utilities.py 文件源码

def generate_mask(img_mask_list, h, w, l):
    img_masks, loss_masks = [], []

    for i in range(l):
        # generate image mask
        img_mask = img_mask_list[i]
        img_mask = tf.cast(tf.image.decode_png(img_mask), tf.float32)
        img_mask = tf.reshape(img_mask, (h, w))
        img_masks.append(img_mask)

        # generate loss mask
        s_total   = h * w
        s_mask    = tf.reduce_sum(img_mask)
        def f1(): return img_mask*((s_total-s_mask)/s_mask-1)+1
        def f2(): return tf.zeros_like(img_mask)
        def f3(): return tf.ones_like(img_mask)
        loss_mask = tf.case([(tf.equal(s_mask, 0), f2), \
                             (tf.less(s_mask, s_total/2), f1)],
                             default=f3)

        loss_masks.append(loss_mask)

    return tf.stack(img_masks), tf.stack(loss_masks)