[人工智能]深度学习预处理不同尺寸的图像的方法

代码源自mask-rcnn模型，github地址如下：https://github.com/multimodallearning/pytorch-mask-rcnn/blob/809abba590db89779ac02c42286135f18ea08b53/utils.py#L270

需要将图像处理成正方形，代码如下：

def resize_image(image, min_dim=None, max_dim=None, padding=False):
    h, w = image.shape[:2]
    window = (0, 0, h, w)
    scale = 1

    # Scale?
    if min_dim:
        # Scale up but not down
        scale = max(1, min_dim / min(h, w))
    # Does it exceed max dim?
    if max_dim:
        image_max = max(h, w)
        if round(image_max * scale) > max_dim:
            scale = max_dim / image_max
    # Resize image and mask
    if scale != 1:
        image = scipy.misc.imresize(
            image, (round(h * scale), round(w * scale)))
    # Need padding?
    if padding:
        # Get new height and width
        h, w = image.shape[:2]
        top_pad = (max_dim - h) // 2
        bottom_pad = max_dim - h - top_pad
        left_pad = (max_dim - w) // 2
        right_pad = max_dim - w - left_pad
        padding = [(top_pad, bottom_pad), (left_pad, right_pad), (0, 0)]
        image = np.pad(image, padding, mode='constant', constant_values=0)
        window = (top_pad, left_pad, h + top_pad, w + left_pad)
    return image, window, scale, padding

针对不同尺寸的图像，直接将原始图像作为输入，并将batchsize设置为1，然后使用梯度累积变相扩充batchsize，不过我试过这种方法效果不是很好。下次试试上面这个resize将原图变成正方形，观察其效果如何