def test_rot180():
import cudamat
import numpy as np
num_images = 100
img_size = 50
img_tot_size = 50*50
inputs = np.random.randn(num_images, img_tot_size)
inputs[1:] = (np.random.rand(*inputs[1:].shape)<0.5)
inputs[0] = (np.random.rand(*inputs[1].shape)<0.005)
targets = np.random.randn(num_images, img_tot_size)
cu_inputs = cudamat.CUDAMatrix(inputs.T)
cu_targets = cudamat.CUDAMatrix(targets.T)
cudamat._cudamat.rot180(cu_inputs.p_mat, cu_targets.p_mat, 0)
cua_targets = cu_targets.asarray().T
targets = np.array([x[::-1,::-1]
for x in inputs.reshape(num_images, img_size, img_size)]).reshape(num_images, img_tot_size)
print abs(targets - cua_targets).max()
from pylab import imshow, subplot, gray
gray()
subplot(221)
imshow(inputs[0].reshape(img_size, img_size), interpolation='nearest')
subplot(222)
imshow(targets[0].reshape(img_size, img_size), interpolation='nearest')
subplot(223)
imshow(cua_targets[0].reshape(img_size, img_size), interpolation='nearest')
def test_copyOutOf():
import cudamat
import numpy as np
num_images = 100
img_size = 50
target_size = 72
img_tot_size = img_size**2
target_tot_size = target_size**2
targets = np.random.randn(target_tot_size, num_images)<-2
inputs = np.zeros((img_tot_size, num_images))
cu_inputs = cudamat.CUDAMatrix(inputs)
cu_targets = cudamat.CUDAMatrix(targets)
assert (target_size - img_size) % 2 == 0
padding = (target_size - img_size)/2
cudamat._cudamat.copy_out_of_center(cu_targets.p_mat, cu_inputs.p_mat, padding, 0)
cua_inputs = cu_inputs.asarray()
#print abs(targets - cua_targets).max()
from pylab import imshow, subplot, gray
gray()
#subplot(221)
#imshow(inputs[0].reshape(img_size, img_size), interpolation='nearest')
subplot(222)
imshow(targets[:,1].reshape(target_size, target_size), interpolation='nearest')
subplot(223)
imshow(cua_inputs[:,1].reshape(img_size, img_size), interpolation='nearest')
def makeTestPair(paths, homography, collection, location=".", size=(250,250), scale = 1.0) :
""" Given a pair of paths to two images and a homography between them,
this function creates two crops and calculates a new homography.
input: paths [strings] (paths to images)
homography [numpy.ndarray] (3 by 3 array homography)
collection [string] (The name of the testset)
location [string] (The location (path) of the testset
size [(int, int)] (The size of an image crop in pixels)
scale [double] (The scale by which we resize the crops after they've been cropped)
out: nothing
"""
# Get width and height
width, height = size
# Load images in black/white
images = map(loadImage, paths)
# Crop part of first image and part of second image:
(top_o, left_o) = (random.randint(0, images[0].shape[0]-height), random.randint(0, images[0].shape[1]-width))
(top_n, left_n) = (random.randint(0, images[1].shape[0]-height), random.randint(0, images[1].shape[1]-width))
# Get two file names
c_path = getRandPath("%s/%s/" % (location, collection))
if not exists(dirname(c_path)) : makedirs(dirname(c_path))
# Make sure we save as gray
pylab.gray()
im1 = images[0][top_o: top_o + height, left_o: left_o + width]
im2 = images[1][top_n: top_n + height, left_n: left_n + width]
im1_scaled = imresize(im1, size=float(scale), interp='bicubic')
im2_scaled = imresize(im2, size=float(scale), interp='bicubic')
pylab.imsave(c_path + "_1.jpg", im1_scaled)
pylab.imsave(c_path + "_2.jpg", im2_scaled)
# Homography for transpose
T1 = numpy.identity(3)
T1[0,2] = left_o
T1[1,2] = top_o
# Homography for transpose back
T2 = numpy.identity(3)
T2[0,2] = -1*left_n
T2[1,2] = -1*top_n
# Homography for scale
Ts = numpy.identity(3)
Ts[0,0] = scale
Ts[1,1] = scale
# Homography for scale back
Tsinv = numpy.identity(3)
Tsinv[0,0] = 1.0/scale
Tsinv[1,1] = 1.0/scale
# Combine homographyies and save
hom = Ts.dot(T2).dot(homography).dot(T1).dot(Tsinv)
hom = hom / hom[2,2]
numpy.savetxt(c_path, hom)
def plotSources(im, sources, schema):
plt.clf()
plt.imshow(im.getArray(), origin='lower', interpolation='nearest',
vmin=-100, vmax=500)
plt.gray()
shapekey = schema.find('shape.sdss').key
xykey = schema.find('centroid.sdss').key
flagkeys = [schema.find(x).key for x in [
'shape.sdss.flags.maxiter', 'shape.sdss.flags.shift',
'shape.sdss.flags.unweighted', 'shape.sdss.flags.unweightedbad']]
flagabbr = ['M','S','U','B']
for source in sources:
quad = source.get(shapekey)
ixx,iyy = quad.getIxx(), quad.getIyy()
x,y = source.get(xykey)
sx,sy = sqrt(ixx),sqrt(iyy)
plt.gca().add_artist(Ellipse([x,y], 2.*sx, 2.*sy, angle=0.,
ec='r', fc='none', lw=2, alpha=0.5))
fs = ''
for j,key in enumerate(flagkeys):
val = source.get(key)
if val:
fs += flagabbr[j]
if len(fs):
plt.text(x+5, y, fs, ha='left', va='center')
def show_weights(layer):
while isinstance(layer, lasagne.layers.InputLayer) == False:
if isinstance(layer, dnn.Conv2DDNNLayer):
first_conv_layer = layer
layer = layer.input_layer
weights = first_conv_layer.get_params()[0].get_value()
weights_no = weights.shape[0]
display_size = int(math.sqrt(weights_no)) + 1
print 'display_size : %s' % display_size
pylab.gray()
for i in range(display_size):
for j in range(display_size):
index = i * display_size + j + 1
if index >= weights_no:
break
print 'index : %s' % index
one_weight = weights[index][0]
pylab.subplot(display_size, display_size, index)
pylab.axis('off')
pylab.imshow(one_weight)
pylab.show()
def _test():
# make a unit circle going counter-clockwise
radius = 60
theta = np.linspace(0, 2*np.pi, 10)
circle_ccw = np.array([radius*np.cos(theta), radius*np.sin(theta)])
area = ContourArea(circle_ccw)
assert area > 0
circle_cw = MakeClockwise(circle_ccw)
area = ContourArea(circle_cw)
assert area < 0
assert (circle_cw == circle_ccw[:,::-1]).all() # it actually got reversed
p = circle_cw + np.array([[280],[430]])
plb.ion()
plb.figure(0)
plb.gray()
i = plb.imread('mri2.png')
i = np.mean(i, axis=2)
plb.imshow(i)
global _contour
_contour, = plb.plot(np.append(p[0,-1], p[0]),np.append(p[1,-1], p[1]))
plb.draw()
Snake2D(i, p, iterations=500)
print 'done'
plb.ioff()
plb.savefig('mri-result.png')
plb.show()
def main():
butils = measDeblend.BaselineUtilsF
foot = buildExample2()
fbb = foot.getBBox()
mask1 = afwImg.MaskU(fbb.getWidth(), fbb.getHeight())
mask1.setXY0(fbb.getMinX(), fbb.getMinY())
afwDet.setMaskFromFootprint(mask1, foot, 1)
if plt:
plt.clf()
plt.imshow(mask1.getArray(), origin='lower', interpolation='nearest',
extent=(fbb.getMinX(), fbb.getMaxX(), fbb.getMinY(), fbb.getMaxY()))
plt.gray()
plt.savefig('foot2.png')
sfoot = butils.symmetrizeFootprint(foot, 355, 227)
mask2 = afwImg.MaskU(fbb.getWidth(), fbb.getHeight())
mask2.setXY0(fbb.getMinX(), fbb.getMinY())
afwDet.setMaskFromFootprint(mask2, sfoot, 1)
plt.clf()
plt.imshow(mask2.getArray(), origin='lower', interpolation='nearest',
extent=(fbb.getMinX(), fbb.getMaxX(), fbb.getMinY(), fbb.getMaxY()))
plt.gray()
plt.savefig('sfoot3.png')
def plot(self):
"""
.. plot::
:include-source:
:width: 80%
from cellnopt.simulate import *
from cellnopt.core import *
pkn = cnodata("PKN-ToyPB.sif")
midas = cnodata("MD-ToyPB.csv")
s = boolean.BooleanSimulator(CNOGraph(pkn, midas))
s.simulate(30)
s.plot()
"""
pylab.clf()
data = numpy.array([self.data[x] for x in self.species if x in self.species])
data = data.transpose()
data = 1 - pylab.flipud(data)
pylab.pcolor(data, vmin=0, vmax=1, edgecolors="k")
pylab.xlabel("species");
pylab.ylabel("Time (tick)");
pylab.gray()
pylab.xticks([0.5+x for x in range(0,30)], self.species, rotation=90)
pylab.ylim([0, self.tick])
pylab.xlim([0, len(self.species)])
def plot_matches(self, name, show_below = True, match_maximum = None):
""" 対応点を線で結んで画像を表示する
入力： im1,im2（配列形式の画像）、locs1,locs2（特徴点座標）
machescores（match()の出力）、
show_below（対応の下に画像を表示するならTrue）"""
im1 = self._image_1.get_array_image()
im2 = self._image_2.get_array_image()
self.appendimages()
im3 = self._append_image
if self._match_score is None:
self.match()
locs1 = self._image_1.get_shift_location()
locs2 = self._image_2.get_shift_location()
if show_below:
im3 = numpy.vstack((im3,im3))
pylab.figure(dpi=160)
pylab.gray()
pylab.imshow(im3, aspect = 'auto')
cols1 = im1.shape[1]
match_num = 0
for i,m in enumerate(self._match_score):
if m > 0 :
pylab.plot([locs1[i][0],locs2[m][0]+cols1], [locs1[i][1],locs2[m][1]], 'c')
match_num = match_num + 1
if match_maximum is not None and match_num >= match_maximum:
break
pylab.axis('off')
pylab.savefig(name, dpi=160)
def demo_graymapping():
"""
Gray space mapping, just change the way of distribution
"""
im = np.array(Image.open('./computer_vision/scenery.jpg').convert('L'))
"""Reverse mapping"""
im_r = 255 - im
"""Mapping gray space to specific range[100, 200]"""
im_f = (100.0 / 255) * im + 100
"""Square pixel value"""
im_s = 255 * (im/ 255.0)**2
"""Show image"""
pl.figure('Gray space mapping')
pl.gray()
pl.subplot(2,2,1)
pl.title('orignal gray image')
pl.imshow(im)
pl.subplot(2,2,2)
pl.title('Reversed gray')
pl.imshow(im_r)
pl.subplot(2,2,3)
pl.title('Mapping to a [100,200]')
pl.imshow(im_f)
pl.subplot(2,2,4)
pl.title('Square pixel value')
pl.imshow(im_s)
pl.show()
def randoms(S=10, N=1, GA=10, GS=10):
butils = measDeblend.BaselineUtilsF
mim = afwImg.MaskedImageF(S, S)
x0, y0 = S/2, S/2
peak = makePeak(x0, y0)
im = mim.getImage().getArray()
for i in range(N):
X, Y = np.meshgrid(np.arange(S), np.arange(S))
R2 = (X-x0)**2 + (Y-y0)**2
im[:, :] = np.random.normal(10, 1, size=im.shape) + GA * np.exp(-0.5 * R2 / GS**2)
if plt:
plt.clf()
ima = dict(vmin=im.min(), vmax=im.max(), origin='lower', interpolation='nearest')
plt.imshow(im, **ima)
plt.gray()
plt.savefig('Rim%i.png' % i)
butils.makeMonotonic(mim.getImage(), peak)
plt.clf()
plt.imshow(mim.getImage().getArray(), **ima)
plt.gray()
plt.savefig('Rim%im.png' % i)
def savepng(pre, img, title=None, **kwargs):
fn = '%s-%s.png' % (pre, idstr)
print 'Saving', fn
plt.clf()
plt.imshow(img, **kwargs)
ax = plt.axis()
if debug:
print len(xplotx),len(allobjx)
for i,(objx,objy,objc) in enumerate(zip(allobjx,allobjy,allobjc)):
plt.plot(objx,objy,'-',c=objc)
tempx = []
tempx.append(xplotx[i])
tempx.append(objx[0])
tempy = []
tempy.append(xploty[i])
tempy.append(objy[0])
plt.plot(tempx,tempy,'-',c='purple')
plt.plot(pointx,pointy,'y.')
plt.plot(xplotx,xploty,'xg')
plt.axis(ax)
if title is not None:
plt.title(title)
plt.colorbar()
plt.gray()
plt.savefig(fn)
def explore_data(data, images, target): # try to determine the type of data... print "data_type belonging to key data:" try: print np.dtype(data) except TypeError as err: print err print "It has dimension", np.shape(data) # plot a 3 # get indices of all threes in target threes = np.where(target == 3) #assert threes is not empty assert(len(threes) > 0) # choose the first 3 three_indx = threes[0] # get the image img = images[three_indx][0] #plot it plot.figure() plot.gray() plot.imshow(img, interpolation = "nearest") plot.show() plot.close()
def visualize_walkthrough():
x_batch = sample_x_from_data_distribution(20)
z_batch = gen(x_batch, test=True)
if use_gpu:
z_batch.to_cpu()
fig = pylab.gcf()
fig.set_size_inches(16.0, 16.0)
pylab.clf()
if config.img_channel == 1:
pylab.gray()
z_a = z_batch.data[:10,:]
z_b = z_batch.data[10:,:]
for col in range(10):
_z_batch = z_a * (1 - col / 9.0) + z_b * col / 9.0
_z_batch = Variable(_z_batch)
if use_gpu:
_z_batch.to_gpu()
_x_batch = dec(_z_batch, test=True)
if use_gpu:
_x_batch.to_cpu()
for row in range(10):
pylab.subplot(10, 10, row * 10 + col + 1)
if config.img_channel == 1:
pylab.imshow(np.clip((_x_batch.data[row] + 1.0) / 2.0, 0.0, 1.0).reshape((config.img_width, config.img_width)), interpolation="none")
elif config.img_channel == 3:
pylab.imshow(np.clip((_x_batch.data[row] + 1.0) / 2.0, 0.0, 1.0).reshape((config.img_channel, config.img_width, config.img_width)), interpolation="none")
pylab.axis("off")
pylab.savefig("%s/walk_through.png" % args.visualization_dir)
def updateColorTable(self, cItem):
print "now viz!"+str(cItem.row())+","+str(cItem.column())
row = cItem.row()
col = cItem.column()
pl.clf()
#pl.ion()
x = pl.arange(self.dataDimen+1)
y = pl.arange(self.dataDimen+1)
X, Y = pl.meshgrid(x, y)
pl.subplot(1,2,1)
pl.pcolor(X, Y, self.mWx[row*self.dataMaxRange+col])
pl.gca().set_aspect('equal')
pl.colorbar()
pl.gray()
pl.title("user 1")
pl.subplot(1,2,2)
pl.pcolor(X, Y, self.mWy[row*self.dataMaxRange+col])
pl.gca().set_aspect('equal')
pl.colorbar()
pl.gray()
pl.title("user 2")
#pl.tight_layout()
pl.draw()
#pl.show()
pl.show(block=False)
def demo(self):
# plt.imshow(self.imgsobel*255/self.sobelmax)
# plt.gray()
# plt.show()
plt.imshow(255-self.cannyOperation())
plt.gray()
plt.show()
def compare_keypoints(im1, im2, pos1, pos2, filename = None, separation = 0) :
""" Show two images next to each other with the keypoints marked
"""
# Construct unified image
im3 = append_images(im1,im2, separation)
# Find the offset and add it
offset = im1.shape[1]
pos2_o = [(x+offset + separation,y) for (x,y) in pos2]
# Create figure
fig = pylab.figure(frameon=False, figsize=(12.0, 8.0))
#ax = pylab.Axes(fig, [0., 0., 1., 1.])
