Python cv2.cornerSubPix() Examples

def get_vectors(image, points, mtx, dist):
    
    # order points
    points = _order_points(points)

    # set up criteria, image, points and axis
    criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)

    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    imgp = np.array(points, dtype='float32')

    objp = np.array([[0.,0.,0.],[1.,0.,0.],
                        [1.,1.,0.],[0.,1.,0.]], dtype='float32')  

    # calculate rotation and translation vectors
    cv2.cornerSubPix(gray,imgp,(11,11),(-1,-1),criteria)
    rvecs, tvecs, _ = cv2.solvePnPRansac(objp, imgp, mtx, dist)

    return rvecs, tvecs 

def calculateCorners(self, gray, points=None):
        '''
        gray is OpenCV gray image,
        points is Marker.points
        >>> marker.calculateCorners(gray)
        >>> print(marker.corners)
        '''
        if points is None: points = self.points
        if points is None: raise TypeError('calculateCorners need a points value')
        '''
        rotations = 0 -> 0,1,2,3
        rotations = 1 -> 3,0,1,2
        rotations = 2 -> 2,3,0,1
        rotations = 3 -> 1,2,3,0
        => A: 1,0,3,2; B: 0,3,2,1; C: 2,1,0,3; D: 3,2,1,0
        '''
        i = self.rotations
        A = (1,0,3,2)[i]; B = (0,3,2,1)[i]; C = (2,1,0,3)[i]; D = (3,2,1,0)[i]
        corners = np.float32([points[A], points[B], points[C], points[D]])
        criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.1)
        self.corners = cv2.cornerSubPix(gray, corners, (5,5), (-1,-1), criteria) 

def add_corners(self, i_frame, subpixel_criteria, frame_folder_path=None,
                    save_image=False, save_chekerboard_overlay=False):
        grey_frame = cv2.cvtColor(self.frame, cv2.COLOR_BGR2GRAY)
        cv2.cornerSubPix(grey_frame, self.current_image_points, (11, 11), (-1, -1), subpixel_criteria)
        if save_image:
            png_path = (os.path.join(frame_folder_path,
                                     "{0:s}{1:04d}{2:s}".format(self.name, i_frame, ".png")))
            cv2.imwrite(png_path, self.frame)
            if save_chekerboard_overlay:
                png_path = (os.path.join(frame_folder_path,
                                         "checkerboard_{0:s}{1:04d}{2:s}".format(self.name, i_frame, ".png")))
                overlay = self.frame.copy()
                cv2.drawChessboardCorners(overlay, self.current_board_dims, self.current_image_points, True)
                cv2.imwrite(png_path, overlay)
        self.usable_frames[i_frame] = len(self.image_points)
        self.image_points.append(self.current_image_points) 

def calculateCorners(self, gray, points=None):
        '''
        gray is OpenCV gray image,
        points is Marker.points
        >>> marker.calculateCorners(gray)
        >>> print(marker.corners)
        '''
        if points is None: points = self.points
        if points is None: raise TypeError('calculateCorners need a points value')
        '''
        rotations = 0 -> 0,1,2,3
        rotations = 1 -> 3,0,1,2
        rotations = 2 -> 2,3,0,1
        rotations = 3 -> 1,2,3,0
        => A: 1,0,3,2; B: 0,3,2,1; C: 2,1,0,3; D: 3,2,1,0
        '''
        i = self.rotations
        A = (1,0,3,2)[i]; B = (0,3,2,1)[i]; C = (2,1,0,3)[i]; D = (3,2,1,0)[i]
        corners = np.float32([points[A], points[B], points[C], points[D]])
        criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.1)
        self.corners = cv2.cornerSubPix(gray, corners, (5,5), (-1,-1), criteria) 

def calculateCorners(self, gray, points=None):
        '''
        gray is OpenCV gray image,
        points is Marker.points
        >>> marker.calculateCorners(gray)
        >>> print(marker.corners)
        '''
        if points is None: points = self.points
        if points is None: raise TypeError('calculateCorners need a points value')
        '''
        rotations = 0 -> 0,1,2,3
        rotations = 1 -> 3,0,1,2
        rotations = 2 -> 2,3,0,1
        rotations = 3 -> 1,2,3,0
        => A: 1,0,3,2; B: 0,3,2,1; C: 2,1,0,3; D: 3,2,1,0
        '''
        i = self.rotations
        A = (1,0,3,2)[i]; B = (0,3,2,1)[i]; C = (2,1,0,3)[i]; D = (3,2,1,0)[i]
        corners = np.float32([points[A], points[B], points[C], points[D]])
        criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.1)
        self.corners = cv2.cornerSubPix(gray, corners, (5,5), (-1,-1), criteria) 

