Python cv2 模块，FastFeatureDetector_create() 实例源码

def __init__(self, videoSource, featurePtMask=None, verbosity=0):
    # cap the length of optical flow tracks
    self.maxTrackLength = 10

    # detect feature points in intervals of frames; adds robustness for
    # when feature points disappear.
    self.detectionInterval = 5

    # Params for Shi-Tomasi corner (feature point) detection
    self.featureParams = dict(
        maxCorners=500,
        qualityLevel=0.3,
        minDistance=7,
        blockSize=7
    )
    # Params for Lucas-Kanade optical flow
    self.lkParams = dict(
        winSize=(15, 15),
        maxLevel=2,
        criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03)
    )
    # # Alternatively use a fast feature detector
    # self.fast = cv2.FastFeatureDetector_create(500)

    self.verbosity = verbosity

    (self.videoStream,
     self.width,
     self.height,
     self.featurePtMask) = self._initializeCamera(videoSource)

def __init__(self, fx, cx, cy):
        self.last_frame = None
        self.R = None
        self.t = None
        self.px_ref = None
        self.px_cur = None
        self.focal = fx
        self.pp = (cx, cy)
        self.detector = cv2.FastFeatureDetector_create(threshold=25,
                                                       nonmaxSuppression=True)

def __init__(self, **kwargs):
        self.detector = cv2.FastFeatureDetector_create(
            threshold=kwargs.get("threshold", 25),
            nonmaxSuppression=kwargs.get("nonmax_suppression", True)
        )

def __init__(self):
        self.fast = cv2.FastFeatureDetector_create()
        self.mask = SkyCamera.getMask()

def fast_thread():
    fast = cv2.FastFeatureDetector_create()
    kps3 = fast.detect(gray, None)
    cv2.drawKeypoints(gray, kps3, img3, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
    cv2.imshow('FAST Algorithm', img3)

def __init__(self, image_topic, feature_detector='FAST'):
        super(OpticalFlowMatcher, self).__init__()

        rospy.init_node('optical_flow_matcher')

        self.cv_bridge = CvBridge()

        self.rectified_image_topic = rospy.Subscriber(
            image_topic,
            Image,
            self.new_image_callback
        )

        self.pub_keypoint_motion = rospy.Publisher(
            'keypoint_motion',
            KeypointMotion,
            queue_size=10
        )

        self.feature_params = None

        if feature_detector == 'FAST':
            self.get_features = self.get_features_fast
            # Initiate FAST detector with default values
            self.fast = cv2.FastFeatureDetector_create()

            self.fast.setThreshold(20)

        elif feature_detector == 'GOOD':
            self.get_features = self.get_features_good
            # params for ShiTomasi 'GOOD' corner detection
            self.feature_params = dict(
                maxCorners=200,
                qualityLevel=0.3,
                minDistance=7,
                blockSize=7
            )
        else:
            raise Exception(
                '{} feature detector not implemented'.format(feature_detector)
            )

        # Parameters for lucas kanade optical flow
        self.lk_params = dict(
            winSize=(15, 15),
            maxLevel=2,
            criteria=(
                cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT,
                10,
                0.03
            )
        )

        self.last_frame_gray = None
        self.good_old = None
        self.good_new = None

def __init__(self, image_topic, feature_detector='SIFT'):
        super(SIFTMatcher, self).__init__()

        rospy.init_node('sift_matcher')

        self.cv_bridge = CvBridge()

        self.rectified_image_topic = rospy.Subscriber(
            image_topic,
            Image,
            self.new_image_callback
        )

        self.pub_keypoint_motion = rospy.Publisher(
            'keypoint_motion',
            KeypointMotion,
            queue_size=10
        )

        self.feature_params = None

        if feature_detector == 'FAST':
            self.get_features = self.get_features_fast
            # Initiate FAST detector with default values
            self.fast = cv2.FastFeatureDetector_create()

            self.fast.setThreshold(20)
        elif feature_detector == 'GOOD':
            self.get_features = self.get_features_good
            # params for ShiTomasi 'GOOD' corner detection
            self.feature_params = dict(
                maxCorners=200,
                qualityLevel=0.3,
                minDistance=7,
                blockSize=7
            )
        elif feature_detector == 'SIFT':
            # OpenCV 3+
            self.sift = cv2.xfeatures2d.SIFT_create()
        else:
            raise Exception(
                '{} feature detector not implemented'.format(feature_detector)
            )

        self.last_frame_gray = None
        self.good_old = None
        self.good_new = None