opencv-python实现sift、orb、asift、denseSift
pip install opencv-python == 3.4.2.16
pip install opencv-contrib-python == 3.4.2.16
1.ORB
# coding:utf-8
import cv2
import numpy as np
from contextlib import contextmanager
def clock():
return cv2.getTickCount() / cv2.getTickFrequency()
@contextmanager
def Timer(msg):
print(msg, '...',)
start = clock()
try:
yield
finally:
print("%.2f ms" % ((clock()-start)*1000))
def filter_matches(kp1, kp2, matches, ratio = 0.75):
mkp1, mkp2 = [], []
for m in matches:
if len(m) == 2 and m[0].distance < m[1].distance * ratio:
m = m[0]
mkp1.append( kp1[m.queryIdx] )
mkp2.append( kp2[m.trainIdx] )
p1 = np.float32([kp.pt for kp in mkp1])
p2 = np.float32([kp.pt for kp in mkp2])
kp_pairs = zip(mkp1, mkp2)
return p1, p2, list(kp_pairs)
with Timer('matching'):
debug=1
if debug==1:
path1 = "./1_IMG_Texture_8Bit.png"
path2 = "./3_IMG_Texture_8Bit.png"
else:
path1 = "./OtherSampleFrame_IMG_Texture_8Bit_48.png"
path2 = "./OtherSampleFrame_IMG_Texture_8Bit_52.png"
img1 = cv2.imread(path1, cv2.IMREAD_GRAYSCALE)
img2 = cv2.imread(path2, cv2.IMREAD_GRAYSCALE)
orb = cv2.ORB_create(500000)
keypoint1, desc1 = orb.detectAndCompute(img1, None)
keypoint2, desc2 = orb.detectAndCompute(img2, None)
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=False)
matches = bf.knnMatch(desc1, desc2, k=2)
#是否按照ratio比率剔除
if 1:
p1, p2, kp_pairs = filter_matches(keypoint1, keypoint2, matches)
else:
mkp1, mkp2 = [], []
for m in matches:
m = m[0]
mkp1.append( keypoint1[m.queryIdx] )
mkp2.append( keypoint2[m.trainIdx] )
p1 = np.float32([kp.pt for kp in mkp1])
p2 = np.float32([kp.pt for kp in mkp2])
kp_pairs = zip(mkp1, mkp2)
kp_pairs = list(kp_pairs)
if len(p1) >= 4:
H, status = cv2.findHomography(p1, p2, cv2.RANSAC, 20.0)
print('%d / %d inliers/matched' % (np.sum(status), len(status)))
kp_pairs = [kpp for kpp, flag in zip(kp_pairs, status) if flag]
img2temp=img2.copy()
img2temp = cv2.cvtColor(img2temp, cv2.COLOR_GRAY2BGR)
H_inv = np.linalg.inv(H)
leftnew_array = np.ones((3, 1), dtype=np.float64)
leftnew_array[0][0]=843.53
leftnew_array[1][0]=230.07
leftnew_array_1 = np.dot(H, leftnew_array)
leftnew_array_2 = leftnew_array_1/leftnew_array_1[2][0]
rightnew_array = np.ones((3, 1), dtype=np.float64)
rightnew_array[0][0]=966.61
rightnew_array[1][0]=447.38
rightnew_array_1 = np.dot(H, rightnew_array)
rightnew_array_2 = rightnew_array_1/rightnew_array_1[2][0]
cv2.rectangle(img2temp, (int(leftnew_array_2[0][0]+0.5), int(leftnew_array_2[1][0]+0.5)),
(int(rightnew_array_2[0][0]+0.5), int(rightnew_array_2[1][0]+0.5)), (0, 255, 0), 3)
#GT
cv2.rectangle(img2temp, (int(239.82 + 0.5), int(704.91 + 0.5)),
(int(389.82 + 0.5), int(985.34 + 0.5)), (0, 0, 255), 3)
cv2.imwrite("./result_orb_rectangle.png", img2temp)
else:
H, status = None, None
