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sudo pip install silx
from silx.image import siftimport cv2img = cv2.imread("./11_IMG_Texture_8Bit.png", cv2.IMREAD_GRAYSCALE)siftp = sift.SiftPlan(img.shape, img.dtype, devicetype="GPU")kp = siftp.keypoints(img) # Python 2/3 compatibilityfrom __future__ import print_functionimport sysimport numpy as npimport cv2 as cv# local modulesfrom common import Timerfrom descriptor import init_feature, filter_matches, explore_matchfrom silx.image import siftimport numpyimport pylabfrom matplotlib.patches import ConnectionPatchimport cv2exp = 0if exp == 0: fn1 = "./save_ply/OtherSampleFrame_IMG_Texture_8Bit_48.png" fn2 = "./save_ply/OtherSampleFrame_IMG_Texture_8Bit_52.png" depfn1 = "./save_ply/OtherSampleFrame_IMG_DepthMap_48.tif" depfn2 = "./save_ply/OtherSampleFrame_IMG_DepthMap_52.tif"else: fn1 = "./save_ply/11_IMG_Texture_8Bit.png" fn2 = "./save_ply/22_IMG_Texture_8Bit.png" depfn1 = "./save_ply/11_IMG_DepthMap.tif" depfn2 = "./save_ply/22_IMG_DepthMap.tif"img1 = cv.imread(fn1, cv.IMREAD_GRAYSCALE)img2 = cv.imread(fn2, cv.IMREAD_GRAYSCALE)depImage1 = cv.imread(depfn1, -1)depImage2 = cv.imread(depfn2, -1)if img1 is None: print('Failed to load fn1:', fn1) sys.exit(1)if img2 is None: print('Failed to load fn2:', fn2) sys.exit(1)siftp = sift.SiftPlan(img1.shape, img1.dtype, devicetype="GPU")keypoints1 = siftp.keypoints(img1)keypoints2 = siftp.keypoints(img2)print("After keypoints1 %i" % keypoints1.shape[0])print("After keypoints2 %i" % keypoints2.shape[0])fig = pylab.figure()sp1 = fig.add_subplot(122)sp2 = fig.add_subplot(121)im1 = sp1.imshow(img1)im2 = sp2.imshow(img2)siftmatch = sift.MatchPlan(devicetype="GPU")matching = siftmatch.match(keypoints1, keypoints2)h1, w1 = img1.shape[:2]h2, w2 = img2.shape[:2]vis = np.zeros((max(h1, h2), w1 + w2), np.uint8)vis[:h1, :w1] = img1vis[:h2, w1:w1 + w2] = img2vis = cv2.cvtColor(vis, cv2.COLOR_GRAY2BGR)green = (0, 255, 0)for left, right in zip(matching[:, 0], matching[:, 1]): cv2.line(vis, (int(left.x), int(left.y)), (int(right.x+w1), int(right.y)), green, thickness=4)cv2.imwrite("./save_ply/No2_FeaturePointElimination.png", vis)
# Python 2/3 compatibilityfrom __future__ import print_functionimport sysimport numpy as npimport cv2 as cv# local modulesfrom common import Timerfrom descriptor import init_feature, filter_matches, explore_matchfrom silx.image import siftimport numpyimport pylabfrom matplotlib.patches import ConnectionPatchimport cv2def affine_skew(tilt, phi, img, mask=None): ''' affine_skew(tilt, phi, img, mask=None) -> skew_img, skew_mask, Ai Ai - is an affine transform matrix from skew_img to img ''' h, w = img.shape[:2] if mask is None: mask = np.zeros((h, w), np.uint8) mask[:] = 255 A = np.float32([[1, 0, 0], [0, 1, 0]]) if phi != 0.0: phi = np.deg2rad(phi) s, c = np.sin(phi), np.cos(phi) A = np.float32([[c,-s], [ s, c]]) corners = [[0, 0], [w, 0], [w, h], [0, h]] tcorners = np.int32( np.dot(corners, A.T) ) x, y, w, h = cv.boundingRect(tcorners.reshape(1,-1,2)) A = np.hstack([A, [[-x], [-y]]]) img = cv.warpAffine(img, A, (w, h), flags=cv.INTER_LINEAR, borderMode=cv.BORDER_REPLICATE) if tilt != 1.0: s = 0.8*np.sqrt(tilt*tilt-1) img = cv.GaussianBlur(img, (0, 0), sigmaX=s, sigmaY=0.01) img = cv.resize(img, (0, 0), fx=1.0/tilt, fy=1.0, interpolation=cv.INTER_NEAREST) A[0] /= tilt if phi != 0.0 or tilt != 1.0: