def match(self):
if self.kp_ocl.size > 0 and self.kp_cpp > 0:
t0 = time.time()
match = feature.sift_match(self.kp_ocl, self.kp_cpp)
t1 = time.time()
print("Number of Keypoints with OpenCL: %i, With C++: %i, Matching keypoints: %i, Processing time: %.3fs" % (self.kp_ocl.size, self.kp_cpp.size, match.shape[0], (t1 - t0)))
self.fig.canvas.draw()
else:
print("No keypoints, cannot match")
def match(self):
if self.kp_ocl.size > 0 and self.kp_cpp > 0:
t0 = time.time()
match = feature.sift_match(self.kp_ocl, self.kp_cpp)
t1 = time.time()
print("Number of Keypoints with OpenCL: %i, With C++: %i, Matching keypoints: %i, Processing time: %.3fs" % (self.kp_ocl.size, self.kp_cpp.size, match.shape[0], (t1 - t0)))
self.fig.canvas.draw()
else:
print("No keypoints, cannot match")
logger.error("Feature is not available to compare results with C++ implementation")
feature = None
res = numpy.empty(0)
img1 = scipy.misc.imread("testimages/img1.jpg")
img2 = scipy.misc.imread("testimages/img2.jpg")
plan = sift.SiftPlan(template=img1, devicetype="gpu")
kp1 = plan.keypoints(img1)
kp2 = plan.keypoints(img2)
fig = pylab.figure()
sp1 = fig.add_subplot(122)
sp2 = fig.add_subplot(121)
sp1.imshow(img1)
sp2.imshow(img2)
match = feature.sift_match(kp1, kp2)
fig.show()
# raw_input("Enter")
for m in match:
con = ConnectionPatch(
xyA=(m["x0"], m["y0"]),
xyB=(m["x1"], m["y1"]),
coordsA="data",
coordsB="data",
axesA=sp1,
axesB=sp2,
color="red",
)
sp1.add_artist(con)
# sp2.add_artist(con)
def test_matching(self):
'''
tests keypoints matching kernel
'''
if hasattr(scipy.misc, "ascent"):
image = scipy.misc.ascent().astype(numpy.float32)
else:
image = scipy.misc.lena().astype(numpy.float32)
if (feature is not None) and USE_CPP_SIFT:
# get the struct keypoints : (x,y,s,angle,[descriptors])
sc = feature.SiftAlignment()
ref_sift = sc.sift(image)
else:
sp = SiftPlan(template=image)
ref_sift = sp.keypoints(image)
ref_sift_2 = numpy.recarray((ref_sift.shape), dtype=ref_sift.dtype)
ref_sift_2[:] = (ref_sift[::-1])
if (feature is not None and USE_CPP_MATCH):
t0_matching = time.time()
siftmatch = feature.sift_match(ref_sift, ref_sift_2)
t1_matching = time.time()
reference = "CPP"
else:
t0_matching = time.time()
siftmatch = match_py(ref_sift, ref_sift_2, raw_results=True)
t1_matching = time.time()
reference = "NumPy"
if (USE_CPU):
wg = 1,
else:
wg = 64,
shape = ref_sift.shape[0] * wg[0],
ratio_th = numpy.float32(0.5329) # sift.cpp : 0.73*0.73
keypoints_start, keypoints_end = 0, min(ref_sift.shape[0],
ref_sift_2.shape[0])
gpu_keypoints1 = pyopencl.array.to_device(self.queue, ref_sift)
gpu_keypoints2 = pyopencl.array.to_device(self.queue, ref_sift_2)
gpu_matchings = pyopencl.array.zeros(
self.queue, (keypoints_end - keypoints_start, 2),
dtype=numpy.int32,
order="C")
keypoints_start, keypoints_end = numpy.int32(
keypoints_start), numpy.int32(keypoints_end)
nb_keypoints = numpy.int32(10000)
counter = pyopencl.array.zeros(self.queue, (1, 1),
dtype=numpy.int32,
order="C")
t0 = time.time()
k1 = self.program.matching(self.queue, shape, wg, gpu_keypoints1.data,
gpu_keypoints2.data, gpu_matchings.data,
counter.data, nb_keypoints, ratio_th,
keypoints_end, keypoints_end)
res = gpu_matchings.get()
cnt = counter.get()
t1 = time.time()
res_sort = res[numpy.argsort(res[:, 0])]
logger.debug("%s", res_sort[0:20])
logger.debug("%s Matching took %.3f ms", reference,
1000.0 * (t1_matching - t0_matching))
logger.debug("OpenCL: %d match / %s : %d match", cnt, reference,
siftmatch.shape[0])
# sort to compare added keypoints
self.assertEqual(cnt, siftmatch.shape[0],
"number of matching element is the same")
delta = abs(res_sort - siftmatch).max()
self.assertEqual(delta, 0, "Matching keypoints are actually the same")
#logger.info("delta=%s" % delta)
if self.PROFILE:
logger.debug("Global execution time: %.3fms." % (1000.0 *
(t1 - t0)))
logger.debug("Matching on device took %.3fms" %
(1e-6 * (k1.profile.end - k1.profile.start)))
y = ref[i, 1]
scale = ref[i, 2]
angle = ref[i, 3]
sp2.annotate("", xy=(x, y), xytext=(x + scale * cos(angle), y + scale * sin(angle)), color="red",
arrowprops=dict(facecolor='red', edgecolor='red', width=1),)