print('%d matches found, not enough for homography estimation' % len(p1))
h1, w1 = img1.shape[:2]
h2, w2 = img2.shape[:2]
vis = np.zeros((h1+h2, w1), np.uint8)
vis[:h1, :w1] = img1
vis[h1:h1+h2, :w1] = img2
vis = cv2.cvtColor(vis, cv2.COLOR_GRAY2BGR)
p1, p2 = [], []
for kpp in kp_pairs:
p1.append(np.int32(kpp[0].pt))
p2.append(np.int32(np.array(kpp[1].pt) + [0, h1]))
green = (0, 255, 0)
for (x1, y1), (x2, y2) in zip(p1, p2):
col = green
r = 1
thickness = 2
cv2.line(vis, (int(x1), int(y1)), (int(x2), int(y2)), col, thickness)
cv2.imwrite("./result_orb.png", vis)
2、sift
# coding:utf-8
import cv2
import numpy as np
from contextlib import contextmanager
def clock():
return cv2.getTickCount() / cv2.getTickFrequency()
@contextmanager
def Timer(msg):
print(msg, '...',)
start = clock()
try:
yield
finally:
print("%.2f ms" % ((clock()-start)*1000))
def filter_matches(kp1, kp2, matches, ratio = 0.75):
mkp1, mkp2 = [], []
for m in matches:
if len(m) == 2 and m[0].distance < m[1].distance * ratio:
m = m[0]
mkp1.append( kp1[m.queryIdx] )
mkp2.append( kp2[m.trainIdx] )
p1 = np.float32([kp.pt for kp in mkp1])
p2 = np.float32([kp.pt for kp in mkp2])
kp_pairs = zip(mkp1, mkp2)
return p1, p2, list(kp_pairs)
with Timer('matching'):
debug=1
if debug==1:
path1 = "./1_IMG_Texture_8Bit.png"
path2 = "./3_IMG_Texture_8Bit.png"
else:
path1 = "./OtherSampleFrame_IMG_Texture_8Bit_48.png"
path2 = "./OtherSampleFrame_IMG_Texture_8Bit_52.png"
img1 = cv2.imread(path1, cv2.IMREAD_GRAYSCALE)
img2 = cv2.imread(path2, cv2.IMREAD_GRAYSCALE)
sift = cv2.xfeatures2d.SIFT_create()
keypoint1, desc1 = sift.detectAndCompute(img1, None)
keypoint2, desc2 = sift.detectAndCompute(img2, None)
flann=1
if flann==1:
"""
FLANN是类似最近邻的快速匹配库
它会根据数据本身选择最合适的算法来处理数据
比其他搜索算法快10倍
"""
#原始是 FLANN_INDEX_KDTREE=0 trees=5
FLANN_INDEX_KDTREE = 1
indexParams = dict(algorithm=FLANN_INDEX_KDTREE, trees=6)
searchParams = dict(checks=50)
flann = cv2.FlannBasedMatcher(indexParams, searchParams)
matches = flann.knnMatch(desc1, desc2, k=2)
else:
bf = cv2.BFMatcher(cv2.NORM_L2, crossCheck=False)
matches = bf.knnMatch(desc1, desc2, k=2)
#是否按照ratio比率剔除
if 1:
p1, p2, kp_pairs = filter_matches(keypoint1, keypoint2, matches)
else:
mkp1, mkp2 = [], []
for m in matches:
m = m[0]
mkp1.append( keypoint1[m.queryIdx] )
mkp2.append( keypoint2[m.trainIdx] )
p1 = np.float32([kp.pt for kp in mkp1])
p2 = np.float32([kp.pt for kp in mkp2])
kp_pairs = zip(mkp1, mkp2)
kp_pairs = list(kp_pairs)
if len(p1) >= 4:
H, status = cv2.findHomography(p1, p2, cv2.RANSAC, 20.0)
print('%d / %d inliers/matched' % (np.sum(status), len(status)))
kp_pairs = [kpp for kpp, flag in zip(kp_pairs, status) if flag]
img2temp=img2.copy()
img2temp = cv2.cvtColor(img2temp, cv2.COLOR_GRAY2BGR)
H_inv = np.linalg.inv(H)
leftnew_array = np.ones((3, 1), dtype=np.float64)
