Python cv2.TERM_CRITERIA_MAX_ITER Examples

def color_quantization(image, k):
    """Performs color quantization using K-means clustering algorithm"""

    # Transform image into 'data':
    data = np.float32(image).reshape((-1, 3))
    # print(data.shape)

    # Define the algorithm termination criteria (the maximum number of iterations and/or the desired accuracy):
    # In this case the maximum number of iterations is set to 20 and epsilon = 1.0
    criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 20, 1.0)

    # Apply K-means clustering algorithm:
    ret, label, center = cv2.kmeans(data, k, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)

    # At this point we can make the image with k colors
    # Convert center to uint8:
    center = np.uint8(center)
    # Replace pixel values with their center value:
    result = center[label.flatten()]
    result = result.reshape(img.shape)
    return result


# Create the dimensions of the figure and set title: 

def _detect_team_color(self, pixels):
        criteria = \
            (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)

        pixels = np.array(pixels.reshape((-1, 3)), dtype=np.float32)

        ret, label, center = cv2.kmeans(
            pixels, 2, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)

        # one is black, another is the team color.

        colors = np.array(center, dtype=np.uint8).reshape((1, 2, 3))
        colors_hsv = cv2.cvtColor(colors, cv2.COLOR_BGR2HSV)
        x = np.argmax(colors_hsv[:, :, 2])
        team_color_bgr = colors[0, x, :]
        team_color_hsv = colors_hsv[0, x, :]

        return {
            'rgb': cv2.cvtColor(colors, cv2.COLOR_BGR2RGB).tolist()[0][x],
            'hsv': cv2.cvtColor(colors, cv2.COLOR_BGR2HSV).tolist()[0][x],
        } 

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 kmeans(samples, k, criteria = None, attempts = 3, flags = None):
    import cv2
    
    if flags == None:
        flags = cv2.KMEANS_RANDOM_CENTERS
    if criteria == None:
        criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
    samples = np.asarray(samples, dtype = np.float32)
    _,labels,centers = cv2.kmeans(samples, k, criteria, attempts, flags)
    labels = util.np.flatten(labels)
    clusters = [None]*k
    for idx, label in enumerate(labels):
        if clusters[label] is None:
            clusters[label] = []
        clusters[label].append(idx)
        
    for  idx, cluster in enumerate(clusters):
        if cluster == None:
            logging.warn('Empty cluster appeared.')
            clusters[idx] = []
            
    return labels, clusters, centers 

def kmeans(samples, k, criteria = None, attempts = 3, flags = None):
    import cv2
    
    if flags == None:
        flags = cv2.KMEANS_RANDOM_CENTERS
    if criteria == None:
        criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
    samples = np.asarray(samples, dtype = np.float32)
    _,labels,centers = cv2.kmeans(samples, k, criteria, attempts, flags)
    labels = util.np.flatten(labels)
    clusters = [None]*k
    for idx, label in enumerate(labels):
        if clusters[label] is None:
            clusters[label] = []
        clusters[label].append(idx)
        
    for  idx, cluster in enumerate(clusters):
        if cluster == None:
            logging.warn('Empty cluster appeared.')
            clusters[idx] = []
            
    return labels, clusters, centers 

def kmeans(samples, k, criteria = None, attempts = 3, flags = None):
    import cv2
    
    if flags == None:
        flags = cv2.KMEANS_RANDOM_CENTERS
    if criteria == None:
        criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
    samples = np.asarray(samples, dtype = np.float32)
    _,labels,centers = cv2.kmeans(samples, k, criteria, attempts, flags)
    labels = util.np.flatten(labels)
    clusters = [None]*k
    for idx, label in enumerate(labels):
        if clusters[label] is None:
            clusters[label] = []
        clusters[label].append(idx)
        
    for  idx, cluster in enumerate(clusters):
        if cluster == None:
            logging.warn('Empty cluster appeared.')
            clusters[idx] = []
            
    return labels, clusters, centers 

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 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 kmeans(vs, ks, niter):
        criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER,
                    niter, 0.01)
        flags = cv2.KMEANS_RANDOM_CENTERS
        compactness, labels, centers = cv2.kmeans(
            vs, ks, criteria, 1, flags)
        return centers 

