I’m working on a project just for fun, and my goal is to play online poker and have the program identify the cards that are on the table. I am using OpenCV with python to isolate the area where the cards are going to be. I have been able to take an image of that area, grayscale and threshold it, and draw a contour around the edge of the card. I am now stuck on how to move forward.
a = sorted(keypoints, key=lambda p: (p[0]) + (p1))[0] # find upper left point
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
I am looking for how to resample a numpy array representing image data at a new size, preferably having a choice of the interpolation method (nearest, bilinear, etc.). I know there is
I’m developing a Python module, with OpenCV, that connects to an RTSP stream to perform some preprocessing on the video (mostly, reducing fps and resolution), and then store it in the file system.
I’m trying to compare images to each other to find out whether they are different. First I tried to make a Pearson correleation of the RGB values, which works also quite good unless the pictures are a litte bit shifted. So if a have a 100% identical images but one is a little bit moved, I get a bad correlation value.
Using the following code I can remove horizontal lines in images. See result below.
What is the best way to represent and solve a maze given an image?
I have been trying to count cars when crossing the line and it works, but the problem is it counts one car many times which is ridiculous because it should only be counted once.
I am working on Retinal fundus images.The image consists of a circular retina on a black background. With OpenCV, I have managed to get a contour which surrounds the whole circular Retina. What I need is to crop out the circular retina from the black background.
After my previous question on finding toes within each paw, I started loading up other measurements to see how it would hold up. Unfortunately, I quickly ran into a problem with one of the preceding steps: recognizing the paws.