# Show images
pylab.gray()
pylab.imshow(im3)
pylab.plot([x for x,y in pos1], [y for x,y in pos1], marker='o', color = '#00aaff', lw=0)
pylab.plot([x for x,y in pos2_o], [y for x,y in pos2_o], marker='o', color = '#00aaff', lw=0)
pylab.axis('off')
pylab.xlim(0,im3.shape[1])
pylab.ylim(im3.shape[0],0)
if filename != None :
fig.savefig(filename, bbox_inches='tight', dpi=300)
def plot_matches(self, name = "harris_match.jpg", show_below = True, match_maximum = None):
""" 対応点を線で結んで画像を表示する
入力：
show_below（対応の下に画像を表示するならTrue）"""
if self._append_image is None:
self.appendimages()
im1 = self._image_1.get_array_image()
im2 = self._image_2.get_array_image()
im3 = self._append_image
if self._image_1.get_harris_point() is None:
self._image_1.make_harris_points()
if self._image_2.get_harris_point() is None:
self._image_2.make_harris_points()
locs1 = self._image_1.get_harris_point()
locs2 = self._image_2.get_harris_point()
if show_below:
im3 = numpy.vstack((im3,im3))
pylab.figure(dpi=160)
pylab.gray()
pylab.imshow(im3, aspect = "auto")
cols1 = im1.shape[1]
if self._match_score is None:
self.match()
if match_maximum is not None:
self._match_score = self._match_score[:match_maximum]
for i,m in enumerate(self._match_score):
if m>0:
pylab.plot([locs1[i][1],locs2[m][1]+cols1],[locs1[i][0],locs2[m][0]],'c')
pylab.axis('off')
pylab.savefig(name, dpi=160)
def main():
"""Compute the SSIM index on two input images specified on the cmd line."""
import pylab
argv = sys.argv
if len(argv) != 3:
print('usage: python -m sp.ssim image1.tif image2.tif', file=sys.stderr)
sys.exit(2)
try:
from PIL import Image
img1 = numpy.asarray(Image.open(argv[1]))
img2 = numpy.asarray(Image.open(argv[2]))
except Exception as e:
e = 'Cannot load images' + str(e)
print(e, file=sys.stderr)
ssim_map = ssim(img1, img2)
ms_ssim = msssim(img1, img2)
pylab.figure()
pylab.subplot(131)
pylab.title('Image1')
pylab.imshow(img1, interpolation='nearest', cmap=pylab.gray())
pylab.subplot(132)
pylab.title('Image2')
pylab.imshow(img2, interpolation='nearest', cmap=pylab.gray())
pylab.subplot(133)
pylab.title('SSIM Map\n SSIM: %f\n MSSSIM: %f' % (ssim_map.mean(), ms_ssim))
pylab.imshow(ssim_map, interpolation='nearest', cmap=pylab.gray())
pylab.show()
return 0
def plotAVTable(experiment):
pylab.figure()
pylab.gray()
pylab.pcolor(experiment.agent.module.params.reshape(81,4).max(1).reshape(9,9),
shading='faceted')
pylab.title("Action-Value table, %s, Run %d" %
(experiment.agent.learner.__class__.__name__, experiment.stepid))
def main():
A = pl.imread(IMAGE_FILE)
i = 1
pc_values = (1, 5, 10, 20, 30, 40)
for num_pcs in pc_values:
# perform (truncated) pca
egvecs, proj, egvals = pca(A, num_pcs)
# reconstruct image
A_rec = np.dot(egvecs, proj).T + np.mean(A, axis=0)
# create sublplot
ax = pl.subplot(2, 3, i, frame_on=False)
ax.xaxis.set_major_locator(pl.NullLocator())
ax.yaxis.set_major_locator(pl.NullLocator())
# draw
pl.imshow(A_rec)
pl.title("{} pc's".format(num_pcs))
pl.gray()
i += 1
pl.show()
def display_head(set_x, set_y, n = 5):
'''
show some figures based on gray image matrixs
@type set_x: TensorSharedVariable,
@param set_x: gray level value matrix of the
@type set_y: TensorVariable,
@param set_y: label of the figures
@type n: int,
@param n: numbers of figure to be display, less than 10, default 5
'''
import pylab
if n > 10: n = 10
img_x = set_x.get_value()[0:n].reshape(n, 28, 28)
img_y = set_y.eval()[0:n]
for i in range(n):
pylab.subplot(1, n, i+1);
pylab.axis('off');
pylab.title(' %d' % img_y[i])
pylab.gray()
pylab.imshow(img_x[i])
def interference_maker(self):
wavelength = input("input wavelength: ")
k = 2*pi/wavelength
amplitude = input("input amplitude: ")
distance_between_centers = input("input distance between centers: ")
length = 100.0
num_points = 500
spacing = length/num_points
first_x = length/2 + distance_between_centers/2
second_x = length/2 - distance_between_centers/2
first_y = length/2
second_y = first_y
points_array = empty([num_points, num_points], float)
for i in range(num_points):
y = spacing*i
for j in range(num_points):
x = spacing*j
r1 = sqrt((x-first_x)**2 + (y - first_y)**2)
r2 = sqrt((x-second_x)**2 + (y - second_y)**2)
points_array[i, j] = amplitude*(sin(k*r1)+sin(k*r2))
imshow(points_array, origin="lower", extent=[0, length, 0, length])
gray()
show()
def apply_conv(name):
import pylab
from PIL import Image
# open random image of dimensions 639x516
img = Image.open(open(name))
# capture image height and width
width,height = img.size
# dimensions are (height, width, channel)
img = numpy.asarray(img, dtype='float64') / 256.
# put image in 4D tensor of shape (1, 3, height, width)
img_ = img.transpose(2, 0, 1).reshape(1, 3, height, width)
filtered_img = f(img_)
# plot original image and first and second components of output
pylab.subplot(1, 3, 1); pylab.axis('off'); pylab.imshow(img)
pylab.gray();
# recall that the convOp output (filtered image) is actually a "minibatch",
# of size 1 here, so we take index 0 in the first dimension:
pylab.subplot(1, 3, 2); pylab.axis('off'); pylab.imshow(filtered_img[0, 0, :, :])
pylab.subplot(1, 3, 3); pylab.axis('off'); pylab.imshow(filtered_img[0, 1, :, :])
pylab.show()
def display_conv_filters(title, layer):
"""
displays the filters as "images"
- one row per feature map in this layer
- one column per input into this layer (one for the first layer,
one per previous layer's feature maps for the next layer)
"""
filters = layer.W # 4D Tensor of dimensions <number of feature maps, number of inputs, height, width>
bias = layer.b # vector of biases, one per feature map
pylab.gray() # make plots greyscale
i = 0
num_feature_maps = len(filters.eval())
for map_num in range(num_feature_maps): # iterate through the feature maps
num_inputs = len(filters.eval()[map_num])
for input_num in range(num_inputs): # iterate through the inputs to this feature map
i += 1
img_data = filters.eval()[map_num][input_num] # extract the (array of) filter values from the tensor slice
pylab.subplot(num_feature_maps, num_inputs, i)
pylab.axis('off')
pylab.imshow(img_data) # Plot it
if i == 1:
pylab.title(title)
pylab.show()
def test():
from PIL import Image
import numpy
import pylab
import os
p = r'C:\repository\research_code\gwb_cropped\gwb_cropped'
imlist = [p+'/'+f for f in os.listdir(p) if 'jpg' in f]
im = numpy.array(Image.open(imlist[0])) #open one image to get the size
m,n = im.shape[0:2] #get the size of the images
imnbr = len(imlist) #get the number of images
#create matrix to store all flattened images
immatrix = numpy.array([numpy.array(Image.open(imlist[i])).flatten() for i in range(imnbr)],'f')
#perform PCA
V,S,immean = pca(immatrix)
#mean image and first mode of variation
immean = immean.reshape(m,n)
mode = V[0].reshape(m,n)
#show the images
pylab.figure()
pylab.gray()
pylab.imshow(immean)
pylab.figure()
pylab.gray()
pylab.imshow(mode)
pylab.show()
def rhoPlot(self, ccaMat, filename, winsize, framesize,dataDimen):
dlen = len(ccaMat)
#x = pl.arange(dlen)
#y = pl.arange(dlen)
print "dlen:"+str(dlen)
order = dataDimen
for i in range(order):
pl.clf()
#pl.ion()
#第一成分はとった
cMat = ccaMat[:,:,i]
Y,X = np.mgrid[slice(0, dlen, 1),slice(0, dlen, 1)]
#print "len X:"+str(len(X))+", X:"+str(X)
#X, Y = np.mgrid[0:dlen:complex(0, dlen), 0:dlen:complex(0, dlen)]
pl.pcolor(X, Y, cMat)
pl.xlim(0,dlen-1)
pl.ylim(0,dlen-1)
name = str(filename) + "_w-" + str(winsize) + "_f-" + str(framesize)+"_"+str(i)
pl.title(name)
pl.colorbar()
pl.gray()
pl.draw()
outname = "cca-eig/"+name+".png"
pl.savefig(outname)
print "eig order:",i," save!"
def demo_histeq():
"""
Gray histogram equalization, make every gray value have same distribution.
This process could imporve contrast of image.
"""
im = np.array(Image.open('./computer_vision/scenery.jpg').convert('L'))
imhist, bins = np.histogram(im.flatten(), bins = 256, normed = True)
cdf = imhist.cumsum()
"""Normalization"""
cdf = 255 * cdf / cdf[-1]
im_n = np.interp(im.flatten(),bins[:-1], cdf)
"""
Image of equalization.
I don't know why reshape() function can't modify shape attribute,
it just change the arrangement of output if you print it out.
I've tried when you create a array with reshape(), it works.
"""
#im_n.reshape(im.shape)
im_n.shape = im.shape
pl.figure('Histogram equalization')
pl.subplot(1,2,1)
pl.gray()
pl.imshow(im)
pl.title('Orignal gray image')
pl.subplot(1,2,2)
pl.gray()
pl.imshow(im_n)
pl.title('equalized image')
pl.show()
def call_run(self):
print('RlOp: running')
# prepare plotting
pylab.gray()
pylab.ion()
for i in range(1000):
# interact with the environment (here in batch mode)
self.experiment.doInteractions(100)
self.agent.learn()
self.agent.reset()
results0 = self.table.params.reshape(2, 4, 5, 20)[0]
results1 = self.table.params.reshape(2, 4, 5, 20)[1]
pp.pprint(results0.argmax(2))
pp.pprint(results1.argmax(2))
# and draw the table
#ar=self.table.params.reshape(2,5,4,5,4)
#for state1 in range(len(constants.SOUNDS)):
# for state2 in range(4):
# pylab.pcolor(ar[1][state1][state2])
# pylab.draw()
results0 = self.table.params.reshape(2, 4, 5, 20)[0]
results1 = self.table.params.reshape(2, 4, 5, 20)[1]
while True:
time.sleep(60)
pp.pprint(results0.argmax(2))
pp.pprint(results1.argmax(2))
def display_output(images, batch_size, layer, num_feature_maps):
"""
Visualises the convolution of the last image to be processed.
NOTE: SAMPLE CODE ONLY - ONLY USED FOR LAYER0
"""
# Create a theano function that computes the layer0 output for a single batch
# This declares to theano what the input source and output expression are
f = theano.function([layer.input], layer.output)
# recast the inputs from (batch_size, num_pixels) to a 4D tensor of size (batch_size, 1, height, width)
# as expected by the convolutional layer (the 1 is the "depth" of the input layer)
img = images.eval()[0: batch_size].reshape(batch_size, 1, IMAGE_HEIGHT, IMAGE_WIDTH)
filtered_img = f(img)
filtered_img = numpy.add(filtered_img, -1. * filtered_img.min()) # Avoid negatives by ensuring the min value is 0
pylab.gray();
# Plot the original image
pylab.subplot(1, 4, 1);
pylab.axis('off');
pylab.imshow(img[0, 0, :, :])
pylab.title("Original image")
# Plot each feature map
for map_num in range(num_feature_maps):
pylab.subplot(1, num_feature_maps + 1, map_num + 2);
pylab.axis('off');
pylab.imshow(filtered_img[0, map_num, :, :])
pylab.title("Feature map " + str(map_num))
pylab.show()
def compare_missed_segm(
input_dir='/datagrid/personal/TextSpotter/FastTextEval/experiments/segmentation',
input_dir2='/datagrid/personal/TextSpotter/FastTextEval/experiments/segmentationg',
showPictures=False):
ft = FASTex()
(ms, dirs) = read_segm_data(input_dir)
(ms2, dirs2) = read_segm_data(input_dir2, 'g')
ms.extend(ms2)
dirs.extend(dirs2)
sumHash = {}
for j in np.arange(0, len(ms)):
missing_segm = ms[j]
for image in missing_segm.keys():
arr = missing_segm[image]
if not sumHash.has_key(image):
sumHash[image] = arr
continue
for i in range(len(arr)):
miss_gt = arr[i]
check = sumHash[image]
hasGt = False
for k in range(len(check)):
miss_gt2 = check[k]
if miss_gt == miss_gt2:
hasGt = True
if not hasGt:
sumHash[image].append(miss_gt)
missing_segm = ms[0]
data = []
dataf = []
gt_id = 0
columns = ['Img', 'GT Id']
for image in sumHash.keys():
arr = sumHash[image]
f = None
for i in range(len(arr)):
orValue = False
miss_gt = arr[i]
row = []
row.append(os.path.basename(image))
row.append(gt_id)
gt_id += 1
rowf = []
for j in np.arange(0, len(ms)):
if gt_id == 1:
columns.append(dirs[j])
msj = ms[j]
hasSegmj = True
val = 1
if msj.has_key(image):
arrj = msj[image]
for k in range(len(arrj)):
miss_gtj = arrj[k]
if miss_gtj == miss_gt:
hasSegmj = False
val = 0
break
row.append(hasSegmj)
rowf.append(val)
orValue = orValue or hasSegmj
if orValue:
rowf.append(1)
else:
rowf.append(0)
if showPictures:
img = cv2.imread(image)
imgg = cv2.imread(image, cv2.IMREAD_GRAYSCALE)
if f == None:
f, axes = plt.subplots(1, 2, figsize=(16, 3))
f.suptitle('Missing segmentation: {0}'.format(image))
ax = axes[0]
ax.imshow(img,
cmap=pylab.gray(),
interpolation='nearest')
ax = axes[1]
ax.imshow(imgg,
cmap=pylab.gray(),
interpolation='nearest')
orBox = miss_gt
segmentations = ft.getCharSegmentations(imgg)
keypoints = ft.getLastDetectionKeypoints()
style = 'rx'
for k in range(5):
maski = keypoints[:, 9] == k + 1
if k == 1:
style = "rv"
if k == 2:
style = "ro"
if k == 4:
style = "bo"
ax.plot(keypoints[maski, 0], keypoints[maski, 1],
style)
for k in range(keypoints.shape[0]):
ax.plot([keypoints[k, 0], keypoints[k, 7]],
[keypoints[k, 1], keypoints[k, 8]], 'r-')
ax = axes[0]
else:
orBox = utils.union(orBox, miss_gt)
line = mlines.Line2D(np.array([
miss_gt[0], miss_gt[2], miss_gt[2], miss_gt[0],
miss_gt[0]
]),
np.array([
miss_gt[1], miss_gt[1],
miss_gt[3], miss_gt[3], miss_gt[1]
]),
lw=5.,
alpha=0.6,
color='r')
ax.add_line(line)
row.append(orValue)
data.append(row)
dataf.append(rowf)
if f != None:
ax = axes[0]
ax.set_xlim(orBox[0] - 20, orBox[2] + 20)
ax.set_ylim(orBox[3] + 20, orBox[1] - 20)
ax = axes[1]
ax.set_xlim(orBox[0] - 20, orBox[2] + 20)
ax.set_ylim(orBox[3] + 20, orBox[1] - 20)
plt.show()
columns.append("OR")
data = np.array(data)
dataf = np.array(dataf)
df = pandas.DataFrame(data=data, columns=columns)
#print(df)
sumCols = dataf.sum(0)
sumCols = dataf.shape[0] - sumCols
print("Missing Segmentations:")
print(sumCols)
indices = np.argsort(sumCols)
bestFactor = indices[1]
missing_segm = ms[bestFactor]
print("Best factor: {0}".format(dirs[bestFactor]))
maskBest = dataf[:, bestFactor] == 0
datafSec = dataf[maskBest, :]
sumCols = datafSec.sum(0)
sumCols = datafSec.shape[0] - sumCols
print("Missing Segmentations 2 best:")
print(sumCols)
indices = np.argsort(sumCols)
bestFactor2 = indices[1]
print("Best factor 2: {0}, missing segmentations: {1} -> {2}".format(
dirs[bestFactor2], datafSec.shape[0], sumCols[indices[1]]))
maskBest = datafSec[:, bestFactor2] == 0
dataf3 = datafSec[maskBest, :]
sumCols = dataf3.sum(0)
sumCols = dataf3.shape[0] - sumCols
indices = np.argsort(sumCols)
bestFactor2 = indices[1]
print("Best factor 3: {0}, missing segmentations: {1} -> {2}".format(
dirs[bestFactor2], dataf3.shape[0], sumCols[indices[1]]))
import harris
from imtools import imresize
import datetime
WID = 5
im1 = np.array(Image.open('.\Resource\crans_1_small.jpg').convert('L'))
im2 = np.array(Image.open('.\Resource\crans_2_small.jpg').convert('L'))
im1 = imresize(im1, (im1.shape[1] / 2, im1.shape[0] / 2))
im2 = imresize(im2, (im2.shape[1] / 2, im2.shape[0] / 2))
harrisim1 = harris.compute_harris_response(im1, 5)
filtered_coords1 = harris.get_harris_points(harrisim1, WID + 1)
d1 = harris.get_descriptors(im1, filtered_coords1, WID)
harrisim2 = harris.compute_harris_response(im2, 5)
filtered_coords2 = harris.get_harris_points(harrisim2, WID + 1)
d2 = harris.get_descriptors(im2, filtered_coords2, WID)
print "start matching:{}".format(datetime.datetime.now())
matches = harris.match_twosided(d1, d2)
pl.figure()
pl.gray()
harris.plot_matches(im1, im2, filtered_coords1, filtered_coords2,
matches[:100])
print "finish matching:{}".format(datetime.datetime.now())
pl.show()
vol_geom['option']['WindowMinX'] += shift
vol_geom['option']['WindowMaxX'] += shift
proj_id2 = astra.create_projector('cuda', proj_geom, vol_geom)
sinogram_id2, sinogram2 = astra.create_sino(P, proj_id2)
# Shift window in y direction
vol_geom['option']['WindowMinX'] -= shift
vol_geom['option']['WindowMaxX'] -= shift
vol_geom['option']['WindowMinY'] += shift
vol_geom['option']['WindowMaxY'] += shift
proj_id3 = astra.create_projector('cuda', proj_geom, vol_geom)
sinogram_id3, sinogram3 = astra.create_sino(P, proj_id3)
pylab.gray()
pylab.figure()
pylab.imshow(P)
pylab.figure()
pylab.imshow(sinogram1)
pylab.figure()
pylab.imshow(sinogram2)
pylab.figure()
pylab.imshow(sinogram3)
pylab.show()
# Free memory
astra.data2d.delete(sinogram_id1)
astra.projector.delete(proj_id1)
astra.data2d.delete(sinogram_id2)
astra.projector.delete(proj_id2)
def Plot_state(t, suffix, SURVEY='PS1'):
'''
Make all the plots we need to assess the state of the tractor. Mainly,
a multi-panel figure of image, synthetic image and chi, for each image being
modelled.