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 k_means(points: np.ndarray):
    """返回一个数组经kmeans分类后的k值以及标签，k值由计算拐点给出

    Args:
        points (np.ndarray): 需分类数据

    Returns:
        Tuple[int, np.ndarry]: k值以及标签数组
    """

    # Define criteria = ( type, max_iter = 10 , epsilon = 1.0 )
    criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)

    # Set flags (Just to avoid line break in the code)
    flags = cv2.KMEANS_RANDOM_CENTERS
    length = []
    max_k = min(10, points.shape[0])
    for k in range(2, max_k + 1):
        avg = 0
        for i in range(5):
            compactness, _, _ = cv2.kmeans(
                points, k, None, criteria, 10, flags)
            avg += compactness
        avg /= 5
        length.append(avg)

    peek_pos = find_peek(length)
    k = peek_pos + 2
    # print(k)
    return k, cv2.kmeans(points, k, None, criteria, 10, flags)[1]  # labels 

def svm_init(C=12.5, gamma=0.50625):
    """Creates empty model and assigns main parameters"""

    model = cv2.ml.SVM_create()
    model.setGamma(gamma)
    model.setC(C)
    model.setKernel(cv2.ml.SVM_RBF)
    model.setType(cv2.ml.SVM_C_SVC)
    model.setTermCriteria((cv2.TERM_CRITERIA_MAX_ITER, 100, 1e-6))

    return model 

def svm_init(C=12.5, gamma=0.50625):
    """Creates empty model and assigns main parameters"""

    model = cv2.ml.SVM_create()
    model.setGamma(gamma)
    model.setC(C)
    model.setKernel(cv2.ml.SVM_LINEAR)
    model.setType(cv2.ml.SVM_C_SVC)
    model.setTermCriteria((cv2.TERM_CRITERIA_MAX_ITER, 100, 1e-6))

    return model 

def svm_init(C=12.5, gamma=0.50625):
    """Creates empty model and assigns main parameters"""

    model = cv2.ml.SVM_create()
    model.setGamma(gamma)
    model.setC(C)
    model.setKernel(cv2.ml.SVM_RBF)
    model.setType(cv2.ml.SVM_C_SVC)
    model.setTermCriteria((cv2.TERM_CRITERIA_MAX_ITER, 100, 1e-6))

    return model 

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 test_kmeans(img):
    ## K均值聚类
    z = img.reshape((-1, 3))
    z = np.float32(z)
    criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
    ret, label, center = cv2.kmeans(z, 20, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)
    center = np.uint8(center)
    res = center[label.flatten()]
    res2 = res.reshape((img.shape))
    cv2.imshow('preview', res2)
    cv2.waitKey() 

def color_quantization(img, n_cluster, iteration, epsilon=1.0):
        Z = img.reshape((-1, 3))
        Z = np.float32(Z)

        criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, iteration, epsilon)
        ret, label, center = cv2.kmeans(Z, n_cluster, None, criteria, iteration, cv2.KMEANS_PP_CENTERS)

        labels = label.reshape((img.shape[0], img.shape[1], 1))
        # center = np.uint(center)
        # visual = center[label.flatten()]
        # visual = visual.reshape(img.shape)
        # visual = np.uint8(visual)

        return labels 

def kmeans(array: np.ndarray, k: int = 3):
        n_channels = 3 if len(np.shape(array)) == 3 else 1
        arr_values = array.reshape((-1, n_channels))
        arr_values = np.float32(arr_values)
        criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.2)
        _, labels, (centers) = cv2.kmeans(arr_values, k, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)
        centers = np.uint8(centers)
        clustered_arr = centers[labels.flatten()]
        clustered_arr = clustered_arr.reshape(array.shape)
        return clustered_arr 

def get_dominant_color(pixels, clusters, attempts):
        """
        Given a (N, Channels) array of pixel values, compute the dominant color via K-means
        """
        clusters = min(clusters, len(pixels))
        flags = cv2.KMEANS_RANDOM_CENTERS
        criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_MAX_ITER, 1, 10)
        _, labels, centroids = cv2.kmeans(pixels.astype(np.float32), clusters, None, criteria, attempts, flags)
        _, counts = np.unique(labels, return_counts=True)
        dominant = centroids[np.argmax(counts)]
        return dominant 

