Category: opencv Python

OpenCV Python – Image Contours ”; Previous Next Contour is a curve joining all the continuous points along the boundary having the same color or intensity. The contours are very useful for shape analysis and object detection. Find Contour Before finding contours, we should apply threshold or canny edge detection. Then, by using findContours() method, we can find the contours in the binary image. The command for the usage of findContours()function is as follows − cv.findContours(image, mode, method, contours) Parameters The parameters of the findContours() function are as follows − image − Source, an 8-bit single-channel image. mode − Contour retrieval mode. method − Contour approximation method. The mode parameter’s values are enumerated as follows − cv.RETR_EXTERNAL − Retrieves only the extreme outer contours. cv.RETR_LIST − Retrieves all of the contours without establishing any hierarchical relationships. cv.RETR_CCOMP − Retrieves all of the contours and organizes them into a twolevel hierarchy. cv.RETR_TREE − Retrieves all of the contours and reconstructs a full hierarchy of nested contours. On the other hand, approximation method can be one from the following − cv.CHAIN_APPROX_NONE − Stores absolutely all the contour points. cv.CHAIN_APPROX_SIMPLE − Compresses horizontal, vertical, and diagonal segments and leaves only their end points. Draw Contour After detecting the contour vectors, contours are drawn over the original image by using the cv.drawContours() function. The command for the cv.drawContours() function is as follows − cv.drawContours(image, contours, contourIdx, color) Parameters The parameters of the drawContours() function are as follows − image − Destination image. contours − All the input contours. Each contour is stored as a point vector. contourIdx − Parameter indicating a contour to draw. If it is negative, all the contours are drawn. color − Color of the contours. Example Following code is an example of drawing contours on an input image having three shapes filled with black colours. In the first step, we obtain a gray image and then perform the canny edge detection. On the resultant image, we then call findContours() function. Its result is a point vector. We then call the drawContours() function. The complete code is as below − import cv2 import numpy as np img = cv2.imread(”shapes.png”) cv2.imshow(”Original”, img) gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) canny = cv2.Canny(gray, 30, 200) contours, hierarchy = cv2.findContours(canny, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) print(“Number of Contours = ” ,len(contours)) cv2.imshow(”Canny Edges”, canny) cv2.drawContours(img, contours, -1, (0, 255, 0), 3) cv2.imshow(”Contours”, img) cv2.waitKey(0) cv2.destroyAllWindows() Output The original image, after canny edge detection and one with contours drawn will be displayed in separate windows as shown here − After the canny edge detection, the image will be as follows − After the contours are drawn, the image will be as follows − Print Page Previous Next Advertisements ”;

OpenCV Python – Color Spaces ”; Previous Next A color space is a mathematical model describing how colours can be represented. It is described in a specific, measurable, and fixed range of possible colors and luminance values. OpenCV supports following well known color spaces − RGB Color space − It is an additive color space. A color value is obtained by combination of red, green and blue colour values. Each is represented by a number ranging between 0 to 255. HSV color space − H, S and V stand for Hue, Saturation and Value. This is an alternative color model to RGB. This model is supposed to be closer to the way a human eye perceives any colour. Hue value is between 0 to 179, whereas S and V numbers are between 0 to 255. CMYK color space − In contrast to RGB, CMYK is a subtractive color model. The alphabets stand for Cyan, Magenta, Yellow and Black. White light minus red leaves cyan, green subtracted from white leaves magenta, and white minus blue returns yellow. All the values are represented on the scale of 0 to 100 %. CIELAB color space − The LAB color space has three components which are L for lightness, A color components ranging from Green to Magenta and B for components from Blue to Yellow. YCrCb color space − Here, Cr stands for R-Y and Cb stands for B-Y. This helps in separation of luminance from chrominance into different channels. OpenCV supports conversion of image between color spaces with the help of cv2.cvtColor() function. The command for the cv2.cvtColor() function is as follows − cv.cvtColor(src, code, dst) Conversion Codes The conversion is governed by following predefined conversion codes. Sr.No. Conversion Code & Function 1 cv.COLOR_BGR2BGRA Add alpha channel to RGB or BGR image. 2 cv.COLOR_BGRA2BGR Remove alpha channel from RGB or BGR image. 3 cv.COLOR_BGR2GRAY Convert between RGB/BGR and grayscale. 4 cv.COLOR_BGR2YCrCb Convert RGB/BGR to luma-chroma 5 cv.COLOR_BGR2HSV Convert RGB/BGR to HSV 6 cv.COLOR_BGR2Lab Convert RGB/BGR to CIE Lab 7 cv.COLOR_HSV2BGR Backward conversions HSV to RGB/BGR Example Following program shows the conversion of original image with RGB color space to HSV and Gray schemes − import cv2 img = cv2.imread(”messi.jpg”) img1 = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY ) img2 = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) # Displaying the image cv2.imshow(”original”, img) cv2.imshow(”Gray”, img1) cv2.imshow(”HSV”, img2) Output Print Page Previous Next Advertisements ”;

