That's fantastic, and you should absolutely keep that up. Okay, let's say you did for whatever reason really look closely and carefully at an object. That's normal, and it's how human brains - and specifically human memory - works. Sure, you've seen all kinds of textures in your daily life, but have you really taken any time to really look at them? In all likelihood, you saw them at a glance, your brain tucked the smallest bit of that information away while throwing away that which it deemed unimportant, and you went on with your life. Your brain doesn't simply start off with any real knowledge of all the kinds of textures that exist in the world, so you don't really have much to pull from when you're looking to add detail to a drawing. Now, before we get into why that matters, first we have to take a bit of a detour.īefore we even think about how to go about adding texture and detail to a drawing, we must first learn to slow down and observe. They cease to be an independent object, but rather become a part of this texture that can be applied to any other surface. If you were to strip down this fishy wallpaper and wrap it around a box instead, the fish would come along with it. The fish is now a part of the wall itself. If, however, you take a bunch of fish and use it to wallpaper your bedroom, it becomes a texture - and the way we draw it changes. We apply constructional means - drawing through our forms, defining their silhouettes with outlines, describing how their surfaces move through space with contour lines, etc. If you've got a fish swimming in the ocean, then we draw it similarly to how we draw the boxes and sausage forms we've tackled thus far. The only difference is that these forms adhere to the surface of some other object - and this difference fundamentally changes how we approach drawing it.Īn example I like to use is fish. While we treat it a little differently, texture is also made up of three dimensional forms. Texture - that is, what people tend to think of as detail - isn't actually all that different. This last point is something we'll focus on a great deal in the next section. Things with volume to them that occupy space and relate to one another in that space. 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).Up until this point, we've largely explored matters relating to solid, three dimensional forms. 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. 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 a modified image, 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. Im2, contours, hierarchy = cv.findContours(thresh, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
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