def plot_matches(im1, im2, matches):
"""
Displays two images side by side with matches highlighted
Args:
im1, im2: paths to the two input images
matches: 2D numpy array of size 4xN containing a list of matches (a
list of pairs of points, each pair being represented by x1, y1, x2,
y2)
Returns:
path to the resulting image, to be displayed
"""
# load images
img1 = piio.read(im1).astype(np.uint8)
img2 = piio.read(im2).astype(np.uint8)
# if images have more than 3 channels, keep only the first 3
if img1.shape[2] > 3:
img1 = img1[:, :, 0:3]
if img2.shape[2] > 3:
img2 = img2[:, :, 0:3]
# build the output image
h1, w1 = img1.shape[:2]
h2, w2 = img2.shape[:2]
w = w1 + w2
h = max(h1, h2)
out = np.zeros((h, w, 3), np.uint8)
out[:h1, :w1] = img1
out[:h2, w1:w] = img2
# define colors, according to min/max intensity values
out_min = min(np.nanmin(img1), np.nanmin(img2))
out_max = max(np.nanmax(img1), np.nanmax(img2))
green = [out_min, out_max, out_min]
blue = [out_min, out_min, out_max]
# plot the matches
for i in range(len(matches)):
x1 = matches[i, 0]
y1 = matches[i, 1]
x2 = matches[i, 2] + w1
y2 = matches[i, 3]
# convert endpoints to int (nn interpolation)
x1, y1, x2, y2 = np.round([x1, y1, x2, y2])
plot_line(out, x1, y1, x2, y2, blue)
out[y1, x1] = green
out[y2, x2] = green
# save the output image, and return its path
outfile = common.tmpfile('.png')
piio.write(outfile, out)
return outfile
def compute_height_map(P1, P2, H1, H2, f_disp, f_mask, fout_height, fout_proj_err):
import piio
import common
P1 = common.matrix_read(P1,3,4)
P2 = common.matrix_read(P2,3,4)
H1 = common.matrix_read(H1,3,3)
H2 = common.matrix_read(H2,3,3)
disp = piio.read(f_disp)[:,:,0]
mask = piio.read(f_mask)[:,:,0]
height,proj_err = disp_to_h_projective(P1,P2,H1,H2,disp,mask)
piio.write(fout_height ,height)
piio.write(fout_proj_err,proj_err)
def register_heights(im1, im2):
"""
Affine registration of heights.
Args:
im1: first height map
im2: second height map, to be registered on the first one
Returns
path to the registered second height map
"""
# remove high frequencies with a morphological zoom out
im1_low_freq = common.image_zoom_out_morpho(im1, 4)
im2_low_freq = common.image_zoom_out_morpho(im2, 4)
# first read the images and store them as numpy 1D arrays, removing all the
# nans and inf
i1 = piio.read(im1_low_freq).ravel() #np.ravel() gives a 1D view
i2 = piio.read(im2_low_freq).ravel()
ind = np.logical_and(np.isfinite(i1), np.isfinite(i2))
h1 = i1[ind]
h2 = i2[ind]
# for debug
print np.shape(i1)
print np.shape(h1)
# # 1st option: affine
# # we search the (u, v) vector that minimizes the following sum (over
# # all the pixels):
# #\sum (im1[i] - (u*im2[i]+v))^2
# # it is a least squares minimization problem
# A = np.vstack((h2, h2*0+1)).T
# b = h1
# z = np.linalg.lstsq(A, b)[0]
# u = z[0]
# v = z[1]
#
# # apply the affine transform and return the modified im2
# out = common.tmpfile('.tif')
# common.run('plambda %s "x %f * %f +" > %s' % (im2, u, v, out))
# 2nd option: translation only
v = np.mean(h1 - h2)
out = common.tmpfile('.tif')
common.run('plambda %s "x %f +" -o %s' % (im2, v, out))
return out
def register_heights(im1, im2):
"""
Affine registration of heights.