def calculateCorners(self, gray, points=None):
        '''
        gray is OpenCV gray image,
        points is Marker.points
        >>> marker.calculateCorners(gray)
        >>> print(marker.corners)
        '''
        if points is None: points = self.points
        if points is None: raise TypeError('calculateCorners need a points value')
        '''
        rotations = 0 -> 0,1,2,3
        rotations = 1 -> 3,0,1,2
        rotations = 2 -> 2,3,0,1
        rotations = 3 -> 1,2,3,0
        => A: 1,0,3,2; B: 0,3,2,1; C: 2,1,0,3; D: 3,2,1,0
        '''
        i = self.rotations
        A = (1,0,3,2)[i]; B = (0,3,2,1)[i]; C = (2,1,0,3)[i]; D = (3,2,1,0)[i]
        corners = np.float32([points[A], points[B], points[C], points[D]])
        criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.1)
        self.corners = cv2.cornerSubPix(gray, corners, (5,5), (-1,-1), criteria) 

def _get_corners(self, image):
        """Find subpixel chessboard corners in image."""
        temp = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        ret, corners = cv2.findChessboardCorners(temp,
                                                 (self.rows, self.columns))
        if not ret:
            raise ChessboardNotFoundError("No chessboard could be found.")
        cv2.cornerSubPix(temp, corners, (11, 11), (-1, -1),
                         (cv2.TERM_CRITERIA_MAX_ITER + cv2.TERM_CRITERIA_EPS,
                          30, 0.01))
        return corners 

def cube(img):
    #img_in = cv2.imread("Picture 27.jpg")
    #img = cv2.resize(img_in,None,fx=0.5, fy=0.5, interpolation = cv2.INTER_CUBIC)
        #cv2.imshow('img',img)
    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
        #cv2.imshow('gray',gray)
    ret, corners = cv2.findChessboardCorners(gray, (8,7),None)
        # print ret,corners

    criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
    objp = np.zeros((7*8,3), np.float32)
    objp[:,:2] = np.mgrid[0:8,0:7].T.reshape(-1,2)

    #axis = np.float32([[3,0,0], [0,3,0], [0,0,-3]]).reshape(-1,3)
    axis = np.float32([[0,0,0], [0,3,0], [3,3,0], [3,0,0],
                       [0,0,-3],[0,3,-3],[3,3,-3],[3,0,-3] ])

    if ret == True:
        cv2.cornerSubPix(gray,corners,(11,11),(-1,-1),criteria)
            
            
                # Find the rotation and translation vectors.
        rvecs, tvecs, inliers = cv2.solvePnPRansac(objp, corners, mtx, dist)

                # project 3D points to image plane
        imgpts, jac = cv2.projectPoints(axis, rvecs, tvecs, mtx, dist)
        #print imgpts
        img = draw2(img,corners,imgpts)
        
    return img 

def processImage(fn):
        print('processing %s... ' % fn)
        img = cv.imread(fn, 0)
        if img is None:
            print("Failed to load", fn)
            return None

        assert w == img.shape[1] and h == img.shape[0], ("size: %d x %d ... " % (img.shape[1], img.shape[0]))
        found, corners = cv.findChessboardCorners(img, pattern_size)
        if found:
            term = (cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_COUNT, 30, 0.1)
            cv.cornerSubPix(img, corners, (5, 5), (-1, -1), term)

        if debug_dir:
            vis = cv.cvtColor(img, cv.COLOR_GRAY2BGR)
            cv.drawChessboardCorners(vis, pattern_size, corners, found)
            _path, name, _ext = splitfn(fn)
            outfile = os.path.join(debug_dir, name + '_chess.png')
            cv.imwrite(outfile, vis)

        if not found:
            print('chessboard not found')
            return None

        print('           %s... OK' % fn)
        return (corners.reshape(-1, 2), pattern_points) 

def get_K_and_D(checkerboard, imgsPath):

