To improve the quality of the training set To increase the size of the training set provided to a machine learning modelģ. Image rotation is a common image processing practice with applications in numerous image-based algorithms. Rotating an image is a feature that allows us to turn an image in the clockwise or counter-clockwise direction by the specified number of degrees. # The number of images in the grid is 2*2 =4 Resize_linear = cv2.resize(img, (0, 0), fx = 0.5, fy = 0.5, interpolation = cv2.INTER_LINEAR) Resize_cubic = cv2.resize(img, (0, 0), fx = 0.5, fy = 0.5, interpolation = cv2.INTER_CUBIC) Resize_area = cv2.resize(img, (0, 0), fx = 0.5, fy = 0.5, interpolation = cv2.INTER_AREA) # Scaling original image using different interpolation methods Implementation of Image Resizing with interpolation functions # Importing cv2 # Scaling the image to larger size of the original image
Plt.imshow(img) # Scaling the image to smaller size of the original image Img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) Img = cv2.imread(r'C:\Users\tushi\Downloads\PythonGeeks\sunflower.jpg') Implementation of Image Resizing with the scaling factor # Importing cv2 It is the Bilinear interpolation method and it is the default interpolation function in OpenCV. cv2.INTER_LINEAR: This is used when zooming is required. cv2.INTER_CUBIC: It is the Bicubic interpolation methodģ. cv2.INTER_AREA: Resamples using pixel area relation. OpenCV offers multiple functions for interpolation, but in this article, we’ll be covering the following methods which are most frequent for resizing purposes:ġ. Interpolation: interpolation function used It is calculated using dsize = Size(round(fx*src.cols), round(fy*src.rows))įx: It is the scaling factor along the horizontal axisįy: It is the scaling factor along the vertical axis Its size is dsize or the size of the source image.ĭsize: Output image size. Src: It is the source image or the input imageĭst: It is the output image. Resize(src, dsize, dst, fx, fy, interpolation) Based on the requirement, the aspect ratio of an image can be preserved. Resizing, by default, only changes the width and the height of the image. To resize an image in Python, resize() function of the OpenCV library is used. It is often used to discard the unnecessary information. Smaller images consume lesser size on network and GPU.ĥ. Resizing is essential to prevent the loss of information.Ĥ. Machine learning models train substantially faster on smaller images.ģ.
Resizing the image is a critical pre-processing step in computer vision processes. Image resizing is necessary to increase or decrease the total number of pixelsĢ. Image interpolation occurs on resizing or distorting the image from a one-pixel grid to another. Scaling of an image in OpenCV can also be accomplished using different interpolation methods. We can manually specify the scaling size of an image, or we can use the scaling factor. Reconstruction means we need to interpolate new pixels. When an image is resized, the pixel information changes, and hence, reducing the size of an image requires resampling of the pixels while increasing the size of an image requires reconstruction of the image. Resizing refers to the scaling of an image. We’ll also understand how we can implement these functions for our computer vision applications.
#ROTATE IMAGE OPENOFFICE HOW TO#
In this article, we’ll look at how to resize and rotate image in opencv through the various built-in functions provided by OpenCV.
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