python类show()的实例源码

def get_sport_clip(clip_name, verbose=True):
    """
    Loads a clip to be fed to C3D for classification.
    TODO: should I remove mean here?

    Parameters
    ----------
    clip_name: str
        the name of the clip (subfolder in 'data').
    verbose: bool
        if True, shows the unrolled clip (default is True).

    Returns
    -------
    Tensor
        a pytorch batch (n, ch, fr, h, w).
    """

    clip = sorted(glob(join('data', clip_name, '*.png')))
    clip = np.array([resize(io.imread(frame), output_shape=(112, 200), preserve_range=True) for frame in clip])
    clip = clip[:, :, 44:44+112, :]  # crop centrally

    if verbose:
        clip_img = np.reshape(clip.transpose(1, 0, 2, 3), (112, 16 * 112, 3))
        io.imshow(clip_img.astype(np.uint8))
        io.show()

    clip = clip.transpose(3, 0, 1, 2)  # ch, fr, h, w
    clip = np.expand_dims(clip, axis=0)  # batch axis
    clip = np.float32(clip)

    return torch.from_numpy(clip)

def selectiveSearch(image):
    segments = felzenszwalb(image, scale=kFelzenszwalbScale)
    numRegions = segments.max()

    rectangles = []

    for regionTag in range(numRegions):
        selectedRegion = segments == regionTag
        regionPixelIndices = np.transpose(np.nonzero(selectedRegion))
        rectangle = aabb(regionPixelIndices)
        rectangles.append(rectangle)

    # Implement similarities, neighbourhood merging.
    # Felzenszwalb's segmentation is ridiculously good already.

    def debug():
        marked = np.zeros(image.shape, dtype=np.uint8)

        for rectangle in rectangles:
            rr, cc = rectangle.pixels(marked.shape)
            randcolor = randint(0, 255), randint(0, 255), randint(0, 255)
            marked[rr, cc] = randcolor

        print(image.shape, segments.shape, marked.shape)

        io.imshow_collection([image, segments, marked])
        io.show()

    # debug()

    return rectangles