    CHECKERBOARD = checkerboard
    subpix_criteria = (cv2.TERM_CRITERIA_EPS+cv2.TERM_CRITERIA_MAX_ITER, 30, 0.1)
    calibration_flags = cv2.fisheye.CALIB_RECOMPUTE_EXTRINSIC+cv2.fisheye.CALIB_CHECK_COND+cv2.fisheye.CALIB_FIX_SKEW
    objp = np.zeros((1, CHECKERBOARD[0]*CHECKERBOARD[1], 3), np.float32)
    objp[0,:,:2] = np.mgrid[0:CHECKERBOARD[0], 0:CHECKERBOARD[1]].T.reshape(-1, 2)
    _img_shape = None
    objpoints = []
    imgpoints = []
    images = glob.glob(imgsPath + '/*.png')
    for fname in images:
        img = cv2.imread(fname)
        if _img_shape == None:
            _img_shape = img.shape[:2]
        else:
            assert _img_shape == img.shape[:2], "All images must share the same size."

        gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
        ret, corners = cv2.findChessboardCorners(gray, CHECKERBOARD,cv2.CALIB_CB_ADAPTIVE_THRESH+cv2.CALIB_CB_FAST_CHECK+cv2.CALIB_CB_NORMALIZE_IMAGE)
        if ret == True:
            objpoints.append(objp)
            cv2.cornerSubPix(gray,corners,(3,3),(-1,-1),subpix_criteria)
            imgpoints.append(corners)
    N_OK = len(objpoints)
    K = np.zeros((3, 3))
    D = np.zeros((4, 1))
    rvecs = [np.zeros((1, 1, 3), dtype=np.float64) for i in range(N_OK)]
    tvecs = [np.zeros((1, 1, 3), dtype=np.float64) for i in range(N_OK)]
    rms, _, _, _, _ = cv2.fisheye.calibrate(
        objpoints,
        imgpoints,
        gray.shape[::-1],
        K,
        D,
        rvecs,
        tvecs,
        calibration_flags,
        (cv2.TERM_CRITERIA_EPS+cv2.TERM_CRITERIA_MAX_ITER, 30, 1e-6)
    )
    DIM = _img_shape[::-1]
    print("Found " + str(N_OK) + " valid images for calibration")
    print("DIM=" + str(_img_shape[::-1]))
    print("K=np.array(" + str(K.tolist()) + ")")
    print("D=np.array(" + str(D.tolist()) + ")")
    return DIM, K, D 

def load_frame_images(self):
        """
        Load images (or image pairs) from self.full_frame_folder_path
        """
        print("Loading frames from '{0:s}'".format(self.full_frame_folder_path))
        all_files = [f for f in os.listdir(self.full_frame_folder_path)
                     if osp.isfile(osp.join(self.full_frame_folder_path, f)) and f.endswith(".png")]
        all_files.sort()

        usable_frame_ct = sys.maxsize

        frame_number_sets = []

        for video in self.videos:
            # assume matching numbers in corresponding left & right files
            files = [f for f in all_files if f.startswith(video.name)]
            files.sort()  # added to be explicit

            cam_frame_ct = 0
            frame_numbers = []
            for ix_pair in range(len(files)):
                frame = cv2.imread(osp.join(self.full_frame_folder_path, files[ix_pair]))
                frame_number = int(re.search(r'\d\d\d\d', files[ix_pair]).group(0))
                frame_numbers.append(frame_number)
                found, corners = cv2.findChessboardCorners(frame, self.board_dims)
                if not found:
                    raise ValueError("Could not find corners in image '{0:s}'".format(files[ix_pair]))
                grey = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
                cv2.cornerSubPix(grey, corners, (11, 11), (-1, -1), self.criteria_subpix)
                video.image_points.append(corners)
                video.usable_frames[frame_number] = ix_pair
                cam_frame_ct += 1
            usable_frame_ct = min(usable_frame_ct, cam_frame_ct)
            frame_number_sets.append(frame_numbers)

