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1 Laboratory of Applied Robotics OpenCV: Shape Detection Paolo Bevilacqua
2 RGB (Red-Green-Blue): Color Spaces RGB and HSV Color defined in relation to primary colors Correlated channels, information on both color and brightness encoded by the combination of all the channels Source:
3 HSV (Hue-Saturation-Value): Color Spaces RGB and HSV Color defined by 3 different channels: 1. Hue: dominant color family (primary/secondary). Expressed as an angle (0-360 ) 2. Saturation: intensity (purity) of the color. Reducing the saturation makes the color faded. When saturation goes to 0 the color becomes gray 3. Value: in conjunction with saturation, it models the brightness of the color. A value of 0 corresponds to black Similar to human perception Source: The hue and brightness are represented by different channels Simpler to filter image colors, independently from the brightness
4 OpenCV Color Spaces BGR space used by default Simple function to convert color space of an image: cvtcolor(src_mat, dst_mat, COLOR_BGR2HSV); Support for various color spaces: COLOR_BGR2GRAY (grayscale), COLOR_BGR2YCrCb BGR-HSV
5 OpenCV Color Spaces Using HSV color space, simple to filter pixels based on color (hue component): inrange(src, Scalar(low_h,low_s,low_v), Scalar(high_h,high_s,high_v), dst); Remember: hue in OpenCV ranges from 0 to 180 (i.e. values of hue on HSV cylinder must be divided by 2) 45 <= hue <= 75 Source:
6 OpenCV Shape Detection To trace boundaries of blobs of pixels (binary image): findcontours(in_mask, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE); RETR_EXTERNAL: retrieve only extreme outer contours (for each blob) Possibility to retrieve all the boundaries, and establish a hierarchical relationship (e.g. holes inside a blob). Use flag RETR_TREE CHAIN_APPROX_SIMPLE: compress horizontal, vertical and diagonal segments (leave only endpoints) Utility function to draw the found contours: drawcontours(img, contours, -1, cv::scalar(40,190,40), 1, cv::line_aa); All the contours Line color Thickness
7 OpenCV Shape Detection findcontours may return contours with a large number of vertices (up to one for each pixel along the boundary) if blob is jagged Possible to fit an (approximating) polygon with less vertices: approxpolydp(contours[i], approx_curve, 3, true); Maximum allowed distance from the original curve Closed curve
8 OpenCV Filtering Digital Image Printing - Camera Capture (Color alteration, noise) Ideal HSV HSV to process
9 OpenCV Filtering Noise produces alterations of colors, especially along boundaries of shapes Often the selection (filtering) of one color produces some unwanted effects: Small pixel blobs belonging to other shapes of different colors: false positives Small holes (especially along boundaries) of correctly identified blobs: false negatives Solution: correct these unwanted effects by post-processing and filtering the identified color mask Source:
10 OpenCV Filtering Convolution: In the general case: ff gg In the discrete case: ff gg tt = ff ττ gg tt ττ dddd nn = mm= ff mm gg nn mm For an image: Source: Source: Kernel: a fixed size array of numerical coefficients with an anchor point (usually at the center) Different kernel structures correspond to different (linear) filtering operations
11 OpenCV Filtering Erosion/Dilation: Similar to convolution, but using more complex, non-linear operations 1. Erosion: a pixel is set to 1 if all the pixels under the kernel are 1 (logical AND) remove isolated pixels 2. Dilation: a pixel is set to 1 if at least one pixel under the kernel is 1 (logical OR) fill holes Original Gaussian Gradient Dilation Erosion
12 OpenCV Filtering Using OpenCV: Mat element = getstructuringelement( type, k_size, anchor ); Shape of kernel: MORPH_RECT, MORPH_ELLIPSE, MORPH_CROSS Kernel size (usually odd) Anchor position (default (-1,-1) i.e. center) erode( src, erosion_dst, element ); Apply erosion to image src, using kernel element, and storing result into erosion_dst. Function dilate can be used (same syntax) to dilate an image. GaussianBlur, medianblur, can be used to blur and filter the image. For more info see imgproc filter.html
13 OpenCV Real Shapes Detection
14 OpenCV Line Detection Hough Transform: Lines: A line can be expressed parametrically as: xx cos θθ + yy sin θθ = rr rr is the length of a normal from the origin to this line θθ is the angle of this normal with respect to the X-axis We know xx ii and yy ii for each pixel ii of the image We want to determine the values of rr and θθ Source:
15 OpenCV Line Detection Solution: Each point (xx ii, yy ii ) in the cartesian image is mapped to a set of sinusoids (parametrized by rr and θθ) in the polar Hough parameter space Collinear points in the cartesian image space map to set of curves that intersect at a common rr, θθ point The parameters of the intersection point correspond to the parameters of the straight line in the original cartesian image
16 OpenCV Circle Detection The same kind of transform can be applied to detect circular shapes in the binary image: Use the parametric equation: xx aa 2 + yy bb 2 = rr 2 aa and bb are the coordinates of the center of the circle rr is the radius of the circle If rr is known the Hough space is still composed by two parameters If rr is unknown the accumulator becomes three-dimensional, and the complexity of the algorithm increases Source:
17 OpenCV Circle Detection The Hough transform algorithm to detect circles in an image is already implemented in OpenCV: HoughCircles(img_mask, circles, HOUGH_GRADIENT, dp, mindst, param1, param2, minradius, maxradius); dp: Inverse ratio of the accumulator resolution to the image resolution mindst: Inverse ratio of the accumulator resolution to the image resolution param1: Threshold used for the Canny edge detector param2: Accumulator threshold for circle centers at detection stage (a smaller value may produce more false positives)
18 OpenCV Circle Detection
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