######################################################## 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.libraryimport*from.arrayimport*from.featuresimport*
[docs]deffast(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)))returnout
[docs]defharris(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)))returnout
[docs]deforb(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))returnfeat,desc
[docs]defhamming_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)))returnindex,dist
[docs]defnearest_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))returnindex,dist
[docs]defmatch_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))returnout
[docs]defsusan(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)))returnout
[docs]defdog(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))returnout
[docs]defsift(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)
[docs]defgloh(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)
[docs]defhomography(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)