leftnew_array[0][0]=843.53
leftnew_array[1][0]=230.07
leftnew_array_1 = np.dot(H, leftnew_array)
leftnew_array_2 = leftnew_array_1/leftnew_array_1[2][0]
rightnew_array = np.ones((3, 1), dtype=np.float64)
rightnew_array[0][0]=966.61
rightnew_array[1][0]=447.38
rightnew_array_1 = np.dot(H, rightnew_array)
rightnew_array_2 = rightnew_array_1/rightnew_array_1[2][0]
cv2.rectangle(img2temp, (int(leftnew_array_2[0][0]+0.5), int(leftnew_array_2[1][0]+0.5)),
(int(rightnew_array_2[0][0]+0.5), int(rightnew_array_2[1][0]+0.5)), (0, 255, 0), 3)
#GT
cv2.rectangle(img2temp, (int(239.82 + 0.5), int(704.91 + 0.5)),
(int(389.82 + 0.5), int(985.34 + 0.5)), (0, 0, 255), 3)
cv2.imwrite("./result_sift_rectangle.png", img2temp)
else:
H, status = None, None
print('%d matches found, not enough for homography estimation' % len(p1))
h1, w1 = img1.shape[:2]
h2, w2 = img2.shape[:2]
vis = np.zeros((h1+h2, w1), np.uint8)
vis[:h1, :w1] = img1
vis[h1:h1+h2, :w1] = img2
vis = cv2.cvtColor(vis, cv2.COLOR_GRAY2BGR)
p1, p2 = [], []
for kpp in kp_pairs:
p1.append(np.int32(kpp[0].pt))
p2.append(np.int32(np.array(kpp[1].pt) + [0, h1]))
green = (0, 255, 0)
for (x1, y1), (x2, y2) in zip(p1, p2):
col = green
r = 1
thickness = 2
cv2.line(vis, (int(x1), int(y1)), (int(x2), int(y2)), col, thickness)
cv2.imwrite("./result_sift.png", vis)3、ASIFT
# coding:utf-8
import cv2
import numpy as np
from contextlib import contextmanager
def clock():
return cv2.getTickCount() / cv2.getTickFrequency()
@contextmanager
def Timer(msg):
print(msg, '...',)
start = clock()
try:
yield
finally:
print("%.2f ms" % ((clock()-start)*1000))
FLANN_INDEX_KDTREE = 1 # bug: flann enums are missing
FLANN_INDEX_LSH = 6
def init_feature(name):
chunks = name.split('-')
if chunks[0] == 'sift':
detector = cv2.xfeatures2d.SIFT_create()
norm = cv2.NORM_L2
elif chunks[0] == 'surf':
detector = cv2.xfeatures2d.SURF_create(800)
norm = cv2.NORM_L2
elif chunks[0] == 'orb':
detector = cv2.ORB_create(400)
norm = cv2.NORM_HAMMING
elif chunks[0] == 'akaze':
detector = cv2.AKAZE_create()
norm = cv2.NORM_HAMMING
elif chunks[0] == 'brisk':
detector = cv2.BRISK_create()
norm = cv2.NORM_HAMMING
else:
return None, None
if 'flann' in chunks:
if norm == cv2.NORM_L2:
flann_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
else:
flann_params= dict(algorithm = FLANN_INDEX_LSH,
table_number = 6, # 12
key_size = 12, # 20
multi_probe_level = 1) #2
matcher = cv2.FlannBasedMatcher(flann_params, {}) # bug : need to pass empty dict (#1329)
else:
matcher = cv2.BFMatcher(norm)
return detector, matcher
def filter_matches(kp1, kp2, matches, ratio = 0.75):
mkp1, mkp2 = [], []
for m in matches:
if len(m) == 2 and m[0].distance < m[1].distance * ratio:
m = m[0]
mkp1.append( kp1[m.queryIdx] )
mkp2.append( kp2[m.trainIdx] )
p1 = np.float32([kp.pt for kp in mkp1])