sp1.annotate("", xy=(x, y), xytext=(x + scale * cos(angle), y + scale * sin(angle)), color="red",
arrowprops=dict(facecolor='red', edgecolor='red', width=1),)
for i in range(kp.shape[0]):
x = kp[i, 0]
y = kp[i, 1]
scale = kp[i, 2]
angle = kp[i, 3]
sp1.annotate("", xy=(x, y), xytext=(x + scale * cos(angle), y + scale * sin(angle)), color="blue",
arrowprops=dict(facecolor='blue', edgecolor='blue', width=1),)
#print numpy.degrees((ref[numpy.argsort(res.scale)][:392] - kp[numpy.argsort(kpg.scale)][:392])[:,3])
#print res[:5]
#print ""*80
#print kpg[:5]
match = feature.sift_match(res, kpg)
#print match
print match.shape
fig.show()
fig.show()
raw_input()
def test_matching(self):
'''
tests keypoints matching kernel
'''
image = scipy.misc.ascent().astype(numpy.float32)
try:
import feature
except:
logger.error("WARNING: feature module is not available to compare results with C++ implementation. Matching cannot be tested.")
feature = None
if (feature is not None):
# get the struct keypoints : (x,y,s,angle,[descriptors])
sc = feature.SiftAlignment()
ref_sift = sc.sift(image)
ref_sift_2 = numpy.recarray((ref_sift.shape), dtype=ref_sift.dtype)
ref_sift_2[:] = (ref_sift[::-1])
t0_matching = time.time()
siftmatch = feature.sift_match(ref_sift, ref_sift_2)
t1_matching = time.time()
ref = ref_sift.desc
if (USE_CPU):
wg = 1,
else:
wg = 64,
shape = ref_sift.shape[0] * wg[0],
ratio_th = numpy.float32(0.5329) # sift.cpp : 0.73*0.73
keypoints_start, keypoints_end = 0, min(ref_sift.shape[0], ref_sift_2.shape[0])
gpu_keypoints1 = pyopencl.array.to_device(self.queue, ref_sift)
gpu_keypoints2 = pyopencl.array.to_device(self.queue, ref_sift_2)
gpu_matchings = pyopencl.array.zeros(self.queue, (keypoints_end - keypoints_start, 2), dtype=numpy.int32, order="C")
keypoints_start, keypoints_end = numpy.int32(keypoints_start), numpy.int32(keypoints_end)
nb_keypoints = numpy.int32(10000)
counter = pyopencl.array.zeros(self.queue, (1, 1), dtype=numpy.int32, order="C")
t0 = time.time()
k1 = self.program.matching(self.queue, shape, wg,
gpu_keypoints1.data, gpu_keypoints2.data, gpu_matchings.data, counter.data,
nb_keypoints, ratio_th, keypoints_end, keypoints_end)
res = gpu_matchings.get()
cnt = counter.get()
t1 = time.time()
# ref_python, nb_match = my_matching(kp1, kp2, keypoints_start, keypoints_end)
t2 = time.time()
res_sort = res[numpy.argsort(res[:, 1])]
# ref_sort = ref[numpy.argsort(ref[:,1])]
logger.info("%s", res[0:20])
# print ref_sort[0:20]
logger.info("C++ Matching took %.3f ms" , 1000.0 * (t1_matching - t0_matching))
logger.info("OpenCL: %d match / C++ : %d match" , cnt, siftmatch.shape[0])
# sort to compare added keypoints
'''
delta = abs(res_sort-ref_sort).max()
self.assert_(delta == 0, "delta=%s" % (delta)) #integers
logger.info("delta=%s" % delta)
'''
if self.PROFILE:
logger.info("Global execution time: CPU %.3fms, GPU: %.3fms." % (1000.0 * (t2 - t1), 1000.0 * (t1 - t0)))
logger.info("Matching took %.3fms" % (1e-6 * (k1.profile.end - k1.profile.start)))
logger.error("Feature is not available to compare results with C++ implementation")
feature = None
res = numpy.empty(0)
img1 = scipy.misc.imread("testimages/img1.jpg")
img2 = scipy.misc.imread("testimages/img2.jpg")
plan = sift.SiftPlan(template=img1, devicetype="gpu")
kp1 = plan.keypoints(img1)
kp2 = plan.keypoints(img2)
fig = pylab.figure()
sp1 = fig.add_subplot(122)
sp2 = fig.add_subplot(121)
sp1.imshow(img1)
sp2.imshow(img2)
match = feature.sift_match(kp1, kp2)
fig.show()
#raw_input("Enter")
for m in match:
con = ConnectionPatch(xyA=(m["x0"], m["y0"]), xyB=(m["x1"], m["y1"]), coordsA="data", coordsB="data", axesA=sp1, axesB=sp2, color="red")
sp1.add_artist(con)
# sp2.add_artist(con)
x = m["x0"]
y = m["y0"]
scale = m["scale0"]
angle = m["angle0"]
x0 = x + scale * cos(angle)
y0 = y + scale * sin(angle)
sp1.annotate("", xy=(x, y), xytext=(x0, y0), color="blue",
arrowprops=dict(facecolor='blue', edgecolor='blue', width=1),)
def test_matching(self):
'''
tests keypoints matching kernel
'''
image = scipy.misc.lena().astype(numpy.float32)
try:
import feature
except:
logger.error(
"WARNING: feature module is not available to compare results with C++ implementation. Matching cannot be tested."
