Python cv2.createLineSegmentDetector方法代碼示例

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import createLineSegmentDetector [as 別名]
def lsdWrap(img, LSD=None, **kwargs):
    '''
    Opencv implementation of
    Rafael Grompone von Gioi, Jérémie Jakubowicz, Jean-Michel Morel, and Gregory Randall,
    LSD: a Line Segment Detector, Image Processing On Line, vol. 2012.
    [Rafael12] http://www.ipol.im/pub/art/2012/gjmr-lsd/?utm_source=doi
    @img
        input image
    @LSD
        Constructing by cv2.createLineSegmentDetector
        https://docs.opencv.org/3.0-beta/modules/imgproc/doc/feature_detection.html#linesegmentdetector
        if LSD is given, kwargs will be ignored
    @kwargs
        is used to construct LSD
        work only if @LSD is not given
    '''
    if LSD is None:
        LSD = cv2.createLineSegmentDetector(**kwargs)

    if len(img.shape) == 3:
        img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)

    lines, width, prec, nfa = LSD.detect(img)
    if lines is None:
        return np.zeros_like(img), np.array([])
    edgeMap = LSD.drawSegments(np.zeros_like(img), lines)[..., -1]
    lines = np.squeeze(lines, 1)
    edgeList = np.concatenate([lines, width, prec, nfa], 1)
    return edgeMap, edgeList 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import createLineSegmentDetector [as 別名]
def robust_edge_detection(img):
    # Find edges
    kernel_size = 5
    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    blur_gray = cv2.GaussianBlur(gray, (kernel_size, kernel_size), 0)
    # io.imagesc(blur_gray)
    edges = cv2.Canny((blur_gray * 255).astype(np.uint8), 10, 200, apertureSize=5)
    # io.imagesc(edges)
    lsd = cv2.createLineSegmentDetector(0)
    lines = lsd.detect(edges)[0]  # Position 0 of the returned tuple are the detected lines

    long_lines = []
    for j in range(lines.shape[0]):
        x1, y1, x2, y2 = lines[j, 0, :]
        if np.linalg.norm(np.array([x1, y1]) - np.array([x2, y2])) > 50:
            long_lines.append(lines[j, :, :])

    lines = np.array(long_lines)
    edges = 1 * np.ones_like(img)
    drawn_img = lsd.drawSegments(edges, lines)
    edges = (drawn_img[:, :, 2] > 1).astype(np.float32)

    kernel = np.ones((7, 7), np.uint8)

    edges = cv2.morphologyEx(edges, cv2.MORPH_CLOSE, kernel)
    kernel = np.ones((3, 3), np.uint8)
    edges = cv2.morphologyEx(edges, cv2.MORPH_OPEN, kernel)

    return edges 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import createLineSegmentDetector [as 別名]
def __detect_lines(self, img):
        """
        Detects lines using OpenCV LSD Detector
        """
        # Convert to grayscale if required
        if len(img.shape) == 3:
            img_copy = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        else:
            img_copy = img

        # Create LSD detector with default parameters
        lsd = cv2.createLineSegmentDetector(0)

        # Detect lines in the image
        # Returns a NumPy array of type N x 1 x 4 of float32
        # such that the 4 numbers in the last dimension are (x1, y1, x2, y2)
        # These denote the start and end positions of a line
        lines = lsd.detect(img_copy)[0]

        # Remove singleton dimension
        lines = lines[:, 0]

        # Filter out the lines whose length is lower than the threshold
        dx = lines[:, 2] - lines[:, 0]
        dy = lines[:, 3] - lines[:, 1]
        lengths = np.sqrt(dx * dx + dy * dy)
        mask = lengths >= self._length_thresh
        lines = lines[mask]

        # Store the lines internally
        self.__lines = lines

        # Return the lines
        return lines