FeatherNets_Face-Anti-spoofing-Attack-Detection-Challenge-CVPR2019
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SoftwareGift
请问一下，输入一张人脸如何预测是否为真人照片呢 ？
看测试的代码，其实和普通的预测代码没有多大区别，也可以打印一下网络的输出也就知道了。
谢谢您！但是我把real和fake都试了几张，输出的都试[0]。
image1 = Image.open('./image/f01.jpg') image1 = transform(image1).unsqueeze(0) output1 = net(image1) soft_output = torch.softmax(output1, dim=-1) preds = soft_output.to('cpu').detach().numpy() _, predicted = torch.max(soft_output.data, 1) predicted = predicted.to('cpu').detach().numpy() print(predicted)
Its amazing its working! thanks i use these code: return 1 to fake photo and 0 in real face [face cropped required]
the next code print output:
./images/fake.jpg: FAKE!
./images/real.jpg: REAL
PDTA: I could export to tensorflow.js, some recomendation? thanks
import sys
sys.path.insert(0,'.')
sys.path.insert(0,'..')
import torch
import tensorflow as tf
from pytorch2keras.converter import pytorch_to_keras
from tensorflow.keras.models import Model
from models import FeatherNet
from torchsummary import summary
from PIL import Image
import torchvision.transforms as transforms
def check_spoofing(image_path):
image1 = Image.open(image_path)
image1 = transform(image1).unsqueeze(0)
output1 = model(image1)
#print(output1)
soft_output = torch.softmax(output1, dim=-1)
#print(soft_output,soft_output.data)
preds = soft_output.to('cpu').detach().numpy()
#print(preds)
_, predicted = torch.max(soft_output.data, 1)
predicted = predicted.to('cpu').detach().numpy()
if(predicted[0]==1):
print(image_path+": FAKE!")
else:
print(image_path + ": REAL")
if __name__=='__main__':
input_size=224
img_size=224
ratio = 224.0 / float(img_size)
global normalize, transform
# Data loading code
normalize = transforms.Normalize(mean=[0.14300402, 0.1434545, 0.14277956], ##accorcoding to casia-surf val to commpute
std=[0.10050353, 0.100842826, 0.10034215])
name = 'FeatherNetB'
model = FeatherNet(se = True,avgdown=True)
#print(model)
model_path = './checkpoints/FeatherNetB_bs32/_47_best.pth.tar'
checkpoint = torch.load(model_path,map_location = 'cpu')
print('load model:',model_path)
model_dict = {}
state_dict = model.state_dict()
#print(checkpoint)
for (k,v) in checkpoint['state_dict'].items():
print(k)
if k[7:] in state_dict:
model_dict[k[7:]] = v
state_dict.update(model_dict)
model.load_state_dict(state_dict)
#model.load_state_dict(checkpoint['state_dict'])
model.eval()
transform = transforms.Compose([
transforms.Resize(int(256 * ratio)),
transforms.CenterCrop(img_size),
transforms.ToTensor(),
normalize,
])
check_spoofing('./images/fake.jpg')
check_spoofing('./images/real.jpg')
谢谢您！但是我把real和fake都试了几张，输出的都试[0]。
image1 = Image.open('./image/f01.jpg') image1 = transform(image1).unsqueeze(0) output1 = net(image1) soft_output = torch.softmax(output1, dim=-1) preds = soft_output.to('cpu').detach().numpy() _, predicted = torch.max(soft_output.data, 1) predicted = predicted.to('cpu').detach().numpy() print(predicted)
参考楼下的,楼下测试成功了,可能你忘记切换到eval模式了.
执行过model.eval( )，我换了很多张图仍然全输出0，请问我使用手机对着电脑屏幕上的人脸拍照，然后识别手机拍的这张照片，这种情况可以被识别吗？
@punisher97 Can you please share the fake and real images that you used? I am getting real for any image I have tested with.
@SoftwareGift 作者您好，请问预测时使用的是深度相机得到的深度图 还是 普通RGB图片呢？因为我看了您写的代码，训练时好像只使用了depth和label的数据，是这样吗？
@SoftwareGift 作者您好 感谢您的分享. 想请教
- 使用RGB作为feathernetB的 input是可以的吗? 因为看您的input有三个channel, 还是说depth map 也有三个channel呢?
- 想请问按照您上面的叙述, 这样不就只考虑1024 feature vector中的index 0/1而已了吗?
