connected-components

问题 I am trying to implement the Light Speed Labeling algorithm from https://www.lri.fr/~lacas/Publications/JRTIP10.pdf. I tried to follow the algorithmic approach described in the paper as closely as possible (pgs 9-12) but the output doesn't make sense after the equivalence stage. Anyone have any idea what the problem is? void segment(const unsigned * Xi, const unsigned& N, unsigned * ERi, unsigned *RLCi, unsigned& ner) { unsigned x1 = 0; unsigned f = 0; unsigned er = 0; for (unsigned j = 0; j

问题 Two pass connected component algorithm is detecting separate components in one image, and after each detection i am saving every component as a different image. To display every component on separate image i am using multiple if conditions but these if conditions are increasing whenever there are alot of shapes in an image for every component, so far i have used 7 if conditions but it is increaing. Any ideas how to use loop for it or how to deal with it. for (x, y) in labels: component = uf

问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 2 years ago . I got the following error in OpenCV (python) and have googled a lot but have not been able to resolve. I would be grateful if anyone could provide me with some clue. OpenCV Error: Assertion failed (L.channels() == 1 && I.channels() == 1) in connectedComponents_sub1, file /home/snoopy/opencv- 3.1.0/modules

问题 I want to reduce the amount of white pixels in this image to just some candidate or representative points in the output image (goal is to model different types of shapes) if you just connect the gray points in the output image together you have the same path but with less white pixels. this path should have only one starting point and one ending point and covers all path from starting to ending. I can solve it using CCA(connected component analysis) and some if then else rules ! but it seems

问题 My table has account_id and device_id . One account_id could have multiple device_id s and vice versa. I am trying to count the depth of each connected many-to-many relationship. Ex: account_id | device_id 1 | 10 1 | 11 1 | 12 2 | 10 3 | 11 3 | 13 3 | 14 4 | 15 5 | 15 6 | 16 How do I construct a query that knows to combine accounts 1-3 together, 4-5 together, and leave 6 by itself? All 7 entries of accounts 1-3 should be grouped together because they all touched the same account_id or device

问题 I'm trying to find the connected components for friends in a city. My data is a list of edges with an attribute of city. City | SRC | DEST Houston Kyle -> Benny Houston Benny -> Charles Houston Charles -> Denny Omaha Carol -> Brian etc. I know the connectedComponents function of pyspark's GraphX library will iterate over all the edges of a graph to find the connected components and I'd like to avoid that. How would I do so? edit: I thought I could do something like select connected_components

问题 I have a list of records (person_id, start_date, end_date) as follows: person_records = [['1', '08/01/2011', '08/31/2011'], ['1', '09/01/2011', '09/30/2011'], ['1', '11/01/2011', '11/30/2011'], ['1', '12/01/2011', '12/31/2011'], ['1', '01/01/2012', '01/31/2012'], ['1', '03/01/2012', '03/31/2012']] The records for each person are sorted in an ascending order of start_date. The periods are consolidated by combining the records based on the dates and recording the start_date of the first period

问题 I am labeling images using the watershed algorithm in OpenCV (similar to this tutorial: https://docs.opencv.org/3.4/d3/db4/tutorial_py_watershed.html) such that at the end I obtain an array of labels where each region has an integer value corresponding to its label. Now, I want to obtain the coordinates of the bounding boxes and areas of each region. I know this is easily done with skimage.measure.regionprops() but for considerations of speed of execution I would like to achieve this without

问题 I have a thresholded binary image as shown below: I want to find all connected object in the image. The code will take an input image stream and gives no. of connected components as output. I have already implemented it in C where matrices are stored and can be accessed directly by A[][] format. But in HLS images come in as a stream, which I have converted in hls::Mat . I am not sure whether I can perform an element-wise operation on Mat and whether mat is available for offline operations

问题 I think the tags explain quite well my problem :) I've been trying to write a Gremlin traversal to compute the connected components of the simple graph described at the end of the post. I tried with g.V().repeat(both('e')).until(cyclicPath()).dedup().tree().by('name').next() obtaining ==>a={b={a={}, c={b={}}, d={c={d={}}}}, c={d={c={}}}} ==>e={f={e={}, g={f={}}}, h={f={h={}}}} ==>g={f={g={}}} which is bad, since the cyclicPath filter terminated the traversal starting from e before reaching g