def loop(self):
while not rospy.is_shutdown():
ranges = np.array(self.scan.ranges)
angles = np.array([self.scan.angle_min + self.scan.angle_increment * i for i in range(len(ranges))])
# Filter out ranges (and corresponding angles) not in the interval [range_min, range_max].
good_indices = np.where(np.logical_and(ranges > self.scan.range_min, ranges < self.scan.range_max))
ranges = ranges[good_indices]
angles = angles[good_indices]
# Skip iteration if too few good values.
if np.size(good_indices) < 2:
continue
# Points in Cartesian coordinates.
points = np.array([pol2cart(dist, angle) for dist, angle in itertools.izip(ranges, angles)])
# Split points in the middle.
left_points = points[:len(points) / 2]
right_points = points[len(points) / 2:]
# Fit line models with RANSAC algorithm.
left_model, left_inliers = ransac(left_points, LineModel, min_samples=5, residual_threshold=0.1, max_trials=100)
right_model, right_inliers = ransac(right_points, LineModel, min_samples=5, residual_threshold=0.1, max_trials=100)
# Determine validity of the lines
left_valid = True
right_valid = True
if np.size(left_inliers) < 15:
left_valid = False
if np.size(right_inliers) < 15:
right_valid = False
# Publish row message.
self.publish_wall(left_model, left_valid, right_model, right_valid)
# RViz visualization of lines and which points are considered in/outliers.
# Predict y's using the two outermost x's. This gives us two points on each line.
left_wall_x = np.array([left_points[0][0], left_points[-1][0]])
right_wall_x = np.array([right_points[0][0], right_points[-1][0]])
left_wall_y = left_model.predict_y(left_wall_x)
right_wall_y = right_model.predict_y(right_wall_x)
# Publish markers.
self.publish_visualization_marker(left_wall_x, left_wall_y, Marker.LINE_STRIP, "line_left", (0.2, 1.0, 0.2))
self.publish_visualization_marker(right_wall_x, right_wall_y, Marker.LINE_STRIP, "line_right", (0.2, 0.2, 1.0))
self.publish_visualization_marker(left_points[left_inliers, 0], left_points[left_inliers, 1], Marker.POINTS, "left_inliers", (0.5, 1.0, 0.5))
self.publish_visualization_marker(right_points[right_inliers, 0], right_points[right_inliers, 1], Marker.POINTS, "right_inliers", (0.5, 0.5, 1.0))
left_outliers = left_inliers == False
right_outliers = right_inliers == False
self.publish_visualization_marker(left_points[left_outliers, 0], left_points[left_outliers, 1], Marker.POINTS, "left_outliers", (0.0, 0.5, 0.0))
self.publish_visualization_marker(right_points[right_outliers, 0], right_points[right_outliers, 1], Marker.POINTS, "right_outliers", (0.0, 0.0, 0.5))
self.rate.sleep()
def calc_transformations(self):
print('Calculating each pair translation matrix')
self.images[0].M = numpy.float32([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
bf = cv2.BFMatcher(cv2.NORM_L2, crossCheck=False)
for i in xrange(1, len(self.images)):
image_1 = self.images[i]
image_2 = self.images[i - 1]
matches = bf.knnMatch(image_1.des, image_2.des, k=2)
good = [m for m, n in matches if m.distance <
self.knnRatio * n.distance]
src_pts = numpy.float32(
[image_1.kp[m.queryIdx].pt for m in good]).reshape(-1, 2)
dst_pts = numpy.float32(
[image_2.kp[m.trainIdx].pt for m in good]).reshape(-1, 2)
model_robust, _ = ransac((src_pts, dst_pts), TranslationTransform,
min_samples=6,
residual_threshold=self.ransacThreshold,
max_trials=1000,
stop_sample_num=0.9 * src_pts.shape[0])
tx, ty = model_robust.params
M = numpy.float32([[1, 0, tx], [0, 1, ty], [0, 0, 1]])
image_1.M = M.dot(image_2.M)
tx, ty = image_1.M[0, 2], image_1.M[1, 2]
if ty > 0 and ty > self.drift_y_down:
self.drift_y_down = ty
elif ty < 0 and ty < self.drift_y_up:
self.drift_y_up = ty
self.drift_x_max = tx
def test_ransac_is_data_valid():
np.random.seed(1)
is_data_valid = lambda data: data.shape[0] > 2
model, inliers = ransac(np.empty((10, 2)), LineModel, 2, np.inf, is_data_valid=is_data_valid)
assert_equal(model, None)
assert_equal(inliers, None)
def test_ransac_is_data_valid():
is_data_valid = lambda data: data.shape[0] > 2
model, inliers = ransac(np.empty((10, 2)), LineModelND, 2, np.inf,
is_data_valid=is_data_valid, random_state=1)
assert_equal(model, None)
assert_equal(inliers, None)
def robustEstimate(ptsA, ptsB): """ Perform robust estimation on the given correspondences using RANSAC. Args: ---- ptsA: A 2 x N matrix of points. ptsB: A 2 x N matrix of points. Returns: ------- The number of inliers within the points. """ src, dst, N = [], [], ptsA.shape[1] for i in xrange(N): src.append((ptsA[0, i], ptsA[1, i])) dst.append((ptsB[0, i], ptsB[1, i])) src, dst = np.asarray(src), np.asarray(dst) model = ProjectiveTransform() model.estimate(src, dst) model_robust, inliers = ransac((src, dst), ProjectiveTransform, min_samples=3, residual_threshold=2, max_trials=100) return inliers
def test_ransac_is_model_valid():
def is_model_valid(model, data):
return False
model, inliers = ransac(np.empty((10, 2)), LineModelND, 2, np.inf,
is_model_valid=is_model_valid, random_state=1)
assert_equal(model, None)
assert_equal(inliers, None)
def run3(self):
""" Cette fonction test des alternatives à SIFT et ORB. Ne fonctionne pas."""
for x in xrange(len(self.stack)-1):
print('Traitement image ' + str(x+1))
im1,im2 = 255.*gaussian_filter(self.stack[x,...], sqrt(self.initial_sigma**2 - 0.25)), 255.*gaussian_filter(self.stack[x+1,...], sqrt(self.initial_sigma**2 - 0.25))
im1,im2 = enhance_contrast(normaliser(im1), square(3)), enhance_contrast(normaliser(im2), square(3))
im1, im2 = normaliser(im1), normaliser(im2)
b = cv2.BRISK()
#b.create("Feature2D.BRISK")
k1,d1 = b.detectAndCompute(im1,None)
k2,d2 = b.detectAndCompute(im2,None)
bf = cv2.BFMatcher(cv2.NORM_HAMMING)
matches = bf.match(d1,d2)
g1,g2 = [],[]
for i in matches:
g1.append(k1[i.queryIdx].pt)
g2.append(k2[i.trainIdx].pt)
model, inliers = ransac((np.array(g1), np.array(g2)), AffineTransform, min_samples=3, residual_threshold=self.min_epsilon, max_trials=self.max_trials, stop_residuals_sum=self.min_inlier_ratio)
self.stack[x+1,...] = warp(self.stack[x+1,...], AffineTransform(rotation=model.rotation, translation=model.translation), output_shape=self.stack[x+1].shape)
self.stack = self.stack.astype(np.uint8)
def run4(self):
""" Cette fonction recadre les images grâce à SURF et RANSAC, fonctionne bien."""
for x in xrange(len(self.stack)-1):
print('Traitement image ' + str(x+1))
im1,im2 = 255.*gaussian_filter(self.stack[x,...], sqrt(self.initial_sigma**2 - 0.25)), 255.*gaussian_filter(self.stack[x+1,...], sqrt(self.initial_sigma**2 - 0.25))
im1,im2 = enhance_contrast(normaliser(im1), square(5)), enhance_contrast(normaliser(im2), square(5))
im1, im2 = normaliser(im1), normaliser(im2)
b = cv2.SURF()
#b.create("Feature2D.BRISK")
k1,d1 = b.detectAndCompute(im1,None)
k2,d2 = b.detectAndCompute(im2,None)
bf = cv2.BFMatcher()
matches = bf.knnMatch(d1,d2, k=2)
# Apply ratio test
good = []
for m,n in matches:
if m.distance < 0.75*n.distance:
good.append(m)
g1,g2 = [],[]
for i in good:
g1.append(k1[i.queryIdx].pt)
g2.append(k2[i.trainIdx].pt)
model, inliers = ransac((np.array(g1), np.array(g2)), AffineTransform, min_samples=3, residual_threshold=self.min_epsilon, max_trials=self.max_trials, stop_residuals_sum=self.min_inlier_ratio)
self.stack[x+1,...] = warp(self.stack[x+1,...], AffineTransform(rotation=model.rotation, translation=model.translation), output_shape=self.stack[x+1].shape)
self.stack = self.stack.astype(np.uint8)
def auto_find_center_rings(avg_img, sigma=1, no_rings=4, min_samples=3,
residual_threshold=1, max_trials=1000):
"""This will find the center of the speckle pattern and the radii of the
most intense rings.
Parameters
----------
avg_img : 2D array
shape of the image
sigma : float, optional
Standard deviation of the Gaussian filter.
no_rings : int, optional
number of rings
min_sample : int, optional
The minimum number of data points to fit a model to.
residual_threshold : float, optional
Maximum distance for a data point to be classified as an inlier.
max_trials : int, optional
Maximum number of iterations for random sample selection.
Returns
-------
center : tuple
center co-ordinates of the speckle pattern
image : 2D array
Indices of pixels that belong to the rings,
directly index into an array
radii : list
values of the radii of the rings
Note
----
scikit-image ransac
method(http://www.imagexd.org/tutorial/lessons/1_ransac.html) is used to
automatically find the center and the most intense rings.