作者提供的其实都是深度的预训练模型(除了FeatherNetB_bs32-ir/_54.pth.tar)，没有rgb的，拿rgb来测试自然就和猜的没有两样。
Its amazing its working! thanks i use these code: return 1 to fake photo and 0 in real face [face cropped required]
the next code print output:
./images/fake.jpg: FAKE! ./images/real.jpg: REALPDTA: I could export to tensorflow.js, some recomendation? thanks
import sys sys.path.insert(0,'.') sys.path.insert(0,'..') import torch import tensorflow as tf from pytorch2keras.converter import pytorch_to_keras from tensorflow.keras.models import Model from models import FeatherNet from torchsummary import summary from PIL import Image import torchvision.transforms as transforms def check_spoofing(image_path): image1 = Image.open(image_path) image1 = transform(image1).unsqueeze(0) output1 = model(image1) #print(output1) soft_output = torch.softmax(output1, dim=-1) #print(soft_output,soft_output.data) preds = soft_output.to('cpu').detach().numpy() #print(preds) _, predicted = torch.max(soft_output.data, 1) predicted = predicted.to('cpu').detach().numpy() if(predicted[0]==1): print(image_path+": FAKE!") else: print(image_path + ": REAL") if __name__=='__main__': input_size=224 img_size=224 ratio = 224.0 / float(img_size) global normalize, transform # Data loading code normalize = transforms.Normalize(mean=[0.14300402, 0.1434545, 0.14277956], ##accorcoding to casia-surf val to commpute std=[0.10050353, 0.100842826, 0.10034215]) name = 'FeatherNetB' model = FeatherNet(se = True,avgdown=True) #print(model) model_path = './checkpoints/FeatherNetB_bs32/_47_best.pth.tar' checkpoint = torch.load(model_path,map_location = 'cpu') print('load model:',model_path) model_dict = {} state_dict = model.state_dict() #print(checkpoint) for (k,v) in checkpoint['state_dict'].items(): print(k) if k[7:] in state_dict: model_dict[k[7:]] = v state_dict.update(model_dict) model.load_state_dict(state_dict) #model.load_state_dict(checkpoint['state_dict']) model.eval() transform = transforms.Compose([ transforms.Resize(int(256 * ratio)), transforms.CenterCrop(img_size), transforms.ToTensor(), normalize, ]) check_spoofing('./images/fake.jpg') check_spoofing('./images/real.jpg')
thank you
Its amazing its working! thanks i use these code: return 1 to fake photo and 0 in real face [face cropped required]
the next code print output:
./images/fake.jpg: FAKE! ./images/real.jpg: REALPDTA: I could export to tensorflow.js, some recomendation? thanks
import sys sys.path.insert(0,'.') sys.path.insert(0,'..') import torch import tensorflow as tf from pytorch2keras.converter import pytorch_to_keras from tensorflow.keras.models import Model from models import FeatherNet from torchsummary import summary from PIL import Image import torchvision.transforms as transforms def check_spoofing(image_path): image1 = Image.open(image_path) image1 = transform(image1).unsqueeze(0) output1 = model(image1) #print(output1) soft_output = torch.softmax(output1, dim=-1) #print(soft_output,soft_output.data) preds = soft_output.to('cpu').detach().numpy() #print(preds) _, predicted = torch.max(soft_output.data, 1) predicted = predicted.to('cpu').detach().numpy() if(predicted[0]==1): print(image_path+": FAKE!") else: print(image_path + ": REAL") if __name__=='__main__': input_size=224 img_size=224 ratio = 224.0 / float(img_size) global normalize, transform # Data loading code normalize = transforms.Normalize(mean=[0.14300402, 0.1434545, 0.14277956], ##accorcoding to casia-surf val to commpute std=[0.10050353, 0.100842826, 0.10034215]) name = 'FeatherNetB' model = FeatherNet(se = True,avgdown=True) #print(model) model_path = './checkpoints/FeatherNetB_bs32/_47_best.pth.tar' checkpoint = torch.load(model_path,map_location = 'cpu') print('load model:',model_path) model_dict = {} state_dict = model.state_dict() #print(checkpoint) for (k,v) in checkpoint['state_dict'].items(): print(k) if k[7:] in state_dict: model_dict[k[7:]] = v state_dict.update(model_dict) model.load_state_dict(state_dict) #model.load_state_dict(checkpoint['state_dict']) model.eval() transform = transforms.Compose([ transforms.Resize(int(256 * ratio)), transforms.CenterCrop(img_size), transforms.ToTensor(), normalize, ]) check_spoofing('./images/fake.jpg') check_spoofing('./images/real.jpg')
I think Real is "1" while Fake is "0". If you check txt files present in data folder, you can conclude the actual label value.
I dont know what this line "./checkpoints/FeatherNetB_bs32/_47_best.pth.tar" refrence to? it has just a readme and when I download links in readme one of them is not tar and it is dmg (I dont know even the link language) and the other one is not usable
谢谢您！但是我把real和fake都试了几张，输出的都试[0]。 image1 = Image.open('./image/f01.jpg') image1 = transform(image1).unsqueeze(0) output1 = net(image1) soft_output = torch.softmax(output1, dim=-1) preds = soft_output.to('cpu').detach().numpy() _, predicted = torch.max(soft_output.data, 1) predicted = predicted.to('cpu').detach().numpy() print(predicted)
参考楼下的,楼下测试成功了,可能你忘记切换到eval模式了.
作者,您好, 按照这个代码,的确可以输出预测,可是为啥是[1,1024]维度呢, 不是二分类么?另外我看FeatherNet的定义,n_class=2并没有用上.