Faster RCNN blob.py

最新推荐文章于 2025-02-07 08:45:00 发布

iamzhangzhuping

最新推荐文章于 2025-02-07 08:45:00 发布

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分类专栏： Deep Learning Caffe学习 Faster RCNN Computer Vision

本文链接：https://blog.youkuaiyun.com/iamzhangzhuping/article/details/51387834

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本文介绍如何将图像数据预处理为适合Caffe框架使用的blob格式，包括图像尺寸调整、像素值标准化等步骤。

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def im_list_to_blob(ims):将图像转换成适合caffe输入的blob数据结构（但是本质上还是python的np.ndarray）, 也就是N * C * H * W的四维结构

def im_list_to_blob(ims):
    """Convert a list of images into a network input.

    Assumes images are already prepared (means subtracted, BGR order, ...).
    """
    # ims里不同图片的shape可能不一样，取出其最大值
    max_shape = np.array([im.shape for im in ims]).max(axis=0)
    num_images = len(ims)
    blob = np.zeros((num_images, max_shape[0], max_shape[1], 3),
                    dtype=np.float32)
    for i in xrange(num_images):
        im = ims[i]
        blob[i, 0:im.shape[0], 0:im.shape[1], :] = im
    # Move channels (axis 3) to axis 1
    # Axis order will become: (batch elem, channel, height, width)
    channel_swap = (0, 3, 1, 2)
    blob = blob.transpose(channel_swap)
    return blob

def prep_im_for_blob(im, pixel_means, target_size, max_size)该方法主要是求取图像的缩放比例，然后将图像resize，

def prep_im_for_blob(im, pixel_means, target_size, max_size):
    """Mean subtract and scale an image for use in a blob."""
    im = im.astype(np.float32, copy=False)
    im -= pixel_means
    im_shape = im.shape
    im_size_min = np.min(im_shape[0:2])
    im_size_max = np.max(im_shape[0:2])
    im_scale = float(target_size) / float(im_size_min)
    # Prevent the biggest axis from being more than MAX_SIZE
    if np.round(im_scale * im_size_max) > max_size:
        im_scale = float(max_size) / float(im_size_max)
    im = cv2.resize(im, None, None, fx=im_scale, fy=im_scale,
                    interpolation=cv2.INTER_LINEAR)

    return im, im_scale