Conda Environment specs for running FDA_demo.py + Unable to produce output as the demo
ukdsvl opened this issue · 3 comments
ukdsvl commented
Can you please share the environment you used to run FDA_demo.py ? I am unable to get the translation result as that of FDA_demo.py using the latest versions, and getting artefacts as follows:
The code I used is:
import numpy as np
from PIL import Image
def low_freq_mutate_np( amp_src, amp_trg, L=0.1 ):
a_src = np.fft.fftshift( amp_src, axes=(-2, -1) )
a_trg = np.fft.fftshift( amp_trg, axes=(-2, -1) )
_, h, w = a_src.shape
b = ( np.floor(np.amin((h,w))*L) ).astype(int)
c_h = np.floor(h/2.0).astype(int)
c_w = np.floor(w/2.0).astype(int)
h1 = c_h-b
h2 = c_h+b+1
w1 = c_w-b
w2 = c_w+b+1
a_src[:,h1:h2,w1:w2] = a_trg[:,h1:h2,w1:w2]
a_src = np.fft.ifftshift( a_src, axes=(-2, -1) )
return a_src
def FDA_source_to_target_np( src_img, trg_img, L=0.1 ):
# exchange magnitude
# input: src_img, trg_img
src_img_np = src_img #.cpu().numpy()
trg_img_np = trg_img #.cpu().numpy()
# get fft of both source and target
fft_src_np = np.fft.fft2( src_img_np, axes=(-2, -1) )
fft_trg_np = np.fft.fft2( trg_img_np, axes=(-2, -1) )
# extract amplitude and phase of both ffts
amp_src, pha_src = np.abs(fft_src_np), np.angle(fft_src_np)
amp_trg, pha_trg = np.abs(fft_trg_np), np.angle(fft_trg_np)
# mutate the amplitude part of source with target
amp_src_ = low_freq_mutate_np( amp_src, amp_trg, L=L )
# mutated fft of source
fft_src_ = amp_src_ * np.exp( 1j * pha_src )
# get the mutated image
src_in_trg = np.fft.ifft2( fft_src_, axes=(-2, -1) )
src_in_trg = np.real(src_in_trg)
return src_in_trg
im_src = Image.open("source.png").convert('RGB')
im_trg = Image.open("target.png").convert('RGB')
im_src = im_src.resize( (1024,512), Image.BICUBIC )
im_trg = im_trg.resize( (1024,512), Image.BICUBIC )
im_src = np.asarray(im_src, np.float32)
im_trg = np.asarray(im_trg, np.float32)
im_src = im_src.transpose((2, 0, 1))
im_trg = im_trg.transpose((2, 0, 1))
src_in_trg = FDA_source_to_target_np( im_src, im_trg, L=0.01 )
src_in_trg = src_in_trg.transpose((1,2,0))
# scipy.misc.toimage(src_in_trg, cmin=0.0, cmax=255.0).save('demo_images/src_in_tar.png')
src_in_trg_=Image.fromarray(np.uint8(src_in_trg))
src_in_trg_.save('gfg_dummy_pic.png')
YangyangLi-potato commented
@ukdsvl
hello, I got the same result as yours with the released demo, have you solved this problem?
ukdsvl commented
@YangyangLi-potato @YanchaoYang
I realized that the released code only works for older scipy versions that support .toimage(). Yes, of course we can create a new conda environment with old dependencies, but I found it to be unreasonable. Thus, to make it work well with the latest torch/np/scipy versions, you can simply include the following into your code (and it shall work!):
_errstr = "Mode is unknown or incompatible with input array shape."
def bytescale(data, cmin=None, cmax=None, high=255, low=0):
# Byte scales an array (image).
# Byte scaling means converting the input image to uint8 dtype and scaling
# the range to ``(low, high)`` (default 0-255).
# If the input image already has dtype uint8, no scaling is done.
# This function is only available if Python Imaging Library (PIL) is installed.
# Parameters
# ----------
# data : ndarray
# PIL image data array.
# cmin : scalar, optional
# Bias scaling of small values. Default is ``data.min()``.
# cmax : scalar, optional
# Bias scaling of large values. Default is ``data.max()``.
# high : scalar, optional
# Scale max value to `high`. Default is 255.
# low : scalar, optional
# Scale min value to `low`. Default is 0.
# Returns
# -------
# img_array : uint8 ndarray
# The byte-scaled array.
# Examples
# --------
# >>> from scipy.misc import bytescale
# >>> img = np.array([[ 91.06794177, 3.39058326, 84.4221549 ],
# ... [ 73.88003259, 80.91433048, 4.88878881],
# ... [ 51.53875334, 34.45808177, 27.5873488 ]])
# >>> bytescale(img)
# array([[255, 0, 236],
# [205, 225, 4],
# [140, 90, 70]], dtype=uint8)
# >>> bytescale(img, high=200, low=100)
# array([[200, 100, 192],
# [180, 188, 102],
# [155, 135, 128]], dtype=uint8)
# >>> bytescale(img, cmin=0, cmax=255)
# array([[91, 3, 84],
# [74, 81, 5],
# [52, 34, 28]], dtype=uint8)
if data.dtype == np.uint8:
return data
if high > 255:
raise ValueError("`high` should be less than or equal to 255.")