Args:
im1: first height map
im2: second height map, to be registered on the first one
Returns
path to the registered second height map
"""
# remove high frequencies with a morphological zoom out
im1_low_freq = common.image_zoom_out_morpho(im1, 4)
im2_low_freq = common.image_zoom_out_morpho(im2, 4)
# first read the images and store them as numpy 1D arrays, removing all the
# nans and inf
i1 = piio.read(im1_low_freq).ravel() #np.ravel() gives a 1D view
i2 = piio.read(im2_low_freq).ravel()
ind = np.logical_and(np.isfinite(i1), np.isfinite(i2))
h1 = i1[ind]
h2 = i2[ind]
# for debug
print np.shape(i1)
print np.shape(h1)
# # 1st option: affine
# # we search the (u, v) vector that minimizes the following sum (over
# # all the pixels):
# #\sum (im1[i] - (u*im2[i]+v))^2
# # it is a least squares minimization problem
# A = np.vstack((h2, h2*0+1)).T
# b = h1
# z = np.linalg.lstsq(A, b)[0]
# u = z[0]
# v = z[1]
#
# # apply the affine transform and return the modified im2
# out = common.tmpfile('.tif')
# common.run('plambda %s "x %f * %f +" > %s' % (im2, u, v, out))
# 2nd option: translation only
v = np.mean(h1 - h2)
out = common.tmpfile('.tif')
common.run('plambda %s "x %f +" -o %s' % (im2, v, out))
return out
def recompose(prefix, levels, suffix, wtype, cur=0):
print prefix+str(cur)+suffix
image = piio.read(prefix+str(cur)+suffix)
print image.shape
if levels==1: # if last level
return image
LL = 2 * recompose(prefix,levels-1,suffix, wtype, cur+1) ## 2 compensates the factor used in decompose
coeffs2 = pywt.dwt2(image,wtype,axes=(0,1))
if coeffs2[0].shape != LL.shape:
print "ERROR: Are you sure that \'%s\' is the right wavelet? Sizes don't match!"%wtype
exit()
ncoeffs2 = (LL, coeffs2[1])
ret = pywt.idwt2(ncoeffs2,wtype, axes=(0,1))
# adjust size of the upsampled image
ret = ret[:image.shape[0],:image.shape[1],:]
print ret.shape
return ret
def recompose(prefix, levels, suffix, wtype, cur=0):
print prefix+str(cur)+suffix
image = piio.read(prefix+str(cur)+suffix)
print image.shape
if levels==1: # if last level
return image
LL = 2 * recompose(prefix,levels-1,suffix, wtype, cur+1) ## 2 compensates the factor used in decompose
coeffs2 = pywt.dwt2(image,wtype,axes=(0,1))
if coeffs2[0].shape != LL.shape:
print "ERROR: Are you sure that \'%s\' is the right wavelet? Sizes don't match!"%wtype
exit()
ncoeffs2 = (LL, coeffs2[1])
ret = pywt.idwt2(ncoeffs2,wtype, axes=(0,1))
# adjust size of the upsampled image
ret = ret[:image.shape[0],:image.shape[1],:]
print ret.shape
return ret
def test_one(self, height, width, input, output):
H, W = height, width
inp_chns = 3 if self.args.model == 'color' else 1
self.batch_size = 1 if self.args.model == 'color' else 3
inputs = tf.placeholder(shape=[self.batch_size, H, W, inp_chns],
dtype=tf.float32)
outputs = self.generator(inputs, reuse=False)
sess = tf.Session(config=tf.ConfigProto(gpu_options=tf.GPUOptions(
allow_growth=True)))
self.saver = tf.train.Saver()
self.load(sess, self.train_dir, step=523000)
blur = iio.read(input)
h, w, c = blur.shape
# make sure the width is larger than the height
rot = False
if h > w:
blur = np.transpose(blur, [1, 0, 2])
rot = True
h = int(blur.shape[0])
w = int(blur.shape[1])
resize = False
if h > H or w > W:
scale = min(1.0 * H / h, 1.0 * W / w)
new_h = int(h * scale)
new_w = int(w * scale)