"""
image = img_as_float(color.rgb2gray(avg_img))
edges = feature.canny(image, sigma)
coords = np.column_stack(np.nonzero(edges))
edge_pts_xy = coords[:, ::-1]
radii = []
for i in range(no_rings):
model_robust, inliers = ransac(edge_pts_xy, CircleModel, min_samples,
residual_threshold,
max_trials=max_trials)
if i == 0:
center = int(model_robust.params[0]), int(model_robust.params[1])
radii.append(model_robust.params[2])
rr, cc = draw.circle_perimeter(center[1], center[0],
int(model_robust.params[2]),
shape=image.shape)
image[rr, cc] = i + 1
edge_pts_xy = edge_pts_xy[~inliers]
return center, image, radii
def get_best_matches(k1, k2, matches):
src = k1[matches[:,0]][:,::-1]
dst = k2[matches[:,1]][:,::-1]
# if there are not enough matches, this fails
model_robust, inliers = ransac((src, dst), AffineTransform,
min_samples=20, residual_threshold=1,
max_trials=40)
return model_robust, inliers
def test_ransac_is_model_valid():
np.random.seed(1)
def is_model_valid(model, data):
return False
model, inliers = ransac(np.empty((10, 2)), LineModel, 2, np.inf,
is_model_valid=is_model_valid)
assert_equal(model, None)
assert_equal(inliers, None)
def main(unused_argv):
tf.logging.set_verbosity(tf.logging.INFO)
# Read features.
locations_1, _, descriptors_1, _, _ = feature_io.ReadFromFile(
cmd_args.features_1_path)
num_features_1 = locations_1.shape[0]
tf.logging.info("Loaded image 1's %d features" % num_features_1)
locations_2, _, descriptors_2, _, _ = feature_io.ReadFromFile(
cmd_args.features_2_path)
num_features_2 = locations_2.shape[0]
tf.logging.info("Loaded image 2's %d features" % num_features_2)
# Find nearest-neighbor matches using a KD tree.
d1_tree = cKDTree(descriptors_1)
_, indices = d1_tree.query(
descriptors_2, distance_upper_bound=_DISTANCE_THRESHOLD)
# Select feature locations for putative matches.
locations_2_to_use = np.array([
locations_2[i,]
for i in range(num_features_2)
if indices[i] != num_features_1
])
locations_1_to_use = np.array([
locations_1[indices[i],]
for i in range(num_features_2)
if indices[i] != num_features_1
])
# Perform geometric verification using RANSAC.
_, inliers = ransac(
(locations_1_to_use, locations_2_to_use),
AffineTransform,
min_samples=3,
residual_threshold=20,
max_trials=1000)
tf.logging.info('Found %d inliers' % sum(inliers))
# Visualize correspondences, and save to file.
_, ax = plt.subplots()
img_1 = mpimg.imread(cmd_args.image_1_path)
img_2 = mpimg.imread(cmd_args.image_2_path)
inlier_idxs = np.nonzero(inliers)[0]
plot_matches(
ax,
img_1,
img_2,
locations_1_to_use,
locations_2_to_use,
np.column_stack((inlier_idxs, inlier_idxs)),
matches_color='b')
ax.axis('off')
ax.set_title('DELF correspondences')
plt.savefig(cmd_args.output_image)
def landmark_registration(points_file1, points_file2, out_file, residual_threshold=2, max_trials=100, delimiter="\t"):
points1 = pd.read_csv(points_file1, delimiter=delimiter)
points2 = pd.read_csv(points_file2, delimiter=delimiter)
src = np.concatenate([np.array(points1['x']).reshape([-1,1]), np.array(points1['y']).reshape([-1,1])], axis=-1)
dst = np.concatenate([np.array(points2['x']).reshape([-1,1]), np.array(points2['y']).reshape([-1,1])], axis=-1)
model = AffineTransform()
model_robust, inliers = ransac((src, dst), AffineTransform, min_samples=3,
residual_threshold=residual_threshold, max_trials=max_trials)
pd.DataFrame(model_robust.params).to_csv(out_file, header=None, index=False, sep="\t")
def punch(img):
# Identifiying the Tissue punches in order to Crop the image correctly
# Canny edges and RANSAC is used to fit a circe to the punch
# A Mask is created
distance = 0
r = 0
float_im, orig, ihc = create_bin(img)
gray = rgb2grey(orig)
smooth = gaussian(gray, sigma=3)
shape = np.shape(gray)
l = shape[0]
w = shape[1]
x = l - 20
y = w - 20
rows = np.array([[x, x, x], [x + 1, x + 1, x + 1]])
columns = np.array([[y, y, y], [y + 1, y + 1, y + 1]])
corner = gray[rows, columns]
thresh = np.mean(corner)
print thresh
binar = (smooth < thresh - 0.01)
bin = remove_small_holes(binar, min_size=100000, connectivity=2)
bin1 = remove_small_objects(bin, min_size=5000, connectivity=2)
bin2 = gaussian(bin1, sigma=3)
bin3 = (bin2 > 0)
# eosin = IHC[:, :, 2]
edges = canny(bin3)
coords = np.column_stack(np.nonzero(edges))
model, inliers = ransac(coords, CircleModel, min_samples=4, residual_threshold=1, max_trials=1000)
# rr, cc = circle_perimeter(int(model.params[0]),
# int(model.params[1]),
# int(model.params[2]),
# shape=im.shape)
a, b = model.params[0], model.params[1]
r = model.params[2]
ny, nx = bin3.shape
ix, iy = np.meshgrid(np.arange(nx), np.arange(ny))
distance = np.sqrt((ix - b)**2 + (iy - a)**2)
mask = np.ma.masked_where(distance > r, bin3)
return distance, r, float_im, orig, ihc, bin3
def test_ransac_invalid_input():
with testing.raises(ValueError):
ransac(np.zeros((10, 2)), None, min_samples=2,
residual_threshold=0, max_trials=-1)
with testing.raises(ValueError):
ransac(np.zeros((10, 2)), None, min_samples=2,
residual_threshold=0, stop_probability=-1)
with testing.raises(ValueError):
ransac(np.zeros((10, 2)), None, min_samples=2,
residual_threshold=0, stop_probability=1.01)
def fit(self, line_img):
"""Estimate the tranform matrix self.M based on a binary image
with line detected.
- `line_img`: image with two lines detected, representing the
left and right boundaries of lanes. In the transformed
bird-eye view, the two boundaries should be roughly parallel.
"""
# image shape
H, W = line_img.shape[:2]
# find line coordinates
ys, xs = np.where(line_img > 0)
# clustering of two lines
cluster2 = KMeans(2)
cluster2.fit(np.c_[xs, ys])
# build robust linear model for each line
linear_models = []
for c in [0, 1]:
i = (cluster2.labels_ == c)
robust_model, inliers = ransac(np.c_[xs[i], ys[i]], LineModel,
min_samples=2, residual_threshold=1., max_trials=500)
linear_models.append(robust_model)
# get the vertices of a trapezoid as source points
if self.forward_distance is None:
middle_h = H/2 + 100#160
else:
middle_h = H - self.forward_distance
line0 = [(linear_models[0].predict_x(H), H), (linear_models[0].predict_x(middle_h), middle_h)]
line1 = [(linear_models[1].predict_x(H), H), (linear_models[1].predict_x(middle_h), middle_h)]
src_pts = np.array(line0 + line1, dtype=np.float32)
# get the vertices of destination points
# here simply map it to a rect with same width/length from bottom
bottom_x1, bottom_x2 = line0[0][0], line1[0][0]
v = np.array(line0[1]) - np.array(line0[0])
# it must be the same as source trapzoid length otherwise y_mpp will change
L = H#int(np.sqrt(np.sum( v*v ))) #H
dst_pts = np.array([(bottom_x1, H), (bottom_x1, H-L),
(bottom_x2, H), (bottom_x2, H-L)],
dtype=np.float32)
# estimate the transform matrix
self.M = cv2.getPerspectiveTransform(src_pts, dst_pts)
self.invM = cv2.getPerspectiveTransform(dst_pts, src_pts)
# estimate meter-per-pixel in the transformed image
self.x_mpp = 3 / np.abs(bottom_x1-bottom_x2)
self.y_mpp = self.estimate_ympp(line_img)
return self
def refineBoundaries(img_orig, plate):
np.random.seed(7)
minr, minc, maxr, maxc = plate.bbox
img_window = img_orig[minr: maxr, minc: maxc]
plate_points = [(minc, minr), (maxc, minr), (maxc, maxr), (minc, maxr)]
plate_x = minc
plate_y = minr
img_window = np.absolute(filters.prewitt_v(img_window))
thresh = filters.threshold_otsu(img_window)
img_window = img_window <= thresh
labels = measure.label(img_window)
points = []
for region in measure.regionprops(labels):
minr, minc, maxr, maxc = region.bbox
ratio = float(maxr - minr) / float(maxc - minc)
heigh = maxr - minr
area = region.area
if (ratio > 1 and area > 10 and heigh > 10):
points.append((minc, minr, maxc, maxr))
if len(points) > 1:
points = np.array(points)
x1 = np.min(points[:, 0])
x2 = np.max(points[:, 2])
ransac_model, inliers = measure.ransac(points[:, 0:2], measure.LineModel, 5, 3, max_trials=30)
points = points[inliers]
if ransac_model.params[1] != 0:
average_heigh = int(np.mean(points[:, 3]) - np.mean(points[:, 1]))
pad_t = average_heigh / 2
pad_b = average_heigh + (average_heigh / 3)
y1 = ransac_model.predict_y(x1)
y2 = ransac_model.predict_y(x2)
refined_points = [(x1, y1 - pad_t), (x2, y2 - pad_t), (x2, y2 + pad_b), (x1, y1 + pad_b)]
refined_points = [(x + plate_x, y + plate_y) for (x, y) in refined_points]
return refined_points
return plate_points
def NextGID(self,image):
""" Calculates the next Group ID for the input image """
NewImg = self.LoadImage(image,Greyscale=True,scale=0.25)
self.orb.detect_and_extract(NewImg)
NewImgKeyDescr = (self.orb.keypoints, self.orb.descriptors)
