def display_instances()

in visualize_attention.py [0:0]

• 33 lines of code
• 7 McCabe index (conditional complexity)

def display_instances(image, mask, fname="test", figsize=(5, 5), blur=False, contour=True, alpha=0.5):
    fig = plt.figure(figsize=figsize, frameon=False)
    ax = plt.Axes(fig, [0., 0., 1., 1.])
    ax.set_axis_off()
    fig.add_axes(ax)
    ax = plt.gca()

    N = 1
    mask = mask[None, :, :]
    # Generate random colors
    colors = random_colors(N)

    # Show area outside image boundaries.
    height, width = image.shape[:2]
    margin = 0
    ax.set_ylim(height + margin, -margin)
    ax.set_xlim(-margin, width + margin)
    ax.axis('off')
    masked_image = image.astype(np.uint32).copy()
    for i in range(N):
        color = colors[i]
        _mask = mask[i]
        if blur:
            _mask = cv2.blur(_mask,(10,10))
        # Mask
        masked_image = apply_mask(masked_image, _mask, color, alpha)
        # Mask Polygon
        # Pad to ensure proper polygons for masks that touch image edges.
        if contour:
            padded_mask = np.zeros((_mask.shape[0] + 2, _mask.shape[1] + 2))
            padded_mask[1:-1, 1:-1] = _mask
            contours = find_contours(padded_mask, 0.5)
            for verts in contours:
                # Subtract the padding and flip (y, x) to (x, y)
                verts = np.fliplr(verts) - 1
                p = Polygon(verts, facecolor="none", edgecolor=color)
                ax.add_patch(p)
    ax.imshow(masked_image.astype(np.uint8), aspect='auto')
    fig.savefig(fname)
    print(f"{fname} saved.")
    return