def run(self):
while not self._stop.isSet():
task = self.q.get()
if task != None:
obj, image = task
rect = cv.BoundingRect(obj.cont)
siftimage = siftfastpy.Image(rect[2], rect[3])
cv.CvtColor(image, self.gray, cv.CV_BGR2GRAY)
cv.SetZero(self.mask)
cv.FillPoly(self.mask, [obj.cont], cv.Scalar(255))
cv.And(self.gray, self.mask, self.gray)
gnp = np.asarray(cv.GetSubRect(self.gray, rect))
siftimage.SetData(gnp)
t0 = time.time()
# compute keypoints and time how long it takes
frames,desc = siftfastpy.GetKeypoints(siftimage)
self.stats.append((rect[2]*rect[3], time.time() - t0))
# compute feature vector
tmp = np.concatenate((frames[:,2:4], desc), axis=1).astype('float64')
# search in the flann tree for the feature vectors
n = 2
thr = 1.5
result, dists = self.flann.nn_index(tmp, n, checks=32)
# ismatch contains the indices in the testset for which a match is found
ismatch = dists[:,1] > thr * dists[:,0]
# meta contains the index to object-ID mapping
obj.ids = []
for i, res in enumerate(result):
if ismatch[i]:
obj.ids.append(self.meta[res[0]][0])
# obj.ids = [self.meta[res][0] for i, res in enumerate(result) if ismatch[i]]
# transfer keypoints back to full frame coordinates
frames[:,0] += rect[0]
frames[:,1] += rect[1]
obj.frames = frames
obj.desc = desc
def run(self):
starttime = time.time()
gray = cv.CreateMat(480, 640, cv.CV_8UC1)
im = cv.LoadImage(self.image)
rect = cv.BoundingRect(self.obj.cont)
siftimage = siftfastpy.Image(rect[2], rect[3])
cv.CvtColor(im, gray, cv.CV_BGR2GRAY)
gnp = np.asarray(cv.GetSubRect(gray, rect))
siftimage.SetData(gnp)
print 'initialization in: %fs' % (time.time() - starttime)
frames, desc = siftfastpy.GetKeypoints(siftimage)
self.obj.frames = frames
self.obj.desc = desc
print '%d keypoints found in %fs' % (frames.shape[0],
time.time() - starttime)
def get_sift_keypoints(img):
"""Get sift keypoints from image
Parameters
----------
img: array-like
Input image of shape ``(M, N)`` from which we get sift keypoints
Returns
-------
frames: numpy.ndarray
each row has [col, row, orientation, scale] in this order
desc: numpy.ndarray
descriptors of each frame
"""
if type(img) is not np.ndarray:
img = np.array(img)
if len(img.shape) != 2:
raise ValueError('image should be 2d array: {}'.format(img.shape))
siftimg = siftfastpy.Image(img.shape[1], img.shape[0])
siftimg.SetData(img)
frames, desc = siftfastpy.GetKeypoints(siftimg)
return frames, desc
karton['image'].reverse()
karton['depth'].reverse()
karton['features'] = []
karton['frames'] = []
karton['ratios'] = [[], [], []]
# compute feature vectors for all images
for i, image in enumerate(karton['image']):
siftimage = siftfastpy.Image(image.shape[1], image.shape[0])
gray = cv.CreateMat(image.shape[0], image.shape[1], cv.CV_8UC1)
cv.CvtColor(image, gray, cv.CV_BGR2GRAY)
siftimage.SetData(np.asarray(gray))
# compute keypoints
frames, desc = siftfastpy.GetKeypoints(siftimage)
# compute feature vector
depth = np.zeros((frames.shape[0], 1))
for j in range(frames.shape[0]):
depth[j] = karton['depth'][i][math.floor(frames[j, 1]),
math.floor(frames[j, 0])]
tmp = np.concatenate((frames[:, 2:4], desc, depth),
axis=1).astype('float64')
karton['features'].append(tmp)
karton['frames'].append(frames)
flann = pyflann.FLANN()
for i in range(len(karton['features']) - 1):