p2 = np.float32([kp.pt for kp in mkp2])
kp_pairs = zip(mkp1, mkp2)
return p1, p2, list(kp_pairs)
def explore_match(img1, img2, kp_pairs, status = None, H = None):
h1, w1 = img1.shape[:2]
h2, w2 = img2.shape[:2]
vis = np.zeros((h1 + h2, w1), np.uint8)
vis[:h1, :w1] = img1
vis[h1:h1 + h2, :w1] = img2
vis = cv2.cvtColor(vis, cv2.COLOR_GRAY2BGR)
if status is None:
status = np.ones(len(kp_pairs), np.bool_)
p1, p2 = [], [] # python 2 / python 3 change of zip unpacking
for kpp in kp_pairs:
p1.append(np.int32(kpp[0].pt))
p2.append(np.int32(np.array(kpp[1].pt) + [0, h1]))
green = (0, 255, 0)
for (x1, y1), (x2, y2) in zip(p1, p2):
col = green
r = 1
thickness = 2
cv2.line(vis, (int(x1), int(y1)), (int(x2), int(y2)), col, thickness)
cv2.imwrite("./result_asift.png", vis)
def 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 = cv2.boundingRect(tcorners.reshape(1,-1,2))
A = np.hstack([A, [[-x], [-y]]])
img = cv2.warpAffine(img, A, (w, h), flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REPLICATE)
if tilt != 1.0:
s = 0.8*np.sqrt(tilt*tilt-1)
img = cv2.GaussianBlur(img, (0, 0), sigmaX=s, sigmaY=0.01)
img = cv2.resize(img, (0, 0), fx=1.0/tilt, fy=1.0, interpolation=cv2.INTER_NEAREST)
A[0] /= tilt
if phi != 0.0 or tilt != 1.0:
h, w = img.shape[:2]
mask = cv2.warpAffine(mask, A, (w, h), flags=cv2.INTER_NEAREST)
Ai = cv2.invertAffineTransform(A)
return img, mask, Ai
def affine_detect(detector, 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.
'''
hh, ww = img.shape[:2]
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))
keypointa_all, descrs_all = [], []
for i, (k, d) in enumerate(params):
t, phi = k, d
timg, tmask, Ai = affine_skew(t, phi, img)
#img_disp = cv2.bitwise_and(timg, timg, mask=tmask);
keypoints, descrs = detector.detectAndCompute(timg, tmask)
for kp in keypoints:
x, y = kp.pt
kp.pt = tuple( np.dot(Ai, (x, y, 1)) )
# Out of bounds judgment
if ((kp.pt[0]<0) or (kp.pt[1]<0) or (kp.pt[0] > ww-1) or (kp.pt[1] > hh-1)):
if (kp.pt[0] < 0):
kp.pt = (0, kp.pt[1])
if (kp.pt[1] < 0):
kp.pt = (kp.pt[0], 0)
if (kp.pt[0] > ww-1):
kp.pt = (ww-1, kp.pt[1])
if (kp.pt[1] > hh-1):
kp.pt = (kp.pt[0], hh-1)
if descrs is None:
descrs = []
keypointa_all.extend(keypoints)
descrs_all.extend(descrs)
return keypointa_all, np.array(descrs_all)
with Timer('matching'):
debug=1
if debug==1:
path1 = "./1_IMG_Texture_8Bit.png"
path2 = "./3_IMG_Texture_8Bit.png"
else:
path1 = "./OtherSampleFrame_IMG_Texture_8Bit_48.png"
path2 = "./OtherSampleFrame_IMG_Texture_8Bit_52.png"
img1 = cv2.imread(path1, cv2.IMREAD_GRAYSCALE)
img2 = cv2.imread(path2, cv2.IMREAD_GRAYSCALE)
detector, matcher = init_feature('sift')
kp1, desc1 = affine_detect(detector, img1)
kp2, desc2 = affine_detect(detector, img2)
print('img1 - %d features, img2 - %d features' % (len(kp1), len(kp2)))
