Finds contours in a binary image

contours = cv.findContours(image)
contours = cv.findContours(image, 'OptionName', optionValue, ...)
[contours, hierarchy] = cv.findContours(...)

Input

• image Source, an 8-bit single-channel image. Non-zero pixels are treated as 1's. Zero pixels remain 0's, so the image is treated as binary. You can use cv.compare, cv.inRange, cv.threshold, cv.adaptiveThreshold, cv.Canny, and others to create a binary image out of a grayscale or color one. If mode equals to CComp or FloodFill, the input can also be a 32-bit integer image of labels (int32 class).

Output

• contours Detected contours. Each contour is stored as a vector of points. A cell array of cell array of 2D integer points, of the form: {{[x,y],[x,y],...}, ...}.
• hierarchy Optional output vector containing information about the image topology. It has as many elements as the number of contours. For each i-th contour, contours{i}, the elements hierarchy{i}(1), hierarchy{i}(2), hierarchy{i}(3), and hierarchy{i}(4) are set to 0-based indices in contours of the next and previous contours at the same hierarchical level, the first child contour and the parent contour, respectively. If for the i-th contour there are no next, previous, parent, or nested contours, the corresponding elements of hierarchy{i} will be negative. A cell array of 4-element integer vectors of the form {[next,prev,child,parent], ...}.

Options

• Mode Contour retrieval mode, default is 'List'. One of:
  • External retrieves only the extreme outer contours. It sets hierarchy{i}(3)=hierarchy{i}(4)=-1 for all the contours.
  • List retrieves all of the contours without establishing any hierarchical relationships. It also sets hierarchy{i}(3)=hierarchy{i}(4)=-1 for all the contours.
  • CComp retrieves all of the contours and organizes them into a two-level hierarchy. At the top level, there are external boundaries of the components. At the second level, there are boundaries of the holes. If there is another contour inside a hole of a connected component, it is still put at the top level.
  • Tree retrieves all of the contours and reconstructs a full hierarchy of nested contours
  • FloodFill connected components of a multi-level image (only valid for 32-bit integer images).
• Method Contour approximation method, default is 'Simple'. One of:
  • None stores absolutely all the contour points. That is, any 2 subsequent points (x1,y1) and (x2,y2) of the contour will be either horizontal, vertical or diagonal neighbors, that is, max(abs(x1-x2),abs(y2-y1))==1.
  • Simple compresses horizontal, vertical, and diagonal segments and leaves only their end points. For example, an up-right rectangular contour is encoded with 4 points.
  • TC89_L1, TC89_KCOS applies one of the flavors of the Teh-Chin chain approximation algorithm [TehChin89] (1-curvature or k-cosine curvature).
• Offset Optional offset by which every contour point is shifted. This is useful if the contours are extracted from the image ROI and then they should be analyzed in the whole image context. default [0,0]

The function retrieves contours from the binary image using the algorithm [Suzuki85]. The contours are a useful tool for shape analysis and object detection and recognition.

References

[Suzuki85]:

Satoshi Suzuki and others. "Topological structural analysis of digitized binary images by border following". Computer Vision, Graphics, and Image Processing, 30(1):32-46, 1985.

[TehChin89]:

C-H Teh and Roland T. Chin. "On the detection of dominant points on digital curves". Pattern Analysis and Machine Intelligence, IEEE Transactions on, 11(8):859-872, 1989.