t is a Tractor object, containing a list of images.
'''
for i, image in enumerate(t.images):
if image.name is None:
imname = suffix + str(i)
else:
imname = image.name
chi = t.getChiImage(i)
if SURVEY == 'PS1':
ima, chia, psfa = lenstractor.PS1_imshow_settings(image, chi)
elif SURVEY == 'KIDS':
ima, chia, psfa = lenstractor.KIDS_imshow_settings(image, chi)
else:
# Do the same as for PS1
scale = np.sqrt(np.median(1.0 / image.invvar[image.invvar > 0.0]))
ima = dict(interpolation='nearest',
origin='lower',
vmin=-100. * scale,
vmax=3. * scale)
chia = dict(interpolation='nearest',
origin='lower',
vmin=-5.,
vmax=5.)
psfa = dict(interpolation='nearest', origin='lower')
fig = plt.figure(**figprops)
fig.subplots_adjust(**adjustprops)
plt.clf()
plt.gray()
plt.subplot(py, px, 1)
plt.imshow(-image.data, **ima)
tidyup_plot()
plt.title('Observed image')
model = t.getModelImages()[i]
# print "lenstractor.Plot_state: minmax of model = ",np.min(model),np.max(model)
plt.subplot(py, px, 2)
plt.imshow(-model, **ima)
tidyup_plot()
plt.title('Predicted image')
plt.subplot(py, px, 3)
plt.imshow(-chi, **chia)
tidyup_plot()
if SURVEY == 'KIDS':
# It is not clear why the residual image is not in units of
# sigma. Perhaps this causes problems in the modelling.
# This code is not refactored into kids.py since it should
# not be necessary in the first place.
plt.title('Residuals (flexible scale)')
else:
plt.title('Residuals ($\pm 5\sigma$)')
psfimage = image.psf.getPointSourcePatch(*model.shape).patch
plt.subplot(py, px, 4)
plt.imshow(-psfimage, **psfa)
tidyup_plot()
plt.title('PSF')
plt.savefig(imname + '_' + suffix + '.png')
return
YY = np.linspace(0, 4096, 5)
XX = np.linspace(0, 2048, 5)
yims = []
for y in YY:
xims = []
for x in XX:
im = psf.instantiateAt(x, y)
im /= im.sum()
xims.append(im)
xims = np.hstack(xims)
yims.append(xims)
yims = np.vstack(yims)
plt.clf()
plt.imshow(yims, origin='lower', interpolation='nearest')
plt.gray()
plt.hot()
plt.title('instantiated')
ps.savefig()
for scale in [True, False]:
print('fitting params, scale=', scale)
psf.scale = scale
psf.splines = None
psf.ensureFit()
sims = []
for y in YY:
xims = []
for x in XX:
mog = psf.mogAt(x, y)
def train_test_plot(use_momentum=False,
use_sparsity=False,
for_comparison=False):
"""Entry point for script."""
# load data
train_x, test_x, train_y, test_y = load_mnist(onehot=True)
trx = len(train_x)
print('finished loading data')
# initialize model
x_mat = T.fmatrix('x')
y_mat = T.fmatrix('d')
np.random.seed(10)
rng = np.random.RandomState(123)
theano_rng = RandomStreams(rng.randint(2**30))
corruption_level = 0.1
training_epochs = 25
batch_size = 128
if use_momentum:
print('Using momentum term.')
if use_sparsity:
print('Using sparsity constraint.')
# initialize weights and biases
weight_1 = init_weights(28 * 28, 900)
bias_1 = init_bias(900)
weight_2 = init_weights(900, 625)
bias_2 = init_bias(625)
weight_3 = init_weights(625, 400)
bias_3 = init_bias(400)
# output reconstructed input from 3rd hidden layer
weight_4 = init_weights(400, 28 * 28)
bias_4 = init_bias(28 * 28)
# softmax layer
weight_5 = init_weights(400, 10)
bias_5 = init_bias(10)
# CORRUPT THE PURE
tilde_x = theano_rng.binomial(size=x_mat.shape,
n=1,
p=1 - corruption_level,
dtype=theano.config.floatX) * x_mat
y_1 = T.nnet.sigmoid(T.dot(tilde_x, weight_1) + bias_1)
y_2 = T.nnet.sigmoid(T.dot(y_1, weight_2) + bias_2)
y_3 = T.nnet.sigmoid(T.dot(y_2, weight_3) + bias_3)
# reconstruction layer
z_1 = T.nnet.sigmoid(T.dot(y_3, weight_4) + bias_4)
if use_sparsity:
beta = 0.5
rho = 0.05
term_1 = -T.mean(
T.sum(x_mat * T.log(z_1) + (1 - x_mat) * T.log(1 - z_1), axis=1))
term_2 = beta * T.shape(y_3)[1] * (rho * T.log(rho) +
(1 - rho) * T.log(1 - rho))
term_3 = -beta * rho * T.sum(T.log(T.mean(y_3, axis=0) + 1e-6))
term_4 = - beta * (1 - rho) * \
T.sum(T.log(1 - T.mean(y_3, axis=0) + 1e-6))
cost_1 = term_1 + term_2 + term_3 + term_4
else:
cost_1 = -T.mean(
T.sum(x_mat * T.log(z_1) + (1 - x_mat) * T.log(1 - z_1), axis=1))
params_1 = [
weight_1, bias_1, weight_2, bias_2, weight_3, bias_3, weight_4, bias_4
]
if use_momentum:
updates_1 = sgd_momentum(cost_1, params_1)
else:
updates_1 = sgd(cost_1, params_1)
train_da1 = theano.function(inputs=[x_mat],
outputs=cost_1,
updates=updates_1,
allow_input_downcast=True)
test_da1 = theano.function(inputs=[x_mat],
outputs=[y_1, y_2, y_3, z_1],
allow_input_downcast=True)
# softmax layer
p_y_4 = T.nnet.softmax(T.dot(y_3, weight_5) + bias_5)
y_4 = T.argmax(p_y_4, axis=1)
cost_2 = T.mean(T.nnet.categorical_crossentropy(p_y_4, y_mat))
params_2 = [
weight_1, bias_1, weight_2, bias_2, weight_3, bias_3, weight_5, bias_5
]
if use_momentum:
updates_2 = sgd_momentum(cost_2, params_2)
else:
updates_2 = sgd(cost_2, params_2)
train_ffn = theano.function(inputs=[x_mat, y_mat],
outputs=cost_2,
updates=updates_2,
allow_input_downcast=True)
test_ffn = theano.function(inputs=[x_mat],
outputs=y_4,
allow_input_downcast=True)
# stacked denoising autoencoder
print('training dae1 ...')
train_cost = []
for _ in tqdm(range(training_epochs)):
# go through trainng set
cost = []
for start, end in zip(range(0, trx, batch_size),
range(batch_size, trx, batch_size)):
cost.append(train_da1(train_x[start:end]))
train_cost.append(np.mean(cost, dtype='float64'))
if for_comparison:
# return training cost for comparison later
comp_train_cost = train_cost
else:
# all the plottings
pylab.figure()
pylab.plot(range(training_epochs), train_cost)
pylab.xlabel('iterations')
pylab.ylabel('cross-entropy')
pylab.savefig(os.path.join(CUR_DIR, 'project_2b_train.png'))
print('plotting weight samples')
w_1 = weight_1.get_value()
pylab.figure()
pylab.gray()
for i in tqdm(range(100)):
pylab.subplot(10, 10, i + 1)
pylab.axis('off')
pylab.imshow(w_1[:, i].reshape(28, 28))
pylab.suptitle('layer 1 weight samples')
pylab.savefig(os.path.join(CUR_DIR, 'project_2b_weight1.png'))
w_2 = weight_2.get_value()
pylab.figure()
pylab.gray()
for i in tqdm(range(100)):
pylab.subplot(10, 10, i + 1)
pylab.axis('off')
pylab.imshow(w_2[:, i].reshape(30, 30))
pylab.suptitle('layer 2 weight samples')
pylab.savefig(os.path.join(CUR_DIR, 'project_2b_weight2.png'))
w_3 = weight_3.get_value()
pylab.figure()
pylab.gray()
for i in tqdm(range(100)):
pylab.subplot(10, 10, i + 1)
pylab.axis('off')
pylab.imshow(w_3[:, i].reshape(25, 25))
pylab.suptitle('layer 3 weight samples')
pylab.savefig(os.path.join(CUR_DIR, 'project_2b_weight3.png'))
ind = np.random.randint(low=0, high=1900)
layer_1, layer_2, layer_3, output = test_da1(train_x[ind:ind + 100, :])
# show input image
print('plotting inputs...')
pylab.figure()
pylab.gray()
for i in tqdm(range(100)):
pylab.subplot(10, 10, i + 1)
pylab.axis('off')
pylab.imshow(train_x[ind + i:ind + i + 1, :].reshape(28, 28))
pylab.suptitle('input images')
pylab.savefig(os.path.join(CUR_DIR, 'project_2b_input.png'))
# hidden layer activations
print('plotting hidden layer activations...')
pylab.figure()
pylab.gray()
for i in tqdm(range(100)):
pylab.subplot(10, 10, i + 1)
pylab.axis('off')
pylab.imshow(layer_1[i, :].reshape(30, 30))
pylab.suptitle('layer 1 activations')
pylab.savefig(os.path.join(CUR_DIR, 'project_2b_layer1.png'))
pylab.figure()
pylab.gray()
for i in tqdm(range(100)):
pylab.subplot(10, 10, i + 1)
pylab.axis('off')
pylab.imshow(layer_2[i, :].reshape(25, 25))
pylab.suptitle('layer 2 activations')
pylab.savefig(os.path.join(CUR_DIR, 'project_2b_layer2.png'))
pylab.figure()
pylab.gray()
for i in tqdm(range(100)):
pylab.subplot(10, 10, i + 1)
pylab.axis('off')
pylab.imshow(layer_3[i, :].reshape(20, 20))
pylab.suptitle('layer 3 activations')
pylab.savefig(os.path.join(CUR_DIR, 'project_2b_layer3.png'))
# reconstructed outputs
print('plotting reconstructed outputs...')
pylab.figure()
pylab.gray()
for i in tqdm(range(100)):
pylab.subplot(10, 10, i + 1)
pylab.axis('off')
pylab.imshow(output[i, :].reshape(28, 28))
pylab.suptitle('reconstructed outputs')
pylab.savefig(os.path.join(CUR_DIR, 'project_2b_output.png'))
# softmax
print('training ffn ...')
train_cost = []
test_accr = []
for _ in tqdm(range(training_epochs)):
# go through trainng set
cost = []
for start, end in zip(range(0, trx, batch_size),
range(batch_size, trx, batch_size)):
cost.append(train_ffn(train_x[start:end], train_y[start:end]))
train_cost.append(np.mean(cost, dtype='float64'))
test_accr.append(
np.mean(np.argmax(test_y, axis=1) == test_ffn(test_x)))
# output max accuracy at # iterations
print('%.1f accuracy at %d iterations' %
(np.max(test_accr) * 100, np.argmax(test_accr) + 1))
if for_comparison:
# return arrays for plotting later
return comp_train_cost, train_cost, test_accr
else:
# all the plottings
pylab.figure()
pylab.plot(range(training_epochs), train_cost)
pylab.xlabel('iterations')
pylab.ylabel('cross-entropy')
pylab.savefig(os.path.join(CUR_DIR, 'project_2b_train_ffn.png'))
pylab.figure()
pylab.plot(range(training_epochs), test_accr)
pylab.xlabel('iterations')
pylab.ylabel('test accuracy')
pylab.savefig(os.path.join(CUR_DIR, 'project_2b_test_ffn.png'))
pylab.show()
def train(self):
# Load decoder weight to GAN's discriminator
# self.combined.summary()
# self.combined = load_model('CAE_weights.h5')
color_mode = 'grayscale' if self.channels == 1 else 'rgb'
train_datagen = ImageDataGenerator(rescale=1./255)
train_set = train_datagen.flow_from_directory(self.train_dir,
target_size=[self.img_rows, self.img_cols],
batch_size=self.batch_size,
color_mode=color_mode, # 'rgb'
class_mode='binary')
test_set = train_datagen.flow_from_directory(self.test_dir,
target_size=[self.img_rows, self.img_cols],
batch_size=self.batch_size,
color_mode=color_mode, # 'rgb'
class_mode='binary')
epoch = 0
i = 0
for x_batch, y_label in train_set:
x_batch = x_batch - 0.5
y_label = y_label.reshape(-1, 1)
# For N class, real / fake has 1:1
half_batch = self.batch_size // 2
cw1 = {0: 1, 1: 1}
# For N class, each class has 'K/N' images while there are total 'K' fake images
# So, each 'real img' has 'N' times more weight than 'fake img' (--> weight = 1 / frequency)
cw2 = {i: self.num_class / half_batch for i in range(self.num_class)}
cw2[self.num_class] = 1 / half_batch
# Adversarial ground truths
# Use x_batch.shape[0] instead of batch_size because of last batch's size ( < batch_size)
valid = np.ones((x_batch.shape[0], 1))
fake = np.zeros((x_batch.shape[0], 1))
# ---------------------
# Train Discriminator
# ---------------------
self.discriminator.trainable = True
noise = np.random.uniform(-1, 1, (x_batch.shape[0], self.latent_dim))
# Generate a batch of new images
gen_imgs = self.generator.predict(noise)
# One-hot encoding of labels
# fake_label will be one-hot encoded to 'N+1'th column
labels = to_categorical(y_label, num_classes=self.num_class+1)
fake_labels = to_categorical(np.full((x_batch.shape[0], 1), self.num_class), num_classes=self.num_class+1)
# Train the discriminator
d_loss_real = self.discriminator.train_on_batch(x_batch, [valid, labels], class_weight=[cw1, cw2])
d_loss_fake = self.discriminator.train_on_batch(gen_imgs, [fake, fake_labels], class_weight=[cw1, cw2])
d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
# ---------------------
# Train Generator
# ---------------------
# Train the generator (to have the discriminator label samples as valid)
self.discriminator.trainable = False
g_loss = self.combined.train_on_batch(noise, valid, class_weight=[cw1, cw2])
# Plot the progress
d_lr = K.eval(self.discriminator.optimizer.lr)
g_lr = K.eval(self.combined.optimizer.lr)
print ("epoch : %d step : %d / %d [D loss: %f, acc.: %.2f%%, op_acc: %.2f%%] [G loss: %f] [d_lr : %f] [g_lr :%f]"
% (epoch, i, train_set.__len__(), d_loss[0], 100*d_loss[3], 100*d_loss[4], g_loss, d_lr, g_lr))
# If at save interval => save generated image samples
if (i + 1) % 20 == 0:
noise = np.random.uniform(-1, 1, (x_batch.shape[0], self.latent_dim))
img = self.generator.predict(noise)
img = 0.5 * img + 0.5
fig = plt.gcf()
fig.set_size_inches(3, 3)
if self.channels == 1:
img = img[0].squeeze(axis=2)
#img = cv2.GaussianBlur(img, (3, 3), 0)
plt.imshow(img, cmap=pylab.gray()) # if gray scale :img[0].squeeze(axis=2), cmap=pylab.gray()
else:
plt.imshow(img[0])
plt.show(block=False)
plt.pause(0.001)
if (i + 1) % 100 == 0:
plt.savefig("result_{}_{}.png.".format(str(epoch), str(i)))
plt.close()
# Test the discriminator
x_batch_t, y_labels_t = test_set.next()
labels_t = to_categorical(y_labels_t, num_classes=self.num_class + 1)
print(self.discriminator.metrics_names)
print(self.discriminator.evaluate(x_batch_t, [valid, labels_t], verbose=1))
if (i + 1) % train_set.__len__() == 0:
epoch += 1
i = 0
i += 1
def tt_plot_func(train_x, train_y, test_x, test_y, func=sgd):
"""Train, test and plot using a particular update function.