OpenCV Python – Template Matching ”; Previous Next The technique of template matching is used to detect one or more areas in an image that matches with a sample or template image. Cv.matchTemplate() function in OpenCV is defined for the purpose and the command for the same is as follows: cv.matchTemplate(image, templ, method) Where image is the input image in which the templ (template) pattern is to be located. The method parameter takes one of the following values − cv.TM_CCOEFF, cv.TM_CCOEFF_NORMED, cv.TM_CCORR, cv.TM_CCORR_NORMED, cv.TM_SQDIFF, cv.TM_SQDIFF_NORMED This method slides the template image over the input image. This is a similar process to convolution and compares the template and patch of input image under the template image. It returns a grayscale image, whose each pixel denotes how much it matches with the template. If the input image is of size (WxH) and template image is of size (wxh), the output image will have a size of (W-w+1, H-h+1). Hence, that rectangle is your region of template. Example In an example below, an image having Indian cricketer Virat Kohli’s face is used as a template to be matched with another image which depicts his photograph with another Indian cricketer M.S.Dhoni. Following program uses a threshold value of 80% and draws a rectangle around the matching face − import cv2 import numpy as np img = cv2.imread(”Dhoni-and-Virat.jpg”,1) cv2.imshow(”Original”,img) gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) template = cv2.imread(”virat.jpg”,0) cv2.imshow(”Template”,template) w,h = template.shape[0], template.shape[1] matched = cv2.matchTemplate(gray,template,cv2.TM_CCOEFF_NORMED) threshold = 0.8 loc = np.where( matched >= threshold) for pt in zip(*loc[::-1]): cv2.rectangle(img, pt, (pt[0] + w, pt[1] + h), (0,255,255), 2) cv2.imshow(”Matched with Template”,img) Output The original image, the template and matched image of the result as follows − Original image The template is as follows − The image when matched with template is as follows − Print Page Previous Next Advertisements ”;

OpenCV Python – Feature Matching ”; Previous Next OpenCV provides two techniques for feature matching. Brute force matching and FLANN matcher technique. Example Following example uses brute-force method import numpy as np import cv2 img1 = cv2.imread(”lena.jpg”) img2 = cv2.imread(”lena-test.jpg”) # Convert it to grayscale img1_bw = cv2.cvtColor(img1,cv2.COLOR_BGR2GRAY) img2_bw = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY) orb = cv2.ORB_create() queryKeypoints, queryDescriptors = orb.detectAndCompute(img1_bw,None) trainKeypoints, trainDescriptors = orb.detectAndCompute(img2_bw,None) matcher = cv2.BFMatcher() matches = matcher.match(queryDescriptors,trainDescriptors) img = cv2.drawMatches(img1, queryKeypoints, img2, trainKeypoints, matches[:20],None) img = cv2.resize(img, (1000,650)) cv2.imshow(“Feature Match”, img) Output Print Page Previous Next Advertisements ”;

