def matches(im1, im2, matches, dist=None, options={}):
""" show a figure with lines joining the accepted matches in im1 and im2
input: im1,im2 (images as arrays), locs1,locs2 (location of features),
matchscores (as output from 'match').
"""
scale = options.get("scale", 1)
filename = options.get("filename", None)
max_dist = options.get("max_dist", 100)
line_width = options.get("line_width", 0.8)
size = options.get("size", (12, 8))
separation = options.get("separation", 20)
if scale != 1:
s = numpy.array([scale, scale])
im1_size = numpy.array(im1.shape[1::-1] * s, dtype=numpy.uint16)
im2_size = numpy.array(im2.shape[1::-1] * s, dtype=numpy.uint16)
if scale < 0.5:
im1_scaled = imaging.get_thumbnail(im1, size=im1_size)
im2_scaled = imaging.get_thumbnail(im2, size=im2_size)
else:
im1_scaled = imaging.open_img(im1, size=im1_size)
im2_scaled = imaging.open_img(im2, size=im2_size)
im3 = append_images(im1_scaled, im2_scaled, separation)
matches = [(m[0] * s, m[1] * s) for m in matches]
else:
im3 = append_images(im1, im2, separation)
im1_scaled = im1
# Create figure
fig = pylab.figure(frameon=False, figsize=size)
ax = pylab.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
# Display image
ax.imshow(im3)
# Get colors
if dist != None and len(dist) == len(matches):
cs = [
getRedGreen(numpy.log(d + 1) / numpy.log(max_dist)) for d in dist
]
else:
cs = ['#00aaff' for m in matches]
# Plot all lines
offset_x = im1_scaled.shape[1]
for i, ((x1, y1), (x2, y2)) in enumerate(matches):
ax.plot([x1, x2 + offset_x + separation], [y1, y2],
color=cs[i],
lw=line_width)
pylab.xlim(0, im3.shape[1])
pylab.ylim(im3.shape[0], 0)
if filename != None:
fig.savefig(filename, bbox_inches='tight', dpi=72)
def matches(im1, im2, matches, dist = None, options = {}) :
""" show a figure with lines joining the accepted matches in im1 and im2
input: im1,im2 (images as arrays), locs1,locs2 (location of features),
matchscores (as output from 'match').
"""
scale = options.get("scale", 1)
filename = options.get("filename", None)
max_dist = options.get("max_dist", 100)
line_width = options.get("line_width", 0.8)
size = options.get("size", (12, 8))
separation = options.get("separation", 20)
if scale != 1 :
s = numpy.array([scale, scale])
im1_size = numpy.array(im1.shape[1::-1] * s, dtype=numpy.uint16)
im2_size = numpy.array(im2.shape[1::-1] * s, dtype=numpy.uint16)
if scale < 0.5 :
im1_scaled = imaging.get_thumbnail(im1, size = im1_size)
im2_scaled = imaging.get_thumbnail(im2, size = im2_size)
else :
im1_scaled = imaging.open_img(im1, size = im1_size)
im2_scaled = imaging.open_img(im2, size = im2_size)
im3 = append_images(im1_scaled, im2_scaled, separation)
matches = [(m[0] * s, m[1] * s) for m in matches]
else :
im3 = append_images(im1,im2, separation)
im1_scaled = im1
# Create figure
fig = pylab.figure(frameon=False, figsize=size)
ax = pylab.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
# Display image
ax.imshow(im3)
# Get colors
if dist != None and len(dist) == len(matches) :
cs = [getRedGreen(numpy.log(d+1)/numpy.log(max_dist)) for d in dist]
else :
cs = ['#00aaff' for m in matches]
# Plot all lines
offset_x = im1_scaled.shape[1]
for i,((x1,y1),(x2,y2)) in enumerate(matches) :
ax.plot([x1, x2+offset_x + separation], [y1,y2], color=cs[i], lw=line_width)
pylab.xlim(0,im3.shape[1])
pylab.ylim(im3.shape[0],0)
if filename != None :
fig.savefig(filename, bbox_inches='tight', dpi=72)
def create_image(self, path, max_size, metric):
""" Match an image with itself finding the closest neighbors within that image """
# Open image
img_data = imaging.open_img(path, max_size)
# Get descriptors
keypoints, descriptors = matchutil.get_features(img_data)
# Match
matches = matchutil.flann_match(descriptors, descriptors, k=2)
# Distances and positions
distances = numpy.array([r[1].distance for r in matches])
positions = numpy.array([k.pt for k in keypoints])
# build position_tree
position_tree = BallTree(positions, metric=metric)
# Collect data
self.original = {
"descriptors": descriptors,
"positions": positions,
"distances": distances,
"position_tree": position_tree,
"size": img_data.shape
}
def create_image(self, path, max_size, metric) :
""" Match an image with itself finding the closest neighbors within that image """
# Open image
img_data = imaging.open_img(path, max_size)
# Get descriptors
keypoints, descriptors = matchutil.get_features(img_data)
# Match
matches = matchutil.flann_match(descriptors, descriptors, k=2)
# Distances and positions
distances = numpy.array([r[1].distance for r in matches])
positions = numpy.array([k.pt for k in keypoints])
# build position_tree
position_tree = BallTree(positions, metric = metric)
# Collect data
self.original = {
"descriptors" : descriptors,
"positions" : positions,
"distances" : distances,
"position_tree" : position_tree,
"size" : img_data.shape
}
def get_match_fun(i) :
# Collect matches
paths = get_path(i)
query_path, target_path = paths["A"], paths["C"]
query_cache, target_img = Metric_Cache(query_path), imaging.open_img(target_path)
return match_fun(query_cache, target_img, options = options)
