Opencv find lines

python - How to detect lines in OpenCV? - Stack OverflowDetecting machine-readable zones in passport images

OpenCV: Extract horizontal and vertical lines by using

  1. Apply two very common morphology operators (i.e. Dilation and Erosion), with the creation of custom kernels, in order to extract straight lines on the horizontal and vertical axes. For this purpose, you will use the following OpenCV functions: erode() dilate() getStructuringElement(
  2. A line can be represented by an equation- or in parametric form it can be representated as, as where (ρ) is the perpendicular distance from origin to the line, and ϴ is the angle formed by this perpendicular line and horizontal axis measured in counter-clockwise (This representation is used in OpenCV). Check below image. So if line is passing below the origin, it will have a positive rho and angle less than 180
  3. Everything explained above is encapsulated in the OpenCV function, cv2.HoughLines (). It simply returns an array of (r, 0) values. r is measured in pixels and 0 is measured in radians. import cv2 import numpy as n
  4. LineLength and maxLineGap were the parameters to discard or retain lines, HoughCircles has a
  5. Use the OpenCV functions HoughLines() and HoughLinesP() to detect lines in an image. Theory Note The explanation below belongs to the book Learning OpenCV by Bradski and Kaehler. Hough Line Transform . The Hough Line Transform is a transform used to detect straight lines. To apply the Transform, first an edge detection pre-processing is desirable
  6. Naturally, one of the first things to do in developing a self-driving car is to automatically detect the lane lines using some sort of algorithm. In this project, we will use Python and OpenCV to..
  7. LineLength = 500 maxLineGap = 5 lines = cv2.HoughLinesP(edges,1,np

Hough Transform with OpenCV (C++/Python) [latexpage]In this post, we will learn how to detect lines and circles in an image, with the help of a technique called Hough transform The Hough Line Transform is a transform used to detect straight lines. OpenCV implements three kinds of Hough Line Transforms:(Standard Hough Transform, SHT),(Multi-Scale Hough Transform, MSHT)and (Progressive Probabilistic Hough Transform, PPHT). Theory. In the Cartesian coordinate system, the line can be expressed as y = mx+b Before going into the lines road detection, we need to understand using opencv what is a line and what isn't a line. Hough lines transform: The Houg lines transform is an algorythm used to detect straight lines. One of the most important features of this method is that can detect lines even when some part of it is missing. And this comes really useful in the road when we have dashed lines, or when for some reason some part of the line is not visible for line in lines: for x1,y1,x2,y2 in line: cv2.line(line_image,(x1,y1),(x2,y2),(255,0,0),10) Draw the lines on the original image and return it. lines_edges = cv2.addWeighted(image, 0.8, line_image, 1, 0) return lines_edge

opencv - Drawing grid lines across the image uisng openccvpython - Detect the vertices of a polyline in an image

Line detection in python with OpenCV? - Tutorialspoin

  1. return np.array([left_line, right_line]) Fitting the coordinates into our actual image and then returning the image with the detected line(road with the detected lane): def display_lines(image, lines)
  2. With OpenCV's cv2.HoughLinesP you can easily find lines and join gaps in lines as demonstrated below. The code displayed below can be used to run the example. The code is very basic that imports the necessary packages and uses OpenCV to read image, convert it to binary image
  3. Finding the Lines Histogram def get_histogram ( binary_warped ): histogram = np . sum ( binary_warped [ binary_warped . shape [ 0 ] // 2 :,:], axis = 0 ) return histogram binary_warped , Minv = warp ( combined_binary ) histogram = get_histogram ( binary_warped ) plt . plot ( histogram
  4. Find edges with Canny edge detector; Crop irrelevant parts of the image; Detect lines with Hough transform; Filter image by color. Udacity course task requires to detect both yellow and white lines. For that purpose, they use HSV or HLS conversion. I need to detect only a white line so I decided to use only a grayscale filter
  5. 4. Code for Detecting Lines in Python and C++. The HoughLineP () function finds circles on grayscale images using a Hough Transform. image - The output from the edge detector. This is a grayscale image. rho - Distance resolution in pixels of the Hough grid which is the parameter . theta - Angular resolution in radians of the Hough grid.
  6. In this project, I used Python and OpenCV to find lane lines in the road images. The following techniques are used: Color Selection; Canny Edge Detection; Region of Interest Selection; Hough Transform Line Detection; Finally, I applied all the techniques to process video clips to find lane lines in them. How to Reproduce the result