blur = scipy.misc.imresize(blur, [new_h, new_w], 'bicubic')
resize = True
blurPad = np.pad(blur, ((0, H - new_h), (0, W - new_w), (0, 0)),
'edge')
else:
blurPad = np.pad(blur, ((0, H - h), (0, W - w), (0, 0)), 'edge')
blurPad = np.expand_dims(blurPad, 0)
if self.args.model != 'color':
blurPad = np.transpose(blurPad, (3, 1, 2, 0))
start = time.time()
deblur = sess.run(outputs, feed_dict={inputs: blurPad / 255.0})
duration = time.time() - start
res = deblur[-1]
if self.args.model != 'color':
res = np.transpose(res, (3, 1, 2, 0))
res = im2uint8(res[0, :, :, :])
# crop the image into original size
if resize:
res = res[:new_h, :new_w, :]
res = scipy.misc.imresize(res, [h, w], 'bicubic')
else:
res = res[:h, :w, :]
if rot:
res = np.transpose(res, [1, 0, 2])
iio.write(output, res)
def main():
# verify input
wtype = pick_option(sys.argv, 't', 'db7')
if len(sys.argv) > 3:
image = piio.read(sys.argv[1])
coarse = piio.read(sys.argv[2])
oname = sys.argv[3]
else:
print("Incorrect syntax, use:")
print(" > " + sys.argv[0] + " image coarse result [-t type]")
print(" 2 scale recomposition using wavelets")
print(" available types:")
for family in pywt.families():
print "%s family:" % family, ', '.join(pywt.wavelist(family))
sys.exit(1)
if pywt.Wavelet(wtype).orthogonal == False:
print "Warning \'%s\' is not orthogonal"%wtype
res = recompose(image, coarse, wtype)
piio.write(oname, res);
def main():
# verify input
wtype = pick_option(sys.argv, 't', 'db7')
if len(sys.argv) > 3:
image = piio.read(sys.argv[1])
coarse = piio.read(sys.argv[2])
oname = sys.argv[3]
else:
print("Incorrect syntax, use:")
print(" > " + sys.argv[0] + " image coarse result [-t type]")
print(" 2 scale recomposition using wavelets")
print(" available types:")
for family in pywt.families():
print "%s family:" % family, ', '.join(pywt.wavelist(family))
sys.exit(1)
if pywt.Wavelet(wtype).orthogonal == False:
print "Warning \'%s\' is not orthogonal"%wtype
res = recompose(image, coarse, wtype)
piio.write(oname, res);
def main():
# verify input
wtype = pick_option(sys.argv, 't', 'db7')
if len(sys.argv) > 4:
image = piio.read(sys.argv[1])
prefix = sys.argv[2]
levels = int(sys.argv[3])
suffix = sys.argv[4]
else:
print("Incorrect syntax, use:")
print(" > " + sys.argv[0] + " input prefix levels suffix [-t type]")
print(" multiscale decomposition using wavelets")
print(" available types:")
for family in pywt.families():
print "%s family:" % family, ', '.join(pywt.wavelist(family))
sys.exit(1)
if pywt.Wavelet(wtype).orthogonal == False:
print "Warning \'%s\' is not orthogonal"%wtype
decompose(image, prefix, levels, suffix, wtype)
def main():
# verify input
wtype = pick_option(sys.argv, 't', 'db7')
if len(sys.argv) > 4:
image = piio.read(sys.argv[1])
prefix = sys.argv[2]
levels = int(sys.argv[3])
suffix = sys.argv[4]
else:
print("Incorrect syntax, use:")
print(" > " + sys.argv[0] + " input prefix levels suffix [-t type]")
print(" multiscale decomposition using wavelets")
print(" available types:")
for family in pywt.families():
print "%s family:" % family, ', '.join(pywt.wavelist(family))
sys.exit(1)
if pywt.Wavelet(wtype).orthogonal == False:
print "Warning \'%s\' is not orthogonal" % wtype
decompose(image, prefix, levels, suffix, wtype)
def mosaic(fout, w, h, list_tiles, tw, th, ov):
"""
Compose several tiles of the same size into a bigger image.
