def blurthday():
from imread import imread
from pprint import pprint
imfuckingshowalready = lambda mx: Image.fromarray(mx).show()
identity = LUT()
amatorka = LUT('amatorka')
#miss_etikate = LUT('miss_etikate')
#soft_elegance_1 = LUT('soft_elegance_1')
#soft_elegance_2 = LUT('soft_elegance_2')
im1 = imread(
AppDirectoriesFinder().storages.get(
'django_instakit').path(join(
'django_instakit', 'img', '06-DSCN4771.JPG')))
im2 = imread(
AppDirectoriesFinder().storages.get(
'django_instakit').path(join(
'django_instakit', 'img',
'430023_3625646599363_1219964362_3676052_834528487_n.jpg')))
pprint(identity)
pprint(amatorka)
im9 = amatorka.transform(im1)
pprint(im9)
imfuckingshowalready(im9)
print im1
print im2
def blurthday():
from imread import imread
from pprint import pprint
imfuckingshowalready = lambda mx: Image.fromarray(mx).show()
identity = LUT()
amatorka = LUT('amatorka')
#miss_etikate = LUT('miss_etikate')
#soft_elegance_1 = LUT('soft_elegance_1')
#soft_elegance_2 = LUT('soft_elegance_2')
im1 = imread(static.path(join('img', '06-DSCN4771.JPG')))
im2 = imread(
static.path(
join('img',
'430023_3625646599363_1219964362_3676052_834528487_n.jpg')))
pprint(identity)
pprint(amatorka)
im9 = amatorka.transform(im1)
pprint(im9)
imfuckingshowalready(im9)
print im1
print im2
def Mix(self, Folder, filen, Sim):
img = imread(os.path.join(Folder, "ForestInitial.png"))
fstr = str(filen).zfill(4)
#Path and name of the file
PathFile = os.path.join(Folder, "Plots", "Plots" + str(Sim),
"forest" + fstr + ".png")
# Reads the basic forest
img2 = imread(PathFile)
# Over plot
gca().set_axis_off()
subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0)
margins(0, 0)
gca().xaxis.set_major_locator(NullLocator())
gca().yaxis.set_major_locator(NullLocator())
plt.imshow(img, zorder=0)
plt.imshow(img2, zorder=1)
plt.savefig(PathFile, dpi=200, bbox_inches='tight', pad_inches=0)
plt.close("all")
import numpy as np
from scipy,misc import imread, imsave, imresize
mean = np.load('./TFD/mean.npy')
std = np.load('./TFD/std.npy')
for i in range(4):
img = imread('~/report_visu/%d.png' % i, True)
img = imresize(img, (48,48), 'bilinear')
imsave('~/report_visu/%d_good.png'%i, img)
inDirAtt ='/Users/edoardocalvello/Documents/Representation_Learning_2020/celebA/list_attr_celeba.txt'
inDirIm='/Users/edoardocalvello/Documents/Representation_Learning_2020/celebA/img_align_celeba'
IMSIZE=64
f = open(inDirAtt)
noSamples = int(f.readline())
print('There are %d samples' % noSamples)
labels = f.readline().split(' ')
print(labels, type(labels))
dataX = []
dataY = []
for i, line in enumerate(f):
imName, labels = line.split(' ')[0], line.split(' ')[1:]
label = np.loadtxt(labels)
print(imName, label)
print(i)
im = imread(join(inDirIm, imName))
im = Image.fromarray(im)
im = fit(im, size=(IMSIZE, IMSIZE))
label = label.astype('int')
im = np.transpose(im, (2, 0, 1))
dataX.append(im)
dataY.append(label)
print(np.shape(dataX))
print(np.shape(dataY))
np.save('/Users/edoardocalvello/Documents/Representation_Learning_2020/xTrain.npy', np.asarray(dataX))
np.save('/Users/edoardocalvello/Documents/Representation_Learning_2020/yAllTrain.npy', np.asarray(dataY))
stopwords = newstop
stopwords.add("DIA")
stopwords.add("CIO")
stopwords.add("use")
stopwords.add("s")
