Chris's profile - activity

Use contour finding and bounding rect to get the rectangle of the collection of white pixels. Loop through all pixels in

I wonder if the issue could be related to what is being masked. According to the docs, https://docs.opencv.org/4.2.0/df/

You could use OpenCV's inrange function that will threshold based on color channel ranges. https://docs.opencv.org/3.4/d

Not sure OpenCV has feature detectors or descriptors for your 3d adjacency data. Have you looked at 3d descriptors like

Here is my version of your functions and test code. I get very similar results for the Mat and vector reading using fst

Maybe this will help. https://www.pyimagesearch.com/2015/09/07/blur-detection-with-opencv/

OpenCV has a couple techniques you can try and there is another that I don't think is in OpenCV. warp polar I cropped

I have the same question. Before I buy a new camera, I would like to know if it will work with OpenCV.

We have found hough circles to be unreliable and sensitive to its input parameters. Our preferred method is to use OpenC

Would something like the following work? Here are the steps for the images below. convert the image to mono with cvtC

Interesting question. It doesn't look like there is a way to do this in the current code. It looks like imread_ calls m

Can you share some real images with us? Will the camera and object be fixed other than the rotation at the hinge? Will

Sounds like this may not really be an OpenCV question. Does opencv even have any keyboard input features? Stack Overflow

It is a program I've been working on that allows you to test various computer vision algorithms and parameters. I plan t

Set an roi (21x21) around the mouse position in the source image. Decide on a wd & ht value for each pixel in the ma

The code doesn't seem to match the question

Use a mask. Get the user selection as a contour then draw it as filled in a separate blank image. The 2nd image from the

I am not quite sure what you mean by " put the correct color in the ball and the limits. because they vary with luminosi

In the past, we have used principal component analysis (PCA) for this. https://docs.opencv.org/4.0.0/d3/d8d/classcv_1_1

Can you share some original images that have not been Canny filtered? I would like to see why template matching would no

If you give matchTemplate two images that are the same size, it will return a single value or score. This score will be

When I use template matching with TM_CCOEFF_NORMED, I get 0.83 score for Image1 & Image2 and 0.37 score for Image

OpenCV has a useful tool called PCA (principal component analysis) https://robospace.wordpress.com/2013/10/09/object-ori

Below are four images. The first is the red channel extracted from the original image. The 2nd is this extracted image t

Here's one solution. Do a binary inverse threshold so the letters are white and background is dark. Use findContours fun

Yes, orientation is a common problem in computer vision. Can you share some images with us?

You may want to look at template matching https://docs.opencv.org/4.0.0/df/dfb/group__imgproc__object.html#ga586ebfb0a7

OpenCV's contours will work if you filter the contours by aspect ratio. In the image below the 2nd from the left is thre

Also, don't underestimate the need for a high quality lens for this fine resolution work. We recently worked on a projec

One Opencv tool you should try is lineIterator. Create some radial lines and step across the boundary of the bottle ope

For more robust circle/ellipse finding, I like to use the line iterator. Set up radial lines either from the center of

I recommend you contact and talk to these guys: http://www.physimetrics.com/

It would be helpful if you could share some images with us.

No, these results came from another, larger c++ program

Opencv has great tools for this problem. The 1st image from the left below is the blue channel extracted. The 2nd im

Often machine vision challenges are not so much software as what I call staging. By staging I mean foreground, backgroun

After finding all the contours, if you take each one alone and dilate it with the same kernel, you should get a boundary

The morphology looks something like: Size kernelSize(3, 3); Mat kernel = getStructuringElement(MORPH_RECT, kernelSize);

The morphology looks something like: Size kernelSize(3, 3); Mat kernel = getStructuringElement(MORPH_RECT, kernelSize);

The morphology looks something like: Size kernelSize(3, 3); Mat kernel = getStructuringElement(MORPH_RECT, kernelSize);

I was able to get the image below with just a few OpenCV steps: threshold the image with binary_inverse and threshold

Here are the steps that seem to work well from left to right in the image below. original image apply Sobel filter wit

Would this help? I opened the above image as mono, did one iteration of erosion with 3x3 Rect kernel, did a threshold at

Would this help? I opened the above image as mono, did one iteration of erosion with 3x3 Rect kernel, did a threshold at

Yes, please share some sample images with us. Hard to suggest anything without seeing what there is to work with.

OpenCV works with many image types: single channel, multi channel, 8bit, 16bit, integral types and floating point types.

Hey Bhavesh, I am not sure what the original tank image looks like. Is the background unique somehow? If so, you can ta