Arguments:
train_x, train_y, test_x, test_y: train and test data
func: update function to use, default to nn_cnn.sgd
"""
# train and test
train, predict, test = cnn(update_func=func)
test_accr = []
train_cost = []
for i in tqdm(range(NO_ITERS)):
train_x, train_y = shuffle_data(train_x, train_y)
test_x, test_y = shuffle_data(test_x, test_y)
cost = 0.0
train_length = len(train_x)
starts = range(0, train_length, BATCH_SIZE)
ends = range(BATCH_SIZE, train_length, BATCH_SIZE)
for start, end in zip(starts, ends):
cost += train(train_x[start:end], train_y[start:end])
# average out the cost for one epoch
cost = cost / (train_length // BATCH_SIZE)
train_cost += [cost]
test_accr.append(np.mean(np.argmax(test_y, axis=1) == predict(test_x)))
# output max accuracy at # iterations
print('%.1f accuracy at %d iterations' %
(np.max(test_accr) * 100, np.argmax(test_accr) + 1))
# plot test accuracy
pylab.figure()
pylab.plot(range(NO_ITERS), test_accr)
pylab.xlabel('epochs')
pylab.ylabel('test accuracy')
pylab.savefig(os.path.join(CUR_DIR, 'project_2a_test.png'))
# plot training cost
pylab.figure()
pylab.plot(range(NO_ITERS), train_cost)
pylab.xlabel('epochs')
pylab.ylabel('training cost')
pylab.savefig(os.path.join(CUR_DIR, 'project_2a_train.png'))
# pick a random image
ind = np.random.randint(low=0, high=2000)
conv_1, pool_1, conv_2, pool_2 = test(test_x[ind:ind + 1, :])
# show input image
pylab.figure()
pylab.gray()
pylab.axis('off')
pylab.imshow(test_x[ind, :].reshape(28, 28))
pylab.title('input image')
pylab.savefig(os.path.join(CUR_DIR, 'img_input.png'))
# show convolved and pooled feature maps
pylab.figure()
pylab.gray()
for i in range(15):
pylab.subplot(3, 5, i + 1)
pylab.axis('off')
pylab.imshow(conv_1[0, i, :].reshape(20, 20))
pylab.suptitle('layer 1 convolved feature maps')
pylab.savefig(os.path.join(CUR_DIR, 'img_conv_1.png'))
pylab.figure()
pylab.gray()
for i in range(15):
pylab.subplot(3, 5, i + 1)
pylab.axis('off')
pylab.imshow(pool_1[0, i, :].reshape(10, 10))
pylab.suptitle('layer 1 pooled feature maps')
pylab.savefig(os.path.join(CUR_DIR, 'img_pooled_1.png'))
pylab.figure()
pylab.gray()
for i in range(20):
pylab.subplot(4, 5, i + 1)
pylab.axis('off')
pylab.imshow(conv_2[0, i, :].reshape(6, 6))
pylab.suptitle('layer 2 convolved feature maps')
pylab.savefig(os.path.join(CUR_DIR, 'img_conv_2.png'))
pylab.figure()
pylab.gray()
for i in range(20):
pylab.subplot(4, 5, i + 1)
pylab.axis('off')
pylab.imshow(pool_2[0, i, :].reshape(3, 3))
pylab.suptitle('layer 2 pooled feature maps')
pylab.savefig(os.path.join(CUR_DIR, 'img_pooled_2.png'))
pylab.show()
def main():
'''
Runs the deblender and creates plots for the "design document",
doc/design.tex. See the file NOTES for how to get set up to the
point where you can actually run this on data.
'''
from optparse import OptionParser
parser = OptionParser()
parser.add_option('--root',
dest='root',
help='Root directory for Subaru data')
parser.add_option('--outroot',
'-o',
dest='outroot',
help='Output root directory for Subaru data')
parser.add_option('--sources', help='Read a FITS table of sources')
parser.add_option('--calexp', help='Read a FITS calexp')
parser.add_option('--psf', help='Read a FITS PSF')
parser.add_option('--drill',
'-D',
dest='drill',
action='append',
type=str,
default=[],
help='Drill down on individual source IDs')
parser.add_option(
'--drillxy',
dest='drillxy',
action='append',
type=str,
default=[],
help='Drill down on individual source positions, eg 132,46;54,67')
parser.add_option('--visit',
dest='visit',
type=int,
default=108792,
help='Suprimecam visit id')
parser.add_option('--ccd',
dest='ccd',
type=int,
default=5,
help='Suprimecam CCD number')
parser.add_option('--prefix',
dest='prefix',
default='design-',
help='plot filename prefix')
parser.add_option('--suffix',
dest='suffix',
default=None,
help='plot filename suffix (default: ".png")')
parser.add_option(
'--pat',
dest='pat',
help=
'Plot filename pattern: eg, "design-%(pid)04i-%(name).png"; overrides --prefix and --suffix'
)
parser.add_option('--pdf',
dest='pdf',
action='store_true',
default=False,
help='save in PDF format?')
parser.add_option('-v', dest='verbose', action='store_true')
parser.add_option('--figw',
dest='figw',
type=float,
help='Figure window width (inches)',
default=4.)
parser.add_option('--figh',
dest='figh',
type=float,
help='Figure window height (inches)',
default=4.)
parser.add_option('--order',
dest='order',
type=str,
help='Child order: eg 3,0,1,2')
parser.add_option('--sdss',
dest='sec',
action='store_const',
const='sdss',
help='Produce plots for the SDSS section.')
parser.add_option('--mono',
dest='sec',
action='store_const',
const='mono',
help='Produce plots for the "monotonic" section.')
parser.add_option('--median',
dest='sec',
action='store_const',
const='median',
help='Produce plots for the "median filter" section.')
parser.add_option('--ramp',
dest='sec',
action='store_const',
const='ramp',
help='Produce plots for the "ramp edges" section.')
parser.add_option(
'--ramp2',
dest='sec',
action='store_const',
const='ramp2',
help='Produce plots for the "ramp edges + stray flux" section.')
parser.add_option('--patch',
dest='sec',
action='store_const',
const='patch',
help='Produce plots for the "patch edges" section.')
opt, args = parser.parse_args()
# Logging
if opt.verbose:
lsst.log.setLevel('', lsst.log.DEBUG)
else:
lsst.log.setLevel('', lsst.log.INFO)
if opt.sec is None:
opt.sec = 'sdss'
if opt.pdf:
if opt.suffix is None:
opt.suffix = ''
opt.suffix += '.pdf'
if not opt.suffix:
opt.suffix = '.png'
if opt.pat:
plotpattern = opt.pat
else:
plotpattern = opt.prefix + '%(pid)04i-%(name)s' + opt.suffix
if opt.order is not None:
opt.order = [int(x) for x in opt.order.split(',')]
invorder = np.zeros(len(opt.order))
invorder[opt.order] = np.arange(len(opt.order))
def mapchild(i):
if opt.order is None:
return i
return invorder[i]
def savefig(pid, figname):
fn = plotpattern % dict(pid=pid, name=figname)
plt.savefig(fn)
# Load data using the butler, if desired
dr = None
if opt.sources is None or opt.calexp is None:
print('Creating DataRef...')
dr = getSuprimeDataref(opt.visit,
opt.ccd,
rootdir=opt.root,
outrootdir=opt.outroot)
print('Got', dr)
# Which parent ids / deblend families are we going to plot?
keepids = None
if len(opt.drill):
keepids = []
for d in opt.drill:
for dd in d.split(','):
keepids.append(int(dd))
print('Keeping parent ids', keepids)
keepxys = None
if len(opt.drillxy):
keepxys = []
for d in opt.drillxy:
for dd in d.split(';'):
xy = dd.split(',')
assert (len(xy) == 2)
keepxys.append((int(xy[0]), int(xy[1])))
print('Keeping parents at xy', keepxys)
# Read from butler or local file
cat = readCatalog(opt.sources,
None,
dataref=dr,
keepids=keepids,
keepxys=keepxys,
patargs=dict(visit=opt.visit, ccd=opt.ccd))
print('Got', len(cat), 'sources')
# Load data from butler or local files
if opt.calexp is not None:
print('Reading exposure from', opt.calexp)
exposure = afwImage.ExposureF(opt.calexp)
else:
exposure = dr.get('calexp')
print('Exposure', exposure)
mi = exposure.getMaskedImage()
if opt.psf is not None:
print('Reading PSF from', opt.psf)
psf = afwDet.Psf.readFits(opt.psf)
print('Got', psf)
elif dr:
psf = dr.get('psf')
else:
psf = exposure.getPsf()
sigma1 = get_sigma1(mi)
fams = getFamilies(cat)
print(len(fams), 'deblend families')
if False:
for j, (parent, children) in enumerate(fams):
print('parent', parent)
print('children', children)
plotDeblendFamily(mi,
parent,
children,
cat,
sigma1,
ellipses=False)
fn = '%04i.png' % parent.getId()
plt.savefig(fn)
print('wrote', fn)
def nlmap(X):
return np.arcsinh(X / (3. * sigma1))
def myimshow(im, **kwargs):
kwargs = kwargs.copy()
mn = kwargs.get('vmin', -5 * sigma1)
kwargs['vmin'] = nlmap(mn)
mx = kwargs.get('vmax', 100 * sigma1)
kwargs['vmax'] = nlmap(mx)
plt.imshow(nlmap(im), **kwargs)
plt.figure(figsize=(opt.figw, opt.figh))
plt.subplot(1, 1, 1)
plt.subplots_adjust(left=0.01,
right=0.99,
bottom=0.01,
top=0.99,
wspace=0.05,
hspace=0.1)
# Make plots for each deblend family.
for j, (parent, children) in enumerate(fams):
print('parent', parent.getId())
print('children', [ch.getId() for ch in children])
print('parent x,y', parent.getX(), parent.getY())
pid = parent.getId()
fp = parent.getFootprint()
bb = fp.getBBox()
pim = footprintToImage(parent.getFootprint(), mi).getArray()
pext = getExtent(bb)
imargs = dict(interpolation='nearest',
origin='lower',
vmax=pim.max() * 0.95,
vmin=-3. * sigma1)
pksty = dict(linestyle='None',
marker='+',
color='r',
mew=3,
ms=20,
alpha=0.6)
plt.clf()
myimshow(afwImage.ImageF(mi.getImage(), bb).getArray(), **imargs)
plt.gray()
plt.xticks([])
plt.yticks([])
savefig(pid, 'image')
# Parent footprint
plt.clf()
myimshow(pim, extent=pext, **imargs)
plt.gray()
pks = fp.getPeaks()
plt.plot([pk.getIx() for pk in pks], [pk.getIy() for pk in pks],
**pksty)
plt.xticks([])
plt.yticks([])
plt.axis(pext)
savefig(pid, 'parent')
from lsst.meas.deblender.baseline import deblend
xc = int((bb.getMinX() + bb.getMaxX()) / 2.)
yc = int((bb.getMinY() + bb.getMaxY()) / 2.)
if hasattr(psf, 'getFwhm'):
psf_fwhm = psf.getFwhm(xc, yc)
else:
psf_fwhm = psf.computeShape().getDeterminantRadius() * 2.35
# Each section of the design doc runs the deblender with different args.
kwargs = dict(sigma1=sigma1, verbose=opt.verbose, getTemplateSum=True)
basic = kwargs.copy()
basic.update(fit_psfs=False,
median_smooth_template=False,
monotonic_template=False,
lstsq_weight_templates=False,
assignStrayFlux=False,
rampFluxAtEdge=False,
patchEdges=False)
if opt.sec == 'sdss':
# SDSS intro
kwargs = basic
kwargs.update(lstsq_weight_templates=True)
elif opt.sec == 'mono':
kwargs = basic
kwargs.update(lstsq_weight_templates=True, monotonic_template=True)
elif opt.sec == 'median':
kwargs = basic
kwargs.update(lstsq_weight_templates=True,
median_smooth_template=True,
monotonic_template=True)
elif opt.sec == 'ramp':
kwargs = basic
kwargs.update(median_smooth_template=True,
monotonic_template=True,
rampFluxAtEdge=True)
elif opt.sec == 'ramp2':
kwargs = basic
kwargs.update(median_smooth_template=True,
monotonic_template=True,
rampFluxAtEdge=True,
assignStrayFlux=True)
elif opt.sec == 'patch':
kwargs = basic
kwargs.update(median_smooth_template=True,
monotonic_template=True,
patchEdges=True)
else:
raise 'Unknown section: "%s"' % opt.sec
print('Running deblender with kwargs:', kwargs)
res = deblend(fp, mi, psf, psf_fwhm, **kwargs)
# print('got result with', [x for x in dir(res) if not x.startswith('__')])
# for pk in res.peaks:
# print('got peak with', [x for x in dir(pk) if not x.startswith('__')])
# print(' deblend as psf?', pk.deblend_as_psf)
# Find bounding-box of all templates.
tbb = fp.getBBox()
for pkres, pk in zip(res.peaks, pks):
tbb.include(pkres.template_foot.getBBox())
print('Bounding-box of all templates:', tbb)
# Sum-of-templates plot
tsum = np.zeros((tbb.getHeight(), tbb.getWidth()))
tx0, ty0 = tbb.getMinX(), tbb.getMinY()