HoughLine: How to Detect Lines using OpenCV. In OpenCV, line detection using Hough Transform is implemented in the function HoughLines and HoughLinesP [Probabilistic Hough Transform]. This function takes the following arguments: edges: Output of the edge detector. lines: A vector to store the coordinates of the start and end of the line We are going to use OpenCV to process the input images to discover any lane lines held within and also for rendering out a representation of the lane. Additionally, images are really just dense.. In this tutorial, we are going to build a spring boot application that can remove or detect grid lines(horizontal,vertical lines) fro

Video: Line detection in python with OpenCV Houghline method

OpenCV for detecting Edges, lines and shape

In the next blog, I will try to put everything together, install opencv in raspberry pi and make a small prototype car that can identify the lane lines and navigate. Stay tuned Yogesh Ojh Step 2 :Apply OpenCv To Uploaded Image. 2.1 Load the image. 2.2 Convert RGB to Gray image. 2.3 Detect and get points of horizontal and vertical lines. 2.4 Overwrite lines on the image. Step 3 : Test Application. Step 1: Create a Spring Boot Application. Go to the Spring Initializr website and download the project. Dependencies we are going to. Line detection with Canny. Canny is an algorithm made for edge detection. This is the base algorithm for any line edge or contour detection for his accuracy and his ease to use. The example presented below will show how to detect lines into an image with the canny algorithm. Note that the canny algoirthm use the sobel algorithm in the background In this paper, we share the opencv using Hough transform for line detection of the specific code, for your reference, the specific content is as follows . 1. The simplest Hough transform is to recognize the straight line in the image. In the plane rectangular coordinate system (X-Y), a straight line can be expressed as follows: y = KX + B Identify Lane Line Pixels. We now need to identify the pixels on the warped image that make up lane lines. Looking at the warped image, we can see that white pixels represent pieces of the lane lines. We start lane line pixel detection by generating a histogram to locate areas of the image that have high concentrations of white pixels

How to detect the text above lines using OpenCV in Python: pframe: 0: 963: Apr-14-2020, 09:53 AM Last Post: pframe : Display the bottom image of the line and cut the upper image using Opencv: jenkins43: 1: 1,697: May-27-2019, 06:56 AM Last Post: heiner55 : circle node with horizontal line in python graphviz: mandana: 0: 975: May-05-2019, 10:39. Opencv uses Hough transform to detect lines for image correction. Time:2020-7-3. The line is detected by Hough transform, and the oblique image is corrected. #include<opencv2\opencv.hpp> #include <iostream> using namespace cv; using namespace std; #define ERROR 1234 //Degree conversion double DegreeTrans(double theta) { double res = theta. OpenCV - Drawing Arrowed Lines. You can draw an arrowed line on an image using the method arrowedLine () of the imgproc class. Following is the syntax of this method −. mat − A Mat object representing the image on which the arrowed line is to be drawn. pt1 and pt2 − Two Point objects representing the points between which the arrowed line.

QR code is detected and decoded by using detectAndDecode method. It allows to get decoded data and an array of vertices of the found QR code. import org.opencv.core.*; If a QR code was found we print the decoded data and draw a bounding box around the detected QR Code Simulate Self-Driving Cars with Computer Vision & Deep Learning - Full Course on sale for $10! (normally $200): https://www.udemy.com/applied-deep-learningtm..

Feature matching between images in OpenCV can be done with Brute-Force matcher or FLANN based matcher. BF Matcher matches the descriptor of a feature from one image with all other features of another image and returns the match based on the distance. It is slow since it checks match with all the features Otherwise, Lines 51-53 compute the rotated bounding box of the current object (using cv2.cv.BoxPoints for OpenCV 2.4 and cv2.boxPoints for OpenCV 3). A call to order_points on Line 59 rearranges the bounding box (x, y)-coordinates in top-left, top-right, bottom-right, and bottom-left order, which as we'll see, is important when we go to.

