[OpenCV’s color image segmentation technology based on HSV space reports an error: ValueError: the input array must be have a shape =]= (.., ..,[ ..,]

Article directory summary Image segmentation concept Segment blue target Split yellow target Split orange target Split green target summary Summary Whenever you see an image, it is usually made up of various elements and objects. In some cases, you may want to extract a specific object from an image. What do you do? The first […]

Matlab/C++ source code realizes the conversion of RGB channel and HSV channel (the effect is compared with Halcon)

The meaning of HSV channel The HSV channel refers to a color model in image processing, which consists of three channels: hue (Hue), saturation (Saturation) and brightness (Value). Hue represents the type of color, saturation represents the purity or vividness of the color, and value represents the brightness of the color. The HSV channel is […]

Digital image processing – mutual conversion between Lab, YCbCr, HSV, RGB

Lab The “Lab” image format usually refers to the CIELAB color space, also known as the Lab color space. It is a device-independent color space used to describe the color of human visual perception, which is different from the common RGB and CMYK color spaces. CIELAB was defined by the International Commission on Illumination (CIE) […]

SSE Image Algorithm Optimization Series Thirty-One: Instruction Set Optimization of RGB2HSL/RGB2HSV and HSL2RGB/HSV2RGB – Part 1.

The mutual conversion between RGB and HSL/HSV color space is widely used in our image processing. Whether it is image adjustment or some skin color algorithms, HSL/HSV color space is very useful. It provides RGB color Space does not have some unique characteristics, but due to the complexity of the HSL/HSV color space, the conversion […]

Python’s RGB and HSV slider threshold segmentation tool

Effect image Operation interface diagram: RGB code: from tkinter import * from tkinter.filedialog import askopenfilename import cv2 import numpy as np def selectPath(): # path_ = askdirectory() path_ = askopenfilename() path.set(path_) print(path_) def stackImages(scale, imgArray): rows = len(imgArray) cols = len(imgArray[0]) rowsAvailable = isinstance(imgArray[0], list) width = imgArray[0][0].shape[1] height = imgArray[0][0].shape[0] if rowsAvailable: for x […]