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Articles by Shahid Akhtar Khan
Page 7 of 17
How 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 Moretorch.argmax() Method in Python PyTorch
To find the indices of the maximum value of the elements in an input tensor, we can apply the torch.argmax() function. It returns the indices only, not the element value. If the input tensor has multiple maximal values, then the function will return the index of the first maximal element. We can apply the torch.argmax() function to compute the indices of the maximum values of a tensor across a dimension..Syntaxtorch.argmax(input)StepsWe could use the following steps to find the indices of the maximum values of all elements in the input tensor −Import the required library. In all the following examples, the ...
Read MoreHow to estimate the gradient of a function in one or more dimensions in PyTorch?
To estimate the gradient of a function, we can apply the torch.gradient() function. This function estimates the gradient using the second-order accurate central differences method. We can estimate the gradient in one or more dimensions. The function of which the gradient is to be estimated may be defined on a real or complex domain. In the process of estimating the gradients, the gradient is estimated by estimating each partial derivative of the function independently.Syntaxtorch.gradient(values)where the parameter values is the tensor that represents the values of the function.StepsWe could use the following steps to estimate the gradient of a function −Import ...
Read MoreHow to compute the inverse hyperbolic sine in PyTorch?
The torch.asinh() method computes the inverse hyperbolic sine of each element of the input tensor. It supports both real and complex-valued inputs. It supports any dimension of the input tensor.Syntaxtorch.asinh(input)where input is the input tensor.OutputIt returns a tensor inverse hyperbolic sine of each element.StepsTo compute the inverse hyperbolic sine of each element in the input tensor, you could follow the steps given below −Import the required library. In all the following examples, the required Python library is torch. Make sure you have already installed it.import torchCreate a torch tensor and print it.input = torch.randn(3, 4) print("Input Tensor:", input)Compute the inverse ...
Read Moretorch.rsqrt() Method in Python PyTorch
The torch.rsqrt() method computes the reciprocal of square-root of each element of the input tensor. It supports both real and complex-valued inputs. If an element in the input tensor is zero, then the corresponding element in the output tensor is NaN.Syntaxtorch.rsqrt(input)Parametersinput – Input tensorOutputIt returns a tensor with reciprocal of square-root.StepsImport the required library. In all the following examples, the required Python library is torch. Make sure you have already installed it.import torchCreate a torch tensor and print it.input = torch.randn(3, 4) print("Input Tensor:", input)Compute the reciprocal of the square-root of each element in the input tensor using torch.rsqrt(input). Here ...
Read MoreHow to compute the element-wise angle of the given input tensor in PyTorch?
To compute the elementwise angle of the given input tensor, we apply torch.angle(). It takes an input tensor and returns a tensor with angle in radian computed element wise. To convert the angles into the degree we multiply the angle in radian by 180/np.pi. It supports both real and complex-valued tensors.Syntaxtorch.angle(input)StepsTo compute the elementwise angle, you could follow the steps given below −Import the required library. In all the following examples, the required Python library is torch. Make sure you have already installed it.import torchDefine torch tensors and print them.input = torch.tensor([1 + 1j, -1 -4j, 3-2j])Compute torch.angle(input). It is ...
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