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e f< q¼W q–W G dd„ deeƒZdS )é    Né   )ÚFeatureDetectorÚDescriptorExtractorÚ_mask_border_keypointsÚ_prepare_grayscale_input_2D)Úcorner_fastÚcorner_orientationsÚcorner_peaksÚcorner_harris)Úpyramid_gaussian)Ú	assert_nDé   )Ú	_orb_loop)é   r   é   é   é   é   é   é
   é	   é   é   é   iñÿÿÿé   c               @   sJ   e Zd ZdZddd	„Zd
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dS )ÚORBaù  Oriented FAST and rotated BRIEF feature detector and binary descriptor
    extractor.

    Parameters
    ----------
    n_keypoints : int, optional
        Number of keypoints to be returned. The function will return the best
        `n_keypoints` according to the Harris corner response if more than
        `n_keypoints` are detected. If not, then all the detected keypoints
        are returned.
    fast_n : int, optional
        The `n` parameter in `skimage.feature.corner_fast`. Minimum number of
        consecutive pixels out of 16 pixels on the circle that should all be
        either brighter or darker w.r.t test-pixel. A point c on the circle is
        darker w.r.t test pixel p if ``Ic < Ip - threshold`` and brighter if
        ``Ic > Ip + threshold``. Also stands for the n in ``FAST-n`` corner
        detector.
    fast_threshold : float, optional
        The ``threshold`` parameter in ``feature.corner_fast``. Threshold used
        to decide whether the pixels on the circle are brighter, darker or
        similar w.r.t. the test pixel. Decrease the threshold when more
        corners are desired and vice-versa.
    harris_k : float, optional
        The `k` parameter in `skimage.feature.corner_harris`. Sensitivity
        factor to separate corners from edges, typically in range ``[0, 0.2]``.
        Small values of `k` result in detection of sharp corners.
    downscale : float, optional
        Downscale factor for the image pyramid. Default value 1.2 is chosen so
        that there are more dense scales which enable robust scale invariance
        for a subsequent feature description.
    n_scales : int, optional
        Maximum number of scales from the bottom of the image pyramid to
        extract the features from.

    Attributes
    ----------
    keypoints : (N, 2) array
        Keypoint coordinates as ``(row, col)``.
    scales : (N, ) array
        Corresponding scales.
    orientations : (N, ) array
        Corresponding orientations in radians.
    responses : (N, ) array
        Corresponding Harris corner responses.
    descriptors : (Q, `descriptor_size`) array of dtype bool
        2D array of binary descriptors of size `descriptor_size` for Q
        keypoints after filtering out border keypoints with value at an
        index ``(i, j)`` either being ``True`` or ``False`` representing
        the outcome of the intensity comparison for i-th keypoint on j-th
        decision pixel-pair. It is ``Q == np.sum(mask)``.

    References
    ----------
    .. [1] Ethan Rublee, Vincent Rabaud, Kurt Konolige and Gary Bradski
          "ORB: An efficient alternative to SIFT and SURF"
          http://www.vision.cs.chubu.ac.jp/CV-R/pdf/Rublee_iccv2011.pdf

    Examples
    --------
    >>> from skimage.feature import ORB, match_descriptors
    >>> img1 = np.zeros((100, 100))
    >>> img2 = np.zeros_like(img1)
    >>> np.random.seed(1)
    >>> square = np.random.rand(20, 20)
    >>> img1[40:60, 40:60] = square
    >>> img2[53:73, 53:73] = square
    >>> detector_extractor1 = ORB(n_keypoints=5)
    >>> detector_extractor2 = ORB(n_keypoints=5)
    >>> detector_extractor1.detect_and_extract(img1)
    >>> detector_extractor2.detect_and_extract(img2)
    >>> matches = match_descriptors(detector_extractor1.descriptors,
    ...                             detector_extractor2.descriptors)
    >>> matches
    array([[0, 0],
           [1, 1],
           [2, 2],
           [3, 3],
           [4, 4]])
    >>> detector_extractor1.keypoints[matches[:, 0]]
    array([[ 42.,  40.],
           [ 47.,  58.],
           [ 44.,  40.],
           [ 59.,  42.],
           [ 45.,  44.]])
    >>> detector_extractor2.keypoints[matches[:, 1]]
    array([[ 55.,  53.],
           [ 60.,  71.],
           [ 57.,  53.],
           [ 72.,  55.],
           [ 58.,  57.]])

