python类hough_circle()的实例源码

def test_image_transformation():
    s = XSeries([generate_image(False) for _ in range(100)])

    try:
        image_transformer = ImageTransformer().fit()
        assert False
    except:
        assert True

    image_transformer = ImageTransformer(skimage_transform.hough_circle, radius=5).fit()
    s_transformed = image_transformer.transform(s)

    assert s_transformed.data_type == np.ndarray

    image_transformer = ImageTransformer(skimage_transform.resize, output_shape=(10, 10)).fit()
    s_transformed = image_transformer.transform(s)

    assert s_transformed.data_type == np.ndarray

def find_circles(edges_img, min_radius, max_radius):
    """
    Finds circles using the Hough transformation
    For each radius a Hough transformation matrix is calculated and retured
    """
    hough_radii = numpy.arange(min_radius, max_radius, 1)
    hough_res = hough_circle(edges_img, hough_radii)

    return (hough_radii, hough_res)

def process(self, im):
        (width, height, _) = im.image.shape

        img_adapted = im.prep(self.transform)

        if width > self.max_resized or height > self.max_resized:
            scale_height = self.max_resized / height
            scale_width = self.max_resized / width
            scale = min(scale_height, scale_width)
            img_adapted = resize(img_adapted, (int(width * scale), int(height * scale)))

        edges = canny(img_adapted, sigma=self.sigma)

        # Detect two radii
        # Calculate image diameter
        shape = im.image.shape
        diam = math.sqrt(shape[0] ** 2 + shape[1] ** 2)
        radii = np.arange(diam / 3, diam * 0.8, 2)
        hough_res = hough_circle(edges, radii)

        accums = []
        for radius, h in zip(radii, hough_res):
            # For each radius, extract two circles
            peaks = peak_local_max(h, num_peaks=1, min_distance=1)
            if len(peaks) > 0:
                accums.extend(h[peaks[:, 0], peaks[:, 1]])

        if len(accums) == 0:  # TODO: fix, should not happen
            return [0]

        idx = np.argmax(accums)
        return [accums[idx]]