Programming Articles - Page 589 of 3363

The morphological gradient is computed as the difference between the dilation and erosion of an image. We use cv2.morphologyEx() method to compute the morphological gradients. Morphological gradient is used in segmentation, edge detection and to find the outline of an object. Syntax Here is the syntax used for this method − cv2.morphologyEx(img, op, kernel) Where, img − The original input image. op − Type of morphological operation. We use cv2.MORPH_GRADIENT. kernel − The kernel. We can define the kernel as a numpy matrix of all ones of dtye uint8. Steps You can use the following steps to ... Read More

In this tutorial, we will see how to apply two different low-pass filters to smooth (remove noise from) the image. The two filters are filter2D and boxFilter. These filters are 2D filters in space. Applying 2D filters to images is also known as the "2D Convolution operation". These filters are commonly referred to as averaging filters. The main disadvantage of these filters is that they also smooth the edges in the image. If you don't want to smooth the edges, you can apply a "bilateral filter". A bilateral filter operation preserves the edges.Syntax Following are the syntaxes of Filter2D and ... Read More

In Perspective Transformation, the straight lines remain straight even after the transformation. To apply a perspective transformation, we need a 3×3 perspective transformation matrix. We need four points on the input image and corresponding four points on the output image. We apply the cv2.getPerspectiveTransform() method to find the transformation matrix. Its syntax is as follows − M = cv2.getPerspectiveTransform(pts1, pts2) where, pts1 − An array of four points on the input image and pts2 − An array of corresponding four points on the output image. The Perspective Transformation matrix M is a numpy array. We pass M ... Read More

A color (RGB) 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. An HLS image also has three channels, the Hue, Lightness and Saturation channels. In OpenCV, the values of the Hue channel range from 0 to 179 whereas the Lightness and Saturation channels range from 0 to 255. In OpenCV, the color image loaded using cv2.imread() function is always in BGR format. To ... Read More

OpenCV provides the function cv2.arrowedLine() to draw an arrowed line on an image. This function takes different arguments to draw the line. See the syntax below. cv2.arrowedLine(img, start, end, color, thickness, line_type, shift, tip_length) img − The input image on which the line is to be drawn. Start − Start coordinate of the line in (width, height) format. End − End coordinate of the line in (width, height) format. Color − Color of the line. For a red color in BGR format we pass (0, 0, 255) Thickness − Thickness of the line in pixels. line_type − Type ... Read More

In Affine Transformation, all parallel lines in the original image will still be parallel in the output image. To apply affine transformation on an image, we need three points on the input image and corresponding point on the output image. So first, we define these points and pass to the function cv2.getAffineTransform(). It will create a 2×3 matrix, we term it a transformation matrix M. We can find the transformation matrix M using the following syntax − M = cv2.getAffineTransform(pts1, pts2) Where pts1 is an array of three points on the input image and pts2 is an array of ... Read More

A very important application of bitwise AND operation in computer vision or image processing is for creating masks of the image. We can also use the operator to add watermarks to an image. The pixel values of an image are represented as a numpy ndarray. The pixel values are stored using 8-bit unsigned integers (uint8) in a range from 0 to 255. The bitwise AND operation between two images is performed on the binary representation of these pixel values of corresponding images. Given below is the syntax to perform bitwise AND operation on two images − cv2.bitwise_and(img1, img2, mask=None) ... Read More

To compute the histogram in OpenCV, we use the cv2.calcHist() function. In this tutorial, we will show how to compute the histogram for different colors (Blue, Green, and Red) of the input image. To compute and plot the histograms of different colors of an image, one could follow the steps given below − Steps Import the required libraries OpenCV and matplotlib. Make sure you have already installed them. import cv2 import matplotlib.pyplot as plt Read the images using cv2.imread() method. The width and height of images must be the same. img1 = cv2.imread('birds.jpg') Compute the histograms for different ... Read More

To perform image rotation by an angle, we first need to get the rotation matrix. To find the rotation matrix, we apply cv2.getRotationMatrix2D() function. The syntax for this function is − M = cv2.getRotationMatrix2D(cr, degree, scale) Where cr is the center of rotation, degree is the angle by which image is rotated and scale is scale factor to scale up or scale down the image. The rotation matrix M is a 2×2 matrix (numpy array). We pass the rotation matrix M to cv2.warpAffine() function as an argument. See the syntax below − Syntax cv2.warpAffine(img, M, (width, height)) Here, ... Read More

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 More