gaussian_blur | Image Gaussian Blur - 图片高斯模糊

I have the following image. I was able to use watershed to detect all the particles using the code below.

However, now I need to calculate the size of each particles in the figure and if I use the "labels" image, for some reasons I am not capable of using the function cv2.findContours.

Anyone willing to share some ideas? If you propose some code, please include explanation because I am a beginner. :)

Many thanks!

I am implementing a paper on image segmentation in pytorch. I am required to do some preprocessing steps but as I am trying it the first time so I am unable to incorporate them in the traditional pipeline. Following are the preprocessing steps-

1) N(w, h) = I(w, h) − G(w, h), (1) where N is the normalized image, I is the original image, and G is the Gaussian blurred image with kernel size 65*65 and 0 mean and standard deviation 10.

2)Normalizing the mean image and dividing each pixel by average standard deviation.

Following is my code snippet for the above steps-

I am working on a project of Image Quality Assessment in which, I have used LIVE database of images. So far, I have understood that how the images have been distributed with respect to each distortion. When it comes to DMOS, I am unable to understand that how to know that which distorted image belongs to which DMOS value.

In readme.txt file, I have found,

The file dmos.mat has two arrays of length 982 each: dmos and orgs. orgs(i)==0 for distorted images. The arrays dmos and orgs are arranged by concatenating the dmos (and orgs) variables for each database as follows:

dmos=[dmos_jpeg2000(1:227) dmos_jpeg(1:233) white_noise(1:174) gaussian_blur(1:174) fast_fading(1:174)] where dmos_distortion(i) is the dmos value for image "distortion\imgi.bmp" where distortion can be one of the five described above.

But, I am still confused how can traverse through the DMOS with respect to the images. Please help me understand.

I am trying to implement a lane tracking system using Raspberry Pi. Therefore I am processing a video inside Raspberry Pi using OpenCV library (with a python code). But when I am capturing the video, it is lagging and not playing properly. Same code is working fine on the Windows OS environment but not inside the Raspberry Pi. I'd appreciate any help.

Raspberry Pi Hardware specification

Raspberry Pi 3 Model B (1.2Ghz 1GB ram) Camera module v2 (8 megapixel)

I have been working on LaneDetection learning from sentdex from his YouTube channel stating PYTHON PLAYS GTA V series.

Ahead I want to apply my own Lane Detection code but I encounter tuple error I am new to OpenCV and thus failing to grasp the concepts for now

Below is the hough_line function error is being generated which is being called from process function.

I am trying to take an image and convert it to grayscale, adding some gaussian blur to that image, and detecting the edges. I am having trouble displaying the image with matplotlib's pyplot.

I'm using Python to get images from an IP camera over an ethernet connection, and then process them looking for specific targets. I am using GRIP to generate code to look for the specific targeted areas. (For those unfamiliar with GRIP: it basically offers you a GUI desktop interface where you can see a live video feed and alter parameters until you get the desired output. Then you can auto generate a piece of code—mine is in Python—that will perform that processing 'pipeline' on any image you feed into it in your code).

After extensively debugging my connection code, I finally got a successful working connection that gets the image from the IP camera and send it into the GRIP pipeline. However, the processing of the image is failing, and it's returning a Segmentation Fault, with no indicated line numbers. Here is the pipeline code (auto generated):