Text Detection and recognition in Android using OpenCV

// Show found lines Mat drawnLines (image); LSD线特征提取方法+Opencv实现C++,LSD - Line Segment Detector on digital images, LSD: A Fast Line Segment Detector with a False Detection Control by Rafael Grompone von Gioi, Jeremie Jakubowicz, Jean-Michel Morel. Figure 2: Detecting the top of a soda can using circle detection with OpenCV. Again, our Python script is able to detect the circular region of the can. Now, let's try the 8 circle problem. In this problem we have one large circle, followed by seven circles placed inside the large one.. Since this is a much smaller image than the previous ones (and we are detecting multiple circles), I'm. Line following robot with OpenCV and contour-based approach. Constantin Toporov. Oct 23, 2018 · 4 min read. In my previous story, I told about PiTanq — a robot-tank I built. Then a big goal is to make it autonomous. I was inspired by Udacity course for self-driving cars and the first task of this course is to recognize lanes on roads We will use the OpenCV HoughLines() function to find all lines in the image and select only the 4 of our interest. Once the 4 lines are detected we just need to use the OpenCV line() function to draw the corresponding table edges. The obtained image can then be overlaid on top of the original image to complete the task as shown below

How to calculate an epipolar line with a stereo pair of

OpenCV: Hough Line Transfor

I have managed to detect horizontal line (unbroken/continuous), however I am having trouble detecting all the dotted/broken lines in an image. Here is my test image, as you can see there are dotted lines and some text/boxes etc. my code is below: import cv2. import numpy as np. img=cv2.imread ('test.jpg' Introduction. In OpenCV, one can draw numerous shapes such as point, line, circle etc. There is an optional for filling a shape. The following code is self-explanatory which shows how shapes are drawn We have learned how to detect shapes like lines an circles with the Hough transform and we explained how to approximate detected contours. In the next post, we will talk about image segmentation. [1] Find the Center of a Blob (Centroid) using OpenCV (C++/Python

Lane line detection using OpenCV - Where good ideas find you

Lines can be detected in an image using Hough lines. OpenCV provides an HouhLines function in which you have to pass the threshold value. The threshold is the minimum vote for it to be considered a line. For a detailed overview, check the below code for complet­e implementation For line detection using Hough lines in OpenCV It's all well and good to detect a hand in am image and draw lines from the center of the hand, but that information will be pretty useless without accurate measurements of those lines. Concept The idea for this system is based loosely on the image belo In the previous tutorial, we have seen how you can detect edges in an image.However, that's not usually enough in the image processing phase. In this tutorial, you will learn how you can detect shapes (mainly lines and circles) in images using Hough Transform technique in Python using OpenCV library.. The Hough Transform is a popular feature extraction technique to detect any shape within an.

How to detect/find checkbox contours using OpenCV. It is working well, however it fails whenever a box touches a line on one of its sides, which occurs fairly frequently in my use case. I have included two examples, one original and one is the image after being processed with canny. Checkbox intersecting line Checkbox after cann A line can be represented as or in parametric form, as where is the perpendicular distance from origin to the line, and is the angle formed by this perpendicular line and horizontal axis measured in counter-clockwise ( That direction varies on how you represent the coordinate system. This representation is used in OpenCV). Check below image

Hough Line Transformation. Hough Transform is a technique to detect any shape that can be represented mathematically. For example, it can detect shapes like rectangles, circles, triangles, or lines. We are interested in detecting lane markings that can be represented as lines. I strongly suggest you check out the Hough Transformation documentation First, we import OpenCV using the line, import cv2 Next, we read in the image, which in this case is, Boxes.png We then create a grayscale version of the image. In order to use the harris corner detection method, the grayscale image must be converted to float32 type. We then apply the harris corner detection method to the grayscale image using. Use the OpenCV functions cv::HoughLines and cv::HoughLinesP to detect lines in an image. Theory Note The explanation below belongs to the book Learning OpenCV by Bradski and Kaehler. Hough Line Transform . The Hough Line Transform is a transform used to detect straight lines. To apply the Transform, first an edge detection pre-processing is. Use Hough Transformation to find the curve lines in your image. (OpenCV only has the Hough transform for straight lines, you can write your own one for detecting curves. Now go through the curve lines pixel by pixel left to right and delete it when it is thin (when only have a little number of same colour pixels above and below) and keep it. Now this is easy for OpenCV to detect contours: contours, hierarchy = cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) image = cv2.drawContours(image, contours, -1, (0, 255, 0), 2) Copy. The above code finds contours within the binary image and draw them with a thick green line to the image, let's show it