for PreImgKeyDescr in reversed(self.ImagesKeypointsDescriptors):
# Check for overlap
matcheOfDesc = match_descriptors(PreImgKeyDescr[1], NewImgKeyDescr[1], cross_check=True)
# Select keypoints from the source (image to be registered)
# and target (reference image)
src = NewImgKeyDescr[0][matcheOfDesc[:, 1]][:, ::-1]
dst = PreImgKeyDescr[0][matcheOfDesc[:, 0]][:, ::-1]
model_robust, inliers = ransac((src, dst), ProjectiveTransform,
min_samples=4, residual_threshold=1, max_trials=300)
NumberOfTrueMatches = np.sum(inliers) #len(inliers[inliers])
if NumberOfTrueMatches > 100 :
# Image has overlap
logger.debug('Image {0} found a match! (No: of Matches={1})'.format(image,NumberOfTrueMatches))
break
else :
logger.debug('Image {0} not matching..(No: of Matches={1})'.format(image,NumberOfTrueMatches))
continue
else:
# None of the images in the for loop has any overlap...So this is a new Group
self.ImagesKeypointsDescriptors = [] # Erase all previous group items
# self.ImagesWithOverlap = []
# Increment Group ID
self.CurrentGroupID += 1
logger.debug('Starting a new Panorama group (GID={0})'.format(self.CurrentGroupID))
# Append the latest image to the current group
self.ImagesKeypointsDescriptors.append(NewImgKeyDescr)
# self.ImagesWithOverlap.append(NewImg)
# Return the current group ID
return self.CurrentGroupID
def _orb_ransac_shift(self, im1, im2, template):
descriptor_extractor = ORB() #n_keypoints=self.parameters['n_keypoints'])
key1, des1 = self._find_key_points(descriptor_extractor, im1)
key2, des2 = self._find_key_points(descriptor_extractor, im2)
matches = match_descriptors(des1, des2, cross_check=True)
# estimate affine transform model using all coordinates
src = key1[matches[:, 0]]
dst = key2[matches[:, 1]]
# robustly estimate affine transform model with RANSAC
model_robust, inliers = ransac((src, dst), AffineTransform,
min_samples=3, residual_threshold=1,
max_trials=100)
# diff = []
# for p1, p2 in zip(src[inliers], dst[inliers]):
# diff.append(p2-p1)
# return np.mean(diff, axis=0)
return model_robust.translation
def test_ransac_geometric():
np.random.seed(1)
# generate original data without noise
src = 100 * np.random.random((50, 2))
model0 = AffineTransform(scale=(0.5, 0.3), rotation=1, translation=(10, 20))
dst = model0(src)
# add some faulty data
outliers = (0, 5, 20)
dst[outliers[0]] = (10000, 10000)
dst[outliers[1]] = (-100, 100)
dst[outliers[2]] = (50, 50)
# estimate parameters of corrupted data
model_est, inliers = ransac((src, dst), AffineTransform, 2, 20)
# test whether estimated parameters equal original parameters
assert_almost_equal(model0._matrix, model_est._matrix)
assert np.all(np.nonzero(inliers == False)[0] == outliers)
def match_from_toa(fk, fd, tk, td, min_matches=10):
# get matching keypoints between images (from to) or (previous, base) or (next, base)
try:
matches = matcher.knnMatch(fd, td, k=2)
matches_subset = filter_matches(matches)
src = [fk[match.queryIdx] for match in matches_subset]
# target image is base image
dst = [tk[match.trainIdx] for match in matches_subset]
src = np.asarray(src)
dst = np.asarray(dst)
if src.shape[0] > min_matches:
# TODO - select which transform to use based on sensor data?
model_robust, inliers = ransac((src, dst), AffineTransform, min_samples=8, residual_threshold=1)
accuracy = float(inliers.shape[0]) / float(src.shape[0])
ransac_matches = matches_subset[inliers]
return model_robust, ransac_matches, accuracy
except Exception, e:
logging.error(e)
def test_ransac_shape():
# generate original data without noise
model0 = CircleModel()
model0.params = (10, 12, 3)
t = np.linspace(0, 2 * np.pi, 1000)
data0 = model0.predict_xy(t)
# add some faulty data
outliers = (10, 30, 200)
data0[outliers[0], :] = (1000, 1000)
data0[outliers[1], :] = (-50, 50)
data0[outliers[2], :] = (-100, -10)
# estimate parameters of corrupted data
model_est, inliers = ransac(data0, CircleModel, 3, 5,
random_state=1)
# test whether estimated parameters equal original parameters
assert_equal(model0.params, model_est.params)
for outlier in outliers:
assert outlier not in inliers
def RANSAC(image):
np.random.seed(seed=1)
# generate coordinates of line
x = np.arange(-200, 200)
y = 0.2 * x + 20
data = np.column_stack([x, y])
# add faulty data
faulty = np.array(30 * [(180., -100)])
faulty += 5 * np.random.normal(size=faulty.shape)
data[:faulty.shape[0]] = faulty
# add gaussian noise to coordinates
noise = np.random.normal(size=data.shape)
data += 0.5 * noise
data[::2] += 5 * noise[::2]
data[::4] += 20 * noise[::4]
# fit line using all data
model = LineModel()
model.estimate(data)
# robustly fit line only using inlier data with RANSAC algorithm
model_robust, inliers = ransac(data, LineModel, min_samples=2,
residual_threshold=1, max_trials=1000)
outliers = inliers == False
# generate coordinates of estimated models
line_x = np.arange(-250, 250)
line_y = model.predict_y(line_x)
line_y_robust = model_robust.predict_y(line_x)
plt.plot(data[inliers, 0], data[inliers, 1], '.b', alpha=0.6,
label='Inlier data')
plt.plot(data[outliers, 0], data[outliers, 1], '.r', alpha=0.6,
label='Outlier data')
plt.plot(line_x, line_y, '-k', label='Line model from all data')
plt.plot(line_x, line_y_robust, '-b', label='Robust line model')
plt.legend(loc='lower left')
plt.show()
def match_from_to(fk, fd, tk, td, min_matches=10):
# get matching keypoints between images (from to) or (previous, base) or (next, base)
ransac_matches = np.zeros((0, 2))
matches = np.zeros((0, 2))
inliers = np.zeros((0, 2))
# try:
if 1:
# opencv way
matches = matcher.knnMatch(fd, td, k=2)
matches_subset = filter_matches(matches)
matches_subset = np.array([[match.trainIdx, match.queryIdx] for match in matches_subset])
src = np.asarray(fk[matches_subset[:, 1]])
dst = np.asarray(tk[matches_subset[:, 0]])
logging.info("STARTING MATCH src: %d dst %d" % (src.shape[0], dst.shape[0]))
if src.shape[0] > min_matches:
# TODO - select which transform to use based on sensor data?
try:
model_robust, inliers = ransac(
(src, dst),
AffineTransform,
min_samples=min_matches,
stop_sample_num=100,
max_trials=2000,
stop_probability=0.995,
residual_threshold=2,
)
except Exception, e:
logging.error(e)
logging.info("FOUND inliers %d" % inliers.shape[0])
if inliers.shape[0]:
num_correct = inliers.shape[0]
num_matches = src.shape[0]
num_false = num_matches - num_correct
ransac_matches = matches_subset[inliers]
perc_correct = 1 - float(num_false) / float(num_matches)
return model_robust, ransac_matches, matches, inliers, perc_correct
else:
logging.info("Not enough matches: %d < min_matches: %d" % (src.shape[0], min_matches))
def find_two_matches(base_img, img, base_k, img_k, base_d, img_d, min_matches=10):
matches = match_descriptors(base_d, img_d, cross_check=True)
# * src (image to be registered):
# * dst (reference image):
src = img_k[matches[:,1]][:,::-1]
dst = base_k[matches[:,0]][:,::-1]
if matches.shape[0] > min_matches:
# model_robust, inliers = ransac((src, dst), ProjectiveTransform,
# min_samples=10, residual_threshold=10,
# stop_sample_num=100, max_trials=300)
#
model_robust, inliers = ransac((src, dst), AffineTransform,
min_samples=6, residual_threshold=3,
max_trials=100)
ransac_matches = matches[inliers]
inlierRatio = ransac_matches.shape[0]/float(matches.shape[0])
return model_robust, ransac_matches, inlierRatio
else:
return np.zeros((0, 2)), np.zeros((0, 2)), 0.0
def match_from_to_compare(fk, fd, tk, td, min_matches=10):
# get matching keypoints between images (from to) or (previous, base) or (next, base)
ransac_matches = np.zeros((0, 2))
matches = np.zeros((0, 2))
inliers = np.zeros((0, 2))
try:
# skimage way
# may need to reverse
matches = match_descriptors(fd, td, cross_check=True)
src = tk[matches[:, 1]][::-1]
dst = fk[matches[:, 0]][::-1]
logging.info("STARTING MATCH src: %d dst %d" % (src.shape[0], dst.shape[0]))
if src.shape[0] > min_matches:
# TODO - select which transform to use based on sensor data?
try:
model_robust, inliers = ransac(
(src, dst),
AffineTransform,
min_samples=min_matches,
stop_sample_num=100,
max_trials=2000,
stop_probability=0.995,
residual_threshold=2,
)
except Exception, e:
logging.error(e)
logging.info("FOUND inliers %d" % inliers.shape[0])
if inliers.shape[0]:
num_correct = inliers.shape[0]
num_matches = src.shape[0]
num_false = num_matches - num_correct
ransac_matches = matches[inliers]
perc_correct = 1 - float(num_false) / float(num_matches)
return model_robust, ransac_matches, matches, inliers, perc_correct
else:
def run2(self):
""" Cette fonction recadre les images grâce à SIFT et RANSAC, fonctionne bien."""
