def match_thumbs(img, cache, thumb_size = (400, 400)) :
# Load target and find descriptors and size
target = get_thumbnail(img, thumb_size)
t_orig_size = get_size(img)
get_features(target)
t_keypoints, t_descriptors = get_features(target)
# Similar for query
q_descriptors = cache.thumb["descriptors"]
q_distances = cache.thumb["distances"]
q_pos = cache.thumb["positions"]
# match thumbnails and find ratio
matcher = cv2.BFMatcher(cv2.NORM_L2, crossCheck=True)
matches = [m for m in matcher.knnMatch(q_descriptors, t_descriptors, k=1) if len(m) > 0]
ratios = numpy.array([m[0].distance / q_distances[m[0].queryIdx] for m in matches])
# Get positions of points and their scaling factor
t_pos = [t_keypoints[m[0].trainIdx].pt for m in matches]
t_ratio_x = t_orig_size[0]/float(target.shape[1])
t_ratio_y = t_orig_size[1]/float(target.shape[0])
t_ratio = numpy.array([t_ratio_x, t_ratio_y])
q_pos = numpy.array([cache.thumb["positions"][m[0].queryIdx] for m in matches])
q_ratio_x = cache.original["size"][0]/float(cache.thumb["size"][0])
q_ratio_y = cache.original["size"][1]/float(cache.thumb["size"][1])
q_ratio = numpy.array([q_ratio_x, q_ratio_y])
# Sort ratios and scale positions
indices = numpy.argsort(ratios)
pos_scaled = numpy.array([(q_p * q_ratio, t_p * t_ratio) for q_p, t_p in zip(q_pos, t_pos)])
return pos_scaled[indices], ratios[indices]
def match_thumbs(img, cache, thumb_size=(400, 400)):
# Load target and find descriptors and size
target = get_thumbnail(img, thumb_size)
t_orig_size = get_size(img)
get_features(target)
t_keypoints, t_descriptors = get_features(target)
# Similar for query
q_descriptors = cache.thumb["descriptors"]
q_distances = cache.thumb["distances"]
q_pos = cache.thumb["positions"]
# match thumbnails and find ratio
matcher = cv2.BFMatcher(cv2.NORM_L2, crossCheck=True)
matches = [
m for m in matcher.knnMatch(q_descriptors, t_descriptors, k=1)
if len(m) > 0
]
ratios = numpy.array(
[m[0].distance / q_distances[m[0].queryIdx] for m in matches])
# Get positions of points and their scaling factor
t_pos = [t_keypoints[m[0].trainIdx].pt for m in matches]
t_ratio_x = t_orig_size[0] / float(target.shape[1])
t_ratio_y = t_orig_size[1] / float(target.shape[0])
t_ratio = numpy.array([t_ratio_x, t_ratio_y])
q_pos = numpy.array(
[cache.thumb["positions"][m[0].queryIdx] for m in matches])
q_ratio_x = cache.original["size"][0] / float(cache.thumb["size"][0])
q_ratio_y = cache.original["size"][1] / float(cache.thumb["size"][1])
q_ratio = numpy.array([q_ratio_x, q_ratio_y])
# Sort ratios and scale positions
indices = numpy.argsort(ratios)
pos_scaled = numpy.array([(q_p * q_ratio, t_p * t_ratio)
for q_p, t_p in zip(q_pos, t_pos)])
return pos_scaled[indices], ratios[indices]
def create_thumbnail(self, path, max_size):
""" Get relevant data for thumbnail """
# Create thumbnail
thumbnail = imaging.get_thumbnail(path, max_size)
# Get thumbnail features
keypoints, descriptors = matchutil.get_features(thumbnail)
# Get nearest neighbor within image (vector with touples of feature points and distances)
matches = matchutil.bf_match(descriptors, descriptors, k=2)
# Distances to nearest neighbor and positions
nn_distances = [r[1].distance for r in matches]
positions = [k.pt for k in keypoints]
# Collect data
self.thumb = {
"descriptors": descriptors,
"positions": positions,
"distances": nn_distances,
"size": thumbnail.shape
}
def create_thumbnail(self, path, max_size) :
""" Get relevant data for thumbnail """
# Create thumbnail
thumbnail = imaging.get_thumbnail(path, max_size)
# Get thumbnail features
keypoints, descriptors = matchutil.get_features(thumbnail)
# Get nearest neighbor within image (vector with touples of feature points and distances)
matches = matchutil.bf_match(descriptors, descriptors, k = 2)
# Distances to nearest neighbor and positions
nn_distances = [r[1].distance for r in matches]
positions = [k.pt for k in keypoints]
# Collect data
self.thumb = {
"descriptors" : descriptors,
"positions" : positions,
"distances" : nn_distances,
"size" : thumbnail.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 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
}