# Sum-of-deblended children plot(s)
# "heavy" bbox == template bbox.
hsum = np.zeros((tbb.getHeight(), tbb.getWidth()))
hsum2 = np.zeros((tbb.getHeight(), tbb.getWidth()))
# Sum of templates from the deblender itself
plt.clf()
t = res.templateSum
myimshow(t.getArray(), extent=getExtent(t.getBBox()), **imargs)
plt.gray()
plt.xticks([])
plt.yticks([])
savefig(pid, 'tsum1')
# Make plots for each deblended child (peak)
k = 0
for pkres, pk in zip(res.peaks, pks):
heavy = pkres.get_flux_portion()
if heavy is None:
print('Child has no HeavyFootprint -- skipping')
continue
kk = mapchild(k)
w = pkres.template_weight
cfp = pkres.template_foot
cbb = cfp.getBBox()
cext = getExtent(cbb)
# Template image
tim = pkres.template_mimg.getImage()
timext = cext
tim = tim.getArray()
(x0, x1, y0, y1) = timext
print('tim ext', timext)
tsum[y0 - ty0:y1 - ty0, x0 - tx0:x1 - tx0] += tim
# "Heavy" image -- flux assigned to child
him = footprintToImage(heavy).getArray()
hext = getExtent(heavy.getBBox())
(x0, x1, y0, y1) = hext
hsum[y0 - ty0:y1 - ty0, x0 - tx0:x1 - tx0] += him
# "Heavy" without stray flux
h2 = pkres.get_flux_portion(strayFlux=False)
him2 = footprintToImage(h2).getArray()
hext2 = getExtent(h2.getBBox())
(x0, x1, y0, y1) = hext2
hsum2[y0 - ty0:y1 - ty0, x0 - tx0:x1 - tx0] += him2
if opt.sec == 'median':
try:
med = pkres.median_filtered_template
except Exception:
med = pkres.orig_template
for im, nm in [(pkres.orig_template, 'symm'), (med, 'med')]:
# print('im:', im)
plt.clf()
myimshow(im.getArray(), extent=cext, **imargs)
plt.gray()
plt.xticks([])
plt.yticks([])
plt.plot([pk.getIx()], [pk.getIy()], **pksty)
plt.axis(pext)
savefig(pid, nm + '%i' % (kk))
# Template
plt.clf()
myimshow(pkres.template_mimg.getImage().getArray() / w,
extent=cext,
**imargs)
plt.gray()
plt.xticks([])
plt.yticks([])
plt.plot([pk.getIx()], [pk.getIy()], **pksty)
plt.axis(pext)
savefig(pid, 't%i' % (kk))
# Weighted template
plt.clf()
myimshow(tim, extent=cext, **imargs)
plt.gray()
plt.xticks([])
plt.yticks([])
plt.plot([pk.getIx()], [pk.getIy()], **pksty)
plt.axis(pext)
savefig(pid, 'tw%i' % (kk))
# "Heavy"
plt.clf()
myimshow(him, extent=hext, **imargs)
plt.gray()
plt.xticks([])
plt.yticks([])
plt.plot([pk.getIx()], [pk.getIy()], **pksty)
plt.axis(pext)
savefig(pid, 'h%i' % (kk))
# Original symmetric template
plt.clf()
t = pkres.orig_template
foot = pkres.orig_foot
myimshow(t.getArray(), extent=getExtent(foot.getBBox()), **imargs)
plt.gray()
plt.xticks([])
plt.yticks([])
plt.plot([pk.getIx()], [pk.getIy()], **pksty)
plt.axis(pext)
savefig(pid, 'o%i' % (kk))
if opt.sec == 'patch' and pkres.patched:
pass
if opt.sec in ['ramp', 'ramp2'] and pkres.has_ramped_template:
# Ramped template
plt.clf()
t = pkres.ramped_template
myimshow(t.getArray(), extent=getExtent(t.getBBox()), **imargs)
plt.gray()
plt.xticks([])
plt.yticks([])
plt.plot([pk.getIx()], [pk.getIy()], **pksty)
plt.axis(pext)
savefig(pid, 'r%i' % (kk))
# Median-filtered template
plt.clf()
t = pkres.median_filtered_template
myimshow(t.getArray(), extent=getExtent(t.getBBox()), **imargs)
plt.gray()
plt.xticks([])
plt.yticks([])
plt.plot([pk.getIx()], [pk.getIy()], **pksty)
plt.axis(pext)
savefig(pid, 'med%i' % (kk))
# Assigned flux
plt.clf()
t = pkres.portion_mimg.getImage()
myimshow(t.getArray(), extent=getExtent(t.getBBox()), **imargs)
plt.gray()
plt.xticks([])
plt.yticks([])
plt.plot([pk.getIx()], [pk.getIy()], **pksty)
plt.axis(pext)
savefig(pid, 'p%i' % (kk))
if opt.sec == 'ramp2':
# stray flux
if pkres.stray_flux is not None:
s = pkres.stray_flux
strayim = footprintToImage(s).getArray()
strayext = getExtent(s.getBBox())
plt.clf()
myimshow(strayim, extent=strayext, **imargs)
plt.gray()
plt.xticks([])
plt.yticks([])
plt.plot([pk.getIx()], [pk.getIy()], **pksty)
plt.axis(pext)
savefig(pid, 's%i' % (kk))
# Assigned flux, omitting stray flux.
plt.clf()
myimshow(him2, extent=hext2, **imargs)
plt.gray()
plt.xticks([])
plt.yticks([])
plt.plot([pk.getIx()], [pk.getIy()], **pksty)
plt.axis(pext)
savefig(pid, 'hb%i' % (kk))
k += 1
# sum of templates
plt.clf()
myimshow(tsum, extent=getExtent(tbb), **imargs)
plt.gray()
plt.xticks([])
plt.yticks([])
plt.plot([pk.getIx() for pk in pks], [pk.getIy() for pk in pks],
**pksty)
plt.axis(pext)
savefig(pid, 'tsum')
# sum of assigned flux
plt.clf()
myimshow(hsum, extent=getExtent(tbb), **imargs)
plt.gray()
plt.xticks([])
plt.yticks([])
plt.plot([pk.getIx() for pk in pks], [pk.getIy() for pk in pks],
**pksty)
plt.axis(pext)
savefig(pid, 'hsum')
plt.clf()
myimshow(hsum2, extent=getExtent(tbb), **imargs)
plt.gray()
plt.xticks([])
plt.yticks([])
plt.plot([pk.getIx() for pk in pks], [pk.getIy() for pk in pks],
**pksty)
plt.axis(pext)
savefig(pid, 'hsum2')
k = 0
for pkres, pk in zip(res.peaks, pks):
heavy = pkres.get_flux_portion()
if heavy is None:
continue
print('Template footprint:', pkres.template_foot.getBBox())
print('Template img:', pkres.template_mimg.getBBox())
print('Heavy footprint:', heavy.getBBox())
cfp = pkres.template_foot
cbb = cfp.getBBox()
cext = getExtent(cbb)
tim = pkres.template_mimg.getImage().getArray()
(x0, x1, y0, y1) = cext
frac = tim / tsum[y0 - ty0:y1 - ty0, x0 - tx0:x1 - tx0]
msk = afwImage.ImageF(cbb.getWidth(), cbb.getHeight())
msk.setXY0(cbb.getMinX(), cbb.getMinY())
afwDet.setImageFromFootprint(msk, cfp, 1.)
msk = msk.getArray()
frac[msk == 0.] = np.nan
# Fraction of flux assigned to this child.
plt.clf()
plt.imshow(frac,
extent=cext,
interpolation='nearest',
origin='lower',
vmin=0,
vmax=1)
# plt.plot([x0,x0,x1,x1,x0], [y0,y1,y1,y0,y0], 'k-')
plt.gray()
plt.xticks([])
plt.yticks([])
plt.plot([pk.getIx()], [pk.getIy()], **pksty)
plt.gca().set_axis_bgcolor((0.9, 0.9, 0.5))
plt.axis(pext)
savefig(pid, 'f%i' % (mapchild(k)))
k += 1
def main():
# Import data
mnist = input_data.read_data_sets('../data/mnist', one_hot=True)
trainX, trainY = mnist.train.images[:12000], mnist.train.labels[:12000]
testX, testY = mnist.test.images[:2000], mnist.test.labels[:2000]
# Create the model
x = tf.placeholder(tf.float32, [None, 784])
# Define loss and optimizer
y_ = tf.placeholder(tf.float32, [None, 10])
# Build the graph for the deep net
W_conv1, h_conv1, h_pool1, h_conv2, h_pool2, y_conv, keep_prob = cnn(x)
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_,
logits=y_conv)
cross_entropy = tf.reduce_mean(cross_entropy)
train_step = tf.train.GradientDescentOptimizer(1e-3).minimize(
cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
correct_prediction = tf.cast(correct_prediction, tf.float32)
accuracy = tf.reduce_mean(correct_prediction)
N = len(trainX)
idx = np.arange(N)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
test_acc = []
for i in range(no_epochs):
np.random.shuffle(idx)
trainX, trainY = trainX[idx], trainY[idx]
for start, end in zip(range(0, N, batch_size),
range(batch_size, N, batch_size)):
train_step.run(feed_dict={
x: trainX[start:end],
y_: trainY[start:end],
keep_prob: 0.5
})
test_acc.append(
accuracy.eval(feed_dict={
x: testX,
y_: testY,
keep_prob: 1.0
}))
print('iter %d: test accuracy %g' % (i, test_acc[i]))
pylab.figure()
pylab.plot(np.arange(no_epochs), test_acc, label='gradient descent')
pylab.xlabel('epochs')
pylab.ylabel('test accuracy')
pylab.legend(loc='lower right')
pylab.savefig('./figures/7.3_1.png')
W_conv1_ = sess.run(W_conv1)
W_conv1_ = np.array(W_conv1_)
pylab.figure()
pylab.gray()
for i in range(32):
pylab.subplot(8, 4, i + 1)
pylab.axis('off')
pylab.imshow(W_conv1_[:, :, 0, i])
pylab.savefig('./figures/7.3_2.png')
ind = np.random.randint(low=0, high=55000)
X = mnist.train.images[ind, :]
pylab.figure()
pylab.gray()
pylab.axis('off')
pylab.imshow(X.reshape(28, 28))
pylab.savefig('./figures/7.3_3.png')
h_conv1_, h_pool1_, h_conv2_, h_pool2_ = sess.run(
[h_conv1, h_pool1, h_conv2, h_pool2], {x: X.reshape(1, 784)})
pylab.figure()
pylab.gray()
h_conv1_ = np.array(h_conv1_)
for i in range(32):
pylab.subplot(4, 8, i + 1)
pylab.axis('off')
pylab.imshow(h_conv1_[0, :, :, i])
pylab.savefig('./figures/7.3_4.png')
pylab.figure()
pylab.gray()
h_pool1_ = np.array(h_pool1_)
for i in range(32):
pylab.subplot(4, 8, i + 1)
pylab.axis('off')
pylab.imshow(h_pool1_[0, :, :, i])
pylab.savefig('./figures/7.3_5.png')
pylab.figure()
pylab.gray()
h_conv2_ = np.array(h_conv2_)
for i in range(64):
pylab.subplot(8, 8, i + 1)
pylab.axis('off')
pylab.imshow(h_conv2_[0, :, :, i])
pylab.savefig('figures/7.3_6.png')
pylab.figure()
pylab.gray()
h_pool2_ = np.array(h_pool2_)
for i in range(64):
pylab.subplot(8, 8, i + 1)
pylab.axis('off')
pylab.imshow(h_pool2_[0, :, :, i])
pylab.savefig('./figures/7.3_7.png')
pylab.show()
def plot(scorelists, output, qrange=0.1, labels=None, publish=False, ylim=0.0):
linestyle = ['-', '-', '-', '-', '-', '-', '-', '-', '-']
linecolors = [(0.0, 0.0, 0.0), (0.8, 0.4, 0.0), (0.0, 0.45, 0.70),
(0.8, 0.6, 0.7), (0.0, 0.6, 0.5), (0.9, 0.6, 0.0),
(0.95, 0.9, 0.25), (0.35, 0.7, 0.9), (0.43, 0.17, 0.60)]
# linestyle = [ '-', '-', '-', '-', '-', '--', '-', '-', '-' ]
# linecolors = [ (0.0, 0.0, 0.0),
# (0.8, 0.4, 0.0),
# (0.35, 0.7, 0.9),
# (0.8, 0.6, 0.7),
# (0.0, 0.6, 0.5),
# (0.9, 0.6, 0.0),
# (0.95, 0.9, 0.25),
# (0.35, 0.7, 0.9),
# (0.43, 0.17, 0.60)]
if publish:
matplotlib.rcParams['text.usetex'] = True
matplotlib.rcParams['font.size'] = 14
matplotlib.rcParams['legend.fontsize'] = 20
matplotlib.rcParams['xtick.labelsize'] = 24
matplotlib.rcParams['ytick.labelsize'] = 24
matplotlib.rcParams['axes.labelsize'] = 22
xlabel = 'q-value'
ylabel = 'Significant PSMs'
pylab.clf()
pylab.xlabel(xlabel)
pylab.ylabel(ylabel)
pylab.gray()
h = -1
for i, (q, p) in enumerate(scorelists):
h = max(itertools.chain([h], (b for a, b in zip(q, p) if a <= qrange)))
pylab.plot(q,
p,
color=linecolors[i],
linewidth=2,
linestyle=linestyle[i])
# if publish:
# yt, _ = pylab.yticks()
# if all(v % 1000 == 0 for v in yt):
# # yl = list('$%d$' % int(v/1000) for v in yt)
# yl = []
# ytt = []
# for v in yt:
# if v < h:
# print(v)
# print(float(v)/float(1000))
# yl.append('$%f$' % float(v)/float(1000))
# ytt.append(v)
# pylab.yticks(ytt, yl)
# pylab.ylabel(ylabel + ' (in 1000\'s)')
pylab.xlim([0, qrange])
pylab.ylim([ylim, h])
pylab.legend(labels, loc='lower right', fontsize=20)
print("Evaluated %d methods, saving plot to %s" % (len(labels), output))
pylab.savefig(output, bbox_inches='tight')
def main():
trainX, trainY = load_data('data_batch_1')
print(trainX.shape, trainY.shape)
testX, testY = load_data('test_batch_trim')
print(testX.shape, testY.shape)
#Scaling the data
testX = (testX - np.min(trainX, axis = 0))/np.max(trainX, axis = 0)
trainX = (trainX - np.min(trainX, axis = 0))/np.max(trainX, axis = 0)
# Create the model
x = tf.placeholder(tf.float32, [None, IMG_SIZE*IMG_SIZE*NUM_CHANNELS])
y_ = tf.placeholder(tf.float32, [None, NUM_CLASSES])
c1,p1,c2,p2,logits = cnn(x)
cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(labels=y_, logits=logits)
loss = tf.reduce_mean(cross_entropy)
train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)
correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(y_, 1))
correct_prediction = tf.cast(correct_prediction, tf.float32)
accuracy = tf.reduce_mean(correct_prediction)
N = len(trainX)
idx = np.arange(N)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
test_acc = []
training_loss =[]
for e in range(epochs):
np.random.shuffle(idx)
trainX, trainY = trainX[idx], trainY[idx]
for start, end in zip(range(0, N, batch_size), range(batch_size, N, batch_size)):
train_step.run(feed_dict={x: trainX[start:end], y_: trainY[start:end]})
training_loss.append(loss.eval(feed_dict={x: trainX, y_: trainY}))
test_acc.append(accuracy.eval(feed_dict={x: testX, y_: testY}))
#_, loss_ = sess.run([train_step, loss], {x: trainX, y_: trainY})
print('epoch', e, 'entropy', training_loss[e], 'test accuracy', test_acc[e])
test1 = testX[0]
test2 = testX[1]
plt.figure(1)
plt.plot(range(epochs), test_acc)
plt.ylabel('Test Accuracy')
plt.xlabel('Number of iterations')
plt.savefig('./A1-Test_accuracy.png')
plt.figure(2)
plt.plot(range(epochs), training_loss)
plt.xlabel('Number of iterations')
plt.ylabel('Training Loss')
plt.savefig('./A1-Training_loss.png')
plt.show()
h_conv1_, h_pool1_, h_conv2_, h_pool2_ = sess.run([c1, p1, c2, p2],{x: test1.reshape(1,3072)})
plt.figure(3)
plt.gray()
plt.imshow(test1.reshape(32,32,3))
plt.savefig('./A1-test1-original.png')
plt.figure(4)
plt.gray()
for i in range(50):
plt.subplot(10, 5, i+1); plt.axis('off'); plt.imshow(h_conv1_[0,:,:,i])
plt.savefig('./A1-test1-c1.png')
plt.figure(5)
plt.gray()
for i in range(50):
plt.subplot(10, 5, i+1); plt.axis('off'); plt.imshow(h_pool1_[0,:,:,i])
plt.savefig('./A1-test1-p1.png')
plt.figure(6)
plt.gray()
for i in range(60):
plt.subplot(10, 6, i+1); plt.axis('off'); plt.imshow(h_conv2_[0,:,:,i])
plt.savefig('./A1-test1-c2.png')
plt.figure(7)
plt.gray()
for i in range(60):
plt.subplot(10, 6, i+1); plt.axis('off'); plt.imshow(h_pool2_[0,:,:,i])
plt.savefig('./A1-test1-p2.png')
plt.show()
h_conv1_, h_pool1_, h_conv2_, h_pool2_ = sess.run([c1, p1, c2, p2],{x: test2.reshape(1,3072)})
plt.figure(8)
plt.gray()
plt.imshow(test2.reshape(32,32,3))
plt.savefig('./A1-test2-original.png')
plt.figure(9)
plt.gray()
for i in range(50):
plt.subplot(10, 5, i+1); plt.axis('off'); plt.imshow(h_conv1_[0,:,:,i])
plt.savefig('./A1-test2-c1.png')
plt.figure(10)
plt.gray()
for i in range(50):
plt.subplot(10, 5, i+1); plt.axis('off'); plt.imshow(h_pool1_[0,:,:,i])
plt.savefig('./A1-test2-p1.png')
plt.figure(11)
plt.gray()
for i in range(60):
plt.subplot(10, 6, i+1); plt.axis('off'); plt.imshow(h_conv2_[0,:,:,i])
plt.savefig('./A1-test2-c2.png')
plt.figure(12)
plt.gray()
for i in range(60):
plt.subplot(10, 6, i+1); plt.axis('off'); plt.imshow(h_pool2_[0,:,:,i])
plt.savefig('./A1-test2-p2.png')
plt.show()
def main(n_doc=100):
# Generate toy dataset
datagen = LdaDataGenerator(n_topic=10, alpha=0.5)
n_DxV, p_DxK = (), ()
for d in xrange(n_doc):
n_V, p_K = datagen.generate_document(n_word=100)
n_DxV += n_V,
p_DxK += p_K,
n_DxV = np.array(n_DxV)
p_DxK = np.array(p_DxK)
# Init LDA learner
lda = LatentDirichletAllocationGibbsSampler(n_topic=datagen.n_topic,
alpha=1.0,
beta=1.0,
n_itr=100)
# Define callback method
pl.ion()
pl.figure('Word topic dist.')
