A torch tensor defined on CPU can be moved to GPU and vice versa. For high-dimensional tensor computation, the GPU utilizes the power of parallel computing to reduce the compute time.High-dimensional tensors such as images are highly computation-intensive and takes too much time if run over the CPU. So, we ... Read More

A tensor in PyTorch can be normalized using the normalize() function provided in the torch.nn.functional module. This is a non-linear activation function.It performs Lp normalization of a given tensor over a specified dimension.It returns a tensor of normalized value of the elements of original tensor.A 1D tensor can be normalized ... Read More

We can join two or more tensors using torch.cat(), and torch.stack(). torch.cat() is used to concatenate two or more tensors, whereas torch.stack() is used to stack the tensors. We can join the tensors in different dimensions such as 0 dimension, -1 dimension.Both torch.cat() and torch.stack() are used to join the ... Read More

A PyTorch tensor is like numpy.ndarray. The difference between these two is that a tensor utilizes the GPUs to accelerate numeric computation. We convert a numpy.ndarray to a PyTorch tensor using the function torch.from_numpy(). And a tensor is converted to numpy.ndarray using the .numpy() method.StepsImport the required libraries. Here, the ... Read More

The ToPILImage() transform converts a torch tensor to PIL image. The torchvision.transforms module provides many important transforms that can be used to perform different types of manipulations on the image data. ToPILImage() accepts torch tensors of shape [C, H, W] where C, H, and W are the number of channels, ... Read More

To detect a rectangle and square in an image, we first detect all the contours in the image. Then Loop over all contours. Find the approximate contour for each of the contours. If the number of vertex points in the approximate contour is 4 then we compute the aspect ratio ... Read More

In OpenCV, to change the contrast and brightness of an image we could use cv2.convertScaleAbs(). The syntax we use for this method is as follows − cv2.convertScaleAbs(image, alpha, beta) Where image is the original input image. alpha is the contrast value. To lower the contrast, use 0 < ... Read More

We use the function cv2.normalize() to normalize an image in OpenCV. This function accepts the parameters- src, dst, alpha, beta, norm_type, dtype and mask. src and dst are input image and output of the same size as input, alpha is lower norm value for range normalization, beta is upper norm ... Read More

We can apply a mask to an image by computing the cv2.bitwise_and() between the mask and the image. To track a color, we define a mask in HSV color space using cv2.inRange() passing lower and upper limits of color values in HSV.Also Read: Color Identification in Images using Python and OpenCV ... Read More

To compare two images, we use the Mean Square Error (MSE) of the pixel values of the two images. Similar images will have less mean square error value. Using this method, we can compare two images having the same height, width and number of channels. Steps You can use the ... Read More