if low < 0:
raise ValueError("`low` should be greater than or equal to 0.")
if high < low:
raise ValueError("`high` should be greater than or equal to `low`.")
if cmin is None:
cmin = data.min()
if cmax is None:
cmax = data.max()
cscale = cmax - cmin
if cscale < 0:
raise ValueError("`cmax` should be larger than `cmin`.")
elif cscale == 0:
cscale = 1
scale = float(high - low) / cscale
bytedata = (data - cmin) * scale + low
return (bytedata.clip(low, high) + 0.5).astype(np.uint8)
def toimage(arr, high=255, low=0, cmin=None, cmax=None, pal=None,
mode=None, channel_axis=None):
# Takes a numpy array and returns a PIL image.
# This function is only available if Python Imaging Library (PIL) is installed.
# The mode of the PIL image depends on the array shape and the `pal` and
# `mode` keywords.
# For 2-D arrays, if `pal` is a valid (N,3) byte-array giving the RGB values
# (from 0 to 255) then ``mode='P'``, otherwise ``mode='L'``, unless mode
# is given as 'F' or 'I' in which case a float and/or integer array is made.
# .. warning::
# This function uses `bytescale` under the hood to rescale images to use
# the full (0, 255) range if ``mode`` is one of ``None, 'L', 'P', 'l'``.
# It will also cast data for 2-D images to ``uint32`` for ``mode=None``
# (which is the default).
# Notes
# -----
# For 3-D arrays, the `channel_axis` argument tells which dimension of the
# array holds the channel data.
# For 3-D arrays if one of the dimensions is 3, the mode is 'RGB'
# by default or 'YCbCr' if selected.
# The numpy array must be either 2 dimensional or 3 dimensional.
data = np.asarray(arr)
if np.iscomplexobj(data):
raise ValueError("Cannot convert a complex-valued array.")
shape = list(data.shape)
valid = len(shape) == 2 or ((len(shape) == 3) and
((3 in shape) or (4 in shape)))
if not valid:
raise ValueError("'arr' does not have a suitable array shape for "
"any mode.")
if len(shape) == 2:
shape = (shape[1], shape[0]) # columns show up first
if mode == 'F':
data32 = data.astype(np.float32)
image = Image.frombytes(mode, shape, data32.tobytes())
return image
if mode in [None, 'L', 'P']:
bytedata = bytescale(data, high=high, low=low,
cmin=cmin, cmax=cmax)
image = Image.frombytes('L', shape, bytedata.tobytes())
if pal is not None:
image.putpalette(np.asarray(pal, dtype=np.uint8).tobytes())
# Becomes a mode='P' automagically.
elif mode == 'P': # default gray-scale
pal = (np.arange(0, 256, 1, dtype=np.uint8)[:, np.newaxis] *
np.ones((3,), dtype=np.uint8)[np.newaxis, :])
image.putpalette(np.asarray(pal, dtype=np.uint8).tobytes())
return image
if mode == '1': # high input gives threshold for 1
bytedata = (data > high)
image = Image.frombytes('1', shape, bytedata.tobytes())
return image
if cmin is None:
cmin = np.amin(np.ravel(data))
if cmax is None:
cmax = np.amax(np.ravel(data))
data = (data*1.0 - cmin)*(high - low)/(cmax - cmin) + low
if mode == 'I':
data32 = data.astype(np.uint32)
image = Image.frombytes(mode, shape, data32.tobytes())
else:
raise ValueError(_errstr)
return image
# if here then 3-d array with a 3 or a 4 in the shape length.
# Check for 3 in datacube shape --- 'RGB' or 'YCbCr'
if channel_axis is None:
if (3 in shape):
ca = np.flatnonzero(np.asarray(shape) == 3)[0]
else:
ca = np.flatnonzero(np.asarray(shape) == 4)
if len(ca):
ca = ca[0]
else:
raise ValueError("Could not find channel dimension.")
else:
ca = channel_axis
numch = shape[ca]
if numch not in [3, 4]:
raise ValueError("Channel axis dimension is not valid.")
bytedata = bytescale(data, high=high, low=low, cmin=cmin, cmax=cmax)
if ca == 2:
strdata = bytedata.tobytes()
shape = (shape[1], shape[0])
elif ca == 1:
strdata = np.transpose(bytedata, (0, 2, 1)).tobytes()
shape = (shape[2], shape[0])
elif ca == 0:
strdata = np.transpose(bytedata, (1, 2, 0)).tobytes()
shape = (shape[2], shape[1])
if mode is None:
if numch == 3:
mode = 'RGB'
else:
mode = 'RGBA'
if mode not in ['RGB', 'RGBA', 'YCbCr', 'CMYK']:
raise ValueError(_errstr)
if mode in ['RGB', 'YCbCr']:
if numch != 3:
raise ValueError("Invalid array shape for mode.")
if mode in ['RGBA', 'CMYK']:
if numch != 4:
raise ValueError("Invalid array shape for mode.")
# Here we know data and mode is correct
image = Image.frombytes(mode, shape, strdata)
return image
YangyangLi-potato commented
@ukdsvl
Thank you very much! I follow your solution and it works.