def lda_monitoring(lda, itr):
if itr != 0 and (itr % 10 != 0) and itr != lda.n_itr - 1: return
phi_KxV = lda.get_word_topic_distribution()
gs = gridspec.GridSpec(1, 2)
pl.subplot(gs[0])
pl.title('True')
visualize_squared_shape_word_dist(datagen.phi_KxV)
pl.subplot(gs[1])
pl.title('Inferred at %d' % itr)
visualize_squared_shape_word_dist(phi_KxV)
pl.draw()
pl.show()
# Infer
lda.fit(n_DxV, monitoring=lda_monitoring)
# Show document topic distribution
theta_KxD = lda.get_document_topic_distribution()
# Find correspondence between true topics and inferred ones
dist_KxK = (p_DxK.T**2).sum(axis=1)[:, np.newaxis] \
- 2 * np.dot(p_DxK.T, theta_KxD.T) \
+ (theta_KxD.T**2).sum(axis=0)[np.newaxis]
corr_K = dist_KxK.argmin(axis=1)
pl.figure('Topic dist. of training documents')
pl.subplot(211)
pl.title('true p(z|d)')
pl.imshow(p_DxK.T, interpolation='none', cmap=pl.gray())
pl.subplot(212)
pl.title('inferred p(z|theta_d)')
pl.imshow(theta_KxD[corr_K, :], interpolation='none', cmap=pl.gray())
pl.show()
# Generate unseen documents
n_us_DxV, p_us_DxK = (), ()
for d in xrange(20):
n_V, p_K = datagen.generate_document(n_word=100)
n_us_DxV += n_V,
p_us_DxK += p_K,
n_us_DxV = np.array(n_us_DxV)
p_us_DxK = np.array(p_us_DxK)
# Infer unseen documents with trained model
theta_us_KxD = lda.infer_unseen_document(n_us_DxV)
# Show topic distributions of unseen documents
pl.ioff()
pl.figure('Topic dist. of unseen documents')
pl.subplot(211)
pl.title('true p(z|d)')
pl.imshow(p_us_DxK.T, interpolation='none', cmap=pl.gray())
pl.subplot(212)
pl.title('inferred p(z|theta_d)')
pl.imshow(theta_us_KxD[corr_K], interpolation='none', cmap=pl.gray())
pl.show()
def train(self):
class_mode = 'grayscale' if self.channels == 1 else 'rgb'
train_set, test_set = prepare_dataset(input_size=(self.img_shape[0], self.img_shape[1]),
train_dir=self.train_dir,
test_dir=self.test_dir,
train_batch_size=self.train_batch_size,
test_batch_size=self.test_batch_size,
class_mode='input',
color_mode=class_mode
)
self.Conv_AutoEncoder.fit_generator(train_set,
epochs=self.epochs,
validation_data=test_set)
self.Conv_AutoEncoder.save(self.save_model_name)
'''
for step, real_img in enumerate(train_set):
loss = self.Conv_AutoEncoder.train_on_batch(real_img, real_img)
'''
fig = plt.figure()
for i in range(10):
img, _ = train_set.next()
re_img = self.Conv_AutoEncoder.predict(np.expand_dims(img[0], 0))
print(img[0].shape)
if self.channels == 1:
fig.add_subplot(1, 2, 1)
plt.imshow(img[0].squeeze(axis=2), cmap=pylab.gray())
fig.add_subplot(1, 2, 2)
plt.imshow(re_img.squeeze(axis=(0, 3)), cmap=pylab.gray())
else:
fig.add_subplot(1, 2, 1)
plt.imshow(img[0])
fig.add_subplot(1, 2, 2)
plt.imshow(re_img.squeeze(axis=0))
plt.savefig("CAE_result_{}png.".format(str(i)))
plt.show(block=False)
plt.pause(1)
print("\n\n\n\n\n\n\n\n\n\n\n\n\n")
def gendata(enable, os, downsample, textid=None, seed=2313, verbose=False):
"""
Generate the MNIST+ dataset.
:param enable: dictionary of flags with keys ['texture', 'azimuth',
'rotation', 'elevation'] to enable/disable a given factor of variation.
:param textid: if enable['texture'], id number of the Brodatz texture to
load. If textid is None, we load a random texture for each MNIST image.
:param os: output size (width and height) of MNIST+ images.
:param downsample: factor by which to downsample texture.
:param seed: integer for seeding RNG.
:param verbose: bool
"""
rng = numpy.random.RandomState(seed)
data = mnist.MNIST('train')
test = mnist.MNIST('test')
data.X = numpy.vstack((data.X, test.X))
data.y = numpy.hstack((data.y, test.y))
del test
output = {}
output['data'] = numpy.zeros((len(data.X), os * os))
output['label'] = numpy.zeros(len(data.y))
if enable['azimuth']:
output['azimuth'] = numpy.zeros(len(data.y))
if enable['elevation']:
output['elevation'] = numpy.zeros(len(data.y))
if enable['rotation']:
output['rotation'] = numpy.zeros(len(data.y))
if enable['texture']:
output['texture_id'] = numpy.zeros(len(data.y))
output['texture_pos'] = numpy.zeros((len(data.y), 2))
for i in xrange(len(data.X)):
# get MNIST image
frgd_img = to_img(data.X[i], 28)
frgd_img = frgd_img.convert('L')
if enable['rotation']:
rot = rng.randint(0, 360)
output['rotation'][i] = rot
frgd_img = frgd_img.rotate(rot, Image.BILINEAR)
frgd_img = frgd_img.resize((os, os), Image.BILINEAR)
if enable['texture']:
if textid is None:
# extract patch from texture database. Note that texture #14
# does not exist.
textid = 14
while textid == 14:
textid = rng.randint(1, 113)
patch_img, (px, py) = extract_patch(textid, os, downsample)
patch_arr = to_array(patch_img)
# store output details
output['texture_id'][i] = textid
output['texture_pos'][i] = (px, py)
# generate binary mask for digit outline
frgd_arr = to_array(frgd_img)
mask_arr = frgd_arr > 0.1
# copy contents of masked-MNIST image into background texture
blend_arr = copy(patch_arr)
blend_arr[mask_arr] = frgd_arr[mask_arr]
# this now because the image to emboss
frgd_img = to_img(blend_arr, os)
azi = 45
if enable['azimuth']:
azi = rng.randint(0, 360)
output['azimuth'][i] = azi
ele = 18.
if enable['elevation']:
ele = rng.randint(0, 60)
output['elevation'][i] = ele
mboss_img = emboss(frgd_img, azi=azi, ele=ele)
mboss_arr = to_array(mboss_img)
output['data'][i] = mboss_arr
output['label'][i] = data.y[i]
if verbose:
pl.imshow(mboss_arr.reshape(os, os))
pl.gray()
pl.show()
fname = 'mnistplus'
if enable['azimuth']:
fname += "_azi"
if enable['rotation']:
fname += "_rot"
if enable['texture']:
fname += "_tex"
fp = open(fname + '.pkl', 'w')
pickle.dump(output, fp, protocol=pickle.HIGHEST_PROTOCOL)
fp.close()
###############################################################################
results_df = single_snp(variants_maf5_lines_filtered,
phenotypes_online,
leave_out_one_chrom=False,
output_file_name='Test-Fast-Lmm')
import pylab
import fastlmm.util.util as flutil
flutil.manhattan_plot(results_df.as_matrix(["Chr", "ChrPos", "PValue"]),
pvalue_line=1e-5,
xaxis_unit_bp=False)
pylab.show()
from fastlmm.util.stats import plotp
plotp.qqplot(results_df["PValue"].values, xlim=[0, 5], ylim=[0, 5])
# print head of results data frame
import pandas as pd
pd.set_option('display.width', 1000)
results_df.head(n=10)
results_df.to_csv(
'../Outputs/Fast-Lmm-Outputs/Quality-Control-Tergite-Online-Adjusted-Phenotype-Leave-False.txt',
sep='\t')
from pysnptools.standardizer import Unit
from fastlmm.inference.fastlmm_predictor import _snps_fixup, _pheno_fixup, _kernel_fixup, _SnpTrainTest
test_snps = _snps_fixup(test_snps)
K_causal = test_snps.read_kernel(Unit()).standardize()
pylab.imshow(K_causal.val, cmap=pylab.gray(), vmin=0, vmax=1)
def __init__(self, parent, id, pos, size, style, name):
self._init_ctrls(parent)
##Create a matplotlib figure/canvas in this panel
##the background colour will be the same as the panel
##the size will also be the same as the panel
##calculate size in inches
pixels_width, pixels_height = self.GetSizeTuple()
self.dpi = 96.0
inches_width = pixels_width / self.dpi
inches_height = pixels_height / self.dpi
##calculate colour in RGB 0 to 1
colour = self.GetBackgroundColour()
self.fig = Figure(figsize=(inches_width,inches_height), dpi = self.dpi\
,facecolor=(colour.Red()/255.0, colour.Green()/255.0, colour.Blue()/255.0)\
,edgecolor=(colour.Red()/255.0, colour.Green()/255.0, colour.Blue()/255.0))
##left : the left side of the subplots of the figure
## | right : the right side of the subplots of the figure
## | bottom : the bottom of the subplots of the figure
## | top : the top of the subplots of the figure
## | wspace : the amount of width reserved for blank space between subplots
## | hspace : the amount of height reserved for white space between subplots
## |
self.canvas = FigureCanvasWxAgg(self, -1, self.fig)
##now put everything in a sizer
sizer = wx.BoxSizer(wx.VERTICAL)
# This way of adding to sizer allows resizing
sizer.Add(self.canvas, 1, wx.LEFT | wx.TOP | wx.GROW)
self.SetSizer(sizer)
self.Fit()
##now finally create the actual plot
##self.axes = self.fig.add_subplot(111)
self.axes = self.fig.add_axes(
(0.08, 0.08, 0.90, 0.85)) ##left,bottom,width,height
self.naohistoryplot = self.axes.plot([0, 0], [0, 0],
'r',
animated=True)
self.naohistoryx = list()
self.naohistoryy = list()
self.positionmeasurementplot = self.axes.plot([0, 0], [0, 0],
'blue',
marker='o',
markersize=5,
linewidth=0,
markeredgewidth=0,
animated=True)
self.orientationmeasurementplot = self.axes.plot([0, 0], [0, 0],
'blue',
linewidth=2,
animated=True)
self.shapeplot = self.axes.plot([0, 0], [0, 0],
'blue',
marker='o',
markersize=2,
linewidth=0,
markeredgewidth=0,
animated=True)
self.estimateplot = self.axes.plot([0, 0], [0, 0],
'red',
linewidth=2,
animated=True)
self.particleplot = self.axes.quiver([0, 0], [0, 0], [1, 1],
[0.5, -0.5], [1, 1],
cmap=pylab.gray(),
animated=True)
##plot formatting
self.axes.set_title('Nao Image', fontsize='10')
self.axes.set_xlabel('y (cm)', fontsize='10')
self.axes.set_ylabel('x (cm)', fontsize='10')
ticks = numpy.arange(-25, 25 + 5, 5)
labels = [str(tick) for tick in ticks]
self.axes.set_yticks(ticks)
self.axes.set_yticklabels(labels, fontsize=8)
self.axes.set_ylim(ticks[0], ticks[-1])
ticks = -numpy.arange(-50, 50 + 5, 5)
labels = [str(tick) for tick in ticks]
self.axes.set_xticks(ticks)
self.axes.set_xticklabels(labels, fontsize=8)
self.axes.set_xlim(ticks[0], ticks[-1])
self.canvas.draw()
self.canvas.gui_repaint()
# save the clean slate background -- everything but the animated line
# is drawn and saved in the pixel buffer background
self.background = self.canvas.copy_from_bbox(self.axes.bbox)
def __init__(self, dfore, bw):
self.dfore = dfore
self.bw = bw
def sub_bg(self, frame):
return (self.dfore[frame], self.bw[frame])
bg = BG(dfore, bw)
hind = hindsight.Hindsight(tracks, bg)
hind.initialize_milestones()
print 'milestones: '
print hind.milestones
print 'tracks: '
print tracks
for t in range(2, len(tracks)):
hind.fixerrors(t)
mpl.gray()
for t in range(len(tracks)):
mpl.imshow(bw[t])
for [id, e] in tracks[t].iteritems():
drawellipse(e, colors[id])
mpl.axis('tight')
mpl.title('Frame %d' % t)
mpl.show()
def run(self, fname, i):
print_info("# %s" % (fname))
print_info("=== %s %-3d" % (fname, i))
raw = ocrolib.read_image_gray(fname)
self.dshow(raw, "input")
# perform image normalization
image = raw - amin(raw)
if amax(image) == amin(image):
print_info("# image is empty: %s" % (fname))
return
image /= amax(image)
if not self.param['nocheck']:
check = self.check_page(amax(image) - image)
if check is not None:
print_error(fname + " SKIPPED. " + check +
" (use -n to disable this check)")
return
# check whether the image is already effectively binarized
if self.param['gray']:
extreme = 0
else:
extreme = (np.sum(image < 0.05) +
np.sum(image > 0.95)) * 1.0 / np.prod(image.shape)
if extreme > 0.95:
comment = "no-normalization"
flat = image
else:
comment = ""
# if not, we need to flatten it by estimating the local whitelevel
print_info("flattening")
m = interpolation.zoom(image, self.param['zoom'])
m = filters.percentile_filter(m,
self.param['perc'],
size=(self.param['range'], 2))
m = filters.percentile_filter(m,
self.param['perc'],
size=(2, self.param['range']))
m = interpolation.zoom(m, 1.0 / self.param['zoom'])
if self.param['debug'] > 0:
clf()
imshow(m, vmin=0, vmax=1)
ginput(1, self.param['debug'])
w, h = minimum(array(image.shape), array(m.shape))
flat = clip(image[:w, :h] - m[:w, :h] + 1, 0, 1)
if self.param['debug'] > 0:
clf()
imshow(flat, vmin=0, vmax=1)
ginput(1, self.param['debug'])
# estimate low and high thresholds
print_info("estimating thresholds")
d0, d1 = flat.shape
o0, o1 = int(self.param['bignore'] * d0), int(self.param['bignore'] *
d1)
est = flat[o0:d0 - o0, o1:d1 - o1]
if self.param['escale'] > 0:
# by default, we use only regions that contain
# significant variance; this makes the percentile
# based low and high estimates more reliable
e = self.param['escale']
v = est - filters.gaussian_filter(est, e * 20.0)
v = filters.gaussian_filter(v**2, e * 20.0)**0.5
v = (v > 0.3 * amax(v))
v = morphology.binary_dilation(v, structure=ones((int(e * 50), 1)))
v = morphology.binary_dilation(v, structure=ones((1, int(e * 50))))
if self.param['debug'] > 0:
imshow(v)
ginput(1, self.param['debug'])
est = est[v]
lo = stats.scoreatpercentile(est.ravel(), self.param['lo'])
hi = stats.scoreatpercentile(est.ravel(), self.param['hi'])
# rescale the image to get the gray scale image
print_info("rescaling")
flat -= lo
flat /= (hi - lo)
flat = clip(flat, 0, 1)
if self.param['debug'] > 0:
imshow(flat, vmin=0, vmax=1)
ginput(1, self.param['debug'])
binarized = 1 * (flat > self.param['threshold'])
# output the normalized grayscale and the thresholded images
#print_info("%s lo-hi (%.2f %.2f) angle %4.1f %s" % (fname, lo, hi, angle, comment))
print_info("%s lo-hi (%.2f %.2f) %s" % (fname, lo, hi, comment))
print_info("writing")
if self.param['debug'] > 0 or self.param['show']:
clf()
gray()
imshow(binarized)
ginput(1, max(0.1, self.param['debug']))
base, _ = ocrolib.allsplitext(fname)
ocrolib.write_image_binary(base + ".bin.png", binarized)
ocrolib.write_image_gray(base + ".nrm.png", flat)
# print("########### File path : ", base+".nrm.png")
# write_to_xml(base+".bin.png")
return base + ".bin.png"
def predict(self, index):
pl.gray()
pl.matshow(random_forest.data.images[index])
pl.show()
prediction = self.classifier.predict([self.X[index]])
print(f"prediction is => {prediction} ")
def single_test(self, type):
shape = np.random.randint(*range2d, size=2)
# these rectangles are biased, but that shouldn't matter
rect_min = [np.random.randint(0, a) for a in shape]
rect_max = [
np.random.randint(rect_min[i] + 1, shape[i] + 1)
for i in range(len(shape))
]
if True:
#pixsize = 0.5 + np.random.random(size=2)
pixsize = np.array([0.5, 0.5]) + np.random.random()
origin = 10 * np.random.random(size=2)
else:
pixsize = (1., 1.)