for x in xrange(len(self.stack)-1):
print('Traitement image ' + str(x+1))
im1,im2 = 255.*gaussian_filter(self.stack[x,...], sqrt(self.initial_sigma**2 - 0.25)), 255.*gaussian_filter(self.stack[x+1,...], sqrt(self.initial_sigma**2 - 0.25))
im1,im2 = enhance_contrast(normaliser(im1), square(3)), enhance_contrast(normaliser(im2), square(3))
im1, im2 = normaliser(im1), normaliser(im2)
sift = cv2.SIFT()
# find the keypoints and descriptors with SIFT
kp1, des1 = sift.detectAndCompute(im1,None)
kp2, des2 = sift.detectAndCompute(im2,None)
# BFMatcher with default params
bf = cv2.BFMatcher()
matches = bf.knnMatch(des1,des2, k=2)
# Apply ratio test
good = []
for m,n in matches:
if m.distance < 0.75*n.distance:
good.append(m)
# good est une liste de DMatch
g1,g2 = [],[]
for i in good:
g1.append(kp1[i.queryIdx].pt)
g2.append(kp2[i.trainIdx].pt)
model, inliers = ransac((np.array(g1), np.array(g2)), AffineTransform, min_samples=3, residual_threshold=self.min_epsilon, max_trials=self.max_trials, stop_residuals_sum=self.min_inlier_ratio)
self.stack[x+1,...] = warp(self.stack[x+1,...], AffineTransform(rotation=model.rotation, translation=model.translation), output_shape=self.stack[x+1].shape)
self.stack = self.stack.astype(np.uint8)
def get_translation_tool(self, n_keypoints=1000):
# Convert images to grayscale
src_image = rgb2gray(self.src_image)
dst_image = rgb2gray(self.dst_image)
# Initiate an ORB class object which can extract features & descriptors from images.
# Set the amount of features that should be found (more = more accurate)
descriptor_extractor = ORB(n_keypoints=n_keypoints)
# Extract features and descriptors from source image
descriptor_extractor.detect_and_extract(src_image)
self.keypoints1 = descriptor_extractor.keypoints
descriptors1 = descriptor_extractor.descriptors
# Extract features and descriptors from destination image
descriptor_extractor.detect_and_extract(dst_image)
self.keypoints2 = descriptor_extractor.keypoints
descriptors2 = descriptor_extractor.descriptors
# Matches the descriptors and gives them rating as to how similar they are
self.matches12 = match_descriptors(descriptors1, descriptors2, cross_check=True)
# Selects the coordinates from source image and destination image based on the
# indices given from the match_descriptors function.
src = self.keypoints1[self.matches12[:, 0]][:, ::-1]
dst = self.keypoints2[self.matches12[:, 1]][:, ::-1]
# Filters out the outliers and generates the transformation matrix based on only the inliers
model_robust, inliers = \
ransac((src, dst), ProjectiveTransform,
min_samples=4, residual_threshold=2)
# This returns the object "model_robust" which contains the tranformation matrix and
# uses that to translate any coordinate point from source to destination image.
return model_robust, inliers
def count_i(land_name):
data = land_name.split('/')[-1].split('.')[0]
#file1 = open('txt_file_m18_m/'+data+'.txt','w')
maxNum = 0
maxName = ''
for name in glob(feature_db + '*'):
#print (name)
name2 = name.split('/')
number = name2[-1].split('.')[0]
# Read features.
locations_1, _, descriptors_1, _, _ = feature_io.ReadFromFile(
land_name)
#cmd_args.features_1_path)
num_features_1 = locations_1.shape[0]
#tf.logging.info("Loaded image 1's %d features" % num_features_1)
locations_2, _, descriptors_2, _, _ = feature_io.ReadFromFile(name)
#cmd_args.features_2_path)
num_features_2 = locations_2.shape[0]
#tf.logging.info("Loaded image 2's %d features" % num_features_2)
# Find nearest-neighbor matches using a KD tree.
d1_tree = cKDTree(descriptors_1)
_, indices = d1_tree.query(descriptors_2,
distance_upper_bound=_DISTANCE_THRESHOLD)
# Select feature locations for putative matches.
locations_2_to_use = np.array([
locations_2[i, ] for i in range(num_features_2)
if indices[i] != num_features_1
])
locations_1_to_use = np.array([
locations_1[indices[i], ] for i in range(num_features_2)
if indices[i] != num_features_1
])
# Perform geometric verification using RANSAC.
try:
_, inliers = ransac((locations_1_to_use, locations_2_to_use),
AffineTransform,
min_samples=3,
residual_threshold=20,
max_trials=1000)
#print(sum(inliers))
#print(maxNum)
#"""
#tf.logging.info('Found %d inliers' % sum(inliers))
#print(sum(inliers))
#print(maxNum)
score = int(sum(inliers))
#file1.write(name+'\t'+str(sum(inliers))+'\n')
#maxName = num2name[number]
num = name.split('/')[-1].split('.')[0]
if score > 35 and score > maxNum:
maxNum = score
maxName = num2name[number]
print(maxName)
print(maxNum)
#break
#"""
except:
#print('fail')
a = 0
#break
#print (maxName)
#result_dic[data] = maxName
return maxName
def read_csv(name, nl="\n", dl=","):
cloud = []
with open(name, newline=nl) as csvfile:
csvreader = reader(csvfile, delimiter=dl)
for xx, yy, zz in csvreader:
cloud.append([float(xx), float(yy), float(zz)])
return cloud
cloud = numpy.array(read_csv("cloud_r.xyz"))
model_robotus, inliers = ransac(cloud,
LineModelND,
min_samples=2,
residual_threshold=1,
max_trials=1000)
outliers = inliers == False
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(cloud[inliers][:, 0],
cloud[inliers][:, 1],
cloud[inliers][:, 2],
c='g',
marker='o',
label='inliers')
# ax.scatter(cloud[outliers][:,0],cloud[outliers][:,1],cloud[outliers][:,2],c='r',marker='o',label='outliers')
img0 = img_list[0]
coords0 = corner_list[0]
matching_corners = [
match_locations(img0, img1, coords0, coords1, min_dist)
for img1, coords1 in zip(img_list, corner_list)
]
############################################################################
# Once all the points are registered to the reference points, robust
# relative affine transformations can be estimated using the RANSAC method.
src = np.array(coords0)
trfm_list = [
measure.ransac((dst, src),
transform.EuclideanTransform,
min_samples=3,
residual_threshold=2,
max_trials=100)[0].params for dst in matching_corners
]
fig, ax_list = plt.subplots(6, 2, figsize=(6, 9), sharex=True, sharey=True)
for idx, (im, trfm, (ax0, ax1)) in enumerate(zip(img_list, trfm_list,
ax_list)):
ax0.imshow(im, cmap="gray", vmin=0, vmax=1)
ax1.imshow(transform.warp(im, trfm), cmap="gray", vmin=0, vmax=1)
if idx == 0:
ax0.set_title(f"Tilted images")
ax0.set_ylabel(f"Reference Image\n(PSNR={psnr_ref:.2f})")
ax1.set_title(f"Registered images")
def MatchFeatures(query_locations,
query_descriptors,
index_image_locations,
index_image_descriptors,
ransac_seed=None,
feature_distance_threshold=0.9,
ransac_residual_threshold=10.0):
"""Matches local features using geometric verification.
First, finds putative local feature matches by matching `query_descriptors`
against a KD-tree from the `index_image_descriptors`. Then, attempts to fit an
affine transformation between the putative feature corresponces using their
locations.
Args:
query_locations: Locations of local features for query image. NumPy array of
shape [#query_features, 2].
query_descriptors: Descriptors of local features for query image. NumPy
array of shape [#query_features, depth].
index_image_locations: Locations of local features for index image. NumPy
array of shape [#index_image_features, 2].
index_image_descriptors: Descriptors of local features for index image.
NumPy array of shape [#index_image_features, depth].
ransac_seed: Seed used by RANSAC. If None (default), no seed is provided.
feature_distance_threshold: Distance threshold below which a pair of
features is considered a potential match, and will be fed into RANSAC.
ransac_residual_threshold: Residual error threshold for considering matches
as inliers, used in RANSAC algorithm.
Returns:
score: Number of inliers of match. If no match is found, returns 0.
Raises:
ValueError: If local descriptors from query and index images have different
dimensionalities.
"""
num_features_query = query_locations.shape[0]
num_features_index_image = index_image_locations.shape[0]
if not num_features_query or not num_features_index_image:
return 0
local_feature_dim = query_descriptors.shape[1]
if index_image_descriptors.shape[1] != local_feature_dim:
raise ValueError(
'Local feature dimensionality is not consistent for query and index '
'images.')