OpenCV Python – Histogram ”; Previous Next Histogram shows the intensity distribution in an image. It plots the pixel values (0 to 255) on X axis and number of pixels on Y axis. By using histogram, one can understand the contrast, brightness and intensity distribution of the specified image. The bins in a histogram represent incremental parts of the values on X axis. In our case, it is the pixel value and the default bin size is one. In OpenCV library, the function cv2.calcHist() function which computes the histogram from the input image. The command for the function is as follows − cv.calcHist(images, channels, mask, histSize, ranges) Parameters The cv2.calcHist() function’s parameters are as follows − images − It is the source image of type uint8 or float32, in square brackets, i.e., “[img]”. channels − It is the index of the channel for which we calculate histogram. For a grayscale image, its value is [0]. For BGR images, you can pass [0], [1] or [2] to calculate the histogram of each channel. mask − Mask image is given as “None” for full image. For a particular region of image, you have to create a mask image for that and give it as a mask. histSize − This represents our BIN count. ranges − Normally, it is [0,256]. Histogram using Matplotlib A histogram plot can be obtained either with the help of Matplotlib’s pyplot.plot() function or by calling Polylines() function from OpenCV library. Example Following program computes histogram for each channel in the image (lena.jpg) and plots the intensity distribution for each channel − import numpy as np import cv2 as cv from matplotlib import pyplot as plt img = cv.imread(”lena.jpg”) color = (”b”,”g”,”r”) for i,col in enumerate(color): hist = cv.calcHist([img],[i],None,[256],[0,256]) plt.plot(hist, color = col) plt.xlim([0,256]) plt.show() Output Print Page Previous Next Advertisements ”;

OpenCV Python – Draw Shapes and Text ”; Previous Next In this chapter, we will learn how to draw shapes and text on images with the help of OpenCV-Python. Let us begin by understanding about drawing shapes on images. Draw Shapes on Images We need to understand the required functions in OpenCV-Python, which help us to draw the shapes on images. Functions The OpenCV-Python package (referred as cv2) contains the following functions to draw the respective shapes. Function Description Command cv2.line() Draws a line segment connecting two points. cv2.line(img, pt1, pt2, color, thickness) cv2.circle() Draws a circle of given radius at given point as center cv2.circle(img, center, radius, color, thickness) cv2.rectangle Draws a rectangle with given points as top-left and bottom-right. cv2.rectangle(img, pt1, pt2, color, thickness) cv2.ellipse() Draws a simple or thick elliptic arc or fills an ellipse sector. cv2.ellipse(img, center, axes, angle, startAngle, endAngle, color, thickness) Parameters The common parameters to the above functions are as follows − Sr.No. Function & Description 1 img The image where you want to draw the shapes 2 color Color of the shape. for BGR, pass it as a tuple. For grayscale, just pass the scalar value. 3 thickness Thickness of the line or circle etc. If -1 is passed for closed figures like circles, it will fill the shape. 4 lineType Type of line, whether 8-connected, anti-aliased line etc. Example Following example shows how the shapes are drawn on top of an image. The program for the same is given below − import numpy as np import cv2 img = cv2.imread(”LENA.JPG”,1) cv2.line(img,(20,400),(400,20),(255,255,255),3) cv2.rectangle(img,(200,100),(400,400),(0,255,0),5) cv2.circle(img,(80,80), 55, (255,255,0), -1) cv2.ellipse(img, (300,425), (80, 20), 5, 0, 360, (0,0,255), -1) cv2.imshow(”image”,img) cv2.waitKey(0) cv2.destroyAllWindows() Output Draw Text The cv2.putText() function is provided to write a text on the image. The command for the same is as follows − img, text, org, fontFace, fontScale, color, thickness) Fonts OpenCV supports the following fonts − Font Name Font Size FONT_HERSHEY_SIMPLEX 0 FONT_HERSHEY_PLAIN 1 FONT_HERSHEY_DUPLEX 2 FONT_HERSHEY_COMPLEX 3 FONT_HERSHEY_TRIPLEX 4 FONT_HERSHEY_COMPLEX_SMALL 5 FONT_HERSHEY_SCRIPT_SIMPLEX 6 FONT_HERSHEY_SCRIPT_COMPLEX 7 FONT_ITALIC 16 Example Following program adds a text caption to a photograph showing Lionel Messi, the famous footballer. import numpy as np import cv2 img = cv2.imread(”messi.JPG”,1) txt=”Lionel Messi” font = cv2.FONT_HERSHEY_SIMPLEX cv2.putText(img,txt,(10,100), font, 2,(255,255,255),2,cv2.LINE_AA) cv2.imshow(”image”,img) cv2.waitKey(0) cv2.destroyAllWindows() Output Print Page Previous Next Advertisements ”;

