I want to use Canny edge or Sobel edge algorithm in opencv to detect my image's edge.
It works well when I take the object(paper) with dark background.
But the quality is low when the background color is similar to the object's.
Only the edge brodered on the shadow is clear. If the shadow is not so clear, the quality is not good enough.
So is there any good ways to improve it ?
Remember an edge means a boundary which represents the change in the distribution of the colour or texture..So if u keep an object in a similar color background then how can a edge detector finds strong edges...
Anyway the problem has a solution too,
If the Background and object colors are different then for better edge detection you can use Frei-Chen edge detector
If your background and object are a bit similar in RGB space then try to convert it into HSV or Ycbcr space and apply edge detection or segmentation techniques...
Hope it is uesful...
Related
I'm coming from a Direct3D 9 background, and recently switched my custom game engine over to Direct3D12. After some research, it looked like using one of the *_SRGB formats was the way to go, because it corrected the gamma level.
Immediately, I noticed that everything nearly doubled in brightness, which was unexpected. When correcting a curve, I would expect some values to be brighter and some to be darker, but everything just appears brighter. However, I just accepted it and moved on. But now I'm noticing some other strange issues, and I'm not sure what's going on. Maybe someone could help me understand what I'm missing?
When I draw a primitive with a color value in either HLSL or C++, such as color(128,128,128,255) or float4(0.5,0.5,0.5,1.0), the resulting color I see on the screen is actually RGB 188,188,188. Is this to be expected? I'm reading the values of these colors in Adobe Photoshop 2022, which is in SRGB mode. Should the values not match up if both applications are using SRGB?
128 to 188 is really strange, but 0.5 to 0.73 is even stranger. How do I manually construct a color that comes out the way I constructed it? For example, one might use 0.5 to scale by "half brightness", but 0.73 is definitely not half brightness. It's almost white.
If our textures are painted on a PC, such as in Substance Painter or Photoshop, what is the point of converting all of these colors? If the artist can see the same color space that will be used to render, why tell the display to show us something else?
Before I switched to sRGB, I modeled in Blender, and my textures always looked the same between Blender and my game engine. If I start using sRGB, I'm worried that will not be the case. How are artists making that work?
Images that I've seen that were gamma corrected are often brighter and washed out. And images that were not gamma correct are usually dark and rich. Does gamma correction cause some type of saturation loss in darker color?
I appreciate any guidance. I've done research on this topic, but most of the information goes on endlessly about linear color space. Linear is nice, because it makes math easier, but half of the stuff we deal with in a 3D app is non-linear. At this point, I'm not sure its worth it.
1&2: Gamma correction is designed to convert between light intensity as it exists in the real world, i.e. the amount of photons that hit a camera sensor, and how human beings perceive light. So if a light source is emitting 50% less photons, we see it as as around 74% of the light (individual curves may vary) so 128 should become 188.
3&4: The point of linear color is to allow us to process images in a space where an increase in the number of photons is linearly related to the increase in the intensity values. Then the linear colors are gamma corrected before presenting them to the user. When you work in those programs, you are looking at gamma corrected images.
Basically, people don't look at linear color spaces. They look wrong to us. They only exist to allow the computer to do some processing. If you have shaders that do work in linear color space, saving your images in a linear format so that they don't have to remove the gamma, do the processing, and then reapply the gamma can have performance benefits.
The problem may be that you are gamma correcting images that are already gamma corrected. If the images look right to you, they may be gamma corrected, if they look dark, with the lighter areas seemingly emphasized, they may be linear. If you are adding colors/images that look right to you, before gamma is applied, you will have to put the colors/images through inverse gamma correction.
How Applications Display/Convert Color Spaces: (Edit)
Photoshop interprets what the numbers in an image mean through the currently applied color space. It is possible to both "assign" a color space, which changes how photoshop interprets the numbers, and "convert" a color space, which changes the numbers so that they look the same (or as close as possible) when interpreted through the new color space.
This first image is in the sRGB color space. I've painted a gray dot with the values of (127,127,127).
In this second image I have converted the image to a linear color space. It looks almost the same, because photoshop always applies gamma correction so that it looks right to you, but the first dot now has the value (54,54,54). I've added a second dot with the values (127,127,127) in this color space.
In this third image, I have assigned the sRGB color space. Now photoshop thinks the numbers are in the sRGB color space, so it thinks the image already has gamma correction, and is showing us something like the way linear color space looks.
For the final image, I did everything the opposite direction, drawing a dot with a value of (127,127,127), then converting back. The last dot now has a value of (187,187,187)
I'm trying to render some label textures with transparent background using OpenTK in Xamarin. At first the labels seemed display properly (see picture 1) but when the view rotated, the some label background are not transparent any more (see picture 2).
The enabled BlendFunc is GL.BlendFunc(BlendingFactorSrc.SrcAlpha, BlendingFactorDest.OneMinusSrcAlpha).