# Find nearest-neighbor matches using a KD tree.
index_image_tree = spatial.cKDTree(index_image_descriptors)
_, indices = index_image_tree.query(
query_descriptors, distance_upper_bound=feature_distance_threshold)
# Select feature locations for putative matches.
query_locations_to_use = np.array([
query_locations[i,]
for i in range(num_features_query)
if indices[i] != num_features_index_image
])
index_image_locations_to_use = np.array([
index_image_locations[indices[i],]
for i in range(num_features_query)
if indices[i] != num_features_index_image
])
# If there are not enough putative matches, early return 0.
if query_locations_to_use.shape[0] <= _MIN_RANSAC_SAMPLES:
return 0
# Perform geometric verification using RANSAC.
_, inliers = measure.ransac(
(index_image_locations_to_use, query_locations_to_use),
transform.AffineTransform,
min_samples=_MIN_RANSAC_SAMPLES,
residual_threshold=ransac_residual_threshold,
max_trials=_NUM_RANSAC_TRIALS,
random_state=ransac_seed)
if inliers is None:
inliers = []
return sum(inliers)
# find correspondences using simple weighted sum of squared differences
src = []
dst = []
for coord in coords_orig_subpix:
src.append(coord)
dst.append(match_corner(coord))
src = np.array(src)
dst = np.array(dst)
# estimate affine transform model using all coordinates
model = AffineTransform()
model.estimate(src, dst)
# robustly estimate affine transform model with RANSAC
model_robust, inliers = ransac((src, dst), AffineTransform, min_samples=3,
residual_threshold=2, max_trials=100)
outliers = inliers == False
# compare "true" and estimated transform parameters
print tform.scale, tform.translation, tform.rotation
print model.scale, model.translation, model.rotation
print model_robust.scale, model_robust.translation, model_robust.rotation
# visualize correspondences
img_combined = np.concatenate((img_orig_gray, img_warped_gray), axis=1)
fig, ax = plt.subplots(nrows=2, ncols=1)
plt.gray()
def calculate_point_MCMC(kps_left, sco_left, des_left, kps_right, sco_right,
des_right):
#Flann特征匹配
FLANN_INDEX_KDTREE = 1
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
search_params = dict(checks=40)
flann = cv2.FlannBasedMatcher(index_params, search_params)
matches = flann.knnMatch(des_left, des_right, k=2)
goodMatch = []
locations_1_to_use = []
locations_2_to_use = []
dis = []
# 匹配对筛选
# min_dist = 1000
# max_dist = 0
disdif_avg = 0
# 统计平均距离差
for m, n in matches:
disdif_avg += n.distance - m.distance
disdif_avg = disdif_avg / len(matches)
for m, n in matches:
#自适应阈值
if n.distance > m.distance + 1 * disdif_avg:
goodMatch.append(m)
p2 = cv2.KeyPoint(kps_right[m.trainIdx][0],
kps_right[m.trainIdx][1], 1)
p1 = cv2.KeyPoint(kps_left[m.queryIdx][0], kps_left[m.queryIdx][1],
1)
locations_1_to_use.append([p1.pt[0], p1.pt[1]])
locations_2_to_use.append([p2.pt[0], p2.pt[1]])
dis.append([n.distance, m.distance])
# if
#goodMatch = sorted(goodMatch, key=lambda x: x.distance)
# print('match num is %d' % len(goodMatch))
locations_1_to_use = np.array(locations_1_to_use)
locations_2_to_use = np.array(locations_2_to_use)
dis = np.array(dis)
# Perform geometric verification using RANSAC.
_, inliers = measure.ransac((locations_1_to_use, locations_2_to_use),
transform.AffineTransform,
min_samples=3,
residual_threshold=_RESIDUAL_THRESHOLD,
max_trials=1000)
# print('Found %d inliers' % sum(inliers))
inlier_idxs = np.nonzero(inliers)[0]
# 筛选距离最近的前60%个数据
inlier_idxs = np.nonzero(inliers)[0]
dis_R = dis[inliers]
dis_idx = np.argsort(dis_R[:, 0] - dis_R[:, 1])
dis_sorted = dis_R[dis_idx]
l = dis_idx.shape[0]
end = int(l * 0.6)
#最终匹配结果
inlier_idxs = inlier_idxs[dis_idx[:end]]
# print('sorted inliers:', end)
#最终匹配结果
matches = np.column_stack((inlier_idxs, inlier_idxs))
# print('whole time is %6.3f' % (time.perf_counter() - start0))
return locations_1_to_use[inlier_idxs], locations_2_to_use[inlier_idxs]
# print("data shape", data.shape)
# add faulty data
faulty = np.array(10 * [(180., -100)])
faulty += 10 * np.random.normal(size=faulty.shape)
data[:faulty.shape[0]] = faulty
# print("data shape", data.shape)
# fit line using all data
model = LineModelND()
model.estimate(data)
# robustly fit line only using inlier data with RANSAC algorithm
model_robust, inliers = ransac(data,
LineModelND,
min_samples=2,
residual_threshold=threshold,
max_trials=20)
outliers = inliers == False
# print("inliers", inliers)
# print("outliers", outliers, np.count_nonzero(outliers))
# generate coordinates of estimated models
line_x = np.arange(-250, 250)
line_y = model.predict_y(line_x)
line_y_robust = model_robust.predict_y(line_x)
fig, ax = plt.subplots()
ax.plot(data[inliers, 0],
data[inliers, 1],
'.b',
#Load up the first frame:
currframe = frame(cap,frameorder[0])
weightimage = np.ones(currframe.grayimage.shape)
imagearr = np.zeros((currframe.rawimage.shape[0],currframe.rawimage.shape[1],currframe.rawimage.shape[2],len(frameorder)))/0.
imagearr[:,:,:,0] = ski_exp.rescale_intensity(ski_util.img_as_float(currframe.rawimage))
#Go through the frames:
for i in range(1,len(frameorder)):
print i,len(frameorder)-1,frameorder[i],imagearr.shape
newframe = frame(cap,frameorder[i])
#Compute matches:
matches = match_frames_twosided(currframe,newframe,dist_ratio=0.6)
#matches = match_frames_flann(currframe,newframe)
matching_coords_curr = currframe.coords[matches > 0,:]
matching_coords_new = newframe.coords[matches[matches>0],:]
#Compute homology:
model,inliers = ski_measure.ransac((matching_coords_curr,matching_coords_new),ski_trans.ProjectiveTransform,min_samples=25,residual_threshold=1.0,max_trials=2000)
#print currframe.rawimage.max(),currframe.rawimage.min()
test = homology_chisquared(newframe.rawimage,ski_exp.rescale_intensity(ski_util.img_as_float(currframe.rawimage)),model._matrix.reshape(-1))
#print np.sum(test),test.min(),test.max()
#print len(inliers),np.sum(inliers)
homologies[i,:,:] = model._matrix
#Determine where the new frame extrema would be after being transformed:
xmin,xmax,ymin,ymax = compute_extrema(newframe,model._matrix)
jointimage = ski_exp.rescale_intensity(ski_util.img_as_float(currframe.rawimage.copy()))
#ax = plot_matches(newframe.rawimage,currframe.rawimage,matching_coords_new[inliers,:],matching_coords_new[inliers,:],matches[inliers],show_below=False,ax=None)
#ax.figure.savefig('test_matches_{0:d}.jpg'.format(newframe.framenumber))
#print xmin,ymin,xmax,ymax,imagearr.shape,np.sum(inliers)
if xmax > currframe.rawimage.shape[1]*1.5 or ymax > currframe.rawimage.shape[0]*1.5 or xmin < currframe.rawimage.shape[1]*-0.5 or ymin < currframe.rawimage.shape[0]*-0.5:
ski_io.imsave('test_avg_aborted.jpg',currframe.rawimage)
print " ",xmin,xmax,ymin,ymax,currframe.rawimage.shape
sys.exit("Image matching has likely failed, aborting to avoid running out of memory")
matchesMask[i] = [1, 0]
# print(i)
# p2 = cv2.KeyPoint(kp1[m.trainIdx][0], kp1[m.trainIdx][1], 1)
# p1 = cv2.KeyPoint(kp2[m.queryIdx][0], kp2[m.queryIdx][1], 1)
locations_1_to_use.append(
[kp1[m.queryIdx].pt[0], kp1[m.queryIdx].pt[1]])
locations_2_to_use.append(
[kp2[m.trainIdx].pt[0], kp2[m.trainIdx].pt[1]])
locations_1_to_use = np.array(locations_1_to_use)
locations_2_to_use = np.array(locations_2_to_use)
# Perform geometric verification using RANSAC.
_RESIDUAL_THRESHOLD = 30
_, inliers = measure.ransac((locations_1_to_use, locations_2_to_use),
transform.AffineTransform,
min_samples=3,
residual_threshold=_RESIDUAL_THRESHOLD,
max_trials=1000)
inlier_idxs = np.nonzero(inliers)[0]