OpenCV Python – Image Threshold ”; Previous Next In digital image processing, the thresholding is a process of creating a binary image based on a threshold value of pixel intensity. Thresholding process separates the foreground pixels from background pixels. OpenCV provides functions to perform simple, adaptive and Otsu’s thresholding. In simple thresholding, all pixels with value less than threshold are set to zero, rest to the maximum pixel value. This is the simplest form of thresholding. The cv2.threshold() function has the following definition. cv2.threshold((src, thresh, maxval, type, dst) Parameters The parameters for the image thresholding are as follows − Src: Input array. Dst: Output array of same size. Thresh: Threshold value. Maxval: Maximum value. Type: Thresholding type. Types of Thresholding Other types of thresholding are enumerated as below − Sr.No Type & Function 1 cv.THRESH_BINARY dst(x,y) = maxval if src(x,y)>thresh 0 otherwise 2 cv.THRESH_BINARY_INV dst(x,y)=0 if src(x,y)>thresh maxval otherwise 3 cv.THRESH_TRUNC dst(x,y)=threshold if src(x,y)>thresh src(x,y) otherwise 4 cv.THRESH_TOZERO dst(x,y)=src(x,y) if src(x,y)>thresh 0 otherwise 5 cv.THRESH_TOZERO_INV dst(x,y)=0 if src(x,y)>thresh src(x,y)otherwise These threshold types result in operation on input image according to following diagram − The threshold() function returns threshold used and threshold image. Following program produces a binary image from the original with a gradient of grey values from 255 to 0 by setting a threshold to 127. Example Original and resultant threshold binary images are plotted side by side using Matplotlib library. import cv2 as cv import numpy as np from matplotlib import pyplot as plt img = cv.imread(”gradient.png”,0) ret,img1 = cv.threshold(img,127,255,cv.THRESH_BINARY) plt.subplot(2,3,1),plt.imshow(img,”gray”,vmin=0,vmax=255) plt.title(”Original”) plt.subplot(2,3,2),plt.imshow(img1,”gray”,vmin=0,vmax=255) plt.title(”Binary”) plt.show() Output The adaptive thresholding determines the threshold for a pixel based on a small region around it. So, different thresholds for different regions of the same image are obtained. This gives better results for images with varying illumination. The cv2.adaptiveThreshold() method takes following input arguments − cv.adaptiveThreshold( src, maxValue, adaptiveMethod, thresholdType, blockSize, C[, dst] ) The adaptiveMethod has following enumerated values − cv.ADAPTIVE_THRESH_MEAN_C − The threshold value is the mean of the neighbourhood area minus the constant C. cv.ADAPTIVE_THRESH_GAUSSIAN_C − The threshold value is a Gaussianweighted sum of the neighbourhood values minus the constant C. Example In the example below, the original image (messi.jpg is applied with mean and Gaussian adaptive thresholding. import cv2 as cv import numpy as np from matplotlib import pyplot as plt img = cv.imread(”messi.jpg”,0) img = cv.medianBlur(img,5) th1 = cv.adaptiveThreshold(img,255,cv.ADAPTIVE_THRESH_MEAN_C, cv.THRESH_BINARY,11,2) th2 = cv.adaptiveThreshold(img,255,cv.ADAPTIVE_THRESH_GAUSSIAN_C, cv.THRESH_BINARY,11,2) titles = [”Original”, ”Mean Thresholding”, ”Gaussian Thresholding”] images = [img, th1, th2] for i in range(3): plt.subplot(2,2,i+1),plt.imshow(images[i],”gray”) plt.title(titles[i]) plt.xticks([]),plt.yticks([]) plt.show() Output Original as well as adaptive threshold binary images are plotted by using matplotlib as shown below − Example OTSU algorithm determines the threshold value automatically from the image histogram. We need to pass the cv.THRES_OTSU flag in addition to the THRESH-BINARY flag. import cv2 as cv import numpy as np from matplotlib import pyplot as plt img = cv.imread(”messi.jpg”,0) # global thresholding ret1,img1 = cv.threshold(img,127,255,cv.THRESH_BINARY) # Otsu”s thresholding ret2,img2 = cv.threshold(img,0,255,cv.THRESH_BINARY+cv.THRESH_OTSU) plt.subplot(2,2,1),plt.imshow(img,”gray”,vmin=0,vmax=255) plt.title(”Original”) plt.subplot(2,2,2),plt.imshow(img1,”gray”) plt.title(”Binary”) plt.subplot(2,2,3),plt.imshow(img2,”gray”) plt.title(”OTSU”) plt.show() Output The matplotlib’s plot result is as follows − Print Page Previous Next Advertisements ”;