        if len(self.videos) > 1:
            # check that all cameras have the same frame number sets
            if len(frame_number_sets[0]) != len(frame_number_sets[1]):
                raise ValueError(
                    "There are some non-paired frames in folder '{0:s}'".format(self.full_frame_folder_path))
            for i_fn in range(len(frame_number_sets[0])):
                fn0 = frame_number_sets[0][i_fn]
                fn1 = frame_number_sets[1][i_fn]
                if fn0 != fn1:
                    raise ValueError("There are some non-paired frames in folder '{0:s}'." +
                                     " Check frame {1:d} for camera {2:s} and frame {3:d} for camera {4:s}."
                                     .format(self.full_frame_folder_path,
                                             fn0, self.videos[0].name,
                                             fn1, self.videos[1].name))

        for i_frame in range(usable_frame_ct):
            self.object_points.append(self.board_object_corner_set)
        return usable_frame_ct 

def getP(self, dst):
        """
        dst: 标记物关键点

        return self.MTX,self.DIST,self.RVEC,self.TVEC:
        反馈 内参、畸变系数，旋转向量，位移向量

        """
        if self.SceneImage is None:
            return None

        corners = np.float32([dst[1], dst[0], dst[2], dst[3]])
        gray = cv2.cvtColor(self.SceneImage, cv2.COLOR_BGR2GRAY)
        # termination criteria
        criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)

        # prepare object points, like (0,0,0), (1,0,0), (1,0,0), (1,1,0)
        objp = np.zeros((2*2,3), np.float32)
        objp[:,:2] = np.mgrid[0:2,0:2].T.reshape(-1,2)

        corners2 = cv2.cornerSubPix(gray,corners,(11,11),(-1,-1),criteria)

        if self.PTimes < self.PCount or self.PCount == 0:
            # Arrays to store object points and image points from all the images.
            objpoints = self.OBJPoints # 3d point in real world space
            imgpoints = self.IMGPoints # 2d points in image plane.

            if len(imgpoints) == 0 or np.sum(np.abs(imgpoints[-1] - corners2)) != 0:
                objpoints.append(objp)
                imgpoints.append(corners2)

            # Find mtx, dist, rvecs, tvecs
            ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1],None,None)
            if not ret:
                self.PTimes += 1
                return None
            self.OBJPoints = objpoints
            self.IMGPoints = imgpoints
            self.MTX = mtx
            self.DIST = dist
            self.RVEC = rvecs[0]
            self.TVEC = tvecs[0]
        else:
            # Find the rotation and translation vectors.
            _, rvec, tvec, _= cv2.solvePnPRansac(objp, corners2, self.MTX, self.DIST)
            self.RVEC = rvec
            self.TVEC = tvec
        self.PTimes += 1

        return self.MTX,self.DIST,self.RVEC,self.TVEC 

def getP(self, dst):
        """
        dst: 标记物关键点

        return self.MTX,self.DIST,self.RVEC,self.TVEC:
        反馈 内参、畸变系数，旋转向量，位移向量

        """
        if self.SceneImage is None:
            return None

        corners = np.float32([dst[1], dst[0], dst[2], dst[3]])
        gray = cv2.cvtColor(self.SceneImage, cv2.COLOR_BGR2GRAY)
        # termination criteria
        criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)

        # prepare object points, like (0,0,0), (1,0,0), (1,0,0), (1,1,0)
        objp = np.zeros((2*2,3), np.float32)
        objp[:,:2] = np.mgrid[0:2,0:2].T.reshape(-1,2)

        corners2 = cv2.cornerSubPix(gray,corners,(11,11),(-1,-1),criteria)

        if self.PTimes < self.PCount or self.PCount == 0:
            # Arrays to store object points and image points from all the images.
            objpoints = self.OBJPoints # 3d point in real world space
            imgpoints = self.IMGPoints # 2d points in image plane.

            if len(imgpoints) == 0 or np.sum(np.abs(imgpoints[-1] - corners2)) != 0:
                objpoints.append(objp)
                imgpoints.append(corners2)