h, w = img.shape[:2] mask = cv.warpAffine(mask, A, (w, h), flags=cv.INTER_NEAREST) Ai = cv.invertAffineTransform(A) return img, mask, Aidef affine_detect(img, mask=None): ''' affine_detect(detector, img, mask=None, pool=None) -> keypoints, descrs Apply a set of affine transformations to the image, detect keypoints and reproject them into initial image coordinates. See http://www.ipol.im/pub/algo/my_affine_sift/ for the details. ThreadPool object may be passed to speedup the computation. ''' params = [(1.0, 0.0)] for t in 2**(0.5*np.arange(1,6)): for phi in np.arange(0, 180, 72.0 / t): params.append((t, phi)) hh,ww = img.shape[:2] keypointa_all = [] total_size = 0 dtype_kp = numpy.dtype([('x', numpy.float32), ('y', numpy.float32), ('scale', numpy.float32), ('angle', numpy.float32), ('desc', (numpy.uint8, 128)) ]) for i, (k, d) in enumerate(params): t, phi = k, d timg, tmask, Ai = affine_skew(t, phi, img) img_disp = cv.bitwise_and(timg, timg, mask=tmask); siftp = sift.SiftPlan(img_disp.shape, img_disp.dtype, devicetype="GPU") keypoints = siftp.keypoints(img_disp) for kp in keypoints: x = kp.x y = kp.y pt_x,pt_y = tuple(np.dot(Ai, (x, y, 1))) # Out of bounds judgment if ((pt_x<0) or (pt_y<0) or (pt_x > ww-1) or (pt_y > hh-1)): if (pt_x < 0): kp.x, kp.y = (0, pt_y) if (pt_y < 0): kp.x, kp.y = (pt_x, 0) if (pt_x > ww-1): kp.x, kp.y = (ww-1, pt_y) if (pt_y > hh-1): kp.x, kp.y = (pt_x, hh-1) keypointa_all.append(keypoints) total_size += len(keypoints) output = numpy.recarray(shape=(total_size,), dtype=dtype_kp) last = 0 for ds in keypointa_all: l = ds.shape[0] if l > 0: output[last:last + l].x = ds[0:l].x output[last:last + l].y = ds[0:l].y output[last:last + l].scale = ds[0:l].scale output[last:last + l].angle = ds[0:l].angle output[last:last + l].desc = ds[0:l].desc last += l return outputdef main(): feature_name = 'sift' exp = 1 if exp == 0: fn1 = "./save_ply/OtherSampleFrame_IMG_Texture_8Bit_48.png" fn2 = "./save_ply/OtherSampleFrame_IMG_Texture_8Bit_52.png" depfn1 = "./save_ply/OtherSampleFrame_IMG_DepthMap_48.tif" depfn2 = "./save_ply/OtherSampleFrame_IMG_DepthMap_52.tif" else: fn1 = "./save_ply/P1000965.JPG" fn2 = "./save_ply/P1000966.JPG" depfn1 = "./save_ply/11_IMG_DepthMap.tif" depfn2 = "./save_ply/22_IMG_DepthMap.tif" img1 = cv.imread(fn1, cv.IMREAD_GRAYSCALE) img2 = cv.imread(fn2, cv.IMREAD_GRAYSCALE) depImage1 = cv.imread(depfn1, -1) depImage2 = cv.imread(depfn2, -1) if img1 is None: print('Failed to load fn1:', fn1) sys.exit(1) if img2 is None: print('Failed to load fn2:', fn2) sys.exit(1) with Timer('kp1'): keypoints1 = affine_detect(img1) with Timer('kp2'): keypoints2 = affine_detect(img2) print("keypoints1 %i" % len(keypoints1)) print("keypoints2 %i" % len(keypoints2)) siftmatch = sift.MatchPlan(devicetype="GPU") matching = siftmatch.match(keypoints1, keypoints2) h1, w1 = img1.shape[:2] h2, w2 = img2.shape[:2] vis = np.zeros((max(h1, h2), w1 + w2), np.uint8) vis[:h1, :w1] = img1 vis[:h2, w1:w1 + w2] = img2 vis = cv2.cvtColor(vis, cv2.COLOR_GRAY2BGR) green = (0, 255, 0) for left, right in zip(matching[:, 0], matching[:, 1]): cv2.line(vis, (int(left.x), int(left.y)), (int(right.x+w1), int(right.y)), green, thickness=4) cv2.imwrite("./save_ply/No2_FeaturePointElimination.png", vis)if __name__ == '__main__': with Timer('all time:'): main()
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