OpenCV Python – Overview ”; Previous Next OpenCV stands for Open Source Computer Vision and is a library of functions which is useful in real time computer vision application programming. The term Computer vision is used for a subject of performing the analysis of digital images and videos using a computer program. Computer vision is an important constituent of modern disciplines such as artificial intelligence and machine learning. Originally developed by Intel, OpenCV is a cross platform library written in C++ but also has a C Interface Wrappers for OpenCV which have been developed for many other programming languages such as Java and Python. In this tutorial, functionality of OpenCV’s Python library will be described. OpenCV-Python OpenCV-Python is a Python wrapper around C++ implementation of OpenCV library. It makes use of NumPy library for numerical operations and is a rapid prototyping tool for computer vision problems. OpenCV-Python is a cross-platform library, available for use on all Operating System (OS) platforms including, Windows, Linux, MacOS and Android. OpenCV also supports the Graphics Processing Unit (GPU) acceleration. This tutorial is designed for the computer science students and professionals who wish to gain expertise in the field of computer vision applications. Prior knowledge of Python and NumPy library is essential to understand the functionality of OpenCV-Python. Print Page Previous Next Advertisements ”;

OpenCV Python – Reading an image ”; Previous Next The CV2 package (name of OpenCV-Python library) provides the imread() function to read an image. The command to read an image is as follows − img=cv2.imread(filename, flags) The flags parameters are the enumeration of following constants − cv2.IMREAD_COLOR (1) − Loads a color image. cv2.IMREAD_GRAYSCALE (0) − Loads image in grayscale mode cv2.IMREAD_UNCHANGED (-1) − Loads image as such including alpha channel The function will return an image object, which can be rendered using imshow() function. The command for using imshow() function is given below − cv2.imshow(window-name, image) The image is displayed in a named window. A new window is created with the AUTOSIZE flag set. The WaitKey() is a keyboard binding function. Its argument is the time in milliseconds. The function waits for specified milliseconds and keeps the window on display till a key is pressed. Finally, we can destroy all the windows thus created. The function waits for specified milliseconds and keeps the window on display till a key is pressed. Finally, we can destroy all the windows thus created. The program to display the OpenCV logo is as follows − import numpy as np import cv2 # Load a color image in grayscale img = cv2.imread(”OpenCV_Logo.png”,1) cv2.imshow(”image”,img) cv2.waitKey(0) cv2.destroyAllWindows() The above program displays the OpenCV logo as follows − Print Page Previous Next Advertisements ”;

OpenCV Python – Environment Setup ”; Previous Next In most of the cases, using pip should be sufficient to install OpenCV-Python on your computer. The command which is used to install pip is as follows − pip install opencv-python Performing this installation in a new virtual environment is recommended. The current version of OpenCV-Python is 4.5.1.48 and it can be verified by following command − >>> import cv2 >>> cv2.__version__ ”4.5.1” Since OpenCV-Python relies on NumPy, it is also installed automatically. Optionally, you may install Matplotlib for rendering certain graphical output. On Fedora, you may install OpenCV-Python by the below mentioned command − $ yum install numpy opencv* OpenCV-Python can also be installed by building from its source available at http://sourceforge.net Follow the installation instructions given for the same. Print Page Previous Next Advertisements ”;