Question

对于我的神经网络，我希望通过向我的图像添加小的随机旋转和缩放来增强我的训练数据。我遇到的问题是scipy在应用旋转和缩放时会改变图像的大小。如果图像的一部分超出边界，我需要剪切边缘。我的所有图像都必须大小相同。

def loadImageData(img, distort = False):
    c, fn = img
    img = scipy.ndimage.imread(fn, True)

    if distort:
        img = scipy.ndimage.zoom(img, 1 + 0.05 * rnd(), mode = 'constant')
        img = scipy.ndimage.rotate(img, 10 * rnd(), mode = 'constant')
        print(img.shape)

    img = img - np.min(img)
    img = img / np.max(img)
    img = np.reshape(img, (1, *img.shape))

    y = np.zeros(ncats)
    y[c] = 1
    return (img, y)

Answer 1

scipy.ndimage.rotate接受reshape=参数：

重塑：bool，可选

如果reshape为真，则调整输出形状以便输入       数组完全包含在输出中。默认为True。

所以去＃34;剪辑＆＃34;你可以简单地调用scipy.ndimage.rotate(img, ..., reshape=False)。

from scipy.ndimage import rotate
from scipy.misc import face
from matplotlib import pyplot as plt

img = face()
rot = rotate(img, 30, reshape=False)

fig, ax = plt.subplots(1, 2)
ax[0].imshow(img)
ax[1].imshow(rot)

scipy.ndimage.zoom的事情变得更加复杂。

一个天真的方法是zoom整个输入数组，然后使用切片索引和/或零填充来使输出与输入的大小相同。但是，如果您要增加图像的大小，那么插入仅会在边缘处被剪掉的像素是浪费的。

相反，在应用zoom之前，您只能索引将在输出数组范围内的输入部分：

import numpy as np
from scipy.ndimage import zoom


def clipped_zoom(img, zoom_factor, **kwargs):

    h, w = img.shape[:2]

    # For multichannel images we don't want to apply the zoom factor to the RGB
    # dimension, so instead we create a tuple of zoom factors, one per array
    # dimension, with 1's for any trailing dimensions after the width and height.
    zoom_tuple = (zoom_factor,) * 2 + (1,) * (img.ndim - 2)

    # Zooming out
    if zoom_factor < 1:

        # Bounding box of the zoomed-out image within the output array
        zh = int(np.round(h * zoom_factor))
        zw = int(np.round(w * zoom_factor))
        top = (h - zh) // 2
        left = (w - zw) // 2

        # Zero-padding
        out = np.zeros_like(img)
        out[top:top+zh, left:left+zw] = zoom(img, zoom_tuple, **kwargs)

    # Zooming in
    elif zoom_factor > 1:

        # Bounding box of the zoomed-in region within the input array
        zh = int(np.round(h / zoom_factor))
        zw = int(np.round(w / zoom_factor))
        top = (h - zh) // 2
        left = (w - zw) // 2

        out = zoom(img[top:top+zh, left:left+zw], zoom_tuple, **kwargs)

        # `out` might still be slightly larger than `img` due to rounding, so
        # trim off any extra pixels at the edges
        trim_top = ((out.shape[0] - h) // 2)
        trim_left = ((out.shape[1] - w) // 2)
        out = out[trim_top:trim_top+h, trim_left:trim_left+w]

    # If zoom_factor == 1, just return the input array
    else:
        out = img
    return out

例如：

zm1 = clipped_zoom(img, 0.5)
zm2 = clipped_zoom(img, 1.5)

fig, ax = plt.subplots(1, 3)
ax[0].imshow(img)
ax[1].imshow(zm1)
ax[2].imshow(zm2)

Answer 2

我建议使用cv2.resize，因为它比scipy.ndimage.zoom快，可能是因为支持更简单的插值方法。

对于480x640图像：

cv2.resize需要~2 ms
scipy.ndimage.zoom需要~500毫秒
scipy.ndimage.zoom(...,order=0)需要~175毫秒

如果您正在进行数据增加，那么这个加速比非常宝贵，因为它意味着可以在更短的时间内完成更多的实验。

以下是使用clipped_zoom

的cv2.resize版本

def cv2_clipped_zoom(img, zoom_factor):
    """
    Center zoom in/out of the given image and returning an enlarged/shrinked view of 
    the image without changing dimensions
    Args:
        img : Image array
        zoom_factor : amount of zoom as a ratio (0 to Inf)
    """
    height, width = img.shape[:2] # It's also the final desired shape
    new_height, new_width = int(height * zoom_factor), int(width * zoom_factor)

    ### Crop only the part that will remain in the result (more efficient)
    # Centered bbox of the final desired size in resized (larger/smaller) image coordinates
    y1, x1 = max(0, new_height - height) // 2, max(0, new_width - width) // 2
    y2, x2 = y1 + height, x1 + width
    bbox = np.array([y1,x1,y2,x2])
    # Map back to original image coordinates
    bbox = (bbox / zoom_factor).astype(np.int)
    y1, x1, y2, x2 = bbox
    cropped_img = img[y1:y2, x1:x2]

    # Handle padding when downscaling
    resize_height, resize_width = min(new_height, height), min(new_width, width)
    pad_height1, pad_width1 = (height - resize_height) // 2, (width - resize_width) //2
    pad_height2, pad_width2 = (height - resize_height) - pad_height1, (width - resize_width) - pad_width1
    pad_spec = [(pad_height1, pad_height2), (pad_width1, pad_width2)] + [(0,0)] * (img.ndim - 2)

    result = cv2.resize(cropped_img, (resize_width, resize_height))
    result = np.pad(result, pad_spec, mode='constant')
    assert result.shape[0] == height and result.shape[1] == width
    return result

Scipy旋转和缩放图像而不更改其尺寸

2 个答案: