These are the slides of the RoboCV Workshop organized by roboVITics on August 11th-12th, 2012 in TT311 Smart Classroom, VIT University, Vellore. The workshop was delivered by the following people: 1. Mayank Prasad, President of roboVITics 2. Akash Kashyap, President of TEC – The Electronics Club of VIT 3. Akshat Wahi, Asst. Project Manager of roboVITics

1. Contours
Module 5
Mayank Prasad
President, roboVITics
© Copyright Mayank Prasad
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2. Content Information
• This set of slides has been made with respect to the
popular book Learning OpenCV by Gary Bradski
and Adrian Kaehler, O’Reilly Media, Inc.
• The slides are to be used along with the Code
Gallery shared along with it
• Slides made by Mayank Prasad, B.Tech (IVth
Year), ECE, VIT University
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3. Outline
• Memory Storage
• Sequences
o Creating a Sequence
o Deleting a Sequence
o Direct Access
o Slicing, Copying and Moving Data
o Sequences as Stack (Not Discussed)
o Sequences and Arrays (Not Discussed)
• Contour Finding
o Contours and Sequences
o Freeman Chain Codes
o Drawing Contours
• More to do with Contours (Not Discussed)
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4. Introduction
• Algorithms like the Canny edge detector used to
find edge pixels that separate different segments in
an image, but nothing about them as entities.
• Next step – assemble those edge pixels into
contours
• cvFindContours() is used
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5. Memory Storage
Handle memory allocation for dynamic objects in OpenCV
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6. Memory Storage
• Linked lists of memory blocks that allow for fast
allocation and de-allocation of continuous sets of
blocks
• Accessed by OpenCV function which require
memory allocation as a part of their normal
functionality
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7. Routines
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8. Sequences
Sequences stored inside memory storage
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9. Sequences
• Stored inside memory storage
• Linked lists of other structures
• Analogous to generic container classes
• Sequence structure in OpenCV is actually a deque
o Very fast for random access and add/delete from either end
o Little slow for such things in the middle
• Direct access to sequence elements (More Later)
o The most direct way – and the correct way to access any random
element (apart from accessing from the ends)
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10. Sequence Structure
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11. Creating/Deleting
Sequence
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12. Flags – Set 1
Flag Type of Object
CV_SEQ_ELTYPE_POINT (x,y)
CV_SEQ_ELTYPE_CODE Freeman Code: 0…7
CV_SEQ_ELTYPE_POINT Pointer to a point: &(x,y)
CV_SEQ_ELTYPE_INDEX Integer Index of a point: #(x,y)
CV_SEQ_ELTYPE_GRAPH_EDGE &next_o, &next_d, &vtx_o, &vtx_d
CV_SEQ_ELTYPE_GRAPH_VERTEX first_edge, &(x,y)
CV_SEQ_ELTYPE_TRIAN_ATR Vertex of a binary tree
CV_SEQ_ELTYPE_CONNECTED_COMP Connected Component
CV_SEQ_ELTYPE_POINT3D (x,y,z)
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13. Flags – Set 2/3
Flags Nature of Object
CV_SEQ_KIND_SET A set of objects
CV_SEQ_KIND_CURVE A curve defined by the objects
CV_SEQ_KIND_BIN_TREE A binary tree of the objects
CV_SEQ_KIND_GRAPH A graph with objects as nodes
Flags Other Property of Object
CV_SEQ_FLAG_CLOSED Sequence is closed (polygons)
CV_SEQ_FLAG_SIMPLE Sequence is simple (polygons)
CV_SEQ_FLAG_CONVEX Sequence is convex (polygons)
CV_SEQ_FLAG_HOLE Sequence is a hole (polygons)
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14. Direct Access to Sequence
Elements
• The most direct way – and correct way to access
random elements of a sequence (apart from
accessing at the ends)
• Example usage to print elements as a sequence of
points (typecast required)
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15. Copying Sequences
• To deep copy sequences, use cvCloneSeq()
• More general routine is cvSeqSlice()
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16. Slicing Sequences
• Slice can be defined using cvSlice(a,b) or the
macro CV_WHOLE_SEQ
• b can also be set to CV_WHOLE_SEQ_END_INDEX
• Slices used to specify elements to remove/insert into
a sequence
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17. More on Sequences
• Sorting, Searching & Inverting
• Sequences as Stack (Deque)
• Inserting & Removing Elements
• Sequence Block Size
• Sequence Readers/Writers
• Sequences and Arrays
These topics may not be necessary, but will be
discussed as and when necessary
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18. Contour Finding
Contour
List of points representing curve of an Image
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19. Intro to Contours
• List of points representing the curve of an image
• Represented as sequences in which every entry in
the sequence encodes information about the
location of the next point on the curve
• cvFindContours() finds contours from binary
images – working shown in next slide
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20. Contour Trees
Working of
cvFindContours()
Dashed lines represent
the exterior boundaries
of white region
(contours, c)
Dotted lines represent
the exterior boundaries
of black region
(holes, h)
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21. cvFindContours()
• img is treated as binary and used as scratch space
• storage – location for the sequence
• firstContour – pointer to the head of contour tree
• headerSize – tells more about allocated objects
• mode – what to compute
• method – how to compute
• Returns total number of contours found
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22. mode of
cvFindContours()
• Indicates the types of contours we want to be
found and how the result is presented to us
• Determines how tree node variables are hooked up
together
mode Description
CV_RETR_EXTERNAL Retrieves only extreme outer
contours
CV_RETR_LIST Retrieves all the contours and puts
them in the list
CV_RETR_CCOMP Retrieves all the contours and
organizes them into a two-level
hierarchy
CV_RETR_TREE Retrieves all the contours and
reconstructs the full hierarchy of
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23. method of
cvFindContours()
• Determines how contours are approximated
method Description
CV_CHAIN_CODE Outputs contours in the Freeman
chain code; all other methods output
polygons
CV_CHAIN_APPROX_NONE Translates all the points from the
chain code into points
CV_CHAIN_APPROX_SIMPLE Compresses horizontal, vertical, and
diagonal segments, leaving only
their ending points
CV_CHAIN_APPROX_TC89_L1 Applies one of the flavors of the Teh-
CV_CHAIN_APPROX_TC89_KCOS Chin chain approximation algorithm
CV_LINK_RUNS Completely different algorithm that
links horizontal segments of 1s
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24. Contours are Sequences
• Contours are special types of sequences
• Other functions to manipulate them
o cvFindContours()
o cvStartFindContours()
o cvFindNextContour()
o cvSubstituteContour()
o cvEndFindContour()
o cvApproxChains()
o cvApproxPoly()
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25. Freeman Chain Code
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26. Drawing Contours
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27. Acknowledgements
Image Courtesy References
• Learning OpenCV by • Learning OpenCV by
Gary Bradski and Gary Bradski and
Adrian Kaehler, O’Reilly Adrian Kaehler, O’Reilly
Media, Inc. Media, Inc.
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28. Up Next
Module 6
Hardware Interfacing using Visual C++
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29. Contacts
• Mayank Prasad
President, roboVITics
mayank@robovitics.in
• Akshat Wahi
Asst. Project Manager, roboVITics
+91 909 250 3053
akshat@core.robovitics.in
• Akash Kashyap
President, TEC – The Electronics Club of VIT
akash130791@gmail.com
© Copyright Mayank Prasad
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