chasep255 chasep255 - 1 year ago 247
Python Question

Scipy rotate and zoom an image without changing its dimensions

For my neural network I want to augment my training data by adding small random rotations and zooms to my images. The issue I am having is that scipy is changing the size of my images when it applies the rotations and zooms. I need to to just clip the edges if part of the image goes out of bounds. All of my images must be the same size.

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')

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 Source

scipy.ndimage.rotate accepts a reshape= parameter:

reshape : bool, optional

If reshape is true, the output shape is adapted so that the input array is contained completely in the output. Default is True.

So to "clip" the edges you can simply call scipy.ndimage.rotate(img, ..., reshape=False).

from scipy.ndimage import rotate, zoom
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)

enter image description here

Things are more complicated for scipy.ndimage.zoom.

A naive method would be to zoom the entire input array, then use slice indexing and/or zero-padding to make the output the same size as your input. However, in cases where you're increasing the size of the image it's wasteful to interpolate pixels that are only going to get clipped off at the edges anyway.

Instead you could index only the part of the input that will fall within the bounds of the output array before you apply zoom:

import numpy as np
from scipy.ndimage import zoom

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

    h, w = img.shape[:2]

    # width and height of the zoomed image
    zh = int(np.round(zoom_factor * h))
    zw = int(np.round(zoom_factor * w))

    # 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 clip region within the output array
        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 clip region within the input array
        top = (zh - h) // 2
        left = (zw - w) // 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
        out = img
    return out

For example:

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

fig, ax = plt.subplots(1, 3)

enter image description here

Recommended from our users: Dynamic Network Monitoring from WhatsUp Gold from IPSwitch. Free Download