poppinace / libranet Goto Github PK

Hi,
Thanks for your great work. For the cross-dataset evaluation, did you use the same model trained on the source domain for cross-dataset evaluation?

Is there an ETA on when the code will be coming @poppinace? Using RL for this is awesome, and I'm keen to have a look under the hood. For what it's worth, an open license would be great too.

Hi, can you explain more on the ground truth file (.csv in your preprocessed training dataset)? From my understanding, you divide the whole image into 32*32 patches and corresponding count value. So each element in the csv file represents its patch count and its patch relative location. Am I correct?
For example:
I use $ cat 9.csv
and it shows:
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
1,5,14,20,23,16,5,1,10,1,12,23,32,36,40,42
37,37,40,42,39,39,44,44,40,42,39,45,45,47,42,44
26,29,20,20,19,23,23,27,22,22,29,27,32,32,30,30

if recycle_ind < parameters['ACTION_NUMBER'] - 1:
start_mask_random = ( (count_rem + net.A[recycle_ind] >= 0) & (start_ind_random == -1) )
start_ind_random[start_mask_random] = recycle_ind

                **end_mask_random** = ( count_rem + net.A[recycle_ind] < parameters['Interval_N'] )
                **end_ind_random**[end_mask_random] = recycle_ind                
                
            maskselect_end = (sort[recycle_ind]==parameters['ACTION_NUMBER']-1)
            action_sort = sort[recycle_ind]
            
            A_sort = np.squeeze(net.A_mat[action_sort])
            
            _ind_max = (( (count_rem + A_sort < parameters['Interval_N']) & (count_rem + A_sort >= 0) | maskselect_end) & (mask_max_find==0) ) & (mask_last==0)
            action_max[_ind_max] = action_max[_ind_max] + sort[recycle_ind] [_ind_max] #?
            mask_max_find = mask_max_find + ( (count_rem + A_sort < parameters['Interval_N']) & (count_rem + A_sort >= 0) | maskselect_end ).astype(np.int8)

action_random = (start_ind_random + (end_ind_random + 2 - start_ind_random ) * np.random.rand(h, w)).astype(np.int8)

Why don't we generate action_random at random, but start_ ind_ Random and end_ ind_ random?

Thanks for releasing the code for inspiring work!
Do you have preprocessed version for the SHT Part_B dataset?
Thanks in advance!
Joseph

Hi, I really like your topic and I am trying to use it. But I am unable to use it to count people from my custom image. Can you guide me? My image is

Excuse me, I can't tar all the tar file. And I try many ways to solve it but I fail.

Hi there, could you please add a standard license? Thanks!

I notice that your train dataset is not the standard Shanghaitech part-A dataset. You do some data augmentation. Could you share some details about augmentation and what the CSV files mean?
Thanks!