How do I detect the curvy lines in OpenCV? - OpenCV Q&A Foru

Now its time to find lines. This is done with the Hough transform. OpenCV comes with it. So a line of code is all that's needed: vector < Vec2f > lines; HoughLines (outerBox, lines, 1, CV_PI / 180, 200); For now, we'll draw each line. Just to see if the results too now are good enough or not Canny ( shapes_blurred, 100, 200) # Step 1: The Hough transform needs a binary edges images. For this particular. # from the original shapes.png file. ''' A function for creating a Hough Accumulator for lines in an image. '''. # of maximum values equal to num_peaks. You have to be careful here though, if

In this video on OpenCV Python Tutorial For Beginners, I am going to show How to Find contours and draw contours using OpenCV in Python.We will see what con.. Naturally, one of the first things we would like to do in developing a self-driving car is to automatically detect lane lines using an algorithm. In this project we will detect lane lines in images using Python and OpenCV. OpenCV means Open-Source Computer Vision, which is a package that has many useful tools for analyzing images But the numerous lines were not good enough for detecting the location of the puzzle. So we'll do some math today and find out exactly where the puzzle is. We'll also un-distort the puzzle so we have a perfect top-down view of the sudoku puzzle. Merging lines. Each physical line on the image has several mathematical lines associated with it OpenCV. i have two perpendicualr lines how to draw the Angle between them. What I have tried: mport numpy as np. import cv2. btn_down = False. def get_points (im): # Set up data to send to mouse handler. data = {

line detection Learn OpenC

Jul 23, 2012 · 2 min read. Not too long ago I mentioned I was playing around with opencv and python. Well, in between client work I've moved a bit forward. Now I'm calculating the angle of a line between two tracked points relative to the horizontal. And here's the code. [sourcecode language=python wraplines=false collapse. We want openCV to detect all of the shapes we have thresholded for (the black line) so we can process them in the next steps. Since findContours() will edit the image that is inputed, we instead inputted a copy of the thresholded image (which we might need to view later for debugging purposes) In this tutorial we are going to learn how to draw lines in an image, using Python and OpenCV. Being able to draw lines on an image might be useful to mark, for example, regions of interest on an image. This tutorial was tested with version 4.0.0 of OpenCV and version 3.7.2 of Python. The code. We will start our code by importing the cv2 module OpenCV offers several functions to draw different geometric shapes and write text on an image. In this tutorial, we are going to see opencv functions to draw shapes like Line, Rectangle, Circle, Ellipse, and Polygon. Below is the list of functions that we are going to cover - cv2.line(): This function is used to draw line on an image

I used Python and OpenCV to find lines in a real time video. The following techniques are used: 1. Canny Edge Detection 2. Hough Transform Line Detection Finally, I applied these two techniques to process video clips to find lines. There is a good instruction to line tracking in Car-Finding-Lane-Lines. Materials/Prerequisites. You will need a. In this project, I used Python and OpenCV to detect lane lines on the road. I developed a processing pipeline that works on a series of individual images, and applied the result to a video stream. View on GitHub Lane Lines Detection Using Python and OpenCV In this project, I used Python and OpenCV to detect lane lines on the road This OpenCV C++ Tutorial is about Horizontal Line Detection i.e. How to Detect Horizontal Edges or Lines in an Image What is a Line? A line is a straight one-dimensional figure having no thickness and extending infinitely in both directions

OpenCV Line Detection Dynamsoft Developer

OpenCV method uses the input images to find any lane lines command among and also for rendering out an illustration of the lane. The OpenCV tools like colour selection, the region of interest selection, grey scaling, Gaussian smoothing, Canny Edge Detection, and Hough Transform line detection are being employed In my Android App,I have to detect text lines in images.I want to use Hough Transform to detect lines.You can see an image below: . I got edges in image by canny filter,then I applied Hough Transform on canny image:. Size size = canny.size(); Mat lines = new Mat(); int lineGap = 20; double sum1 = 0; Imgproc.HoughLinesP(canny, lines, 1, Math.PI / 180, 100, size.width / 2.f, lineGap)

