o ^i\3@sddlZddlmZmZmZmZddlmZm Z m Z m Z ddl m Z ddlmZddlmZdd lmZed Zgd Zed d D]Zeeee eeedD] Zdededef<qTqBGdddeeZdS)N)FeatureDetectorDescriptorExtractor_mask_border_keypoints_prepare_grayscale_input_2D) corner_fastcorner_orientations corner_peaks corner_harris)pyramid_gaussian)check_nD)NP_COPY_IF_NEEDED) _orb_loop)r)rrrrr r irc@sVeZdZdZ      ddd Zd d Zd d ZddZddZddZ ddZ dS)ORBaOriented 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) >>> rng = np.random.default_rng(19481137) # do not copy this value >>> square = rng.random((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, 4], [4, 3]]) >>> detector_extractor1.keypoints[matches[:, 0]] array([[59. , 59. ], [40. , 40. ], [57. , 40. ], [46. , 58. ], [58.8, 58.8]]) >>> detector_extractor2.keypoints[matches[:, 1]] array([[72., 72.], [53., 53.], [70., 53.], [59., 71.], [72., 72.]]) 333333?rr{Gz?{Gz?cCsF||_||_||_||_||_||_d|_d|_d|_d|_ d|_ dS)N) downscalen_scales n_keypointsfast_nfast_thresholdharris_k keypointsscales responses orientations descriptors)selfr!r"r#r$r%r&r-N/var/www/html/RAG/RAG_venv/lib/python3.10/site-packages/skimage/feature/orb.py__init__ws  z ORB.__init__cCs$t|}tt||jd|jddS)Nr) channel_axis)rlistr r"r!)r,imager-r-r._build_pyramids zORB._build_pyramidc Cs|j}t||j|j}t|dd}t|dkr+tjd|dtjd|dtjd|dfSt|j |dd}||}t ||t }t |d |j d }||dddf|dddff}|||fS) Nr) min_distancer)rrdtype)rrdistancek)methodr9)r6rr$r%r lennpzerosrshaper OFAST_MASKr r&) r, octave_imager6 fast_responser'maskr*harris_responser)r-r-r._detect_octaves      $ zORB._detect_octavecCsPt|d||}g}g}g}g}tt|D]A}t||}t|jdkr+n/||\} } } | | |j || | | tj | j d|j ||j d| | qt|} t|} t|} t|} | j d|jkr| |_| |_| |_| |_dS| dddd|j} | | |_| | |_| | |_| | |_dS)zDetect oriented FAST keypoints along with the corresponding scale. Parameters ---------- image : 2D array Input image. rrr5N)r r3ranger;r<ascontiguousarraysqueezendimrDappendr!fullr>r6vstackhstackr#r'r(r*r)argsort)r,r2pyramidkeypoints_listorientations_list scales_listresponses_listoctaver@r'r*r)r( best_indicesr-r-r.detectsF           z ORB.detectcCsPt|j|dd}tj||tjdtd}tj||ddd}t|||}||fS)Nr7C)r6ordercopyF)rYrZ)rr>r<arrayintprr)r,r@r'r*rBr+r-r-r._extract_octaves zORB._extract_octavecCst|d||}g}g}t|t|jtj}tt|D]6} || k} t | dkrYt || } || } | |j| } || } | | | | \}}| || |q#t |t|_t||_dS)aExtract 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. rrN)r r3r<logr!astyper\rFr;sumrGr]rJrLviewboolr+rMmask_)r,r2r'r(r*rOdescriptors_list mask_listoctavesrT octave_maskr@octave_keypointsoctave_orientationsr+rBr-r-r.extracts(    z ORB.extractcCst|d||}g}g}g}g}g}tt|D]s}t||} t| jdkr-na|| \} } } t| dkrP| | | | | tj dt dq| | | | \} }| ||j |}| || | || | || |j |tj|jdtjd| | qt|dkrtdt|} t|} t|}t|} t|t } | jd|jkr| |_||_| |_| |_| |_dS| dddd|j}| ||_|||_| ||_| ||_| ||_dS)zDetect oriented FAST keypoints and extract rBRIEF descriptors. Note that this is faster than first calling `detect` and then `extract`. Parameters ---------- image : 2D array Input image. rr)rr5znORB found no features. Try passing in an image containing greater intensity contrasts between adjacent pixels.NrE)r r3rFr;r<rGrHrIrDrJr=rbr]r!onesr>r\ RuntimeErrorrLrMrar#r'r(r*r)r+rN)r,r2rOrPrSrRrQrdrTr@r'r*r)r+rBscaled_keypointsr(rUr-r-r.detect_and_extract"sf                zORB.detect_and_extractN)rrrrrr ) __name__ __module__ __qualname____doc__r/r3rDrVr]rjror-r-r-r.rs_ 8 3r)numpyr< feature.utilrrrrcornerrr r r transformr _shared.utilsr _shared.compatrorb_cyrr=r? OFAST_UMAXrFiabsjrr-r-r-r.s     $