def live_calibrate(camera, pattern_shape, n_matches_needed):
    """ Find calibration parameters as the user moves a checkerboard in front of the camera """
    print("Looking for %s checkerboard" % (pattern_shape,))
    criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
    example_3d = np.zeros((pattern_shape[0] * pattern_shape[1], 3), np.float32)
    example_3d[:, :2] = np.mgrid[0 : pattern_shape[1], 0 : pattern_shape[0]].T.reshape(-1, 2)
    points_3d = []
    points_2d = []
    while len(points_3d) < n_matches_needed:
        ret, frame = camera.cap.read()
        assert ret
        gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        ret, corners = cv2.findCirclesGrid(
            gray_frame, pattern_shape, flags=cv2.CALIB_CB_ASYMMETRIC_GRID
        )
        cv2.imshow("camera", frame)
        if ret:
            points_3d.append(example_3d.copy())
            points_2d.append(corners)
            print("Found calibration %i of %i" % (len(points_3d), n_matches_needed))
            drawn_frame = cv2.drawChessboardCorners(frame, pattern_shape, corners, ret)
            cv2.imshow("calib", drawn_frame)
        cv2.waitKey(10)
    ret, camera_matrix, distortion_coefficients, _, _ = cv2.calibrateCamera(
        points_3d, points_2d, gray_frame.shape[::-1], None, None
    )
    assert ret
    return camera_matrix, distortion_coefficients 

def calibrate_intrinsics(camera, image_points,
                         object_points,
                         use_rational_model=True,
                         use_tangential=False,
                         use_thin_prism=False,
                         fix_radial=False,
                         fix_thin_prism=False,
                         max_iterations=30,
                         use_existing_guess=False,
                         test=False):
    flags = 0
    if test:
        flags = flags | cv2.CALIB_USE_INTRINSIC_GUESS
        # fix everything
        flags = flags | cv2.CALIB_FIX_PRINCIPAL_POINT
        flags = flags | cv2.CALIB_FIX_ASPECT_RATIO
        flags = flags | cv2.CALIB_FIX_FOCAL_LENGTH
        # apparently, we can't fix the tangential distance. What the hell? Zero it out.
        flags = flags | cv2.CALIB_ZERO_TANGENT_DIST
        flags = fix_radial_flags(flags)
        flags = flags | cv2.CALIB_FIX_S1_S2_S3_S4
        criteria = (cv2.TERM_CRITERIA_MAX_ITER, 1, 0)
    else:
        if fix_radial:
            flags = fix_radial_flags(flags)
        if fix_thin_prism:
            flags = flags | cv2.CALIB_FIX_S1_S2_S3_S4
        criteria = (cv2.TERM_CRITERIA_MAX_ITER + cv2.TERM_CRITERIA_EPS, max_iterations,
                    2.2204460492503131e-16)
    if use_existing_guess:
        flags = flags | cv2.CALIB_USE_INTRINSIC_GUESS
    if not use_tangential:
        flags = flags | cv2.CALIB_ZERO_TANGENT_DIST
    if use_rational_model:
        flags = flags | cv2.CALIB_RATIONAL_MODEL
        if len(camera.intrinsics.distortion_coeffs) < 8:
            camera.intrinsics.distortion_coeffs.resize((8,))
    if use_thin_prism:
        flags = flags | cv2.CALIB_THIN_PRISM_MODEL
        if len(camera.intrinsics.distortion_coeffs) != 12:
            camera.intrinsics.distortion_coeffs = np.resize(camera.intrinsics.distortion_coeffs, (12,))
    return __calibrate_intrinsics(camera, image_points, object_points, flags, criteria) 

