered such a problem during training. Limited by my graphics card memory, I changed BAZIE to 200. there were an error on the 1803th iteration of epoch1: indexerror:too many indices for arry When I modified the batchsize to 240, I got another error: index 80 is out of the bounds for axis 0 with size 80. Both error locations are near line 126 of lcnn29.py. I have no idea. Hope to get help. thanks a lot
