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OpenCV Articles
Page 7 of 11
How to perform image translation using OpenCV in Python?
Shifting the image location in a particular direction is referred to as Image Translation. To perform image translation, we should first understand what is a translation matrix and how to define it using OpenCV and NumPy. If we want to make a translation in (x, y) direction, let it be (tx, ty). tx is the shift in horizontal direction and ty is the shift in vertical direction. Using (tx, ty), we can define the translation matrix M as below − M = np.float32([[1, 0, tx], [0, 1, ty]]) The translation matrix M is a numpy array of type np.float32. ...
Read MoreHow to find the HSV values of a color using OpenCV Python?
To find the HSV values of a color, we can use color space conversion from BGR to HSV. First we define the color value in BGR format as numpy.ndarray and then convert it to HSV space. We can also find the lower and upper limits of HSV value as [H-10, 100, 100] and [H+10, 255, 255] respectively. These lower and upper limits can be used to track an object of particular color. To find the HSV values of a color, follow the steps given below − Steps Import the required libraries. In all the following Python examples, the required Python ...
Read MoreHow to create a trackbar as the RGB color palette using OpenCV Python?
In OpenCV, a trackbar can be created using cv2.reateTrackbar() function. To access the value of the selected trackbar position, we use cv2.getTrackbarPos() function. Using these two functions, we create a window that contains the trackbars for R, G, B colors and a color window to display the selected color. By changing the position of trackbars RGB colors change between 0 and 255. See the below syntaxes for both functions. Syntax cv2.createTrackbar(trackbar_name, window_name, default_value, max_value, callback_func) cv2.getTrackbarPos(trackbar_name, window_name) Parameters trackbar_name − It's the trackbar name. This name is used to access the trackbar position value. window_name − It is ...
Read MoreHow to convert an RGB image to HSV image using OpenCV Python?
An RGB (colored) image has three channels, Red, Blue and Green. A colored image in OpenCV has a shape in [H, W, C] format, where H, W, and C are image height, width and number of channels. All three channels have a value range between 0 and 255. The HSV image also has three channels, the Hue, Saturation and Value channels. In OpenCV, the values of the Hue channel range from 0 to 179, whereas the Saturation and Value channels range from 0 to 255. In OpenCV, to convert an RGB image to HSV image, we use the cv2.cvtColor() function. ...
Read MoreHow to create a black image and a white image using OpenCV Python?
To create a black image, we could use the np.zeros() method. It creates a numpy n-dimensional array of given size with all elements as 0. As all elements are zero, when we display it using cv2.imshow() or plt.imshow() functions, it displays a balck image. To create a white image, we could use the np.ones() method. It creates a numpy n-dimensional array of given size with all elements as 1. We multiply this array by 255 to create a white image. Now all elements are 255, so when we display it using cv2.imshow() or plt.imshow() functions it gives a white image. ...
Read MoreHow to join two images horizontally and vertically using OpenCV Python?
Images in OpenCV are represented as numpy.ndarray. OpenCV provides two functions − cv2.hconcat() and cv2.vconcat() to join images. The function cv2.hconcat() joins images horizontally and the function cv2.vconcat() joins images vertically. These functions join two or more images. These functions accept a list of images of the same size to join them. The height, width and number of channels of all images must be the same to join them Syntax cv2.hconcat(img_list) cv2.vconcat(img_list) Where img_list is a list of images [img1, img2, …]. To join the images horizontally or vertically, one could follow the steps given below − ...
Read MoreDownsampling an image using OpenCV
In this program, we will down sample an image. Downsampling is decreasing the spatial resolution while keeping the 2D representation of an image. It is typically used for zooming out of an image. We will use the pyrdown() function in the openCV library to complete this task.Original ImageAlgorithmStep 1: Fead the image. Step 2: Pass the image as a parameter to the pyrdown() function. Step 3: Display the output.Example Codeimport cv2 image = cv2.imread('testimage.jpg') print("Size of image before pyrDown: ", image.shape) image = cv2.pyrDown(image) print("Size of image after pyrDown: ", image.shape) cv2.imshow('DownSample', image)OutputSize of image before pyrDown: (350, ...
Read MorePerforming white TopHat operation on images using OpenCV
In this program, we will perform the TopHat operation on images. TopHat operation is a morphological operation that is used to extract small elements and details from given images. TopHat is used to enhance bright objects in a dark background. We will use the morphologyEx(image, cv2.MORPH_TOPHAT, kernel) functionOriginal ImageAlgorithmStep 1: Import cv2. Step 2: Read the image. Step 3: Define the kernel size. Step 4: Pass the image and kernel to the cv2.morphologyex() function. Step 5: Display the output.Example Codeimport cv2 image = cv2.imread('tophat.jpg') filter_size = (5, 5) kernel = cv2.getStructuringElement(cv2.MORPH_RECT, filter_size) image = cv2.morphologyEx(image, cv2.MORPH_TOPHAT, kernel) cv2.imshow('TopHat', image)OutputExplanationAs ...
Read MoreDilating images using the OpenCV function dilate()
In this program, we will dilate an image using the dilate function in the OpenCV library. Dilation adds pixels to the boundaries of objects in an image, i.e., it expands the image on all sides.Original ImageAlgorithmStep 1: Import cv2 and numpy. Step 2: Read the image using opencv.imread(). Step 3: Define the kernel using np.ones() function. Step 4: Pass the image and kernel to the dilate() function. Step 5: Display the imageExample Codeimport cv2 import numpy as np image = cv2.imread('testimage.jpg') kernel = np.ones((3, 3), np.uint8) image = cv2.dilate(image, kernel) cv2.imshow('Dilated Image', image)OutputExplanationAs you can see, the image ...
Read MorePerforming an opening operation on an image using OpenCV
In this program, we will perform the opening operation on image. Opening removes small objects from the foreground of an image, placing them in the background. This technique can also be used to find specific shapes in an image. Opening can be called erosion followed by dilation. The function we will use for this task is cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel).Original ImageAlgorithmStep 1: Import cv2 and numpy. Step 2: Read the image. Step 3: Define the kernel. Step 4: Pass the image and kernel to the cv2.morphologyex() function. Step 4: Display the output.Example Codeimport cv2 import numpy as np image = ...
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