image-processing

问题 I am trying to read and write jpegs wth Adobe RGB colorspace in OpenCV. OpenCV assumes the jpeg has sRGB colorspace and when displaying or writing to file, the image loses some of its color intensity. I found this intensity loss was due to colorspace difference by answers given to my previous question. Is there anyway I can make OpenCV to read Adobe RGB colorspace without casting it to sRGB? 回答1: Some information that is hopefully useful for anyone looking for a work-around for dealing with

问题 I am trying to read and write jpegs wth Adobe RGB colorspace in OpenCV. OpenCV assumes the jpeg has sRGB colorspace and when displaying or writing to file, the image loses some of its color intensity. I found this intensity loss was due to colorspace difference by answers given to my previous question. Is there anyway I can make OpenCV to read Adobe RGB colorspace without casting it to sRGB? 回答1: Some information that is hopefully useful for anyone looking for a work-around for dealing with

问题 I am trying to read and write jpegs wth Adobe RGB colorspace in OpenCV. OpenCV assumes the jpeg has sRGB colorspace and when displaying or writing to file, the image loses some of its color intensity. I found this intensity loss was due to colorspace difference by answers given to my previous question. Is there anyway I can make OpenCV to read Adobe RGB colorspace without casting it to sRGB? 回答1: Some information that is hopefully useful for anyone looking for a work-around for dealing with

问题 I'm doing an at-home project basically just for fun but I'm having more trouble than anticipated. I want to be able to find the pitch mark in this sample image. enter image description here I've followed some tutorials and things to load the image from a location and run simple blob detection on the image. My code currently is as follows -> import cv2 import numpy as np # Read in the image in grayscale img = cv2.imread('/home/pi/Downloads/divot1.jpeg', cv2.IMREAD_GRAYSCALE) params = cv2

问题 I'm doing an at-home project basically just for fun but I'm having more trouble than anticipated. I want to be able to find the pitch mark in this sample image. enter image description here I've followed some tutorials and things to load the image from a location and run simple blob detection on the image. My code currently is as follows -> import cv2 import numpy as np # Read in the image in grayscale img = cv2.imread('/home/pi/Downloads/divot1.jpeg', cv2.IMREAD_GRAYSCALE) params = cv2

问题 I annotated images using PixelAnnotationTool provided here: https://github.com/abreheret/PixelAnnotationTool and using the provided dictionary: { "labels": { "unlabeled": { "categorie": "void", "color": [ 0, 0, 0 ], "id": 0, "id_categorie": 0, "name": "unlabeled" }, "bicycle_motorcycle": { "categorie": "bicycle_motorcycle", "color": [ 119, 11, 32 ], "id": 1, "id_categorie": 1, "name": "bicycle_motorcycle" }, "bus": { "categorie": "bus", "color": [ 102, 51, 0 ], "id": 2, "id_categorie": 2,

问题 I annotated images using PixelAnnotationTool provided here: https://github.com/abreheret/PixelAnnotationTool and using the provided dictionary: { "labels": { "unlabeled": { "categorie": "void", "color": [ 0, 0, 0 ], "id": 0, "id_categorie": 0, "name": "unlabeled" }, "bicycle_motorcycle": { "categorie": "bicycle_motorcycle", "color": [ 119, 11, 32 ], "id": 1, "id_categorie": 1, "name": "bicycle_motorcycle" }, "bus": { "categorie": "bus", "color": [ 102, 51, 0 ], "id": 2, "id_categorie": 2,

问题 I am using opencv-python library to do the liquid level detection. So far I was able to convert the image to gray scale and applying canny edge detection the container has been identified. import numpy as np import cv2 import math from matplotlib import pyplot as plt from cv2 import threshold, drawContours img1 = cv2.imread('botone.jpg') kernel = np.ones((5,5),np.uint8) #convert the image to grayscale imgray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) edges = cv2.Canny(imgray,120,230) I need to

问题 I'm currently using the below function to remove noise from two images of size (240x720). I'm currently computing this on my computer but would like to implement this in real time. However on my computer the function results in significant delays to the program. Is there an alternative to removing noise from an image that could work in real time? Gaussian blur perhaps? fastNlMeansDenoising(ipmOfLeftLaneRobust, ipmOfLeftLaneRobust,10,7,21); 回答1: Given a function fastNlMeansDenoising(InputArray

问题 can the JavaScript check the color mode of a image? I did a lot of search about this but the only thing I saw was color mode convert (but the convert wants you to set the original color mode) I add this: --allow-file-access-from-files to have a full control of img in canvas beacause I am using GoogleChrome Html <canvas id="canvas" width=6000 height=7919></canvas> Js var canvas = document.getElementById("canvas"); var ctx = canvas.getContext("2d"); var img = new Image(); img.crossOrigin =