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vision.py
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#######################################################
# Copyright (c) 2015, ArrayFire
# All rights reserved.
#
# This file is distributed under 3-clause BSD license.
# The complete license agreement can be obtained at:
# http://arrayfire.com/licenses/BSD-3-Clause
########################################################
"""
Computer vision functions (FAST, ORB, etc)
"""
from .library import *
from .array import *
from .features import *
def fast(image, threshold=20.0, arc_length=9, non_max=True, feature_ratio=0.05, edge=3):
"""
FAST feature detector.
Parameters
----------
image : af.Array
A 2D array representing an image.
threshold : scalar. optional. default: 20.0.
FAST threshold for which a pixel of the circle around a central pixel is consdered.
arc_length : scalar. optional. default: 9
The minimum length of arc length to be considered. Max length should be 16.
non_max : Boolean. optional. default: True
A boolean flag specifying if non max suppression has to be performed.
feature_ratio : scalar. optional. default: 0.05 (5%)
Specifies the maximum ratio of features to pixels in the image.
edge : scalar. optional. default: 3.
Specifies the number of edge rows and columns to be ignored.
Returns
---------
features : af.Features()
Contains the location and score. Orientation and size are not computed.
"""
out = Features()
safe_call(backend.get().af_fast(c_pointer(out.feat),
image.arr, c_float_t(threshold), c_uint_t(arc_length), non_max,
c_float_t(feature_ratio), c_uint_t(edge)))
return out
def harris(image, max_corners=500, min_response=1E5, sigma=1.0, block_size=0, k_thr=0.04):
"""
Harris corner detector.
Parameters
----------
image : af.Array
A 2D array specifying an image.
max_corners : scalar. optional. default: 500.
Specifies the maximum number of corners to be calculated.
min_response : scalar. optional. default: 1E5
Specifies the cutoff score for a corner to be considered
sigma : scalar. optional. default: 1.0
- Specifies the standard deviation of a circular window.
- Only used when block_size == 0. Must be >= 0.5 and <= 5.0.
block_size : scalar. optional. default: 0
Specifies the window size.
k_thr : scalar. optional. default: 0.04
Harris constant. must be >= 0.01
Returns
---------
features : af.Features()
Contains the location and score. Orientation and size are not computed.
Note
------
The covariation matrix will be square when `block_size` is used and circular when `sigma` is used.
"""
out = Features()
safe_call(backend.get().af_harris(c_pointer(out.feat),
image.arr, c_uint_t(max_corners), c_float_t(min_response),
c_float_t(sigma), c_uint_t(block_size), c_float_t(k_thr)))
return out
def orb(image, threshold=20.0, max_features=400, scale = 1.5, num_levels = 4, blur_image = False):
"""
ORB Feature descriptor.
Parameters
----------
image : af.Array
A 2D array representing an image.
threshold : scalar. optional. default: 20.0.
FAST threshold for which a pixel of the circle around a central pixel is consdered.
max_features : scalar. optional. default: 400.
Specifies the maximum number of features to be considered.
scale : scalar. optional. default: 1.5.
Specifies the factor by which images are down scaled at each level.
num_levles : scalar. optional. default: 4.
Specifies the number of levels used in the image pyramid.
blur_image : Boolean. optional. default: False.
Flag specifying if the input has to be blurred before computing descriptors.
A gaussian filter with sigma = 2 is applied if True.
Returns
---------
(features, descriptor) : tuple of (af.Features(), af.Array)
- descriptor is an af.Array of size N x 8
"""
feat = Features()
desc = Array()
safe_call(backend.get().af_orb(c_pointer(feat.feat), c_pointer(desc.arr), image.arr,
c_float_t(threshold), c_uint_t(max_features),
c_float_t(scale), c_uint_t(num_levels), blur_image))
return feat, desc
def hamming_matcher(query, database, dim = 0, num_nearest = 1):
"""
Hamming distance matcher.
Parameters
-----------
query : af.Array
A query feature descriptor
database : af.Array
A multi dimensional array containing the feature descriptor database.
dim : scalar. optional. default: 0.