raw_matches = matcher.knnMatch(desc1, trainDescriptors = desc2, k = 2) #2
p1, p2, kp_pairs = filter_matches(kp1, kp2, raw_matches)
if len(p1) >= 4:
H, status = cv2.findHomography(p1, p2, cv2.RANSAC, 50.0)
print('%d / %d inliers/matched' % (np.sum(status), len(status)))
# do not draw outliers (there will be a lot of them)
kp_pairs = [kpp for kpp, flag in zip(kp_pairs, status) if flag]
img2temp=img2.copy()
img2temp = cv2.cvtColor(img2temp, cv2.COLOR_GRAY2BGR)
#H_inv = np.linalg.inv(H)
leftnew_array = np.ones((3, 1), dtype=np.float64)
leftnew_array[0][0]=843.53
leftnew_array[1][0]=230.07
leftnew_array_1 = np.dot(H, leftnew_array)
leftnew_array_2 = leftnew_array_1/leftnew_array_1[2][0]
rightnew_array = np.ones((3, 1), dtype=np.float64)
rightnew_array[0][0]=966.61
rightnew_array[1][0]=447.38
rightnew_array_1 = np.dot(H, rightnew_array)
rightnew_array_2 = rightnew_array_1/rightnew_array_1[2][0]
cv2.rectangle(img2temp, (int(leftnew_array_2[0][0]+0.5), int(leftnew_array_2[1][0]+0.5)),
(int(rightnew_array_2[0][0]+0.5), int(rightnew_array_2[1][0]+0.5)), (0, 255, 0), 3)
#GT
#cv2.rectangle(img2temp, (int(239.82 + 0.5), int(704.91 + 0.5)),
# (int(389.82 + 0.5), int(985.34 + 0.5)), (0, 0, 255), 3)
cv2.imwrite("./result_asift_rectangle.png", img2temp)
'''
Mat H = findHomography(queryCoord, objectCoord, CV_RANSAC, 10, mask);
Mat H_inv = H.inv();
int inliers_cnt = 0, outliers_cnt = 0;
for (unsigned i = 0; i < queryCoord.size(); i++) {
Mat col = Mat::ones(3, 1, CV_64F);
col.at(0) = queryCoord[i].x;
col.at(1) = queryCoord[i].y;
col = H * col;
col /= col.at(2); // 将[x*, y*, #] 转换为 [x*/#, y*/#, 1]
double dist = sqrt(pow(col.at(0) - objectCoord[i].x, 2) +
pow(col.at(1) - objectCoord[i].y, 2)); // 计算误差-欧式距离
if (dist < inlier_threshold) { // 误差小于阈值
queryInliers.push_back(queryCoord[i]);
sceneInliers.push_back(objectCoord[i]);
inliers_cnt++;
}
}
历史图
[
843.5384615384614,
230.07692307692304
],
[
966.6153846153845,
447.38461538461536
]
当前图
[
239.82608695652175,
704.9130434782609
],
[
389.82608695652175,
985.3478260869565
]
'''
else:
H, status = None, None
print('%d matches found, not enough for homography estimation' % len(p1))
explore_match(img1, img2, kp_pairs, None, H) 4、DenseSIFT
# coding:utf-8
import cv2
import numpy as np
from contextlib import contextmanager
def clock():
return cv2.getTickCount() / cv2.getTickFrequency()
@contextmanager
def Timer(msg):
print(msg, '...',)
start = clock()
try:
yield
finally:
print("%.2f ms" % ((clock()-start)*1000))
def filter_matches(kp1, kp2, matches, ratio = 0.75):
mkp1, mkp2 = [], []
for m in matches:
if len(m) == 2 and m[0].distance < m[1].distance * ratio:
m = m[0]
mkp1.append( kp1[m.queryIdx] )
mkp2.append( kp2[m.trainIdx] )
p1 = np.float32([kp.pt for kp in mkp1])
p2 = np.float32([kp.pt for kp in mkp2])