)
feature = None
if (feature != None):
#get the struct keypoints : (x,y,s,angle,[descriptors])
sc = feature.SiftAlignment()
ref_sift = sc.sift(image)
ref_sift_2 = numpy.recarray((ref_sift.shape), dtype=ref_sift.dtype)
ref_sift_2[:] = (ref_sift[::-1])
t0_matching = time.time()
siftmatch = feature.sift_match(ref_sift, ref_sift_2)
t1_matching = time.time()
ref = ref_sift.desc
if (USE_CPU): wg = 1,
else: wg = 64,
shape = ref_sift.shape[0] * wg[0],
ratio_th = numpy.float32(0.5329) #sift.cpp : 0.73*0.73
keypoints_start, keypoints_end = 0, min(ref_sift.shape[0],
ref_sift_2.shape[0])
gpu_keypoints1 = pyopencl.array.to_device(queue, ref_sift)
gpu_keypoints2 = pyopencl.array.to_device(queue, ref_sift_2)
gpu_matchings = pyopencl.array.zeros(
queue, (keypoints_end - keypoints_start, 2),
dtype=numpy.int32,
order="C")
keypoints_start, keypoints_end = numpy.int32(
keypoints_start), numpy.int32(keypoints_end)
nb_keypoints = numpy.int32(10000)
counter = pyopencl.array.zeros(queue, (1, 1),
dtype=numpy.int32,
order="C")
t0 = time.time()
k1 = self.program.matching(queue, shape, wg, gpu_keypoints1.data,
gpu_keypoints2.data, gpu_matchings.data,
counter.data, nb_keypoints, ratio_th,
keypoints_end, keypoints_end)
res = gpu_matchings.get()
cnt = counter.get()
t1 = time.time()
# ref_python, nb_match = my_matching(kp1, kp2, keypoints_start, keypoints_end)
t2 = time.time()
res_sort = res[numpy.argsort(res[:, 1])]
# ref_sort = ref[numpy.argsort(ref[:,1])]
print res[0:20]
print ""
# print ref_sort[0:20]
print("C++ Matching took %.3f ms" % (1000.0 *
(t1_matching - t0_matching)))
print("OpenCL: %d match / C++ : %d match" %
(cnt, siftmatch.shape[0]))
#sort to compare added keypoints
'''
delta = abs(res_sort-ref_sort).max()
self.assert_(delta == 0, "delta=%s" % (delta)) #integers
logger.info("delta=%s" % delta)
'''
if PROFILE:
logger.info("Global execution time: CPU %.3fms, GPU: %.3fms." %
(1000.0 * (t2 - t1), 1000.0 * (t1 - t0)))
logger.info("Matching took %.3fms" %
(1e-6 * (k1.profile.end - k1.profile.start)))
xy=(x, y),
xytext=(x + scale * cos(angle), y + scale * sin(angle)),
color="red",
arrowprops=dict(facecolor='red', edgecolor='red', width=1),
)
for i in range(kp.shape[0]):
x = kp[i, 0]
y = kp[i, 1]
scale = kp[i, 2]
angle = kp[i, 3]
sp1.annotate(
"",
xy=(x, y),
xytext=(x + scale * cos(angle), y + scale * sin(angle)),
color="blue",
arrowprops=dict(facecolor='blue', edgecolor='blue', width=1),
)
#print numpy.degrees((ref[numpy.argsort(res.scale)][:392] - kp[numpy.argsort(kpg.scale)][:392])[:,3])
#print res[:5]
#print ""*80
#print kpg[:5]
match = feature.sift_match(res, kpg)
#print match
print match.shape
fig.show()
fig.show()
raw_input()
