def query(self, imname):
"""Find a list of matching images for imname
"""
h = self.candidates_from_histogram()
matchscores = []
for imid in candidates:
# get name
cand_name = self.con.execute("""
SELECT filename
FROM imlist
WHERE rowid = '%d'""" %
imid).fetchone()
cand_h = self.get_imhistogram(cand_name)
cand_dist = np.sqrt(np.sum((h - cand_h)**2))
matchscores.append((cand_dist, imid))
return matchscores.sort()
def gauss(m, v, x):
"""Evaluate gaussian in d-dimensions with independent
mean m and variance v at the points in (the rows of) x.
"""
if len(x.shape) == 1:
n, d = 1, x.shape[0]
else:
n, d = x.shape
# covariance matrix, subtract mean
S = np.diag(1 / v)
x = x - m
# product of probabilities
y = np.exp(-.05 * np.diag(np.dot(x, np.dot(S, x.T))))
# normalize and return
return y * (2 * np.pi) ** (-d / 2.0) / (np.sqrt(np.prod(v)) + 1e-6)
def L2dist(p1, p2):
return np.sqrt(np.sum((p1 - p2)**2))
(0, 255, 255), 2)
cv2.circle(frame, center, 5, (0, 0, 255), -1)
# update the points queue
pts.appendleft(center)
# loop over the set of tracked points
for i in range(1, len(pts)):
# if either of the tracked points are None, ignore
# them
if pts[i - 1] is None or pts[i] is None:
continue
# otherwise, compute the thickness of the line and
# draw the connecting lines
thickness = int(np.sqrt(args["buffer"] / float(i + 1)) * 2.5)
cv2.line(frame, pts[i - 1], pts[i], (0, 0, 255), thickness)
# show the frame to our screen
cv2.imshow("Frame", frame)
key = cv2.waitKey(1) & 0xFF
# if the 'q' key is pressed, stop the loop
if key == ord("q"):
break
# if we are not using a video file, stop the camera video stream
if not args.get("video", False):
vs.stop()
# otherwise, release the camera