computer-vision

问题 I have this script to capture images from webcam, but it is not saving any image, I donot know why. I am getting this error " Traceback (most recent call last): File "C:/Users/Iram/.PyCharmCE2019.3/config/scratches/scratch_5.py", line 26, in gray = cv2.cvtColor(img_, cv2.COLOR_BGR2GRAY) cv2.error: OpenCV(4.2.0) C:\projects\opencv-python\opencv\modules\imgproc\src\color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function 'cv::cvtColor' [ WARN:0] global C:\projects\opencv-python

问题 I have this script to capture images from webcam, but it is not saving any image, I donot know why. I am getting this error " Traceback (most recent call last): File "C:/Users/Iram/.PyCharmCE2019.3/config/scratches/scratch_5.py", line 26, in gray = cv2.cvtColor(img_, cv2.COLOR_BGR2GRAY) cv2.error: OpenCV(4.2.0) C:\projects\opencv-python\opencv\modules\imgproc\src\color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function 'cv::cvtColor' [ WARN:0] global C:\projects\opencv-python

问题 I'm using python (cv2) to import a .tif file with three layers, but I only seem to be able to import one layer. How can i separately import all three layers? import cv2 img = cv2.imread('LanB2_KD1P2_31603_gut_2.tif', cv2.IMREAD_COLOR) #extract blue channel blue_channel = img[:,:,0] #write blue channel to greyscale image cv2.imwrite('blue_channel.jpg',blue_channel) this extracts the blue channel from the first layer but idk how to get the other layers 回答1: You can simply use cv2.split for

问题 How can I detect the rotation angle from a perspective matrix? I wrote this code ,but the resulted angle doesn't exceed 40 ... Mat mmat; mmat.create(3,3,CV_32FC1); mmat=getPerspectiveTransform(templPoints,imgPoints); cout<< mmat<<endl<<endl; float angle=acos(mmat.at<double>(0,0)); angle=(angle*180)/3.14; cout<<"angle is"<<angle<<endl; 回答1: getPerspectiveTransform returns an homography matrix which can be decomposed like this: [R11,R12,T1] [R21,R22,T2] [ P , P , 1] R represents a rotation

问题 I have tried implementing a basic version of shi-tomasi corner detection algorithm. The algorithm works fine for corners but I came across a strange issue that the algorithm also gives high values for slanted(titled) edges. Here's what i did Took gray scale image computer dx, and dy of the image by convolving it with sobel_x and sobel_y Took a 3 size window and moved it across the image to compute the sum of the elements in the window. computed sum of the window elements from the dy image and

问题 I am working on OCRing a document image. I want to detect all pictures and remove from the document image. I want to retain tables in the document image. Once I detect pictures I will remove and then want to OCR. I tried to find contour tried to detect all the bigger areas. unfortunately it detects tables also. Also how to remove the objects keeping other data in the doc image. I am using opencv and python Here's my code import os from PIL import Image import pytesseract img = cv2.imread(

问题 I used Hough lines to get intersection of horizontal and vertical lines in this image: but the complexity grows with the grid cells count significantly. Is there any simple way to complete the grid without using line detection ? Thank you. 回答1: Here's a potential solution using morphological operations with OpenCV Obtain binary image. Load image, grayscale, Gaussian blur, Otsu's threshold Obtain horizontal/vertical lines masks. Create horizontal/vertical kernel and isolate horizontal/vertical

问题 I used Hough lines to get intersection of horizontal and vertical lines in this image: but the complexity grows with the grid cells count significantly. Is there any simple way to complete the grid without using line detection ? Thank you. 回答1: Here's a potential solution using morphological operations with OpenCV Obtain binary image. Load image, grayscale, Gaussian blur, Otsu's threshold Obtain horizontal/vertical lines masks. Create horizontal/vertical kernel and isolate horizontal/vertical

问题 I have a feature array which is created from ORB with this code part: orb = cv2.ORB_create() #keypoints and descriptors kpO, desO = orb.detectAndCompute(gray_image, None) I create this gray image from colored one. And des0.shape is (500,32). Also shape of my original image(colored one) is (422, 750, 3). And I want to decisionTreeRegression from these arrays for predict the colored version of grayscale image which is I created from colored version. But the problem is starting here , feature

问题 I am trying to implement a paper on Semantic Segmentation and I am confused about how to Upsample the prediction map produced by my segmentation network to match the input image size. For example, I am using a variant of Resnet101 as the segmentation network (as used by the paper). With this network structure, an input of size 321x321 (again used in the paper) produces a final prediction map of size 41x41xC (C is the number of classes). Because I have to make pixel-level predictions, I need