My question is how can I always have labels transparency on despite of their positions?
The same code and shader can run properly on Android Devices by the way.
Ah yes, the good old transparency problem. Unfortunately this is one that a graphics programmer has to solve on his own.
For just a few labels, the most straight foward solution is likely to sort your labels by z-depth and then render them from farthest to closest. You'd probably need to do some matrix math on that label position to adjust for viewport rotation.
For the 3d game I'm working on I have chosen to implement the order-independent transparency method called WBOIT by Morgan McGuire, which is fairly simple to implement and yields relatively good results.
Problem
I'm trying to use opencv2 to detect PlayStation Move Motion Controllers in still images. In an attempt to increase the contrast between the orbs and the backgrounds, I decided to modify the input image to automatically scale the brightness level between the image's mean level and 96 above for each channel, then when converting to grayscale, taking the maximum value instead of the default transform, since some orbs are saturated but not "bright".
However, my best attempts at adjusting the parameters seems to not work well, detecting circles that aren't there over the obvious ones.
What can I do to improve the accuracy of the detection? What other improvements or algorithms do you think I could use?
Samples
In order of best to worst:
2 Wands, 1 Wand detected (showing all 2 detected circles)
2 Wands, 1 Wand detected with many nonexistent circles (showing top 4 circles)
1 Wand (against a dark background), 6 total circles, the lowest-ranked of which is the correct one (showing all 6 circles)
1 Wand (against a dark background), 44 total circles detected, none of which are that Wand (showing all 44 circles)
I am using this function call:
cv2.HoughCircles(img_gray,cv2.HOUGH_GRADIENT,
dp=1, minDist=24, param1=90, param2=25,
minRadius=2, maxRadius=48)
All images are resized and cropped to 640x480 (the resolution of the PS3 Eye). No blur is performed.
I think hough circles is the wrong approach for you, as you are not really looking for circles. You are looking for circular areas with strong intensity. Use e.g. blob detection instead, I linked a guide:
https://www.learnopencv.com/blob-detection-using-opencv-python-c/
In the blob detection, you need to set the parameters to get a proper high-intensity circular area.
as the other user said, hough circles arent the best approach here because hough circles look for perfect circles only. whereas your target is "circular" but not a circle (due to motion blur, light bleed/reflection, noise etc)
I suggest converting the image to HSV then filtering by hue/color and intensities to get a binary threshold instead of using grayscale directly (that will help remove background & noise and limit the search area)
then using findContours() (faster than blob detection), check for contours of high circularity and expected size/area range and maybe even solidity.
area = cv2.contourArea(contour)
perimeter = cv2.arcLength(contour,True)
circularity = 4*np.pi*area / (perimeter**2)
solidity = area/cv2.contourArea(cv2.convexHull(contour))
your biggest problem will be the orb contour merging with the background due to low contrast. so maybe some adaptive threshold could help
I need to write a program that will detect a red square in an image. I would like to do this on my GPU using OpenGl-ES. I have no experience with GPU programming, and haven't found the answer through Google so far.
Is it possible to do this using OpenGL? Does OpenGL-ES give access to the whole matrix of pixels as well as their location in the matrix, allowing a program to go through the pixels, and check the color value of each one as well as their locations in the matrix?
Thank you.
Above all, you are confused to call a few terms. There is 'no matrix of pixels'
If what you meant by that is Convolution, yes, you can put the convolution on Fragment shader to detect edges. However, there is no returning datas, and no way to access each pixel to get the color value. Convolution would work if you just want the shader to draw of square's edge. But if you want to know if a red square exist in the camera frame it must be calculated in CPU not in GPU.
Commonly, techniques such as supersampling or multisampling are used to produce high fidelity images.
I've been messing around on mobile devices with CSS3 3D lately and this trick does a fantastic job of obtaining high quality non-aliased edges on quads.
The way the trick works is that the texture for the quad gains two extra pixels in each dimension forming a transparent one-pixel-wide outline outside the border. Due to texture sampling interpolation, so long as the transformation does not put the camera too close to an edge the effect is not unlike a pre-filtered antialiased rendering approach.
What are the conceptual and technical limitations of taking this sort of approach to render a 3D model, for example?
I think I already have one point that precludes using this kind of trick in the general case. Whenever geometry is not rectangular it does nothing to reduce aliasing: The fact that the result with a transparent 1px outline border is smooth for HTML5 with CSS3 depends on those elements being rectangular so that they rasterize neatly into a pixel grid.
The trick you linked to doesn't seem to have to do with texture interpolation. The CSS added a border that is drawn as a line. The rasterizer in the browser is drawing polygons without antialiasing and is drawing lines with antialiasing.
To answer your question of why you wouldn't want to blend into transparency over a 1 pixel border is that transparency is very difficult to draw correctly and could lead to artifacts when polygons are not drawn from back to front. You either need to presort your polygons based on distance or have opaque polygons that you check occlusion of using a depth buffer and multisampling.