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import numpy as np |
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import cv2 |
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from scipy.spatial import Delaunay |
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def applyAffineTransform(src, srcTri, dstTri, size) : |
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warpMat = cv2.getAffineTransform( np.float32(srcTri), np.float32(dstTri) ) |
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return cv2.warpAffine( src, warpMat, (size[0], size[1]), None, flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT_101 ) |
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def morphTriangle(dst_img, src_img, st, dt) : |
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(h,w,c) = dst_img.shape |
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sr = np.array( cv2.boundingRect(np.float32(st)) ) |
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dr = np.array( cv2.boundingRect(np.float32(dt)) ) |
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sRect = st - sr[0:2] |
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dRect = dt - dr[0:2] |
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d_mask = np.zeros((dr[3], dr[2], c), dtype = np.float32) |
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cv2.fillConvexPoly(d_mask, np.int32(dRect), (1.0,)*c, 8, 0); |
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imgRect = src_img[sr[1]:sr[1] + sr[3], sr[0]:sr[0] + sr[2]] |
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size = (dr[2], dr[3]) |
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warpImage1 = applyAffineTransform(imgRect, sRect, dRect, size) |
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if c == 1: |
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warpImage1 = np.expand_dims( warpImage1, -1 ) |
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dst_img[dr[1]:dr[1]+dr[3], dr[0]:dr[0]+dr[2]] = dst_img[dr[1]:dr[1]+dr[3], dr[0]:dr[0]+dr[2]]*(1-d_mask) + warpImage1 * d_mask |
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def morph_by_points (image, sp, dp): |
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if sp.shape != dp.shape: |
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raise ValueError ('morph_by_points() sp.shape != dp.shape') |
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(h,w,c) = image.shape |
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result_image = np.zeros(image.shape, dtype = image.dtype) |
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for tri in Delaunay(dp).simplices: |
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morphTriangle(result_image, image, sp[tri], dp[tri]) |
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return result_image |