# p1 = locations_2_to_use[inlier_idxs]
# p2 = locations_1_to_use[inlier_idxs]
# Visualize correspondences, and save to file.
#1 绘制匹配连线
plt.rcParams['savefig.dpi'] = 100 #图片像素
plt.rcParams['figure.dpi'] = 100 #分辨率
plt.rcParams['figure.figsize'] = (4.0, 3.0) # 设置figure_size尺寸
_, ax = plt.subplots()
plotmatch.plot_matches(ax,
img_sar_canny,
img_optical_canny,
descriptor_extractor.detect_and_extract(img_right)
keypoints_right = descriptor_extractor.keypoints
descriptors_right = descriptor_extractor.descriptors
matches = match_descriptors(descriptors_left, descriptors_right,
cross_check=True)
print(f'Number of matches: {matches.shape[0]}')
# Estimate the epipolar geometry between the left and right image.
random_seed = 9
rng = np.random.default_rng(random_seed)
model, inliers = ransac((keypoints_left[matches[:, 0]],
keypoints_right[matches[:, 1]]),
FundamentalMatrixTransform, min_samples=8,
residual_threshold=1, max_trials=5000,
random_state=rng)
inlier_keypoints_left = keypoints_left[matches[inliers, 0]]
inlier_keypoints_right = keypoints_right[matches[inliers, 1]]
print(f'Number of inliers: {inliers.sum()}')
# Compare estimated sparse disparities to the dense ground-truth disparities.
disp = inlier_keypoints_left[:, 1] - inlier_keypoints_right[:, 1]
disp_coords = np.round(inlier_keypoints_left).astype(np.int64)
disp_idxs = np.ravel_multi_index(disp_coords.T, groundtruth_disp.shape)
disp_error = np.abs(groundtruth_disp.ravel()[disp_idxs] - disp)
disp_error = disp_error[np.isfinite(disp_error)]
# Array to keep track of all candidates in database.
inliers_counts = []
# Read the resized query image for plotting.
img_1 = mpimg.imread(resized_image)
for index in unique_image_indexes:
locations_2_use_query, locations_2_use_db = get_locations_2_use(
index, indices, accumulated_indexes_boundaries)
if len(locations_2_use_db) <= 3:
continue
# Perform geometric verification using RANSAC.
_, inliers = ransac(
(locations_2_use_db,
locations_2_use_query), # source and destination coordinates
AffineTransform,
min_samples=3,
residual_threshold=20,
max_trials=1000)
# If no inlier is found for a database candidate image, we continue on to the next one.
if inliers is None or len(inliers) == 0:
continue
# the number of inliers as the score for retrieved images.
inliers_counts.append({"index": index, "inliers": sum(inliers)})
print('Found inliers for image {} -> {}'.format(index, sum(inliers)))
# Visualize correspondences.
# _, ax = plt.subplots()
# img_2 = mpimg.imread(db_images[index])
# inlier_idxs = np.nonzero(inliers)[0]
# plot_matches(
# ax,
def MatchFeatures(query_locations,
query_descriptors,
index_image_locations,
index_image_descriptors,
ransac_seed=None,
descriptor_matching_threshold=0.9,
ransac_residual_threshold=10.0,
query_im_array=None,
index_im_array=None,
query_im_scale_factors=None,
index_im_scale_factors=None,
use_ratio_test=False):
"""Matches local features using geometric verification.
First, finds putative local feature matches by matching `query_descriptors`
against a KD-tree from the `index_image_descriptors`. Then, attempts to fit an
affine transformation between the putative feature corresponces using their
locations.
Args:
query_locations: Locations of local features for query image. NumPy array of
shape [#query_features, 2].
query_descriptors: Descriptors of local features for query image. NumPy
array of shape [#query_features, depth].
index_image_locations: Locations of local features for index image. NumPy
array of shape [#index_image_features, 2].
index_image_descriptors: Descriptors of local features for index image.
NumPy array of shape [#index_image_features, depth].
ransac_seed: Seed used by RANSAC. If None (default), no seed is provided.
descriptor_matching_threshold: Threshold below which a pair of local
descriptors is considered a potential match, and will be fed into RANSAC.
If use_ratio_test==False, this is a simple distance threshold. If
use_ratio_test==True, this is Lowe's ratio test threshold.
ransac_residual_threshold: Residual error threshold for considering matches
as inliers, used in RANSAC algorithm.
query_im_array: Optional. If not None, contains a NumPy array with the query
image, used to produce match visualization, if there is a match.
index_im_array: Optional. Same as `query_im_array`, but for index image.
query_im_scale_factors: Optional. If not None, contains a NumPy array with
the query image scales, used to produce match visualization, if there is a
match. If None and a visualization will be produced, [1.0, 1.0] is used
(ie, feature locations are not scaled).
index_im_scale_factors: Optional. Same as `query_im_scale_factors`, but for
index image.
use_ratio_test: If True, descriptor matching is performed via ratio test,
instead of distance-based threshold.
Returns:
score: Number of inliers of match. If no match is found, returns 0.
match_viz_bytes: Encoded image bytes with visualization of the match, if
there is one, and if `query_im_array` and `index_im_array` are properly
set. Otherwise, it's an empty bytes string.
Raises:
ValueError: If local descriptors from query and index images have different
dimensionalities.
"""
num_features_query = query_locations.shape[0]
num_features_index_image = index_image_locations.shape[0]
if not num_features_query or not num_features_index_image:
return 0, b''
local_feature_dim = query_descriptors.shape[1]
if index_image_descriptors.shape[1] != local_feature_dim:
raise ValueError(
'Local feature dimensionality is not consistent for query and index '
'images.')
# Construct KD-tree used to find nearest neighbors.
index_image_tree = spatial.cKDTree(index_image_descriptors)
if use_ratio_test:
distances, indices = index_image_tree.query(query_descriptors,
k=2,
n_jobs=-1)
query_locations_to_use = np.array([
query_locations[i, ] for i in range(num_features_query)
if distances[i][0] < descriptor_matching_threshold *
distances[i][1]
])
index_image_locations_to_use = np.array([
index_image_locations[indices[i][0], ]
for i in range(num_features_query)
if distances[i][0] < descriptor_matching_threshold *
distances[i][1]
])
else:
_, indices = index_image_tree.query(
query_descriptors,
distance_upper_bound=descriptor_matching_threshold,
n_jobs=-1)
# Select feature locations for putative matches.
query_locations_to_use = np.array([
query_locations[i, ] for i in range(num_features_query)
if indices[i] != num_features_index_image
])
index_image_locations_to_use = np.array([
index_image_locations[indices[i], ]
for i in range(num_features_query)
if indices[i] != num_features_index_image
])
# If there are not enough putative matches, early return 0.
if query_locations_to_use.shape[0] <= _MIN_RANSAC_SAMPLES:
return 0, b''
# Perform geometric verification using RANSAC.
_, inliers = measure.ransac(
(index_image_locations_to_use, query_locations_to_use),
transform.AffineTransform,
min_samples=_MIN_RANSAC_SAMPLES,
residual_threshold=ransac_residual_threshold,
max_trials=_NUM_RANSAC_TRIALS,
random_state=ransac_seed)
match_viz_bytes = b''
if inliers is None:
inliers = []
elif query_im_array is not None and index_im_array is not None:
if query_im_scale_factors is None:
query_im_scale_factors = [1.0, 1.0]
if index_im_scale_factors is None:
index_im_scale_factors = [1.0, 1.0]
inlier_idxs = np.nonzero(inliers)[0]
_, ax = plt.subplots()
ax.axis('off')
ax.xaxis.set_major_locator(plt.NullLocator())
ax.yaxis.set_major_locator(plt.NullLocator())
plt.subplots_adjust(top=1,
bottom=0,
right=1,
left=0,
hspace=0,
wspace=0)
plt.margins(0, 0)
feature.plot_matches(ax,
query_im_array,
index_im_array,
query_locations_to_use * query_im_scale_factors,
index_image_locations_to_use *
index_im_scale_factors,
np.column_stack((inlier_idxs, inlier_idxs)),
only_matches=True)
match_viz_io = io.BytesIO()
plt.savefig(match_viz_io,
format='jpeg',
bbox_inches='tight',
pad_inches=0)
match_viz_bytes = match_viz_io.getvalue()
return sum(inliers), match_viz_bytes
src = []
dst = []
for coord in coords_orig_subpix:
src.append(coord)
dst.append(match_corner(coord))
src = np.array(src)
dst = np.array(dst)
# estimate affine transform model using all coordinates
model = AffineTransform()
model.estimate(src, dst)
# robustly estimate affine transform model with RANSAC
model_robust, inliers = ransac((src, dst),
AffineTransform,
min_samples=3,
residual_threshold=2,
max_trials=100)
outliers = inliers == False
# compare "true" and estimated transform parameters
print(tform.scale, tform.translation, tform.rotation)
print(model.scale, model.translation, model.rotation)
print(model_robust.scale, model_robust.translation, model_robust.rotation)
# visualize correspondence
fig, ax = plt.subplots(nrows=2, ncols=1)
plt.gray()
inlier_idxs = np.nonzero(inliers)[0]
keypoints_right, sorces_right, descriptor_right = extract_features(
img_right)
matches = match_descriptors(descriptor_left,
descriptor_right,
cross_check=True)
print('Number of raw matches: %d.' % matches.shape[0])
#print(keypoints_left)
keypoints_ll = keypoints_left[matches[:, 0], :2]
keypoints_rr = keypoints_right[matches[:, 1], :2]
np.random.seed(0)
best_model_, inliers = ransac(
(keypoints_ll, keypoints_rr),
ProjectiveTransform,
min_samples=4,
residual_threshold=4,
max_trials=10000 # res= 2 maxtrial=10000
)
n_inliers = np.sum(inliers)
print('Number of inliers: %d.' % n_inliers)
inlier_keypoints_left = [
cv2.KeyPoint(point[0], point[1], 1)
for point in keypoints_ll[inliers]
]
inlier_keypoints_right = [
cv2.KeyPoint(point[0], point[1], 1)
for point in keypoints_rr[inliers]
]
def main():
parser = argparse.ArgumentParser(
description='COLMAP database building',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("--sequence_root",
type=str,
required=True,
help='root of video sequence')
parser.add_argument("--overwrite_database", action="store_true")
args = parser.parse_args()
sequence_root = Path(args.sequence_root)
overwrite_database = args.overwrite_database
feature_match_path = sequence_root / "feature_matches.hdf5"
database_path = sequence_root / "database.db"
if not overwrite_database:
if database_path.exists():
print("ERROR: database exists already")
exit()
if not feature_match_path.exists():
print("ERROR: feature matches hdf5 file does not exist")
exit()