            # Find mtx, dist, rvecs, tvecs
            ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1],None,None)
            if not ret:
                self.PTimes += 1
                return None
            self.OBJPoints = objpoints
            self.IMGPoints = imgpoints
            self.MTX = mtx
            self.DIST = dist
            self.RVEC = rvecs[0]
            self.TVEC = tvecs[0]
        else:
            # Find the rotation and translation vectors.
            _, rvec, tvec, _= cv2.solvePnPRansac(objp, corners2, self.MTX, self.DIST)
            self.RVEC = rvec
            self.TVEC = tvec
        self.PTimes += 1

        return self.MTX,self.DIST,self.RVEC,self.TVEC 

def process_images(self, filepath):
        """Read images, detect corners, refine corners, and save data."""
        # Arrays to store object points and image points from all the images.
        self.objpoints = []  # 3d point in real world space
        self.imgpoints_l = []  # 2d points in image plane.
        self.imgpoints_r = []  # 2d points in image plane.
        self.calib_successes = [] # polygon ids of left/right image sets with checkerboard corners.

        images_left = glob.glob(filepath + "/left/*")
        images_right = glob.glob(filepath + "/right/*")
        images_left.sort()
        images_right.sort()

        print("\nAttempting to read images for left camera from dir: " +
              filepath + "/left/")
        print("Attempting to read images for right camera from dir: " +
              filepath + "/right/")

        assert len(images_left) != 0, "ERROR: Images not read correctly, check directory"
        assert len(images_right) != 0, "ERROR: Images not read correctly, check directory"

        for image_left, image_right in zip(images_left, images_right):
            img_l = cv2.imread(image_left, 0)
            img_r = cv2.imread(image_right, 0)

            assert img_l is not None, "ERROR: Images not read correctly"
            assert img_r is not None, "ERROR: Images not read correctly"

            print("Finding chessboard corners for %s and %s..." % (os.path.basename(image_left), os.path.basename(image_right)))
            start_time = time.time()

            # Find the chess board corners
            flags = 0
            flags |= cv2.CALIB_CB_ADAPTIVE_THRESH
            flags |= cv2.CALIB_CB_NORMALIZE_IMAGE
            ret_l, corners_l = cv2.findChessboardCorners(img_l, (9, 6), flags)
            ret_r, corners_r = cv2.findChessboardCorners(img_r, (9, 6), flags)

            # termination criteria
            self.criteria = (cv2.TERM_CRITERIA_MAX_ITER +
                             cv2.TERM_CRITERIA_EPS, 30, 0.001)

            # if corners are found in both images, refine and add data
            if ret_l and ret_r:
                self.objpoints.append(self.objp)
                rt = cv2.cornerSubPix(img_l, corners_l, (5, 5),
                                      (-1, -1), self.criteria)
                self.imgpoints_l.append(corners_l)
                rt = cv2.cornerSubPix(img_r, corners_r, (5, 5),
                                      (-1, -1), self.criteria)
                self.imgpoints_r.append(corners_r)
                self.calib_successes.append(polygon_from_image_name(image_left))
                print("\t[OK]. Took %i seconds." % (round(time.time() - start_time, 2)))
            else:
                print("\t[ERROR] - Corners not detected. Took %i seconds." % (round(time.time() - start_time, 2)))

            self.img_shape = img_r.shape[::-1]
        print(str(len(self.objpoints)) + " of " + str(len(images_left)) +
              " images being used for calibration")
        self.ensure_valid_images() 

def calibrate(dirpath, prefix, image_format, square_size, width=9, height=6):
    """ Apply camera calibration operation for images in the given directory path. """
    # prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(8,6,0)
    objp = np.zeros((height*width, 3), np.float32)
    objp[:, :2] = np.mgrid[0:width, 0:height].T.reshape(-1, 2)

    objp = objp * square_size  # Create real world coords. Use your metric.

    # Arrays to store object points and image points from all the images.
    objpoints = []  # 3d point in real world space
    imgpoints = []  # 2d points in image plane.

    # Directory path correction. Remove the last character if it is '/'
    if dirpath[-1:] == '/':
        dirpath = dirpath[:-1]

    # Get the images
    images = glob.glob(dirpath+'/' + prefix + '*.' + image_format)

    # Iterate through the pairs and find chessboard corners. Add them to arrays
    # If openCV can't find the corners in an image, we discard the image.
    for fname in images:
        img = cv2.imread(fname)
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

        # Find the chess board corners
        ret, corners = cv2.findChessboardCorners(gray, (width, height), None)

        # If found, add object points, image points (after refining them)
        if ret:
            objpoints.append(objp)

            corners2 = cv2.cornerSubPix(gray, corners, (11, 11), (-1, -1), criteria)
            imgpoints.append(corners2)

            # Draw and display the corners
            # Show the image to see if pattern is found ! imshow function.
            img = cv2.drawChessboardCorners(img, (width, height), corners2, ret)

    ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)

    return [ret, mtx, dist, rvecs, tvecs] 

def find_chessboard(self, sx=6, sy=9):
        """Finds the corners of an sx X sy chessboard in the image.

