5/11/2024 0 Comments Opencv resize![]() Np.ceil(pad_horz).astype(int) pad_top, pad_bot = 0, 0 else: # square image Pad_left, pad_right = np.floor(pad_horz).astype(int), New_w = np.round(new_h *aspect).astype(int) Pad_left, pad_right = 0, 0 elif aspect < 1: # vertical image Pad_top, pad_bot = np.floor(pad_vert).astype(int), New_h = np.round(new_w /aspect).astype(int) Interp = cv2.INTER_CUBIC # aspect ratio of imageĪspect = w /h # compute scaling and pad sizing Interp = cv2.INTER_AREA else: # stretching image With this math we proportionally convert the image to 200 x 200Ġ7 step # Mount the Image with Pads, if necessary scaled_img = cv2.resize(img, (new_w, new_h), interpolation =interp) scaled_img = cv2.copyMakeBorder(scaled_img, pad_top, pad_bot, pad_left, pad_right, borderType =cv2.BORDER_CONSTANT, value =127) i = plt.imshow(scaled_img, cmap ='gray') scaled_img.shape (200, 200, 3)Ġ8 step #Run It All Together & Check it out import cv2ĭef resizeAndPad(img, size, padColor =0): Print("With this math we proportionally convert the image to 200 x 200") The original aspect ratio is 1.33 Rotimg = cv2.04 step # What Size do You Want to Convert the Image to? size = (200,200)Ġ5 step # Choose an OpenCV Interpolation Method # interpolation methodĠ6 step # Calculate the Pads in case the Images are Horizontal or Vertical # aspect ratio of image Mat = cv2.getRotationMatrix2D(center, 90, 1) We then apply the warpAffine function to the matrix returned by getRotationMatrix2D() function to obtain rotated image.įollowing program rotates the original image by 90 degrees without changing the dimensions − Example GetRotationMatrix2D(center, angle, scale) To find this transformation matrix for rotation, OpenCV provides a function, cv2.getRotationMatrix2D, which is as follows − cv2.warpAffine takes a 2x3 transformation matrix while cv2.warpPerspective takes a 3x3 transformation matrix as input. The cv2 module provides two functions cv2.warpAffine and cv2.warpPerspective, with which you can have all kinds of transformations. The affine transformation is a transformation that can be expressed in the form of a matrix multiplication (linear transformation) followed by a vector addition (translation). ![]() OpenCV uses affine transformation functions for operations on images such as translation and rotation. Res = cv2.resize(img,(int(width/2), int(height/2)), interpolation = Exampleįollowing code resizes the ‘messi.jpg’ image to half its original height and width. Preferable interpolation methods are cv2.INTER_AREA for shrinking and cv2.INTER_CUBIC (slow) & cv2.INTER_LINEAR for zooming. INTER_LANCZOS4 − A Lanczos interpolation over 8x8 pixel neighborhood INTER_CUBIC − A bicubic interpolation over 4x4 pixel neighborhood It is a preferred method for image decimation but when the image is zoomed, it is similar to the INTER_NEAREST method. INTER_AREA − Resampling using pixel area relation. INTER_LINEAR − A bilinear interpolation (used by default) INTER_NEAREST − A nearest-neighbor interpolation. The types of interpolation are as follows − In the above resize() function, interpolation flags determine the type of interpolation used for calculating size of destination image. Interpolation allows us to estimate the values within the gap. When graphical data contains a gap, but data is available on either side of the gap or at a few specific points within the gap. In general, interpolation is a process of estimating values between known data points. Resize(src, dsize, dst, fx, fy, interpolation) The resize() function is used as follows − It is possible to scale up or down an image with the use of cv2.resize() function. In this chapter, we will learn how to resize and rotate an image with the help of OpenCVPython.
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