Args:
fout: path to the output image
w, h: output image dimensions
list_tiles: list containing paths to the input tiles
tw, th: dimensions of a tile (they must all have the same dimensions)
ov: overlap between tiles (in pixels)
Returns:
nothing
"""
N = len(list_tiles)
ntx = np.ceil(float(w - ov) / (tw - ov)).astype(int)
nty = np.ceil(float(h - ov) / (th - ov)).astype(int)
assert (ntx * nty == N)
# default numpy datatype is float64, useless as the ouput file will be
# stored with float32
out = np.zeros([h, w], dtype=np.float32)
count = np.zeros([h, w], dtype=np.uint8)
# loop over all the tiles
for j in range(nty):
for i in range(ntx):
sys.stdout.write("\tPasting tile %02d %02d\r" % (j, i))
sys.stdout.flush()
# top-left and bottom-right corners of the tile in the output full
# image
x0 = i * (tw - ov)
y0 = j * (th - ov)
x1 = min(x0 + tw, w)
y1 = min(y0 + th, h)
# read the tile with piio. If the tile has not been produced,
# nothing needs to be done. The corresponding pixels will get the
# value 'nan' in the output full image.
tile_fname = list_tiles[j * ntx + i]
if os.path.isfile(tile_fname):
tile = piio.read(tile_fname).astype(np.float32)[:, :, 0]
assert (np.shape(tile) == (th, tw))
# count the pixels different from nan and inf
ind = np.isfinite(tile)
count[y0:y1, x0:x1] += ind[:y1 - y0, :x1 - x0]
# replace nan and inf with zeros, then add the tile to the
# output. ~ind is the negation of ind
tile[~ind] = 0
out[y0:y1, x0:x1] += tile[:y1 - y0, :x1 - x0]
# free mem
if 'tile' in locals():
del tile
if 'ind' in locals():
del ind
gc.collect()
sys.stdout.write('\n')
# put nan where count is zero, and take the average where count is nonzero.
sys.stdout.write('\tCounting...\n')
sys.stdout.flush()
ind = (count > 0)
sys.stdout.write('\tAveraging...\n')
sys.stdout.flush()
out[ind] /= count[ind]
sys.stdout.write('\tPutting nans on empty pixels...\n')
sys.stdout.flush()
out[~ind] = np.nan
del count
# saving the 'out' numpy array in TIFF with piio requires to much memory
# (something like twice de file size) because tiled tiff image writing is
# not implemented yet in iio.
# As an alternative, the numpy array is stored in raw and the libtiff util
# 'raw2tiff' is used to produce a tiff file from it.
sys.stdout.write('\twriting raw data to disk...\n')
raw_file = common.tmpfile('')
out.tofile(raw_file)
common.run('raw2tiff -w %d -l %d -d float -c zip %s %s' %
(w, h, raw_file, fout))
def mosaic(fout, w, h, list_tiles, tw, th, ov):
"""
Compose several tiles of the same size into a bigger image.
Args:
fout: path to the output image
w, h: output image dimensions
list_tiles: list containing paths to the input tiles
tw, th: dimensions of a tile (they must all have the same dimensions)
ov: overlap between tiles (in pixels)
Returns:
nothing
"""
N = len(list_tiles)
ntx = np.ceil(float(w - ov) / (tw - ov)).astype(int)
nty = np.ceil(float(h - ov) / (th - ov)).astype(int)
assert(ntx * nty == N)
# default numpy datatype is float64, useless as the ouput file will be
# stored with float32
out = np.zeros([h, w], dtype=np.float32)
count = np.zeros([h, w], dtype=np.uint8)
# loop over all the tiles
for j in range(nty):
for i in range(ntx):
sys.stdout.write("\tPasting tile %02d %02d\r" % (j, i))
sys.stdout.flush()
# top-left and bottom-right corners of the tile in the output full
# image
x0 = i * (tw - ov)
y0 = j * (th - ov)
x1 = min(x0 + tw, w)
y1 = min(y0 + th, h)
# read the tile with piio. If the tile has not been produced,
# nothing needs to be done. The corresponding pixels will get the
# value 'nan' in the output full image.
tile_fname = list_tiles[j * ntx + i]
if os.path.isfile(tile_fname):
tile = piio.read(tile_fname).astype(np.float32)[:, :, 0]
assert(np.shape(tile) == (th, tw))
# count the pixels different from nan and inf
ind = np.isfinite(tile)
count[y0:y1, x0:x1] += ind[:y1 - y0, :x1 - x0]
# replace nan and inf with zeros, then add the tile to the
# output. ~ind is the negation of ind
tile[~ind] = 0
out[y0:y1, x0:x1] += tile[:y1 - y0, :x1 - x0]
# free mem
if 'tile' in locals():
del tile
if 'ind' in locals():
del ind
gc.collect()
sys.stdout.write('\n')
# put nan where count is zero, and take the average where count is nonzero.
sys.stdout.write('\tCounting...\n')
sys.stdout.flush()
ind = (count > 0)
sys.stdout.write('\tAveraging...\n')
sys.stdout.flush()
out[ind] /= count[ind]
sys.stdout.write('\tPutting nans on empty pixels...\n')
sys.stdout.flush()