origin = (0., 0.)
vg = astra.create_vol_geom(shape[1], shape[0],
origin[0] - 0.5 * shape[0] * pixsize[0],
origin[0] + 0.5 * shape[0] * pixsize[0],
origin[1] - 0.5 * shape[1] * pixsize[1],
origin[1] + 0.5 * shape[1] * pixsize[1])
#print(vg)
if type == 'parallel':
pg = gen_random_geometry_parallel()
projector_id = astra.create_projector('line', pg, vg)
elif type == 'parallel_vec':
pg = gen_random_geometry_parallel_vec()
projector_id = astra.create_projector('line', pg, vg)
elif type == 'fanflat':
pg = gen_random_geometry_fanflat()
projector_id = astra.create_projector('line_fanflat', pg, vg)
elif type == 'fanflat_vec':
pg = gen_random_geometry_fanflat_vec()
projector_id = astra.create_projector('line_fanflat', pg, vg)
data = np.zeros((shape[1], shape[0]), dtype=np.float32)
data[rect_min[1]:rect_max[1], rect_min[0]:rect_max[0]] = 1
sinogram_id, sinogram = astra.create_sino(data, projector_id)
#print(pg)
#print(vg)
astra.data2d.delete(sinogram_id)
astra.projector.delete(projector_id)
a = np.zeros(np.prod(astra.functions.geom_size(pg)), dtype=np.float32)
b = np.zeros(np.prod(astra.functions.geom_size(pg)), dtype=np.float32)
c = np.zeros(np.prod(astra.functions.geom_size(pg)), dtype=np.float32)
i = 0
#print( origin[0] + (-0.5 * shape[0] + rect_min[0]) * pixsize[0], origin[0] + (-0.5 * shape[0] + rect_max[0]) * pixsize[0], origin[1] + (-0.5 * shape[1] + rect_min[1]) * pixsize[1], origin[1] + (-0.5 * shape[1] + rect_max[1]) * pixsize[1])
for src, det in gen_lines(pg):
#print(src,det)
# NB: Flipped y-axis here, since that is how astra interprets 2D volumes
# We compute line intersections with slightly bigger (cw) and
# smaller (aw) rectangles, and see if the kernel falls
# between these two values.
(aw, bw, cw) = intersect_line_rectangle_interval(
src, det,
origin[0] + (-0.5 * shape[0] + rect_min[0]) * pixsize[0],
origin[0] + (-0.5 * shape[0] + rect_max[0]) * pixsize[0],
origin[1] + (+0.5 * shape[1] - rect_max[1]) * pixsize[1],
origin[1] + (+0.5 * shape[1] - rect_min[1]) * pixsize[1], 1e-3)
a[i] = aw
b[i] = bw
c[i] = cw
i += 1
# Add weight for pixel / voxel size
try:
detweight = pg['DetectorWidth']
except KeyError:
detweight = np.sqrt(pg['Vectors'][0, 4] * pg['Vectors'][0, 4] +
pg['Vectors'][0, 5] * pg['Vectors'][0, 5])
a *= detweight
b *= detweight
c *= detweight
a = a.reshape(astra.functions.geom_size(pg))
b = b.reshape(astra.functions.geom_size(pg))
c = c.reshape(astra.functions.geom_size(pg))
# Check if sinogram lies between a and c
y = np.min(sinogram - a)
z = np.min(c - sinogram)
x = np.max(np.abs(sinogram -
b)) # ideally this is small, but can be large
# due to discontinuities in line kernel
self.assertFalse(z < 0 or y < 0)
if z < 0 or y < 0:
print(y, z, x)
pylab.gray()
pylab.imshow(data)
pylab.figure()
pylab.imshow(sinogram)
pylab.figure()
pylab.imshow(b)
pylab.figure()
pylab.imshow(a)
pylab.figure()
pylab.imshow(c)
pylab.figure()
pylab.imshow(sinogram - a)
pylab.figure()
pylab.imshow(c - sinogram)
pylab.show()
def PlotStuff():
files_list, max_list = get_stuff()
x = 2
y = 3
INframeW = 1.5
INframeH = 1.5
INgapw = 0.05 * np.ones(x + 1)
INgapw[0] = 0.2
INgaph = 0.05 * np.ones(y + 1)
INgaph[3] = 0.2
INfig = FigArray(INframeW, INframeH, INgapw, INgaph)
(W, H) = INfig.dimensions()
plt.figure(1, figsize=(W, H))
plt.text(-0.08,
1.085,
"L = 1 pix, C = 5 mag",
fontsize=10,
rotation=0,
color='k')
plt.text(0.53,
1.085,
"L = 4 pix, C = 2 mag",
fontsize=10,
rotation=0,
color='k')
plt.text(-0.15,
1.04,
"detector scale model",
fontsize=9,
rotation=90,
color='k')
plt.text(-0.15,
0.65,
"NIRISS sim observation",
fontsize=9,
rotation=90,
color='k')
plt.text(-0.15,
0.25,
"NIRCam sim observation",
fontsize=9,
rotation=90,
color='k')
plt.axis('off')
ctr = 0
for i in range(0, x):
for j in range(0, y):
disp = files_list[ctr]
disp = np.power(disp, PWR)
#disp = disp_psf[30:55,30:55]
dispmax = max_list[ctr]
dispmax = np.power(dispmax, PWR)
a = plt.axes(INfig.axes(i + 1, j + 1))
#plt.text(0,1, '%d' %(ctr), fontsize=6, rotation=0, color='w')
p = plt.imshow(disp,
vmax=dispmax,
vmin=0,
cmap='hot',
interpolation='nearest')
a.xaxis.set_major_locator(plt.NullLocator())
a.yaxis.set_major_locator(plt.NullLocator())
plt.gray() # overrides current and sets default
plt.axis("off")
ctr += 1
plt.savefig(LOC + STUFF_PLOT, dpi=150)
def _process_segment(self, page_image, page, page_xywh, page_id,
input_file, n):
raw = ocrolib.pil2array(page_image)
if len(raw.shape) > 2:
raw = np.mean(raw, 2)
raw = raw.astype("float64")
# perform image normalization
image = raw - amin(raw)
if amax(image) == amin(image):
LOG.info("# image is empty: %s" % (page_id))
return
image /= amax(image)
# check whether the image is already effectively binarized
if self.parameter['gray']:
extreme = 0
else:
extreme = (np.sum(image < 0.05) +
np.sum(image > 0.95)) * 1.0 / np.prod(image.shape)
if extreme > 0.95:
comment = "no-normalization"
flat = image
else:
comment = ""
# if not, we need to flatten it by estimating the local whitelevel
LOG.info("Flattening")
m = interpolation.zoom(image, self.parameter['zoom'])
m = filters.percentile_filter(m,
self.parameter['perc'],
size=(self.parameter['range'], 2))
m = filters.percentile_filter(m,
self.parameter['perc'],
size=(2, self.parameter['range']))
m = interpolation.zoom(m, 1.0 / self.parameter['zoom'])
if self.parameter['debug'] > 0:
clf()
imshow(m, vmin=0, vmax=1)
ginput(1, self.parameter['debug'])
w, h = minimum(array(image.shape), array(m.shape))
flat = clip(image[:w, :h] - m[:w, :h] + 1, 0, 1)
if self.parameter['debug'] > 0:
clf()
imshow(flat, vmin=0, vmax=1)
ginput(1, self.parameter['debug'])
# estimate low and high thresholds
LOG.info("Estimating Thresholds")
d0, d1 = flat.shape
o0, o1 = int(self.parameter['bignore'] * d0), int(
self.parameter['bignore'] * d1)
est = flat[o0:d0 - o0, o1:d1 - o1]
if self.parameter['escale'] > 0:
# by default, we use only regions that contain
# significant variance; this makes the percentile
# based low and high estimates more reliable
e = self.parameter['escale']
v = est - filters.gaussian_filter(est, e * 20.0)
v = filters.gaussian_filter(v**2, e * 20.0)**0.5
v = (v > 0.3 * amax(v))
v = morphology.binary_dilation(v, structure=ones((int(e * 50), 1)))
v = morphology.binary_dilation(v, structure=ones((1, int(e * 50))))
if self.parameter['debug'] > 0:
imshow(v)
ginput(1, self.parameter['debug'])
est = est[v]
lo = stats.scoreatpercentile(est.ravel(), self.parameter['lo'])
hi = stats.scoreatpercentile(est.ravel(), self.parameter['hi'])
# rescale the image to get the gray scale image
LOG.info("Rescaling")
flat -= lo
flat /= (hi - lo)
flat = clip(flat, 0, 1)
if self.parameter['debug'] > 0:
imshow(flat, vmin=0, vmax=1)
ginput(1, self.parameter['debug'])
binarized = 1 * (flat > self.parameter['threshold'])
# output the normalized grayscale and the thresholded images
# print_info("%s lo-hi (%.2f %.2f) angle %4.1f %s" % (fname, lo, hi, angle, comment))
LOG.info("%s lo-hi (%.2f %.2f) %s" % (page_id, lo, hi, comment))
LOG.info("writing")
if self.parameter['debug'] > 0 or self.parameter['show']:
clf()
gray()
imshow(binarized)
ginput(1, max(0.1, self.parameter['debug']))
page_xywh['features'] += ',binarized'
bin_array = array(255 * (binarized > ocrolib.midrange(binarized)), 'B')
bin_image = ocrolib.array2pil(bin_array)
file_id = input_file.ID.replace(self.input_file_grp, self.image_grp)
if file_id == input_file.ID:
file_id = concat_padded(self.image_grp, n)
file_path = self.workspace.save_image_file(
bin_image,
file_id,
page_id=page_id,
file_grp=self.image_grp,
force=self.parameter['force'])
page.add_AlternativeImage(
AlternativeImageType(filename=file_path,
comments=page_xywh['features']))
def plot_forecast_area(ttt, model, outputDir, current_labels = None, t_stop=None, BackgroundFile=None, ForeGroundRGBFile=None, labels_dir = '/opt/users/'+getpass.getuser()+'/PyTroll/scripts/labels/', in_msg = None):
verbose = True
if t_stop is None:
t_stop = ttt
ylabel = "area"
while ttt <= t_stop:
yearS, monthS, dayS, hourS, minS = string_date(ttt)
if verbose:
print("******** read cell properties from shelve")
if current_labels is None:
yearS, monthS, dayS, hourS, minS = string_date(ttt)
filename = 'Labels_%s.shelve'%(yearS+monthS+dayS+hourS+minS)
myShelve = shelve.open(filename)
labels_all = deepcopy(myShelve['labels'])
else:
labels_all = deepcopy(current_labels)
if verbose:
print(labels_all)
unique_labels = np.unique(labels_all[labels_all>0])
if verbose:
print(("... cells with unique labels: ", unique_labels))
forecasted_labels = {}
forecasted_areas = []
at_least_one_cell = False
if verbose:
print("*** computing history backward (", labels_dir, ")")
for interesting_cell in unique_labels:
forecasted_labels["ID"+str(interesting_cell)]=[]
# calculate backward history for 1 hour and save it in labels_dir
ind, area, displacement, time, center = history_backward(ttt, interesting_cell, True, in_msg, ttt-timedelta(hours = 1), labels_dir=labels_dir) #-timedelta(minutes = 10))
# current time, cell_id, backward? time_stop
if area is None or len(area)<=1:
if verbose:
print("new cell or cell with COM outside domain")
continue
at_least_one_cell = True
if len(area)<=3:
# if history is too short, use linear extrapolation
t, y = future_properties(time, area, ylabel, "linear")
else:
t, y = future_properties(time, area, ylabel, model)
if False:
ind1, area1, displacement1, time1, center = history_backward(ttt, interesting_cell, False, ttt+timedelta(hours=1), labels_dir=labels_dir)
print("******** computed history forward")
t2 = time1 #[::-1]
y2 = area1 #[::-1]
nx,ny = labels_all.shape
#if verbose:
# print(nx,ny)
label_cell = np.zeros(labels_all.shape)
label_cell[labels_all==interesting_cell] = 1
#pickle.dump(label_cell, open("test_label.p", "wb" ) )
#quit()
dt = 0
if False:
figure_labels(label_cell, outputDir, ttt, dt, area_plot="ccs4", add_name = "_ID"+str(interesting_cell), verbose=verbose)
area_current = sum(sum(label_cell))
forecasted_areas.append(area_current)
indx = np.where(t==ttt)[0] + 1
if verbose:
print("*** compute displacement ")
if displacement.shape[1]==2:
if len(displacement) == 0:
dx = 0
dy = 0
else:
try:
dx = int(round(displacement[:,0].mean()))
dy = int(round(displacement[:,1].mean()))
except ValueError:
print("VALUE ERROR")
print(displacement)
quit()
print(" computed displacement dx, dy = ", dx, dy)
else:
print("wrong displacement")
quit()
labels_in_time={}
index_stop = 12
print(("*** calculate forecasts for cell ID"+str(interesting_cell)))
if verbose:
print("index time area growth")
print("----------------------------")
for i in range(13):
dt += 5
#if verbose:
# print("... for time ", dt ,", index ", indx + i)
if indx+i >= len(y):
index_stop = deepcopy(i)
break
else:
area_new = y[indx+i]
area_prev = y[indx+i-1]
#if verbose:
# print("area px that will be grown ", area_current)
# print("area forecasted ", area_new)
# print("area forecasted prev ", area_prev)
###growth = sqrt(float(area_new)/float(area_current))
if area_new < 0 or len(area_new)==0 or len(area_prev)==0:
if verbose:
print("the cell is predicted to disappear")
index_stop = deepcopy(i)
break
growth = sqrt(float(area_new)/float(area_prev))
#if verbose:
# print("growing by ", growth)
# print("dx ", dx)
# print("dy ", dy)
if verbose:
print((indx + i, dt, area_new, growth))
#figure_labels(label_cell, outputDir, ttt, dt, area_plot="ccs4", add_name = "before")
shifted_label = resize_array(label_cell, dx, dy, nx, ny)
#figure_labels(shifted_label, outputDir, ttt, dt, area_plot="ccs4", add_name = "before_shifted")
#quit()
if verbose:
print((" after shift ", sum(sum(shifted_label))))
if sum(sum(shifted_label))==0: #the cell is outside the domain
break
#center of mass before resizing
center_before = ndimage.measurements.center_of_mass(shifted_label)
center_before = np.rint(center_before)
#if verbose:
# print(" after shift ", sum(sum(shifted_label)))
resized_label = scipy.misc.imresize(shifted_label,float(growth),'nearest')
resized_label[resized_label >0] = 1
temp_label = np.zeros((nx,ny))
#after resizing, the array is larger/smaller than nx,ny --> create new array that contains all the label region
if resized_label.shape[0]<nx:
temp_label[0:resized_label.shape[0],0:resized_label.shape[1]] = deepcopy(resized_label)
else:
x_start = max(min(np.nonzero(resized_label)[0])-1,0)
y_start = max(min(np.nonzero(resized_label)[1])-1,0)
temp_label[0:min(nx,resized_label.shape[0]-x_start),0:min(ny,resized_label.shape[1]-y_start)] = deepcopy(resized_label[x_start:min(x_start+nx,resized_label.shape[0]),y_start:min(y_start+ny,resized_label.shape[1])])
#if verbose:
# print(np.unique(temp_label))
# print(" after resize ", sum(sum(temp_label)))
#figure_labels(resized_label, outputDir, ttt, dt, area_plot="ccs4", add_name = "before_shifted_resized")
#center of mass after resizing
center_after = ndimage.measurements.center_of_mass(temp_label)
center_after = np.rint(center_after)
dx_new,dy_new = center_before - center_after
shifted_label = resize_array(temp_label,dx_new,dy_new, nx, ny)
#if verbose:
# print(" after shift2 ", sum(sum(shifted_label)))
label_cell = np.zeros((nx,ny))
label_cell[0:,0:] = shifted_label[0:nx,0:ny]
if label_cell.shape[0] != nx or label_cell.shape[1] != ny:
print("incorrect size")
quit()
forecasted_labels["ID"+str(interesting_cell)].append(deepcopy(label_cell))
#indx+=1
label_cell = shifted_label #????????????????????????????????????
