face-landmark-localization
face-landmark-localization copied to clipboard
qiexing
cnn network predict face landmarks (68 points) and head pose (3d pose, yaw,roll,pitch).
再次请教大神！ 我只是训练特征点定位，加上您的模型finetune，只可以把loss降到0.05.请问您在做实验时特征点的loss最低能降到多少呢？ 谢谢！
Traceback (most recent call last): File "landmarkPredict.py", line 252, in func(*sys.argv[2:]) File "landmarkPredict.py", line 231, in predictImage vgg_point_net.set_input_arrays(faces.astype(np.float32),data4DL.astype(np.float32)) File "/home/ggj/lch/caffe/python/caffe/pycaffe.py", line 245, in _Net_set_input_arrays return self._set_input_arrays(data, labels) RuntimeError: data array...
Hello, Thank you for the model and codes. I am a bit confused. There are two deploy files provided. The model/deploy.prototxt consists of a model which gets divided at the...
Hi, thanks for your works! I'm confused that once I have annotated facial landmarks(e.g. 68 pts from 300W dataset), how can I transform(or use some algorithms) to get head-pose labels...
when i run code it gives error,please help me error is print faces[i].shape ^ SyntaxError: Missing parentheses in call to 'print'
Traceback (most recent call last): File "landmarkPredict.py", line 239, in func(*sys.argv[2:]) File "landmarkPredict.py", line 218, in predictImage vgg_point_net.set_input_arrays(faces.astype(np.float32),data4DL.astype(np.float32)) TypeError: _Net_set_input_arrays() takes exactly 4 arguments (3 given)
作者您好，您的工作很棒，我想跟着你用你train文件夹里面的网络结构自己重新训练一下，但是出现了了loss下降到0.7左右时候就没法下降，不论人脸的姿势是怎么样的，训练检测的每张图片的脸的68个点几乎都是端正分布在图中构成一个正面的脸。 请问您训练的过程中有遇到过这个问题吗？针对这个问题您有没有什么意见或建议以便我能寻找这个问题的根源呢，谢谢！期待您的回复
Thanks for sharing the code! very good job! You know, dlib provides good implementation for face landmarks estimation. In order to make it faster, I want to predict only the...