Lines detection with Hough Transform - OpenCV 3

OCR programs typically have to do some sort of page-layout analysis to find out where the text is and carve it up into individual lines and characters. When you hear OCR, you might think about fancy Machine Learning techniques like Neural Nets. But it's a dirty secret of the trade that page layout analysis, a much less glamorous problem. To find out where you python.exe is installed, just run these two lines of code, it would print the location where python is installed. import sys print(sys.executable) Now if you have done these steps successfully, let's move to the code for pedestrian detection OpenCV Python hand gesture recognition - tutorial based on OpenCV software and Python language aiming to recognize the hand gestures. In this tutorial, you can find the program lines that extract from input frames the region of interest (ROI), how to find the contour, how to draw the convex hull, and finally how to find the convexity defects. I am having trouble looping through an image with lines and extract region of interest(roi) above those lines. My code : import cv2 import numpy as np img=cv2.imread('test3.jpg') #img=cv2.resize(img,(500,500)) imgGray=cv2.cvtColor(img,cv2.COLOR_BGR2.. Using OpenCV, I have found that a quite reliable solution is based on (i) the use of the Hough transform, and (ii) the computation of the intersection of the lines we get. For the first part, OpenCV has two main options, the Standard Hough Transform (SHT), and the Progressive Probabilistic Hough Transform (PPHT)

The first parameter gives the window name and the second parameter is the frame to be displayed. cv2.imshow('Edge frame', edge) cv2.imshow ('Edge frame', edge) cv2.imshow ('Edge frame', edge) The next line of code waits for the user to enter a certain character, for instance 'q', to reak out of the loop to quit the window We will do this using the concepts of computer vision using OpenCV library. To detect the lane we have to detect the white markings on both sides on the lane. Road Lane-Line Detection with Python & OpenCV. Using computer vision techniques in Python, we will identify road lane lines in which autonomous cars must run This tutorial begins with how to load , modify and display a video with OpenCV 4.0 in Python. Follow a tutorial to install OpenCV and find a video you want to play with ( I use this video ). Finally, fire your favorite text editor to run this example: Loads and displays a video. cap = cv2

Here, I will use it for preprocessing, to detect the text from an image file. Tesseract requires a clean image to detect the text, this is where OpenCV plays an important role as it performs the operations on an image like converting a colored image to binary image, adjusting the contrast of an image, edge detection, and many more detectMultiScale(image, scaleFactor, minNeighbors): This is a general function to detect objects, in this case, it'll detect faces since we called in the face cascade. If it finds a face, it returns a list of positions of said face in the form Rect(x,y,w,h)., if not, then returns None. Image: The first input is the grayscale image. So make sure the image is in grayscale How draw line in opencv. Please Sign up or sign in to vote. 1.00/5 (1 vote) See more: C++. OpenCV. i had code for the detect object edge. using this one i can draw line around rectangle objects. but now i want draw line cross the rectangle. how i do this. simply say. i want measure width of the rectangle

Finding Driving Lane Line live with OpenCV by Percy

Step 6 : Find Lines. Hough transform is a feature extraction method for detecting simple shapes such as circles, lines etc in an image. Here, you can learn more about that. # it will return line coordinates,it will return 3darray Steps: First we will create a image array using np.zeros () After that we will create a line using cv2.line () Then display the image using cv2.imshow () Wait for keyboard button press using cv2.waitKey () Exit window and destroy all windows using cv2.destroyAllWindows ( Here, we have converted the image into a binary format. Now let us define two kernels to extract the horizontal and vertical lines from these cells. For the horizontal lines, we will do the following. We will first get the entire image dimensions and then using the OpenCV structural element function we will get the horizontal lines

OpenCV Real Time Road Lane Detection - GeeksforGeek

1. Introduction: welcome to the skill share self driving cars tutorial. In this free course, I will show you how to detect lane lines with open CV and python by making use of various computer vision techniques, as would be done for a self driving car. This is really exciting stuff In this tutorial, you will learn how to detect the speed of a car using Python OpenCV library and some basic mathematical calculations. In the area of traffic management, checking the speed of a vehicle passing through a road becomes very crucial when there is the rule of speed limitations The third parameter, contour approximation method, will collect only the endpoint coordinates of straight lines. All the white blobs in the mask will have contours applied. The array list of found contours will be in the contours variable. (Optional) Draw all Contours. OpenCV Find Contours Example cv2.drawContours(frame, contours, -1, (0,255,0), 3 Installing OpenCV. First, you need to find the correct setup file for your operating system. I found that installing OpenCV was the hardest part of the task. If you get strange unexplainable errors, it could be due to library clashes, 32/64 bit differences, and so on

