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)
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)
scipy.ndimage.rotate accepts a
reshape : bool, optional
reshapeis 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) ax.imshow(img) ax.imshow(rot)
Things are more complicated for
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
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 - h) // 2) trim_left = ((out.shape - 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
zm1 = clipped_zoom(img, 0.5) zm2 = clipped_zoom(img, 1.5) fig, ax = plt.subplots(1, 3) ax.imshow(img) ax.imshow(zm1) ax.imshow(zm2)