yukangwang / textfield Goto Github PK
View Code? Open in Web Editor NEWTextField: Learning A Deep Direction Field for Irregular Scene Text Detection (TIP 2019)
License: Other
TextField: Learning A Deep Direction Field for Irregular Scene Text Detection (TIP 2019)
License: Other
I managed to test your model which was already trained on total-text dataset, but I cannot reach your score. I want to know what is your environment? Such as GCC version, opencv-python version. Thanks!
你好,我想向你请教一下怎么用dokcer部署已经训练好的模型,卡了好久了,网上资料也找了好多,这个环境怎么搭?拉取镜像的时候以前安装的环境老师被覆盖掉?
万分感谢!!能加你个联系方式??
Thank you for sharing the approach.
Could you please give an example or commit a demo on how you provide your datasets for training? Your train.py script used the DataLayer and reads data from data_dir='/home/wangyukang/dataset/' I think? But how is it formatted, what is the structure (image and gt in one folder? gt as images or as txt?)
How you trained and evaluated ICDAR2015 dataset?
I managed to retrain your approach with my own dataset and it performance quit well! However, the runtime/inference speed seems to be slower than compared with several other approaches (EAST e.g.) - especially if ported to a non GPU version. To you have any hints/ideas on how to improve the inference speed? Could the model retrained differently to better fit smaller inference scales?
Hi,
TextField is a great work, but I'm confused about the post processing:
We apply a simple dilation δ (with 3 × 3 structuring element) to group the representatives of the same text instance. This is followed by a connected component labeling that forms candidate text instances. The text superpixel grouping is depicted in line 17- 21 of Algorithm 1.
and I found these in your post processing code:
for (int row = 0; row < rows_; row++)
{
float* ending_p = ending.ptr<float>(row);
float* parent_p = parent.ptr<float>(row);
float* dict_p = dict.ptr<float>(row);
for (int col = 0; col < cols_; col++)
{
if (ending_p[col] == 1)
{
for (int dilDepth = 1; dilDepth <= min((int)(1*dict_p[2*col+1]-16), 12); dilDepth++) //
{
p.x = row+(int)parent_p[2*col]*dilDepth;
p.y = col+(int)parent_p[2*col+1]*dilDepth;
if (p.x >= 0 && p.x <= rows_-1 && pt.y >= 0 && pt.y <= cols_-1)
{
float* merged_ending_p = merged_ending.ptr<float>(p.x);
merged_ending_p[p.y] = 1;
}
}
}
}
}
I understand that the dilate element kernel_size = 3x3 is for grouping the representatives of text instance, and the above code is for grouping the rest children pixels belonging to this instance, but I don't understand this: for (int dilDepth = 1; dilDepth <= min((int)(1*dict_p[2*col+1]-16), 12); dilDepth++)
, and dilDepth
stands for the deepest depth, but the magic numbers 16
and 12
represent for what meaning?
Thanks in advance for your reply!
源代码
top[0].data[...] = np.sum((self.distL1**2)*(self.weightPos + self.weightNeg)) / bottom[0].num / 2. / np.sum(self.weightPos + self.weightNeg)
当中bottom[0].num中的num是什么,好像Python当中没有这个
CXX tools/caffe.cpp
CXX tools/test_net.cpp
CXX tools/finetune_net.cpp
CXX examples/TextField/inference.cpp
CXX .build_release/src/caffe/proto/caffe.pb.cc
examples/TextField/inference.cpp: In function ‘int main(int, char**)’:
examples/TextField/inference.cpp:43:3: error: reference to ‘shared_ptr’ is ambiguous
shared_ptr <Net> net_;
^~~~~~~~~~
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