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Easy contour python how to#
Easy contour python code#

How to find the circle in the given images using opencv python (hough circles )?.

How to find upper left most corner of my Contour Bounding Box in Python OpenCV.

Python Opencv drawContours fail when I draw just the bigger contour object.

Python opencv find pentagon contour on image.

How to find the orientation of an object (shape)? - Python Opencv.

Find the color of connectedComponents in Python OpenCV.

Find contour of the set of points in OpenCV.

OpenCV : How to find the pixels inside a contour in c++.

Python OpenCV - Extrapolating the largest rectangle off of a set of contour points.

How to find the contour of a blob using OpenCv + Python.

Or just calculate the angle between them. This wouldn't be useful on a pixel-by-pixel basis, but you could compare, say, pixels N-1,N,N+1 to pixels N+1,N+2,N+3.

Implement an edge follower that moves from one pixel to the next in the contour list, and calculate some kind of "inertia" as the pixel shifts direction.

(Second time I've mentioned R-P-D today it's handy.) Check for vertices with angles that deviate much from 180 degrees.

Use Ramer-Puecker-Douglas to render the outlines as polygons, then choose parameters to ensure those polygons are appropriately simplified.

Do a bunch of curve fits to chunks of pixels from the contour.

There are numerous other ways to do this, but this is quick to implement from scratch, and I think a reasonable starting point: Compare the "geodesic" distance and the Euclidean distance.

Calculate (distanceAlongContour)/(point-to-point distance).

Calculate the distance along the contour from N-D to N+D.

At each element N (a pixel) in the list, get the pixels at N - D and N + D that is the pixels D ahead of the current pixel and D behind the current pixel.

Find the contours as arrays/lists/containers of (x,y) coordinates.

One simple method I've used in the path goes something like this:

Even if there are methods available on the web to do this work for you, it's worth implementing a few basic techniques yourself before you go googling. First image shows points I got with cv.CHAIN_APPROX_NONE (734 points) and second image shows the one with cv.CHAIN_APPROX_SIMPLE (only 4 points).The definition of "local maximum" can be tricky to pin down, but if you start with a simple method you'll develop an intuition to look further. Just draw a circle on all the coordinates in the contour array (drawn in blue color). It removes all redundant points and compresses the contour, thereby saving memory.īelow image of a rectangle demonstrate this technique. This is what cv.CHAIN_APPROX_SIMPLE does. Do you need all the points on the line to represent that line? No, we need just two end points of that line. But actually do we need all the points? For eg, you found the contour of a straight line. If you pass cv.CHAIN_APPROX_NONE, all the boundary points are stored. But does it store all the coordinates ? That is specified by this contour approximation method. It stores the (x,y) coordinates of the boundary of a shape. What does it denote actually?Ībove, we told that contours are the boundaries of a shape with same intensity. This is the third argument in cv.findContours function. Note Last two methods are same, but when you go forward, you will see last one is more useful. To draw all contours, pass -1) and remaining arguments are color, thickness etc.Ĭv.drawContours(img,, 0, (0,255,0), 3) Its first argument is source image, second argument is the contours which should be passed as a Python list, third argument is index of contours (useful when drawing individual contour. It can also be used to draw any shape provided you have its boundary points. To draw the contours, cv.drawContours function is used.

Easy contour python code#

Until then, the values given to them in code sample will work fine for all images. Note We will discuss second and third arguments and about hierarchy in details later. Each individual contour is a Numpy array of (x,y) coordinates of boundary points of the object. Contours is a Python list of all the contours in the image. And it outputs the contours and hierarchy.

See, there are three arguments in cv.findContours() function, first one is source image, second is contour retrieval mode, third is contour approximation method. Contours, hierarchy = cv.findContours(thresh, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)