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