I’ve an input image like below : ] I did some processing and got lines from my input image as below Lined Image : I want to have output with cell detected like this: Output Image I tried to found cells Bounding box using findContours and connectedComponentsWithStats method but they’re not giving me a satisfying results. My Code: For Contours:
Tag: computer-vision
Multi band blending makes seams brighter and more visible
I’m trying to stitch two pre-warped images together seamlessly using multi-band blending. I have two input images (that have already been warped) and one mask. However, when I apply MBB, the area surrounding the seams glow brighter and as a result, they become more visible which is the opposite of the objective here. I have absolutely no idea what I’m
Building wheel for neural-renderer-pytorch (setup.py) …installing multiperson and neural mesh renderer doesn’t work for pytorch 1.6
I am trying to install a github repo named multiperson for PyTorch 1.6 and I get the following error. How can I make it work for PyTorch 1.6? I have: Answer You would need to change all AT_CHECK in neural mesh renderer to TORCH_CHECK
How to resize a depth map from size [400,400] into size [60,60]?
I have a depth map image which was obtained using a kinect camera. In that image I have selected a region of size [400,400] and stored it as another image. Now, I would like to know how to resize this image into a size of [x,y] in python. Answer Same as a normal image
error: (-215:Assertion failed) scn + 1 == m.cols in function ‘cv::perspectiveTransform’
Below is a python script that calculates the homography between two images and then map a desired point from one image to another However, when i display the image that contains the mapped point it returns the following error: According to my knowledge this error means that parameter assigned to the function perspective transform is not correct or not being
OpenCV(4.1.2) error: (-215:Assertion failed) !ssize.empty() in function ‘cv::resize’
I am recieving this error when running this line of code: OpenCV(4.1.2) C:projectsopencv-pythonopencvmodulesimgprocsrcresize.cpp:3720: error: (-215:Assertion failed) !ssize.empty() in function ‘cv::resize’ Answer Your img variable is empty, you probably didn’t load the image correctly. Try printing your img.shape after you load your image. Then have a look at the path you specified, there is probably something wrong with it. (Either you
Canny edge detection not working on Gaussian blurred images
I am trying to detect edges on this lane image. First blurred the image using Gaussian filter and applied Canny edge detection but it gives only blank image without detecting edges. I have done like this: Answer You should always examine your data. Simply running your script step by step and examining intermediate values shows what is going wrong: mpimg.imread
How to generate accurate masks for an image from Mask R-CNN prediction in PyTorch?
I have trained a Mask RCNN network for instance segmentation of apples. I am able to load the weights and generate predictions for my test images. The masks being generated seem to be in the correct location, but the mask itself has no real form.. it just looks like a bunch of pixels Training is done based on the dataset
Get In Focus Pixels of an Image
How to detect which pixels of an image are in focus compared to the blurry ones. Something like the ‘Focus Peaking’ feature lots of cameras have? The idea is to color the pixels that are in focus so that it assists the user while clicking a picture. Looking for an implementation through Python. Answer You can find the edges, which
Understanding contour hierarchies: How to distinguish filled circle/contour and unfilled circle/contour in OpenCV?
I am unable to differentiate between the below two contours. cv2.contourArea() is giving the same value for both. Is there any function to distinguish them in Python? How do I use contour hierarchies to determine the difference? Answer To distinguish between a filled contour and unfilled contour, you can use contour hierarchy when finding contours with cv2.findContours(). Specifically, you can