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
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
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,
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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