        Parameters
        ----------
        sx : int
            Number of chessboard corners in x-direction.
        sy : int
            Number of chessboard corners in y-direction.

        Returns
        -------
        :obj:`list` of :obj:`numpy.ndarray`
            A list containing the 2D points of the corners of the detected
            chessboard, or None if no chessboard found.
        """
        # termination criteria
        criteria = (cv2.TERM_CRITERIA_EPS +
                    cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)

        # prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
        objp = np.zeros((sx * sy, 3), np.float32)
        objp[:, :2] = np.mgrid[0:sx, 0:sy].T.reshape(-1, 2)

        # Arrays to store object points and image points from all the images.
        objpoints = []  # 3d point in real world space
        imgpoints = []  # 2d points in image plane.

        # create images
        img = self.data.astype(np.uint8)
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

        # Find the chess board corners
        ret, corners = cv2.findChessboardCorners(gray, (sx, sy), None)

        # If found, add object points, image points (after refining them)
        if ret:
            objpoints.append(objp)
            cv2.cornerSubPix(gray, corners, (11, 11), (-1, -1), criteria)
            imgpoints.append(corners)

            if corners is not None:
                return corners.squeeze()
        return None 

def export_loader(image, name, experiment, **config):
    has_keypoints = config.get('has_keypoints', True)
    has_descriptors = config.get('has_descriptors', True)

    num_features = config.get('num_features', 0)
    remove_borders = config.get('remove_borders', 0)
    keypoint_predictor = config.get('keypoint_predictor', None)
    do_nms = config.get('do_nms', False)
    nms_thresh = config.get('nms_thresh', 4)
    keypoint_refinement = config.get('keypoint_refinement', False)
    binarize = config.get('binarize', False)
    entries = ['keypoints', 'scores', 'descriptors', 'local_descriptors']

    name = name.decode('utf-8') if isinstance(name, bytes) else name
    path = Path(EXPER_PATH, 'exports', experiment, name+'.npz')
    with np.load(path) as p:
        pred = {k: v.copy() for k, v in p.items()}
    image_shape = image.shape[:2]
    if keypoint_predictor:
        keypoint_config = config.get('keypoint_config', config)
        keypoint_config['keypoint_predictor'] = None
        pred_detector = keypoint_predictor(
            image, name, **{'experiment': experiment, **keypoint_config})
        pred['keypoints'] = pred_detector['keypoints']
        pred['scores'] = pred_detector['scores']
    elif has_keypoints:
        assert 'keypoints' in pred
        if remove_borders:
            mask = keypoints_filter_borders(
                pred['keypoints'], image_shape, remove_borders)
            pred = {**pred,
                    **{k: v[mask] for k, v in pred.items() if k in entries}}
        if do_nms:
            keep = nms_fast(
                pred['keypoints'], pred['scores'], image_shape, nms_thresh)
            pred = {**pred,
                    **{k: v[keep] for k, v in pred.items() if k in entries}}
        if keypoint_refinement:
            criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER,
                        30, 0.001)
            pred['keypoints'] = cv2.cornerSubPix(
                image, np.float32(pred['keypoints']),
                (3, 3), (-1, -1), criteria)
    if num_features:
        keep = np.argsort(pred['scores'])[::-1][:num_features]
        pred = {**pred,
                **{k: v[keep] for k, v in pred.items() if k in entries}}
    if has_descriptors:
        if 'descriptors' in pred:
            pass
        elif 'local_descriptors' in pred:
            pred['descriptors'] = pred['local_descriptors']
        else:
            assert 'local_descriptor_map' in pred
            pred['descriptors'] = sample_descriptors(
                pred['local_descriptor_map'], pred['keypoints'], image_shape,
                input_shape=pred['input_shape'][:2] if 'input_shape' in pred
                else None)
    if binarize:
        pred['descriptors'] = pred['descriptors'] > 0
    return pred