future.graph.merge_hierarchical

[Vighnesh Birodkar]

Hello

Only if in_place = True. If not, the new node could be something entirely 
different.

The new_id is the safer bet.


Thanks
Vighnesh
On Sunday, September 20, 2015 at 11:26:12 PM UTC-4, bricklemacho wrote:
>
> Hi Vighnesh,
>
> That basically what I am after.  If I inject the code at line 130 after 
> the merger then I only need use [dst] to create the label map, correct?
>
> I just submitted a [WIP] pull request, but will update code to reflect 
> these changes.
>
> Thanks for your help.
>
> Michael.
> --
>
>
>
>
> On 21/09/2015 11:13 am, Vighnesh Birodkar wrote:
>
> Hello 
>
> Sorry for the repost, I accidentally submitted incomplete code in the last 
> one. 
>
> For the demo videos in that post I was traversing the graph at each 
> iteration to obtain the segmentation, see 118 of graph_merge.py
>
> https://github.com/vighneshbirodkar/scikit-image/compare/vighneshbirodkar:ha...ha_video
>
> This was because I needed the entire segmentation at each step to display 
> it. Juan, I think you mean I traverse it only once in the code on master ? 
> For the video I was indeed traversing it each time.
>
> The nodes are merged here
>
> https://github.com/scikit-image/scikit-image/blob/master/skimage/future/graph/graph_merge.py#L128
>
> On line 127 you can inject your logic.
>
> So if I understand correctly, you want [src + dst](the regions being 
> merged at that point of time) as one region and the rest of the graph as 
> other ?
>
> label_map = np.arange(labels.max() + 1)
> label_map[:] = 2 # Label the rest of the graph as one region
>
> # Label src as one region
> for l in rag.node[src]['labels']:
>     label_map[l] = 1 
>
>
> for l in rag.node[dst]['labels']:
>     label_map[l] = 1 
>
> seg_list.append(labels[label_map])
>
> Thanks
> Vighnesh
>
>
> On Tuesday, September 1, 2015 at 7:24:31 PM UTC-4, bricklemacho wrote: 
>>
>> Hi All,
>>
>> I am looking at generating some detection proposals, see  Hosang, Jan, et 
>> al. "What makes for effective detection proposals?." *arXiv preprint 
>> arXiv:1502.05082* (2015), http://arxiv.org/pdf/1502.05082.pdf    
>> Starting with the Selective Search algorithm, Section 3 of  Uijlings, 
>> Jasper RR, et al. "Selective search for object recognition." *International 
>> journal of computer vision* 104.2 (2013): 154-171,    
>> https://staff.fnwi.uva.nl/th.gevers/pub/GeversIJCV2013.pdf
>>
>> The basic idea is the performing a hierarchical merging of the image, 
>> where each new merge get added to the list of regions suspected to contain 
>> an object, you can capture objects at all scales.  This reduces the search 
>> space significantly than say compared to floating window.  The output is 
>> NOT a image segmentaiton, rather a list of regions (bounding boxes) of 
>> potential objects (deteciton proposals).
>>
>> I have looked in the gallery at RAG Merging 
>> <http://scikit-image.org/docs/dev/auto_examples/plot_rag_merge.html>
>> http://scikit-image.org/docs/dev/auto_examples/plot_rag_merge.html,  
>> fairly confident I can setup the callback methods to provided the 
>> similarity measure.   I am naively hoping that future.graph.hierarchical(), 
>> even though it seems to output a segmentation (labels), can be easily 
>> adapted to the task.    What would be the best way to have 
>> future.graph.merge_hierarchica() merge regions with the "highest" 
>> similarity measure, rather thana threshold?   What would be the best way 
>> future.graph.merge_hierarchica() save each merged region?   Tried setting  
>> "in_place" to false, but didn't notice any difference.
>>
>> Any help appreciated,
>>
>> Brickle.
>> --
>>
>>
>>
>>
>>
>
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