OpenCV

问题 My overall goal is to crop several regions from an input mirax (.mrxs) slide image to JPEG output files. Here is what one of these images looks like: Note that the darker grey area is part of the image, and the regions I ultimately wish to extract in JPEG format are the 3 black square regions. Now, for the specifics: I'm able to extract the color channels from the mirax image into 3 separate TIFF files using vips on the command line: vips extract_band INPUT.mrxs OUTPUT.tiff[tile,compression

问题 Hello this code is intended to store the coordinates of rectangles drawn with open cv and compile the results into a single image. import numpy as np import cv2 im = cv2.imread('1.jpg') im3 = im.copy() gray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY) blur = cv2.GaussianBlur(gray,(5,5),0) thresh = cv2.adaptiveThreshold(blur,255,1,1,11,2) contours,hierarchy = cv2.findContours(thresh,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE) squares = [] for cnt in contours: if cv2.contourArea(cnt)>50: [x,y,w,h] = cv2

问题 I have a program where I get one image as input and I have to compare it with ~640 known images to see which one is the most similar. To do this I was thinking of using OpenCV's match template, as it seems very fast and effective in doing what I want to do. I noticed that matching two images, both 400x240 px, 1000 times is much slower than matching a 400x240 px image in a 1400x240 px, despite both of them being 1000 matches. My idea was to combine the 640 images in one big image containing

问题 I have a program where I get one image as input and I have to compare it with ~640 known images to see which one is the most similar. To do this I was thinking of using OpenCV's match template, as it seems very fast and effective in doing what I want to do. I noticed that matching two images, both 400x240 px, 1000 times is much slower than matching a 400x240 px image in a 1400x240 px, despite both of them being 1000 matches. My idea was to combine the 640 images in one big image containing

问题 How to integrate opencv in Android Studio? I am facing problem in importing and installing opencv library in android studio 回答1: Install openCV in Android Studio Simple Steps:- (Indirect Method) Download/Install(Extract) OpenCV for Android (you might already have it) Download :-here Documentation :- here Version 2.4.11 is recommended. Open you Android Project on Android Studio. Import Module ( Files >> New >> Import Module ) Browse the path to the samples Folder inside OpenCV for Android

问题 I am developing an android app to scan documents with my phone. I am using openCV and Canny edge detection and it works ok but if I try to scan an document on a background without enough contrast between the document and the background it fails. I have tried other apps in the Play Store and they are still able to scan the document with less contrast. So I was looking for ways to improve my edge detection and found this: https://www.pyimagesearch.com/2019/03/04/holistically-nested-edge

问题 My aim is to identify car logos using HOG descriptors. I am following tutorial linked https://gurus.pyimagesearch.com/lesson-sample-histogram-of-oriented-gradients-and-car-logo-recognition/# . I have testing and training images in separate folders. While extracting HOG features using following code: # import the necessary packages from sklearn.neighbors import KNeighborsClassifier from skimage import exposure from skimage import feature from imutils import paths import argparse import imutils

问题 Hello I am getting an error when using aruco. I am just trying to get an example from the tutorial working. I did everything according to the tutorial but I get: /home/pi/Programs/markerDetection/markerDetection.cpp: In function ‘int main(int, char**)’: /home/pi/Programs/markerDetection/markerDetection.cpp:26:104: error: invalid initialization of reference of type ‘cv::Ptr<cv::aruco::Dictionary>&’ from expression of type ‘cv::aruco::Dictionary’ aruco::detectMarkers(inputImage, dictionary,

问题 My aim is to identify car logos using HOG descriptors. I am following tutorial linked https://gurus.pyimagesearch.com/lesson-sample-histogram-of-oriented-gradients-and-car-logo-recognition/# . I have testing and training images in separate folders. While extracting HOG features using following code: # import the necessary packages from sklearn.neighbors import KNeighborsClassifier from skimage import exposure from skimage import feature from imutils import paths import argparse import imutils

问题 I found way to convert RGB to HSV, but still I am unable to find the upper and lower value of color. How to do i calculate that? I have to take out the pickachu from the image and this my code till now import cv2 import numpy as np cap = cv2.VideoCapture(0) while True: _, frame = cap.read() hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) lower_red = np.array([30,50,50]) upper_red = np.array([255,255,180]) #it is trial and error mask = cv2.inRange(frame, lower_red, upper_red) res = cv2.bitwise