area_current = sum(sum(label_cell))
if verbose:
print(("end ", area_current))
forecasted_areas.append(area_current)
#add check to make sure the area you produced is more or less correct
t_temp = deepcopy(ttt)
forecasted_time = []
for gg in range(len(forecasted_areas)):
forecasted_time.append(t_temp)
t_temp+=timedelta(minutes = 5)
"""
if verbose:
print("******** produce images")
if False:
t_composite = deepcopy(ttt)
for i in range(min(len(y),index_stop)):
yearSf, monthSf, daySf, hourSf, minSf = string_date(t_composite)
contour_file = outputDir + "Forecast"+yearS+monthS+dayS+"_Obs"+hourS+minS+"_Forc"+hourSf+minSf+"_ID"+str(interesting_cell)+".png"
type_image = "_HRV"
#background_file = "/data/COALITION2/PicturesSatellite//"+yearS+"-"+monthS+"-"+dayS+"/"+yearS+"-"+monthS+"-"+dayS+type_image+"_"+"ccs4"+"/MSG"+type_image+"-"+"ccs4"+"_"+yearS[2:]+monthS+dayS+hourS+minS+".png"
background_file = "/data/COALITION2/PicturesSatellite/LEL_results_wind/"+yearS+"-"+monthS+"-"+dayS+"/RGB-HRV_dam/"+yearS+monthS+dayS+"_"+hourS+minS+"*.png"
out_file1 = create_dir( outputDir+"/Contours/")+"Obs"+hourS+minS+"_Forc"+hourSf+minSf+"_ID"+str(interesting_cell)+".png"
if verbose:
print("... create composite "+contour_file+" "+background_file+" "+out_file1)
#subprocess.call("/usr/bin/composite "+contour_file+" "+background_file+" "+out_file1, shell=True)
if verbose:
print("... saved composite: display ", out_file1, " &")
t_composite+=timedelta(minutes=5)
"""
"""
if False:
fig, ax = plt.subplots()
ax.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))
ax.plot_date(t, y, 'o',label="Fit and extrapolation")
ax.plot_date(forecasted_time, forecasted_areas, '*',label="forecasted")
ax.plot_date(t2, y2, '*', label="Observations")
#ax.set_xlim([t[0]-timedelta(minutes = 5), t2[-1]+timedelta(minutes = 5)])
ax.set_ylabel("area")
ax.legend(loc="best");
fig.savefig(yearS+monthS+dayS+"_"+hourS+minS+"_AreaInTime"+"ID"+str(interesting_cell)+".png")
plt.close( fig)
"""
t_composite = deepcopy(ttt)
# merge coalition2 file with
if ForeGroundRGBFile is None:
currentRGB_im_filename = "/opt/users/"+getpass.getuser()+"/PyTroll/scripts/Mecikalski/cosmo/Channels/indicators_in_time/RGB_dam/"+yearS+monthS+dayS+"_"+hourS+minS+"*ccs4.png"
else:
currentRGB_im_filename = ForeGroundRGBFile
currentRGB_im = glob.glob(currentRGB_im_filename)
if len(currentRGB_im)<1:
print("No file found:", currentRGB_im_filename)
# get background file
if BackgroundFile is None:
background_im_filename = '/data/COALITION2/PicturesSatellite/LEL_results_wind/'+yearS+'-'+monthS+'-'+dayS+'/RGB-HRV_dam/'+yearS+monthS+dayS+'_'+hourS+minS+'*.png'
else:
background_im_filename = BackgroundFile
background_im = glob.glob(background_im_filename)
if len(background_im)>0:
im = plt.imread(background_im[0])
back_exists = True
else:
back_exists = False
#img1 = Image.imread(currentRGB_im[0])
obj_area = get_area_def("ccs4")
fig,ax = prepare_figure(obj_area)
if in_msg.nrt == False:
if back_exists:
plt.imshow(np.flipud(im))
else:
# now read the data we would like to forecast
global_data = GeostationaryFactory.create_scene(in_msg.sat_str(), in_msg.sat_nr_str(), "seviri", ttt)
#global_data_RGBforecast = GeostationaryFactory.create_scene(in_msg.sat, str(10), "seviri", time_slot)
# area we would like to read
area2load = "EuropeCanary95" #"ccs4" #c2"#"ccs4" #in_windshift.ObjArea
area_loaded = get_area_def(area2load )#(in_windshift.areaExtraction)
# load product, global_data is changed in this step!
area_loaded = load_products(global_data, ['IR_108'], in_msg, area_loaded )
data = global_data.project("ccs4")
plt.imshow(np.flipud(data['IR_108'].data),cmap = pylab.gray())
# background file form function argument or default
if BackgroundFile is None:
background_im_filename = '/data/COALITION2/PicturesSatellite/LEL_results_wind/'+yearS+'-'+monthS+'-'+dayS+'/RGB-HRV_dam/'+yearS+monthS+dayS+'_'+hourS+minS+'*.png'
else:
if verbose:
print("... BackgroundFile ", BackgroundFile)
background_im_filename = BackgroundFile
# searching background file (wildcards are possible)
background_im = glob.glob(background_im_filename)
if len(background_im) == 0:
print("*** Error in plot_forecast_area (test_forecast.py)")
print(" no background file found: ", background_im_filename)
quit()
elif len(background_im) > 1:
print("*** Warning in plot_forecast_area (test_forecast.py)")
print(" several background files found: ", background_im)
# read background file
im = plt.imread(background_im[0])
#img1 = Image.imread(currentRGB_im[0])
obj_area = get_area_def("ccs4")
fig,ax = prepare_figure(obj_area)
#plt.imshow(np.flipud(im))
# plot contour lines for all cells
if at_least_one_cell:
time_wanted_minutes = [5,20,40,60]
time_wanted = []
color_wanted = []
vmax = 70
for t_want in time_wanted_minutes:
time_wanted.append((t_want-5)/5)
tmp = (mpl.cm.Blues(float(t_want)/vmax))
tmp1 = [tmp]
color_wanted.append(tmp1)
all_labels_in_time = np.zeros((nx,ny))
for i in range(len(time_wanted)-1,-1,-1):
ind_time = time_wanted [i]
for key, forc_labels in forecasted_labels.items(): #forecasted_labels["ID"+str(interesting_cell)]=[]
if len(forc_labels)>ind_time:
#plt.contour(np.flipud(forc_labels[ind_time]),[0.5],colors = color_wanted_cont[i]) #colors='w') #
all_labels_in_time[forc_labels[ind_time]>0] = time_wanted_minutes[i]
forc_labels_tmp = np.ma.masked_where(all_labels_in_time==0,all_labels_in_time)
plt.contourf(np.flipud(forc_labels_tmp), cmap="Blues", vmin=0, vmax=vmax)
if False:
for i in range(len(time_wanted)):
ind_time = time_wanted [i]
for key, forc_labels in forecasted_labels.items(): #forecasted_labels["ID"+str(interesting_cell)]=[]
if len(forc_labels)>ind_time:
plt.contour(np.flipud(forc_labels[ind_time]),[0.5],colors = color_wanted[i]) #colors='w') #
else:
print("*** Warning, no COALITION2 cell detected ")
print(" produce empty figure ...")
PIL_image = fig2img ( fig )
standardOutputName = in_msg.standardOutputName.replace('%y%m%d%H%M',strftime('%y%m%d%H%M',ttt.timetuple()))
#PIL_image.paste(img1, (0, 0), img1)
if in_msg is None:
PIL_image.save(create_dir(outputDir)+"Forecast"+yearS+monthS+dayS+"_Obs"+hourS+minS+".png")
path = (outputDir)+yearS+monthS+dayS+hourS+minS+"Forecast.png"
else:
# dic_figure={}
# if in_msg.nrt == True:
# dic_figure['rgb']= 'Forecast' #'C2rgbForecastTMP-IR-108'
# else:
# dic_figure['rgb']= 'Forecast-C2rgb'
# dic_figure['area']='ccs4'
# PIL_image.save(create_dir(outputFile)+standardOutputName%dic_figure)
# path = (outputFile)+standardOutputName%dic_figure
# if in_msg.nrt == False:
# dic_figure={}
# dic_figure['rgb']= 'C2rgb-Forecast-HRV' #'C2rgbForecastTMP-IR-108'
# dic_figure['area']='ccs4'
# path_output = (outputFile)+standardOutputName%dic_figure
# print ("creating composite: ",currentRGB_im[0],"+",path)
# subprocess.call("/usr/bin/composite "+currentRGB_im[0]+" "+path+" "+path_output, shell=True)
#print ("... display ",path_output," &")
#dic_figure={}
#dic_figure['rgb']= 'Forecast' #'C2rgbForecastTMP-IR-108'
#dic_figure['area']='ccs4'
outputFile = format_name(create_dir(outputDir)+in_msg.outputFile, ttt, rgb='Forecast', area='ccs4', sat_nr=int(in_msg.sat_nr))
#PIL_image.save(create_dir(outputDir)+in_msg.outputFile%dic_figure)
PIL_image.save(outputFile)
#path = (outputDir)+in_msg.outputFile%dic_figure
path = outputFile
print("... display ",path," &")
plt.close( fig)
if True:
if verbose:
print("path foreground", currentRGB_im[0])
if in_msg is None:
path_composite = (outputFile)+yearS+monthS+dayS+"_Obs"+hourS+minS+"Forecast_composite.png"
else:
# dic_figure={}
# dic_figure['rgb']='C2rgb-Forecast-HRV'
# dic_figure['area']='ccs4'
# path_composite = (outputFile)+standardOutputName%dic_figure
#dic_figure = {}
#dic_figure['rgb'] = "_HRV" #'IR-108'
#dic_figure['area']='ccs4'
#path_IR108 = (outputFile)+standardOutputName%dic_figure
#dic_figure={}
#dic_figure['rgb'] = 'C2rgbForecast-IR-108'
#dic_figure['area'] = 'ccs4'
#path_composite = (outputDir) + in_msg.outputFile%dic_figure
path_composite = format_name( outputDir+in_msg.outputFile, ttt, rgb='C2rgbForecast-IR-108', area='ccs4', sat_nr=int(in_msg.sat_nr))
#dic_figure = {}
#dic_figure['rgb'] = 'IR-108'
#dic_figure['area']='ccs4'
#path_IR108 = (outputDir) + in_msg.outputFile%dic_figure
path_IR108 = format_name( outputDir+in_msg.outputFile, ttt, rgb='IR-108', area='ccs4', sat_nr=int(in_msg.sat_nr))
if in_msg.nrt == True:
if verbose:
print("---starting post processing")
#if area in in_msg.postprocessing_areas:
in_msg.postprocessing_composite = deepcopy(in_msg.postprocessing_composite2)
postprocessing(in_msg, ttt, in_msg.sat_nr, "ccs4")
#print ("... display",path_composite,"&")
if in_msg.scpOutput and in_msg.nrt == True and False: #not necessary because already done within postprocessing
print("... secure copy "+path_composite+ " to "+in_msg.scpOutputDir) #
subprocess.call("scp "+in_msg.scpID+" "+path_composite +" "+in_msg.scpOutputDir+" 2>&1 &", shell=True) #BackgroundFile #
if False:
for i in range(12):
contour_files = glob.glob(outputDir + "Forecast"+yearS+monthS+dayS+"_Obs"+hourS+minS+"_Forc"+hourSf+minSf+"_ID*.png")
if verbose:
print(("Files found: ",contour_files))
if len(contour_files)>0:
background_file = "/data/COALITION2/PicturesSatellite/LEL_results_wind/"+yearS+"-"+monthS+"-"+dayS+"/RGB-HRV_dam/"+yearS+monthS+dayS+"_"+hourS+minS+"*.png"
out_file1 = create_dir( outputDir+"/Contours/")+"Obs"+hourS+minS+"_Forc"+hourSf+minSf+".png"
t_composite+=timedelta(minutes=5)
ttt += timedelta(minutes = 5)
for jj in range(Nf):
num += (filt[Mf-1-ii, Nf-1-jj] * image[i-Mf2+ii, j-Nf2+jj])
result[i, j] = num
return result
image = lena()
filter = ones((7,7), dtype='int32')
import time
start = time.time()
result = filter2d(image, filter)
duration = time.time() - start
from scipy.ndimage import convolve
start = time.time()
result = convolve(image, filter)
duration2 = time.time() - start
print "Time for LLVM code = %f\nTime for convolve = %f" % (duration, duration2)
from pylab import subplot, imshow, show, title, gray
subplot(1,2,1)
imshow(image)
title('Original Image')
gray()
subplot(1,2,2)
imshow(result)
title('Filtered Image')
gray()
show()
def s(img):
pl.imshow(img)
pl.gray()
pl.show()
def show_img(img):
#show image
thresh = cv2.adaptiveThreshold(img, 255, 1, 1, 11, 15)
_ = pl.imshow(thresh, cmap=pl.gray())
_ = pl.axis("off")
pl.show()
def s(fig):
pl.imshow(fig)
pl.gray()
pl.show()
from scipy import ndimage
import matplotlib.image as mpimg
from matplotlib.colors import NoNorm
import pylab
from scipy import misc
import numpy as np
#http://scikit-image.org/docs/dev/user_guide/transforming_image_data.html
#im_image = np.uint8(100*np.ones((28, 28)))
#im_image =100.*np.ones((28,28), dtype=np.float32)
im_image = 100 * np.ones((28, 28), dtype=np.uint8)
im_image[1:3, 1:3] = 200
plt.subplot(211)
plt.imshow(im_image, cmap=pylab.gray(), norm=NoNorm())
plt.imsave('C:/pythonwork/images/Pimage1.png',
im_image) # uses the Image module (PIL)
#convert image (np.array) to binary image
#https://stackoverflow.com/questions/40449781/convert-image-np-array-to-binary-image
im_label = im_image < 120
#im_label=np.zeros((28,28), dtype=bool)
#im_label[1:3, 1:3] =np.array([[True, True] , [True, True]])
plt.subplot(212)
plt.imshow(im_label, cmap=plt.cm.binary)
plt.show()
plt.imsave('C:/pythonwork/labels/Plabel1.png',
im_label) # uses the Image module (PIL)
import numpy as np
filename = r'./image_processing/example.png'
'''
img = plt.imread(filename)
plt.imshow(img)
plt.show()
'''
import pylab as plt
from PIL import Image
import numpy as np
img = Image.open(filename)
print(img)
img_gray = img.convert('L') #转换成灰度图像
img = np.array(img)
print(img.shape)
print(img[0])
img_gray = np.array(img_gray)
print(img_gray.shape)
print(img_gray[0])
plt.imshow(
img
) # or plt.imshow(img / 255.0)，matplotlib和matlab一样，如果是float类型的图像，范围是0-1才能正常imshow，如果是uint8图像，范围则需要是0-255
plt.show()
plt.imshow(img_gray, cmap=plt.gray()) # 显示灰度图要设置cmap参数
plt.show()
plt.imshow(Image.open(filename)) # 实际上plt.imshow可以直接显示PIL格式图像
plt.show()
