import numpy as np

def log_images (x,  y_pred,  y_true=None, channel=1):

    images = []

    x_np = x[:, channel].cpu().numpy()

    y_true_np = y_true[:, 0].cpu().numpy()

    y_pred_np = y_pred[:, 0].cpu().numpy()

    for i in range(x_np.shape[0]):

        image = gray2rgb(np.squeeze(x_np[i]))

        image = outline(image, y_pred_np[i], color=[255, 0, 0])

        image = outline(image, y_true_np[i], color=[0, 255, 0])

        images.append(image)

    return images

def gray2rgb(image):

    w, h = image.shape

    image += np.abs(np.min(image))

    image_max = np.abs(np.max(image))

    if image_max > 0:

        image /= image_max

    ret = np.empty((w, h, 3), dtype=np.uint8)

    ret[:, :, 2] = ret[:, :, 1] = ret[:, :, 0] = image * 255

    return ret

def outline(image, mask, color):

    mask = np.round(mask)

    yy, xx = np.nonzero(mask)

    for y, x in zip(yy, xx):

        if 0.0 < np.mean(mask[max(0, y - 1) : y + 2, max(0, x - 1) : x + 2]) < 1.0:

            image[max(0, y) : y + 1, max(0, x) : x + 1] = color

    return image