Specifies the dimension along which feature descriptor lies.
num_nearest: scalar. optional. default: 1.
Specifies the number of nearest neighbors to find.
Returns
---------
(location, distance): tuple of af.Array
location and distances of closest matches.
"""
index = Array()
dist = Array()
safe_call(backend.get().af_hamming_matcher(c_pointer(index.arr), c_pointer(dist.arr),
query.arr, database.arr,
c_dim_t(dim), c_dim_t(num_nearest)))
return index, dist
def nearest_neighbour(query, database, dim = 0, num_nearest = 1, match_type=MATCH.SSD):
"""
Nearest Neighbour matcher.
Parameters
-----------
query : af.Array
A query feature descriptor
database : af.Array
A multi dimensional array containing the feature descriptor database.
dim : scalar. optional. default: 0.
Specifies the dimension along which feature descriptor lies.
num_nearest: scalar. optional. default: 1.
Specifies the number of nearest neighbors to find.
match_type: optional: af.MATCH. default: af.MATCH.SSD
Specifies the match function metric.
Returns
---------
(location, distance): tuple of af.Array
location and distances of closest matches.
"""
index = Array()
dist = Array()
safe_call(backend.get().af_nearest_neighbour(c_pointer(index.arr), c_pointer(dist.arr),
query.arr, database.arr,
c_dim_t(dim), c_dim_t(num_nearest),
match_type.value))
return index, dist
def match_template(image, template, match_type = MATCH.SAD):
"""
Find the closest match of a template in an image.
Parameters
----------
image : af.Array
A multi dimensional array specifying an image or batch of images.
template : af.Array
A multi dimensional array specifying a template or batch of templates.
match_type: optional: af.MATCH. default: af.MATCH.SAD
Specifies the match function metric.
Returns
--------
out : af.Array
An array containing the score of the match at each pixel.
"""
out = Array()
safe_call(backend.get().af_match_template(c_pointer(out.arr),
image.arr, template.arr,
match_type.value))
return out
def susan(image, radius=3, diff_thr=32, geom_thr=10, feature_ratio=0.05, edge=3):
"""
SUSAN corner detector.
Parameters
----------
image : af.Array
A 2D array specifying an image.
radius : scalar. optional. default: 500.
Specifies the radius of each pixel neighborhood.
diff_thr : scalar. optional. default: 1E5
Specifies the intensity difference threshold.
geom_thr : scalar. optional. default: 1.0
Specifies the geometric threshold.
feature_ratio : scalar. optional. default: 0.05 (5%)
Specifies the ratio of corners found to number of pixels.
edge : scalar. optional. default: 3
Specifies the number of edge rows and columns that are ignored.
Returns
---------
features : af.Features()
Contains the location and score. Orientation and size are not computed.
"""
out = Features()
safe_call(backend.get().af_susan(c_pointer(out.feat),
image.arr, c_uint_t(radius), c_float_t(diff_thr),
c_float_t(geom_thr), c_float_t(feature_ratio),
c_uint_t(edge)))
return out
def dog(image, radius1, radius2):
"""
Difference of gaussians.
Parameters
----------
image : af.Array
A 2D array specifying an image.
radius1 : scalar.
The radius of first gaussian kernel.
radius2 : scalar.
The radius of second gaussian kernel.
Returns
--------
out : af.Array
A multi dimensional array containing the difference of gaussians.
Note
------
The sigma values are calculated to be 0.25 * radius.
"""
out = Array()
safe_call(backend.get().af_dog(c_pointer(out.arr),
image.arr, radius1, radius2))
return out
def sift(image, num_layers=3, contrast_threshold=0.04, edge_threshold=10.0, initial_sigma = 1.6,
double_input = True, intensity_scale = 0.00390625, feature_ratio = 0.05):
"""
SIFT feature detector and descriptor.
