hough-transform

问题 I have a binary image with the number of close loops and free curves like shown in image.. My approach so far- (python code): Skeletonize the image to get edge of 1 pixel Use label/bwlabel to get individual curve/edge Applied DFS to get the edge which is not close Apply DFS to get close edge..but not working correctly.. Expected output - Number of close loops =6, the sum of the pixels along the loop or all (x,y) points along the edge of the close curve as highlighted in RED Expected Output-

问题 My goal is to detect the fractured bone using open cv. I tried the following code. and got correct canny detection edges.and also found houghlines.But now my job is to detected the fractured spot in the image. Iam not understanding how to proceed further. In some blogs i found the we can determine angle of houghlines to detect that line is not staright. But not knowing how to find angle of houghline in my code. Can someone help? import cv2 import numpy as np import math img = cv2.imread(

问题 My goal is to detect the fractured bone using open cv. I tried the following code. and got correct canny detection edges.and also found houghlines.But now my job is to detected the fractured spot in the image. Iam not understanding how to proceed further. In some blogs i found the we can determine angle of houghlines to detect that line is not staright. But not knowing how to find angle of houghline in my code. Can someone help? import cv2 import numpy as np import math img = cv2.imread(

问题 I'm writing a C++ program with OpenCV for lunar crater detection which seems to detect only a small fraction of craters accurately. My strategy for this approach was to first convert the image to HSV, then use inRange() to catch the colors in a range of values to produce a threshold, then Gaussian blur it and use HoughCircles() to detect the circles. One thing that I am not fully understanding is that when I give inRange() a low and high threshold around a color, it simply does not return

问题 I'm writing a C++ program with OpenCV for lunar crater detection which seems to detect only a small fraction of craters accurately. My strategy for this approach was to first convert the image to HSV, then use inRange() to catch the colors in a range of values to produce a threshold, then Gaussian blur it and use HoughCircles() to detect the circles. One thing that I am not fully understanding is that when I give inRange() a low and high threshold around a color, it simply does not return

问题 I am trying to detect crop-rows using Segmentation and HoughLines and I have a script from github that I am trying to modify. A merging function was applied after the HoughLines to merge lines that are close to each other based on distance But I don't seems to understand the reason for that. from what I can tell multiple lines where detected for individual crop-row even after varying the HoughLine parameters. So merging the lines was a way to optimize the result of the HoughLine process. def

问题 I am new to OpenCV and trying to see if I can find a way to detect vertical text for the image attached. In this case on row 3 , I would like to get the bounding box around Original Cost and the amount below ($200,000.00). Similarly I would like to get the bounding box around Amount Existing Liens and the associated amount below. I then would use this data to send to an OCR engine to read text. Traditional OCR engines go line by line and extract and loses the context. Here is what I have

问题 I used the Laplacian transform in OpenCV for edge detection and then used Hough Line Transform for detecting lines in it. These identified lines need to eventually removed from the image. import cv2 import numpy as np from matplotlib import pyplot as plt img = cv2.imread('Feb_16-0.jpg',0) kernel = np.ones((1,1),np.uint8) opening = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel) blur = cv2.GaussianBlur(opening,(1,1),0) ret3,th4 = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)

问题 In OpenCV, there are two methods of detecting lines that give similar results in the form of a vector of endpoints - the Line Segments Detector (LSD) and the Probabilistic Hough Transform. (Discounting the standard Hough transform as the output given is in terms of equations, not line endpoints.) I haven't been able to find a compare and contrast of these two line detection methods and their pros/cons. Thus - what is the difference between these two functions? Are there any particular

问题 I want to extract the information from a scanned table and store it a csv. Right now my table extraction algorithm does the following steps. Apply skew correction Apply a gaussian filter for denoising. Do a binarization using Otsu thresholding Do a morphological opening. Canny egde detection Do a hough transform to obtain lines of table. Remove duplicate lines( same lines in the range of 10 pixels) filter the horizontal and vertical lines using slope of line(slope should be less than +/-5