While executing the below code:
scipy.misc.toimage(output * 255, high=255, low=0, cmin=0, cmax=255).save(
params.result_dir + \'final/%5d_00_%d_out.png\'
@Martijn Pieters worked for me but I also found another solution that may suit some people better. You can also use the code below that imports keras.preprocessing.image, array_to_img instead of scipy.misc.toimage which was deprecated in Scipy 1.0.0 as @Martijn Pieters has already mentioned.
So as an example of using keras API to handle converting images:
# example of converting an image with the Keras API
from keras.preprocessing.image import load_img
from keras.preprocessing.image import img_to_array
from keras.preprocessing.image import array_to_img
# load the image
img = load_img('image.jpg')
print(type(img))
# convert to numpy array
img_array = img_to_array(img)
print(img_array.dtype)
print(img_array.shape)
# convert back to image
img_pil = array_to_img(img_array)
print(type(img_pil))
# show image
fig = plt.figure()
ax = fig.add_subplot()
ax.imshow(img_pil)
and to save an image with keras:
from keras.preprocessing.image import save_img
from keras.preprocessing.image import load_img
from keras.preprocessing.image import img_to_array
# load image
img = load_img('image.jpg')
# convert image to a numpy array
img_array = img_to_array(img)
# save the image with a new filename
save_img('image_save.jpg', img_array)
# load the image to confirm it was saved correctly
img = load_img('image_save.jpg')
print(type(img))
print(img.format)
print(img.mode)
print(img.size)