Parameters
----------
image : af.Array
A 2D array representing an image
num_layers : optional: integer. Default: 3
Number of layers per octave. The number of octaves is calculated internally.
contrast_threshold : optional: float. Default: 0.04
Threshold used to filter out features that have low contrast.
edge_threshold : optional: float. Default: 10.0
Threshold used to filter out features that are too edge-like.
initial_sigma : optional: float. Default: 1.6
The sigma value used to filter the input image at the first octave.
double_input : optional: bool. Default: True
If True, the input image will be scaled to double the size for the first octave.
intensity_scale : optional: float. Default: 1.0/255
The inverse of the difference between maximum and minimum intensity values.
feature_ratio : optional: float. Default: 0.05
Specifies the maximum number of features to detect as a ratio of image pixels.
Returns
--------
(features, descriptor) : tuple of (af.Features(), af.Array)
- descriptor is an af.Array of size N x 128
"""
feat = Features()
desc = Array()
safe_call(backend.get().af_sift(c_pointer(feat.feat), c_pointer(desc.arr),
image.arr, num_layers, c_float_t(contrast_threshold), c_float_t(edge_threshold),
c_float_t(initial_sigma), double_input, c_float_t(intensity_scale), c_float_t(feature_ratio)))
return (feat, desc)
def gloh(image, num_layers=3, contrast_threshold=0.04, edge_threshold=10.0, initial_sigma = 1.6,
double_input = True, intensity_scale = 0.00390625, feature_ratio = 0.05):
"""
GLOH feature detector and descriptor.
Parameters
----------
image : af.Array
A 2D array representing an image
num_layers : optional: integer. Default: 3
Number of layers per octave. The number of octaves is calculated internally.
contrast_threshold : optional: float. Default: 0.04
Threshold used to filter out features that have low contrast.
edge_threshold : optional: float. Default: 10.0
Threshold used to filter out features that are too edge-like.
initial_sigma : optional: float. Default: 1.6
The sigma value used to filter the input image at the first octave.
double_input : optional: bool. Default: True
If True, the input image will be scaled to double the size for the first octave.
intensity_scale : optional: float. Default: 1.0/255
The inverse of the difference between maximum and minimum intensity values.
feature_ratio : optional: float. Default: 0.05
Specifies the maximum number of features to detect as a ratio of image pixels.
Returns
--------
(features, descriptor) : tuple of (af.Features(), af.Array)
- descriptor is an af.Array of size N x 272
"""
feat = Features()
desc = Array()
safe_call(backend.get().af_gloh(c_pointer(feat.feat), c_pointer(desc.arr),
image.arr, num_layers, c_float_t(contrast_threshold),
c_float_t(edge_threshold), c_float_t(initial_sigma),
double_input, c_float_t(intensity_scale),
c_float_t(feature_ratio)))
return (feat, desc)
def homography(x_src, y_src, x_dst, y_dst, htype = HOMOGRAPHY.RANSAC,
ransac_threshold = 3.0, iters = 1000, out_type = Dtype.f32):
"""
Homography estimation
Parameters
----------
x_src : af.Array
A list of x co-ordinates of the source points.
y_src : af.Array
A list of y co-ordinates of the source points.
x_dst : af.Array
A list of x co-ordinates of the destination points.
y_dst : af.Array
A list of y co-ordinates of the destination points.
htype : optional: af.HOMOGRAPHY. Default: HOMOGRAPHY.RANSAC
htype can be one of
- HOMOGRAPHY.RANSAC: RANdom SAmple Consensus will be used to evaluate quality.
- HOMOGRAPHY.LMEDS : Least MEDian of Squares is used to evaluate quality.
ransac_threshold : optional: scalar. Default: 3.0
If `htype` is HOMOGRAPHY.RANSAC, it specifies the L2-distance threshold for inliers.
out_type : optional. af.Dtype. Default: Dtype.f32.
Specifies the output data type.
Returns
-------
(H, inliers) : A tuple of (af.Array, integer)
"""
H = Array()
inliers = c_int_t(0)
safe_call(backend.get().af_homography(c_pointer(H), c_pointer(inliers),
x_src.arr, y_src.arr, x_dst.arr, y_dst.arr,
htype.value, ransac_threshold, iters, out_type.value))
return (H, inliers)
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