def orbCompute(self):
"""
detect&descript ORB features and find pattern from the scene image
Values:
- self.kpts1: numpy array of keypoints from pattern image
(shape of (num_of_kpts1, 2))
- self.kpts2: numpy array of keypoints from scene image
(shape of (num_of_kpts2, 2))
- self.matches: numpy array of matches between pattern image and scene image
(shape of (index_of_matched_kpts, 2))
- self.dst: location of four corners in scene image
(shape of (4, 1, 2))
"""
pattern = fun.bgr2rgb(self.pattern)
scene = fun.bgr2rgb(self.scene)
pattern_g = rgb2gray(pattern)
scene_g = rgb2gray(scene)
# Create orb extractor
orb = ORB(downscale=1.2,
n_scales=8,
n_keypoints=self.num_kpts,
fast_n=9,
fast_threshold=0.08,
harris_k=0.04)
orb.detect_and_extract(pattern_g)
self.kpts1 = orb.keypoints
desc1 = orb.descriptors
orb.detect_and_extract(scene_g)
self.kpts2 = orb.keypoints
desc2 = orb.descriptors
self.matches = match_descriptors(desc1, desc2, cross_check=True)
# Find homography matrix
if len(self.matches) > self.min_match_count:
src_pts = np.float32([self.kpts1[m[0]]
for m in self.matches]).reshape(-1, 1, 2)
dst_pts = np.float32([self.kpts2[m[1]]
for m in self.matches]).reshape(-1, 1, 2)
print(src_pts)
print(dst_pts)
# find homography matrix in cv2.RANSAC using good match points
M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)
if M is None:
self.dst = np.zeros((4, 1, 2), float32)
return
h, w = self.pattern.shape[:2]
pts = np.float32([[0, 0], [0, h - 1], [w - 1, h - 1],
[w - 1, 0]]).reshape(-1, 1, 2)
self.dst = cv2.perspectiveTransform(pts, M)
print(M)
def plotMatch(self, **kwargs):
"""
Plot matches between pattern image and scene image
"""
# set some default parameters
kwargs.setdefault('keypoints_color', 'k')
kwargs.setdefault('matches_color', 'g')
kwargs.setdefault('only_matches', False)
fig, ax = plt.subplots(1, 1, figsize=(12, 6))
plot_matches(ax, fun.bgr2rgb(self.pattern), fun.bgr2rgb(self.scene), self.kpts1, self.kpts2, self.matches, **kwargs)
plt.title('SIFT: Keypoints Match (# of matched: %d)' % self.matches.shape[0])
ax.axis('off')
plt.subplots_adjust(left=0.05, bottom=0.05,
right=0.95, top=0.9,
hspace=0.1, wspace=0.05)
def plotFind(self, **kwargs):
"""
Locate the pattern from the scene image
"""
# set some default parameters
# kwargs.setdefault('edgecolor', 'k')
# kwargs.setdefault('facecolor', 'g')
# kwargs.setdefault('linewidth', 2)
# kwargs.setdefault('alpha', 0.5)
fig, ax = plt.subplots(1, 1, figsize=(8, 8))
# plot scene image
# ax.imshow(fun.bgr2rgb(self.scene), cmap=plt.cm.gray)
# draw rectangle
# width = self.dst[2, 0, 0] - self.dst[0, 0, 0]
# height = self.dst[2, 0, 1] - self.dst[0, 0, 1]
# rect = plt.Rectangle((self.dst[0, 0, 0], self.dst[0, 0, 1]), width, height, **kwargs)
# ax.add_patch(rect)
# draw detected object
img = fun.bgr2rgb(self.scene)
Y = np.array([
self.dst[0, 0, 1], self.dst[1, 0, 1], self.dst[2, 0, 1],
self.dst[3, 0, 1]
])
X = np.array([
self.dst[0, 0, 0], self.dst[1, 0, 0], self.dst[2, 0, 0],
self.dst[3, 0, 0]
])
rr, cc = dr.polygon(Y, X)
color = [0, 255, 0]
dr.set_color(img, [rr, cc], color, alpha=0.5)
ax.imshow(img, plt.cm.gray)
# draw center point
ax.plot(self.computeCenter()[0],
self.computeCenter()[1],
'k+',
alpha=0.8)
plt.title('Finded pattern in the scene [center:(%d, %d)]' %
(self.computeCenter()[0], self.computeCenter()[1]))
ax.axis('off')
plt.subplots_adjust(left=0.05,
bottom=0.05,
right=0.95,
top=0.9,
hspace=0.1,
wspace=0.05)