    ç333333ó?r   éô  r   ç{®Gáz´?ç{®Gáz¤?c             C   sF   || _ || _|| _|| _|| _|| _d | _d | _d | _d | _	d | _
d S )N)Ú	downscaleÚn_scalesÚn_keypointsÚfast_nÚfast_thresholdÚharris_kÚ	keypointsÚscalesÚ	responsesÚorientationsÚdescriptors)Úselfr    r!   r"   r#   r$   r%   © r,   ú2lib/python3.7/site-packages/skimage/feature/orb.pyÚ__init__u   s    zORB.__init__c             C   s$   t |ƒ}tt|| jd | jddƒS )Nr   F)Zmultichannel)r   Úlistr   r!   r    )r+   Úimager,   r,   r-   Ú_build_pyramid…   s    zORB._build_pyramidc             C   s¸   t || j| jƒ}t|dd}t|ƒdkrVtjdtjdtjdtjdtjdtjdfS t|j	|dd}|| }t
||tƒ}t|d	| jd
}||d d …df |d d …df f }|||fS )Nr   )Zmin_distancer   )r   r   )Údtype)r   r   )ÚdistanceÚk)Úmethodr4   )r   r#   r$   r	   ÚlenÚnpÚzerosÚdoubler   Úshaper   Ú
OFAST_MASKr
   r%   )r+   Úoctave_imageZfast_responser&   Úmaskr)   Zharris_responser(   r,   r,   r-   Ú_detect_octaveŠ   s     
$zORB._detect_octavec             C   s@  t |dƒ |  |¡}g }g }g }g }x€tt|ƒƒD ]p}t || ¡}|  |¡\}	}
}| |	| j|  ¡ | |
¡ | | j| tj	|	j
d tjd ¡ | |¡ q2W t |¡}	t |¡}
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d | jk rø|	| _|| _|
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| | _|| | _dS )z¥Detect oriented FAST keypoints along with the corresponding scale.

        Parameters
        ----------
        image : 2D array
            Input image.

        r   r   )r2   Néÿÿÿÿ)r   r1   Úranger6   r7   Úascontiguousarrayr>   Úappendr    Úonesr:   ÚintpÚvstackÚhstackr"   r&   r'   r)   r(   Úargsort)r+   r0   ÚpyramidÚkeypoints_listÚorientations_listÚscales_listÚresponses_listÚoctaver<   r&   r)   r(   r'   Úbest_indicesr,   r,   r-   Údetect¢   s8    	
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
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

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ORB.detectc             C   sT   t |j|dd}tj|| tjddd}tj|| tjddd}t|||ƒ}||fS )Né   )r3   ÚCF)r2   ÚorderÚcopy)r   r:   r7   ZarrayrD   r9   r   )r+   r<   r&   r)   r=   r*   r,   r,   r-   Ú_extract_octaveÓ   s    zORB._extract_octavec             C   sÜ   t |dƒ |  |¡}g }g }t |¡t | j¡  tj¡}x|tt|ƒƒD ]l}	||	k}
t 	|
¡dkrHt 
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 }|  |||¡\}}| |¡ | |¡ qHW t |¡ tj¡| _t |¡| _dS )a  Extract rBRIEF binary descriptors for given keypoints in image.

        Note that the keypoints must be extracted using the same `downscale`
        and `n_scales` parameters. Additionally, if you want to extract both
        keypoints and descriptors you should use the faster
        `detect_and_extract`.

        Parameters
        ----------
        image : 2D array
            Input image.
        keypoints : (N, 2) array
            Keypoint coordinates as ``(row, col)``.
        scales : (N, ) array
            Corresponding scales.
        orientations : (N, ) array
            Corresponding orientations in radians.

        r   r   N)r   r1   r7   Úlogr    ZastyperD   r@   r6   ÚsumrA   rT   rB   rE   ÚviewÚboolr*   rF   Zmask_)r+   r0   r&   r'   r)   rH   Údescriptors_listZ	mask_listZoctavesrM   Zoctave_maskr<   Zoctave_keypointsZoctave_orientationsr*   r=   r,   r,   r-   Úextractß   s&    
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

zORB.extractc             C   sÞ  t |dƒ |  |¡}g }g }g }g }g }xàtt|ƒƒD ]Ð}t || ¡}	|  |	¡\}
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        Note that this is faster than first calling `detect` and then
        `extract`.

        Parameters
        ----------
        image : 2D array
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zORB.detect_and_extractN)r   r   r   r   r   r   )Ú__name__Ú
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16r   )Znumpyr7   Zfeature.utilr   r   r   r   Zfeaturer   r   r	   r
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OFAST_UMAXr@   ÚiÚabsÚjr   r,   r,   r,   r-   Ú<module>   s   
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