kp_pairs = zip(mkp1, mkp2)
return p1, p2, list(kp_pairs)
with Timer('matching'):
debug=1
if debug==1:
path1 = "./1_IMG_Texture_8Bit.png"
path2 = "./3_IMG_Texture_8Bit.png"
else:
path1 = "./OtherSampleFrame_IMG_Texture_8Bit_48.png"
path2 = "./OtherSampleFrame_IMG_Texture_8Bit_52.png"
img1 = cv2.imread(path1, cv2.IMREAD_GRAYSCALE)
img2 = cv2.imread(path2, cv2.IMREAD_GRAYSCALE)
sift = cv2.xfeatures2d.SIFT_create()
# keypoint1, desc1 = sift.detectAndCompute(img1, None)
# keypoint2, desc2 = sift.detectAndCompute(img2, None)
rows1, cols1 = img1.shape[:2]
rows2, cols2 = img2.shape[:2]
initXyStep=6
keypoint1 = []
for lrow in range(6, rows1-6, 6):
for lcol in range(6, cols1 - 6, 6):
keypoint = cv2.KeyPoint(lcol, lrow, 6, _class_id=0)
keypoint1.append(keypoint)
keypoint2 = []
for lrow in range(6, rows2-6, 6):
for lcol in range(6, cols2 - 6, 6):
keypoint = cv2.KeyPoint(lcol, lrow, 6, _class_id=0)
keypoint2.append(keypoint)
keypoint1, desc1 = sift.compute(img1, keypoint1)
keypoint2, desc2 = sift.compute(img2, keypoint2)
resultimg = img1.copy()
resultimg=cv2.drawKeypoints(img1,keypoint1,resultimg,flags=cv2.DRAW_MATCHES_FLAGS_DEFAULT)
cv2.imwrite('denseSift_keypoints.png',resultimg)
flann=1
if flann==1:
"""
FLANN是类似最近邻的快速匹配库
它会根据数据本身选择最合适的算法来处理数据
比其他搜索算法快10倍
"""
#原始是 FLANN_INDEX_KDTREE=0 trees=5
FLANN_INDEX_KDTREE = 1
indexParams = dict(algorithm=FLANN_INDEX_KDTREE, trees=6)
searchParams = dict(checks=50)
flann = cv2.FlannBasedMatcher(indexParams, searchParams)
matches = flann.knnMatch(desc1, desc2, k=2)
else:
bf = cv2.BFMatcher(cv2.NORM_L2, crossCheck=False)
matches = bf.knnMatch(desc1, desc2, k=2)
#是否按照ratio比率剔除
if 1:
p1, p2, kp_pairs = filter_matches(keypoint1, keypoint2, matches)
else:
mkp1, mkp2 = [], []
for m in matches:
m = m[0]
mkp1.append( keypoint1[m.queryIdx] )
mkp2.append( keypoint2[m.trainIdx] )
p1 = np.float32([kp.pt for kp in mkp1])
p2 = np.float32([kp.pt for kp in mkp2])
kp_pairs = zip(mkp1, mkp2)
kp_pairs = list(kp_pairs)
if len(p1) >= 4:
H, status = cv2.findHomography(p1, p2, cv2.RANSAC, 20.0)
print('%d / %d inliers/matched' % (np.sum(status), len(status)))
kp_pairs = [kpp for kpp, flag in zip(kp_pairs, status) if flag]
else:
H, status = None, None
print('%d matches found, not enough for homography estimation' % len(p1))
h1, w1 = img1.shape[:2]
h2, w2 = img2.shape[:2]
vis = np.zeros((h1+h2, w1), np.uint8)
vis[:h1, :w1] = img1
vis[h1:h1+h2, :w1] = img2
vis = cv2.cvtColor(vis, cv2.COLOR_GRAY2BGR)
p1, p2 = [], []
for kpp in kp_pairs:
p1.append(np.int32(kpp[0].pt))
p2.append(np.int32(np.array(kpp[1].pt) + [0, h1]))
green = (0, 255, 0)
for (x1, y1), (x2, y2) in zip(p1, p2):
col = green
r = 1
thickness = 2
cv2.line(vis, (int(x1), int(y1)), (int(x2), int(y2)), col, thickness)
cv2.imwrite("./result_denseSift.png", vis)