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 calibrate_cameras(self):
        """Calibrate cameras based on found chessboard corners."""
        criteria = (cv2.TERM_CRITERIA_MAX_ITER + cv2.TERM_CRITERIA_EPS,
                    100, 1e-5)
        flags = (cv2.CALIB_FIX_ASPECT_RATIO + cv2.CALIB_ZERO_TANGENT_DIST +
                 cv2.CALIB_SAME_FOCAL_LENGTH)
        calib = StereoCalibration()
        (calib.cam_mats["left"], calib.dist_coefs["left"],
         calib.cam_mats["right"], calib.dist_coefs["right"],
         calib.rot_mat, calib.trans_vec, calib.e_mat,
         calib.f_mat) = cv2.stereoCalibrate(self.object_points,
                                            self.image_points["left"],
                                            self.image_points["right"],
                                            self.image_size,
                                            calib.cam_mats["left"],
                                            calib.dist_coefs["left"],
                                            calib.cam_mats["right"],
                                            calib.dist_coefs["right"],
                                            calib.rot_mat,
                                            calib.trans_vec,
                                            calib.e_mat,
                                            calib.f_mat,
                                            criteria=criteria,
                                            flags=flags)[1:]
        (calib.rect_trans["left"], calib.rect_trans["right"],
         calib.proj_mats["left"], calib.proj_mats["right"],
         calib.disp_to_depth_mat, calib.valid_boxes["left"],
         calib.valid_boxes["right"]) = cv2.stereoRectify(calib.cam_mats["left"],
                                                      calib.dist_coefs["left"],
                                                      calib.cam_mats["right"],
                                                      calib.dist_coefs["right"],
                                                      self.image_size,
                                                      calib.rot_mat,
                                                      calib.trans_vec,
                                                      flags=0)
        for side in ("left", "right"):
            (calib.undistortion_map[side],
             calib.rectification_map[side]) = cv2.initUndistortRectifyMap(
                                                        calib.cam_mats[side],
                                                        calib.dist_coefs[side],
                                                        calib.rect_trans[side],
                                                        calib.proj_mats[side],
                                                        self.image_size,
                                                        cv2.CV_32FC1)
        # This is replaced because my results were always bad. Estimates are
        # taken from the OpenCV samples.
        width, height = self.image_size
        focal_length = 0.8 * width
        calib.disp_to_depth_mat = np.float32([[1, 0, 0, -0.5 * width],
                                              [0, -1, 0, 0.5 * height],
                                              [0, 0, 0, -focal_length],
                                              [0, 0, 1, 0]])
        return calib 

def color_quantization(image, k):
    """Performs color quantization using K-means clustering algorithm"""

    # Transform image into 'data':
    data = np.float32(image).reshape((-1, 3))
    # print(data.shape)

    # Define the algorithm termination criteria (the maximum number of iterations and/or the desired accuracy):
    # In this case the maximum number of iterations is set to 20 and epsilon = 1.0
    criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 20, 1.0)

    # Apply K-means clustering algorithm:
    ret, label, center = cv2.kmeans(data, k, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)

    # At this point we can make the image with k colors
    # Convert center to uint8:
    center = np.uint8(center)
    # Replace pixel values with their center value:
    result = center[label.flatten()]
    result = result.reshape(img.shape)

    # Build the 'color_distribution' legend.
    # We will use the number of pixels assigned to each center value:
    counter = collections.Counter(label.flatten())
    print(counter)

    # Calculate the total number of pixels of the input image:
    total = img.shape[0] * img.shape[1]

    # Assign width and height to the color_distribution image:
    desired_width = img.shape[1]
    # The difference between 'desired_height' and 'desired_height_colors'
    # will be the separation between the images
    desired_height = 70
    desired_height_colors = 50

    # Initialize the color_distribution image:
    color_distribution = np.ones((desired_height, desired_width, 3), dtype="uint8") * 255
    # Initialize start:
    start = 0

    for key, value in counter.items():
        # Calculate the normalized value:
        value_normalized = value / total * desired_width

        # Move end to the right position:
        end = start + value_normalized

        # Draw rectangle corresponding to the current color:
        cv2.rectangle(color_distribution, (int(start), 0), (int(end), desired_height_colors), center[key].tolist(), -1)
        # Update start:
        start = end

    return np.vstack((color_distribution, result))


# Create the dimensions of the figure and set title: 

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 

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()