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PORotate image and crop out black borders
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copied!<p>My application: I am trying to rotate an image (using OpenCV and Python)</p> <p><img src="https://i.stack.imgur.com/KWoeo.jpg" alt="Rotating Images"></p> <p>At the moment I have developed the below code which rotates an input image, padding it with black borders, giving me A. What I want is B - the largest possible area crop window within the rotated image. I call this the axis-aligned boundED box.</p> <p>This is essentially the same as <a href="https://stackoverflow.com/questions/16255037/rotate-and-crop">Rotate and crop</a>, however I cannot get the answer on that question to work. Additionally, that answer is apparently only valid for square images. My images are rectangular.</p> <p>Code to give A:</p> <pre><code>import cv2 import numpy as np def getTranslationMatrix2d(dx, dy): """ Returns a numpy affine transformation matrix for a 2D translation of (dx, dy) """ return np.matrix([[1, 0, dx], [0, 1, dy], [0, 0, 1]]) def rotateImage(image, angle): """ Rotates the given image about it's centre """ image_size = (image.shape[1], image.shape[0]) image_center = tuple(np.array(image_size) / 2) rot_mat = np.vstack([cv2.getRotationMatrix2D(image_center, angle, 1.0), [0, 0, 1]]) trans_mat = np.identity(3) w2 = image_size[0] * 0.5 h2 = image_size[1] * 0.5 rot_mat_notranslate = np.matrix(rot_mat[0:2, 0:2]) tl = (np.array([-w2, h2]) * rot_mat_notranslate).A[0] tr = (np.array([w2, h2]) * rot_mat_notranslate).A[0] bl = (np.array([-w2, -h2]) * rot_mat_notranslate).A[0] br = (np.array([w2, -h2]) * rot_mat_notranslate).A[0] x_coords = [pt[0] for pt in [tl, tr, bl, br]] x_pos = [x for x in x_coords if x > 0] x_neg = [x for x in x_coords if x < 0] y_coords = [pt[1] for pt in [tl, tr, bl, br]] y_pos = [y for y in y_coords if y > 0] y_neg = [y for y in y_coords if y < 0] right_bound = max(x_pos) left_bound = min(x_neg) top_bound = max(y_pos) bot_bound = min(y_neg) new_w = int(abs(right_bound - left_bound)) new_h = int(abs(top_bound - bot_bound)) new_image_size = (new_w, new_h) new_midx = new_w * 0.5 new_midy = new_h * 0.5 dx = int(new_midx - w2) dy = int(new_midy - h2) trans_mat = getTranslationMatrix2d(dx, dy) affine_mat = (np.matrix(trans_mat) * np.matrix(rot_mat))[0:2, :] result = cv2.warpAffine(image, affine_mat, new_image_size, flags=cv2.INTER_LINEAR) return result </code></pre>

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