# Open the database.
db = COLMAPDatabase.connect(str(database_path))
# For convenience, try creating all the tables upfront.
db.create_tables()
images_root = sequence_root / "images"
image_list = list(images_root.glob("0*.jpg"))
image_list.sort()
image = cv2.imread(str(image_list[0]))
height, width, _ = image.shape
# Create camera model -- PINHOLE CAMERA (fx fy cx cy))
camera_intrinsics_path = sequence_root / "camera_intrinsics_per_view"
with open(str(camera_intrinsics_path), "r") as f:
temp = list()
for i in range(4):
temp.append(f.readline())
model, intrinsics = 1, np.array((temp[0], temp[1], temp[2], temp[3]))
camera_id = db.add_camera(model, width, height, intrinsics)
# Create image ids
image_id_list = list()
for image_path in image_list:
image_id_list.append(db.add_image(image_path.name, camera_id))
# Create matches per image pair
f_matches = h5py.File(str(feature_match_path), 'r')
dataset_matches = f_matches['matches']
start_index = 0
tq = tqdm.tqdm(total=dataset_matches.shape[0])
tq.set_description("Gathering keypoints")
keypoints_dict = dict()
# Keypoint gathering
while start_index < dataset_matches.shape[0]:
header = dataset_matches[start_index, :, 0]
num_matches, id_1, id_2, _ = header
tq.set_postfix(source_frame_index='{:d}'.format(id_1),
target_frame_index='{:d}'.format(id_2))
id_1 += 1
id_2 += 1
id_1 = int(id_1)
id_2 = int(id_2)
pair_matches = dataset_matches[start_index + 1:start_index + 1 +
num_matches, :, 0]
pair_matches = pair_matches.astype(np.long)
matches = np.concatenate(
[(pair_matches[:, 0] + pair_matches[:, 1] * width).reshape(
(-1, 1)),
(pair_matches[:, 2] + pair_matches[:, 3] * width).reshape(
(-1, 1))],
axis=1)
if str(id_1) not in keypoints_dict:
keypoints_dict[str(id_1)] = list(matches[:, 0])
else:
keypoints_dict[str(id_1)] += list(matches[:, 0])
keypoints_dict[str(id_1)] = list(
np.unique(keypoints_dict[str(id_1)]))
if str(id_2) not in keypoints_dict:
keypoints_dict[str(id_2)] = list(matches[:, 1])
else:
keypoints_dict[str(id_2)] += list(matches[:, 1])
keypoints_dict[str(id_2)] = list(
np.unique(keypoints_dict[str(id_2)]))
tq.update(num_matches + 1)
start_index += num_matches + 1
tq.close()
new_keypoints_dict = dict()
# Keypoint indexing building
# val -- 1D location of keypoint, key -- one-based frame index
for key, value in keypoints_dict.items():
value = np.unique(value)
temp = dict()
for idx, val in enumerate(value):
temp[str(val)] = int(idx + 1)
new_keypoints_dict[key] = temp
tq = tqdm.tqdm(total=dataset_matches.shape[0])
tq.set_description("Adding matches to database")
start_index = 0
while start_index < dataset_matches.shape[0]:
header = dataset_matches[start_index, :, 0]
num_matches, id_1, id_2, _ = header
id_1 += 1
id_2 += 1
id_1 = int(id_1)
id_2 = int(id_2)
# new_keypoint_dict -- one-based frame index
keypoint_dict_1 = new_keypoints_dict[str(id_1)]
keypoint_dict_2 = new_keypoints_dict[str(id_2)]
pair_matches = dataset_matches[start_index + 1:start_index + 1 +
num_matches, :, 0]
model, inliers = ransac((pair_matches[:, :2], pair_matches[:, 2:]),
FundamentalMatrixTransform,
min_samples=8,
residual_threshold=10.0,
max_trials=10)
pair_matches = pair_matches.astype(np.long)
pair_matches = np.concatenate(
[(pair_matches[:, 0] + pair_matches[:, 1] * width).reshape(
(-1, 1)),
(pair_matches[:, 2] + pair_matches[:, 3] * width).reshape(
(-1, 1))],
axis=1)
idx_pair_matches = np.zeros_like(pair_matches).astype(np.int32)
# one-based keypoint index per frame
for i in range(pair_matches.shape[0]):
idx_pair_matches[i, 0] = int(keypoint_dict_1[str(pair_matches[i,
0])])
idx_pair_matches[i, 1] = int(keypoint_dict_2[str(pair_matches[i,
1])])
db.add_matches(id_1, id_2, idx_pair_matches)
# Fundamental matrix provided
db.add_two_view_geometry(id_1,
id_2,
idx_pair_matches[inliers],
F=model.params,
config=3)
tq.set_postfix(source_frame_index='{:d}'.format(id_1),
target_frame_index='{:d}'.format(id_2),
inliers_ratio='{:.3f}'.format(
np.sum(inliers) / idx_pair_matches.shape[0]))
tq.update(num_matches + 1)
start_index += num_matches + 1
tq.close()
# Adding keypoints per image to database
for image_id in image_id_list:
if str(image_id) not in keypoints_dict:
continue
temp = np.asarray(keypoints_dict[str(image_id)])
temp_1 = np.floor(temp.reshape((-1, 1)) / width)
temp_2 = np.mod(temp.reshape((-1, 1)), width)
db.add_keypoints(
int(image_id),
np.concatenate([temp_2, temp_1], axis=1).astype(np.int32))
# Write to the database file
db.commit()
fig, ax = plt.subplots(1, 1, figsize=(15, 12))
# Best match subset for pano0 -> pano1
plot_matches(ax, I0, I1, keypoints0, keypoints1, matches01)
ax.axis('off');
from skimage.transform import ProjectiveTransform
from skimage.measure import ransac
# Select keypoints from
# * source (image to be registered): pano1
# * target (reference image): pano0
src = keypoints1[matches01[:, 1]][:, ::-1]
dst = keypoints0[matches01[:, 0]][:, ::-1]
model_robust01, inliers01 = ransac((src, dst), ProjectiveTransform,
min_samples=4, residual_threshold=1, max_trials=300)
print(model_robust01)
fig, ax = plt.subplots(1, 1, figsize=(15, 12))
# Best match subset for pano0 -> pano1
plot_matches(ax, I0, I1, keypoints0, keypoints1, matches01[inliers01])
ax.axis('off');
from skimage.transform import SimilarityTransform
# Shape registration target
r, c = pano0.shape[:2]
def read_LiDAR(self, data):
# sequence_dir = os.path.join(self.data_base_dir, self.sequence_idx, 'velodyne')
# sequence_manager = Ptutils.KittiScanDirManager(sequence_dir)
# scan_paths = sequence_manager.scan_fullpaths
tic = time.time()
rate = rospy.Rate(20)
self.num_frames = 6000
# print('jhloi')
# Pose Graph Manager (for back-end optimization) initialization
# Result saver
self.save_dir = "result/" + self.sequence_idx
if not os.path.exists(self.save_dir): os.makedirs(self.save_dir)
# Scan Context Manager (for loop detection) initialization
# for save the results as a video
fig_idx = 1
num_frames_to_skip_to_show = 5
# num_frames_to_save = np.floor(num_frames/num_frames_to_skip_to_show)
# with writer.saving(fig, video_name, num_frames_to_save): # this video saving part is optional
# @@@ MAIN @@@: data stream
# for for_idx, scan_path in tqdm(enumerate(scan_paths), total=num_frames, mininterval=5.0):
# get current information
self.curr_scan_pts = ptstoxyz.pointcloud2_to_xyz_array(
data, remove_nans=True)
# print (len(self.curr_scan_pts), 'nubetotal')
# plane1 = pyrsc.Circle()
# best_eq, best_inliers = plane1.fit(self.curr_scan_pts, 0.01)
# print(best_inliers)
# print(len(self.curr_scan_pts), 'origin')
model_robust, inliers = ransac(self.curr_scan_pts,
LineModelND,
min_samples=2,
residual_threshold=0.9,
max_trials=3)
# print(type(inliers))
# pos_True = inliers == False
# print(len(inliers[pos_false]) , 'outliers')
# print(self.curr_scan_pts.shape)
# curr_scan_pts = Ptutils.readScan(scan_paths)
var = []
for i in range(0, len(inliers)):
if inliers[i] == False:
var.append(i)
self.curr_scan_pts = self.curr_scan_pts[var[:], :]
print(self.curr_scan_pts.shape)
# save current node
curr_node_idx = self.for_idx # make start with 0
self.curr_scan_down_pts = Ptutils.random_sampling(
self.curr_scan_pts, num_points=self.num_icp_points)
# print(len(self.curr_scan_down_pts), 'random')
if (curr_node_idx == 0):
self.prev_node_idx = curr_node_idx
self.prev_scan_pts = copy.deepcopy(self.curr_scan_pts)
self.icp_initial = np.eye(4)
# continue
if self.for_idx == -10:
self.curr_scan_pts1 = ptstoxyz.pointcloud2_to_xyz_array(
data, remove_nans=True)
x1 = self.curr_scan_pts1[:, 0]
y1 = self.curr_scan_pts1[:, 1]
z1 = self.curr_scan_pts1[:, 2]
print(len(self.curr_scan_pts1), 'origin', len(x1))
# x2= self.curr_scan_pts[:,0]
# y2= self.curr_scan_pts[:,1]
# z2= self.curr_scan_pts[:,2]
x3 = self.curr_scan_pts[:, 0]
y3 = self.curr_scan_pts[:, 1]
z3 = self.curr_scan_pts[:, 2]
print(len(self.curr_scan_pts), 'ransac', len(x3))