out[~ind] = np.nan
del count
# saving the 'out' numpy array in TIFF with piio requires to much memory
# (something like twice de file size) because tiled tiff image writing is
# not implemented yet in iio.
# As an alternative, the numpy array is stored in raw and the libtiff util
# 'raw2tiff' is used to produce a tiff file from it.
sys.stdout.write('\twriting raw data to disk...\n')
raw_file = common.tmpfile('')
out.tofile(raw_file)
common.run('raw2tiff -w %d -l %d -d float -c zip %s %s' % (w, h, raw_file,
fout))
#!/usr/bin/env python
from __future__ import division
import piio
import sys
import os.path
from matplotlib import cm
if __name__ == '__main__':
if '-h' in sys.argv:
print "Usage: {} input=- output=- map=magma min=input.min() max=input.max()".format(os.path.basename(sys.argv[0]))
sys.exit(0)
in_image = piio.read(sys.argv[1] if len(sys.argv) > 1 else '-')[:,:,0]
vmin = float(sys.argv[4]) if len(sys.argv) > 4 else in_image.min()
vmax = float(sys.argv[5]) if len(sys.argv) > 5 else in_image.max()
in_image = (in_image - vmin) / (vmax - vmin)
cmap = cm.get_cmap(sys.argv[3] if len(sys.argv) > 3 else 'magma')
out_image = cmap(in_image)[:,:,:3]
piio.write(sys.argv[2] if len(sys.argv) > 2 else 'TIFF:-', out_image)
import sys, piio, demtk x = piio.read(sys.argv[1]).squeeze() piio.write(sys.argv[2], demtk.render(r))
[2, 3]])]
rgb = np.array([1, 0, 2, 1])
def expand_GrRBGb(in_image, phase):
assert(len(in_image.shape) == 3 and in_image.shape[2] == 4)
w, h = in_image.shape[:2]
out_image = np.zeros((2 * w, 2 * h, 3), dtype = in_image.dtype)
for i in xrange(2):
for j in xrange(2):
chan = pattern[phase][i, j]
out_image[i::2, j::2, rgb[chan]] = in_image[:, :, chan]
return out_image
if __name__ == '__main__':
import piio
import sys
import os.path
if '-h' in sys.argv:
print "Usage: {} [phase [input [output]]]".format(os.path.basename(sys.argv[0]))
sys.exit(1)
phase = int(sys.argv[1]) if len(sys.argv) > 1 else 2
in_image = piio.read(sys.argv[2] if len(sys.argv) > 2 else '-')
out_image = expand_GrRBGb(in_image, phase)
piio.write(sys.argv[3] if len(sys.argv) > 3 else '-', out_image)
#!/usr/bin/env python
import piio
d = piio.read('testimg.tif')
print d.shape
print d[:, :, 0]
piio.write('testimg2.png', d)
#!/usr/bin/env python
import piio
d = piio.read('testimg.tif')
print(d.shape)
print(d[:,:,0] )
piio.write('testimg2.png',d)
#!/usr/bin/env python3
import numpy
import tkinter
import piio
x = piio.read("x.png").squeeze()
x = numpy.uint8(x)
x_bytes = x.data.tobytes()
WIDTH = x.shape[1]
HEIGHT = x.shape[0]
window = tkinter.Tk()
canvas = tkinter.Canvas(window, width=WIDTH, height=HEIGHT)
canvas.pack()
xdata = b"P5 %d %d 255\n" % (WIDTH, HEIGHT) + x_bytes + x_bytes + x_bytes
image = tkinter.PhotoImage(data=xdata, format="PPM")
canvas.create_image(1, 1, image=image, anchor=tkinter.NW)
posx = 100
posy = 100
def helloworld(x):
global posx
global posy
image.put("#ff0000", (posx, posy))
posx = posx + 1
print("hello world! \"%s\"" % x)