Hough Line Transform opencv python. GitHub Gist: instantly share code, notes, and snippets OpenCV implementation is based on Robust Detection of Lines Using the Progressive Probabilistic Hough Transform by Matas, J. and Galambos, C. and Kittler, J.V. [54]. The function used is cv2.HoughLinesP (). It has two new arguments. minLineLength - Minimum length of line. Line segments shorter than this are rejected In a previous article I built a simple line following robot. I had intended to follow this article up with more about building cooperating systems, but I'm afraid I got distracted! In particular I spent some time adding openCV (the open source image processing project) to leJOS. As a result I wanted a project to explore what was possible using openCV on the EV3, this article is the result

Detecting lines in image with OpenCV - Hough Line

Until recently OpenCV Python packages were provided for Windows, Linux (x86_64 and ARM), and macOS (formerly known as OSX) for x86_64 and all was right. Read More » July 26, 2021 . News. OpenCV 4.5.3 . OpenCV 4.5.3 and 3.4.15 have been released. Read More » July 19, 2021 . Competition rho = 1 # distance resolution in pixels of the Hough grid theta = np.pi / 180 # angular resolution in radians of the Hough grid threshold = 15 # minimum number of votes (intersections in Hough grid cell) min_line_length = 50 # minimum number of pixels making up a line max_line_gap = 20 # maximum gap in pixels between connectable line segments. ANN:12 Marks the beginning of finding the moments of the contours found. /ANN:12 let cnt; let Moments; let M00; let M10; M00 is the zeroth moment-the area enclosed by a contour. In OpenCv it is actually the number of pixels enclosed by the contour. M10 and M01 are the x and y coordinate-weighted number of pixels enclosed OpenCV is an open source computer vision library which is very popular for performing basic image processing tasks such as blurring, image blending, enhancing image as well as video quality, thresholding etc. In addition to image processing, it provides various pre-trained deep learning models which can be directly used to solve simple tasks at hand. Using these algorithms to detect and recognize objects in videos requires an understanding of applied mathematics and solid technical knowledge of the algorithms as well as thousands of lines of code. This is a highly technical and time-consuming process, and for those who desire to implement object detection can find the process very inconvenient

GitHub - tatsuyah/Lane-Lines-Detection-Python-OpenCV: Lane

Let's see how well we can find Nemo in an image. The key Python packages you'll need to follow along are NumPy, the foremost package for scientific computing in Python, Matplotlib, a plotting library, and of course OpenCV. This articles uses OpenCV 3.2.0, NumPy 1.12.1, and Matplotlib 2.0.2 # OpenCV Python program to detect cars in video frame # import libraries of python OpenCV import cv2 # capture frames from a video cap = cv2.VideoCapture('video.avi') # Trained XML classifiers describes some features of some object we want to detect car_cascade = cv2.CascadeClassifier('cars.xml') # loop runs if capturing has been initialized

Find & Draw Contours ¶. OpenCV에서 contours를 찾고, 그리기 위해서 아래 2개의 함수를 사용합니다. cv2.findContours (image, mode, method [, contours [, hierarchy [, offset]]]) → image, contours, hierarchy. Parameters: image - 8-bit single-channel image. binary image. mode -. contours를 찾는 방법. cv2.RETR. Before using the face detector we need comvert the captured image to Gray scale. gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) Now lets apply the face detector to detect faces in our captured image. faces = detector.detectMultiScale(gray, 1.3, 5) the above line will get the x,y and height,width of all the faces present in the captured image in a. OpenCV essentially stands for Open Source Computer Vision Library. Although it is written in optimized C/C++, it has interfaces for Python and Java along with C++. OpenCV boasts of an active user base all over the world with its use increasing day by day due to the surge in computer vision applications. OpenCV-Python is the python API for OpenCV If you are trying to build an OpenCV application which uses GStreamer for video-processing, then you need to compile OpenCV from source with GStreamer support. This is easier in Linux, where you can install GStreamer libraries and plugins, and compile OpenCV with with_gstreamer option enabled to get your job done. But in windows, this becomes trickier as the with_gstreamer optio Our goal here is to find all of the corners in this image. I will note that we have some aliasing issues here (jagged-ness in slanted lines), so, if we let it, a lot of corners will be found, and rightly-so. As usual with OpenCV, the hard part is done for us already, and all we need to do is feed in some parameters So lets see step by step how calculate the orientation of each object in the images above. First, read the image and convert it to greyscale. 1. 2. Mat bw, img = imread (test_image.jpg); cvtColor (img, bw, COLOR_BGR2GRAY); Then process the greyscale image and find the objects of interest. 1. 2