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(x1,
y1,
z1,
marker="*",
label="Nube de puntos",
s=0.02,
c='blue')
# ax.scatter(x3,y3,z3, marker = '*',label = "RANSAC outliers" ,s=0.02, c ='red' )
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
plt.legend(loc=2, prop={'size': 10})
ax.legend()
plt.show()
# fig2=plt.figure()
# calc odometry
# print (self.prev_scan_down_pts)
self.prev_scan_down_pts = Ptutils.random_sampling(
self.prev_scan_pts, num_points=self.num_icp_points)
self.odom_transform, _, _ = ICP.icp(self.curr_scan_down_pts,
self.prev_scan_down_pts,
init_pose=self.icp_initial,
max_iterations=100)
# print (self.odom_transform)
# update the current (moved) pose
self.curr_se3 = np.matmul(self.curr_se3, self.odom_transform)
self.icp_initial = self.odom_transform # assumption: constant velocity model (for better next ICP converges)
# print(self.odom_transform)
# add the odometry factor to the graph
# self.for_idx= self.for_idx+1
# renewal the prev information
self.prev_node_idx = curr_node_idx
self.prev_scan_pts = copy.deepcopy(self.curr_scan_pts)
# loop detection and optimize the graph
# if(PGM.curr_node_idx > 1 and PGM.curr_node_idx % self.try_gap_loop_detection == 0):
# # 1/ loop detection
# loop_idx, loop_dist, yaw_diff_deg = SCM.detectLoop()
# if(loop_idx == None): # NOT FOUND
# pass
# else:
# print("Loop event detected: ", PGM.curr_node_idx, loop_idx, loop_dist)
# # 2-1/ add the loop factor
# loop_scan_down_pts = SCM.getPtcloud(loop_idx)
# loop_transform, _, _ = ICP.icp(curr_scan_down_pts, loop_scan_down_pts, init_pose=yawdeg2se3(yaw_diff_deg), max_iterations=20)
# PGM.addLoopFactor(loop_transform, loop_idx)
#
# # 2-2/ graph optimization
# PGM.optimizePoseGraph()
#
# # 2-2/ save optimized poses
# ResultSaver.saveOptimizedPoseGraphResult(PGM.curr_node_idx, PGM.graph_optimized)
# save the ICP odometry pose result (no loop closure)
# ResultSaver.saveUnoptimizedPoseGraphResult(self.curr_se3, curr_node_idx)
self.pose_list = np.vstack(
(self.pose_list, np.reshape(self.curr_se3, (-1, 16))))
# if(curr_node_idx % self.save_gap == 0 or curr_node_idx == self.num_frames):
# save odometry-only poses
filename = "pose" + self.sequence_idx + "unoptimized_" + str(
int(time.time())) + ".csv"
filename = os.path.join(self.save_dir, filename)
np.savetxt(filename, self.pose_list, delimiter=",")
if (self.for_idx % num_frames_to_skip_to_show == 0):
self.x = self.pose_list[:, 3]
self.y = self.pose_list[:, 7]
## z = self.pose_list[:,11]
#
fig = plt.figure(fig_idx)
plt.clf()
plt.plot(-self.y, self.x,
color='blue') # kitti camera coord for clarity
plt.axis('equal')
plt.xlabel('x', labelpad=10)
plt.ylabel('y', labelpad=10)
plt.draw()
plt.pause(
0.01) #is necessary for the plot to update for some reason
# ResultSaver.vizCurrentTrajectory(fig_idx=fig_idx)
self.for_idx = self.for_idx + 1
# writer.grab_frame()
msg.header.stamp = rospy.get_rostime()
msg.header.frame_id = " UTM_COORDINATE "
msg.pose.pose.position.x = -self.y[curr_node_idx - 4]
msg.pose.pose.position.y = self.x[curr_node_idx - 4]
self.pub.publish(msg)
rate.sleep()
toc = time.time()
def localize_using_landmarks(features, est_position, config):
''' Localize the robot by finding the transformation model (rotation + translation)
that match the best the set of feature found to the known one on the table.
This need an estimation of the position.
Parameters
----------
features: list of (N) OrientedCorner object
est_position: list of 3 float. 1. position x; 2. position y; 3. heading
in radian
'''
position = (None, None)
heading = None
if features is None:
return (None, None), None
features_list = np.array([(feature.x, feature.y) for feature in features])
table_landmarks = np.array(
[[0, 0], [config['TABLE_WIDTH'], 0],
[config['TABLE_WIDTH'], config['TABLE_HEIGHT']],
[0, config['TABLE_HEIGHT']]],
dtype=float)
if est_position[1] < config['TABLE_WIDTH'] / 2:
table_landmarks = np.vstack(
(table_landmarks,
np.array([[
0, config['TABLE_HEIGHT'] / 2 -
config['CENTER_OBSTACLE_HALF_WIDTH']
],
[
config['TABLE_WIDTH'], config['TABLE_HEIGHT'] / 2 -
config['CENTER_OBSTACLE_HALF_WIDTH']
]])))
else:
table_landmarks = np.vstack(
(table_landmarks,
np.array([[
0, config['TABLE_HEIGHT'] / 2 +
config['CENTER_OBSTACLE_HALF_WIDTH']
],
[
config['TABLE_WIDTH'], config['TABLE_HEIGHT'] / 2 +
config['CENTER_OBSTACLE_HALF_WIDTH']
]])))
table_rel_landmarks = table_landmarks - est_position[0:2]
table_rel_landmarks = rotatePolygon(table_rel_landmarks,
-est_position[2] + np.pi / 2)
pair_idx = pair_points(table_rel_landmarks, features_list)
src = table_rel_landmarks[pair_idx]
dst = features_list
model_robust, inliers = ransac(
(src, dst),
TransformationModel,
min_samples=2,
residual_threshold=config['MAX_RESIDUAL_LANDMARKS'],
max_trials=100,
is_data_valid=lambda cls, data: TransformationModel.is_data_valid(
cls, data, config['MIN_DISTANCE_VALID_LANDMARKS']))
outliers = inliers == False
if model_robust is not None and any(inliers):
model_robust.estimate(src[inliers], dst[inliers])
heading = np.array(-model_robust.rotation + est_position[2])
position = np.array(est_position[0:2]).T + rotatePolygon(
model_robust.translation, est_position[2] + np.pi / 2).squeeze()
return position, heading
src = []
dst = []
new_match = []
#center = (img1_series['original'].shape[0]/2,img1_series['original'].shape[1]/2)
for match in matches[10:]:
# if np.linalg.norm(np.array(kp1[match.queryIdx].pt)-center)<250:
# new_match.append(match)
src.append(kp1[match.queryIdx].pt)
dst.append(kp2[match.trainIdx].pt)
src = np.array(src)
dst = np.array(dst)
model, inliers = ransac((src, dst),
PolyTF,
min_samples=6,
residual_threshold=0.8,
max_trials=4000)
sk_img1 = img1_series['sk_vessel_mask']
sk_img2 = img2_series['sk_vessel_mask']
sk_img2_warped = transform.warp(sk_img2, model)
img2_warped = skimage2opencv(sk_img2_warped)
print('fit complete.')
blend = (img2_warped * 0.5 + img1_series['vessel_mask'] * 0.5).astype(np.uint8)
cv2.namedWindow('Result', cv2.WINDOW_NORMAL)
# cv2.imshow('Result',blend )
# cv2.waitKey(1)
orb.detect_and_extract(image0)
keypoints1 = orb.keypoints
descriptors1 = orb.descriptors
orb.detect_and_extract(image1)
keypoints2 = orb.keypoints
descriptors2 = orb.descriptors
matches12 = match_descriptors(descriptors1, descriptors2, cross_check=True)
src = keypoints2[matches12[:, 1]][:, ::-1]
dst = keypoints1[matches12[:, 0]][:, ::-1]
transform_model, inliers = \
ransac((src, dst), ProjectiveTransform,
min_samples=4, residual_threshold=2)
r, c = image1.shape[:2]
corners = np.array([[0, 0], [0, r], [c, 0], [c, r]])
warped_corners = transform_model(corners)
all_corners = np.vstack((warped_corners, corners))
corner_min = np.min(all_corners, axis=0)
corner_max = np.max(all_corners, axis=0)
output_shape = (corner_max - corner_min)
output_shape = np.ceil(output_shape[::-1])
faulty = np.array(30 * [(180., -100)])
faulty += 5 * np.random.normal(size=faulty.shape)
data[:faulty.shape[0]] = faulty
# add gaussian noise to coordinates
noise = np.random.normal(size=data.shape)
data += 0.5 * noise
data[::2] += 5 * noise[::2]
data[::4] += 20 * noise[::4]
# fit line using all data
model = LineModel()
model.estimate(data)
# robustly fit line only using inlier data with RANSAC algorithm
model_robust, inliers = ransac(data, LineModel, min_samples=2,
residual_threshold=1, max_trials=1000)
outliers = inliers == False
# generate coordinates of estimated models
line_x = np.arange(-250, 250)
line_y = model.predict_y(line_x)
line_y_robust = model_robust.predict_y(line_x)
plt.plot(data[inliers, 0], data[inliers, 1], '.b', alpha=0.6,
label='Inlier data')
plt.plot(data[outliers, 0], data[outliers, 1], '.r', alpha=0.6,
label='Outlier data')
plt.plot(line_x, line_y, '-k', label='Line model from all data')
plt.plot(line_x, line_y_robust, '-b', label='Robust line model')
plt.legend(loc='lower left')
plt.show()