stopwords.add("IC ")
stopwords.add("IC")
stopwords.add("ITE")
stopwords.add("t")
stopwords.add("ESITE")
print stopwords
# read the mask image
custom_mask = imread(os.path.normpath((os.path.join(path,'Pictures for Mask','soldier.png'))))
wc = WordCloud(background_color="white", max_words=150, mask=custom_mask,
stopwords=stopwords)
# generate word cloud
wc.generate(text)
# store to file
wc.to_file(os.path.normpath((os.path.join(path,'Output','cloudsoldier.png'))))
# show
plt.imshow(wc)
plt.axis("off")
plt.figure()
plt.imshow(custom_mask, cmap=plt.cm.gray)
plt.axis("off")
def read_image(path):
file = imread(path, as_grey=True)
file = scipy.misc.imresize(file, IMAGE_SHAPE). \
reshape(IMAGE_ARRAY_SIZE)
return file
def read_image(path):
file = imread(path, as_grey=True)
file = scipy.misc.imresize(file, IMAGE_SHAPE). \
reshape(IMAGE_ARRAY_SIZE)
return file
def _imread(image_name):
return imread(image_name)
def MultiFireMix(self, Folder, nSims, mode="Scale", probs=[]):
# Read initial forest status
imgForest = imread(os.path.join(Folder, "ForestInitial.png"))
#fstr = str(filen).zfill(4)
# If no probs provided, alpha = 0.25 by default (otherwise, transparency is proportional to the fire probability)
if len(probs) == 0:
probs = np.full(nSims, 1 / nSims)
#Path and name of the file
PathFile = []
for i in range(1, nSims + 1):
#PathFile.append(os.path.join(Folder, "Plots", "Plots"+str(i), "forest" + fstr + ".png"))
ScarPath = os.path.join(Folder, "Plots", "Plots" + str(i))
ScarFiles = os.listdir(ScarPath)
PathFile.append(ScarPath + "/" + ScarFiles[-1])
#print(ScarFiles[-1])
# Reads the basic forest
imgArray = []
for i in range(nSims):
imgArray.append(imread(PathFile[i]))
#print(imgArray[i].shape)
# Summation mode: sum the pixels values for highlighting the intersections of fires (more intense than Scale)
if mode == "Sum":
SumPixels = np.zeros([
imgArray[0].shape[0], imgArray[0].shape[1],
imgArray[0].shape[2]
])
for i in range(nSims):
SumPixels[:, :, ] += imgArray[i][:, :, ]
# Trick for seeing less likely scar
SumPixels[:, :, ] += 1
# Divide by the total number of simulations
SumPixels /= (nSims + 1)
# Over plot
gca().set_axis_off()
subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0)
margins(0, 0)
gca().xaxis.set_major_locator(NullLocator())
gca().yaxis.set_major_locator(NullLocator())
# Plot the initial forest state
plt.imshow(imgForest, zorder=0)
# Sum or Scale
if mode == "Sum":
plt.imshow(SumPixels, zorder=0, alpha=1, vmin=0, vmax=255)
# Plot each fire
else:
for i in range(nSims):
plt.imshow(imgArray[i],
zorder=i,
alpha=probs[i],
vmin=0,
vmax=255)
# Save the combined picture
plt.savefig(os.path.join(Folder, "Plots",
"MultiFire_Plot" + mode + ".png"),
dpi=200,
bbox_inches='tight',
pad_inches=0)
plt.close("all")
detals.append(list(map(eval,line.spilt(","))))
f.close()
#自动绘制
for i in range(len(detals)):
t.pencolor(detals[i][3],detals[i][4],detals[i][5])
t.fd(detals[i][0])
if detals[i][1]:
t.right(detals[1][2])
else:
t.left(detals[i][2])
# 不规则图形词云
import jieba
import wordcloud
import scipy.misc import imread # 自定义词云形状
mask = imread("chinamap.jpg") # 导入词云形状图，需是白底
excludes = {}
f = open("新时代中国特色社会主义.txt","r",encoding = "utf-8")
t = f.read()
f.close()
ls = jieba.lcut(t)
txt = "".join(ls)
w = wordcloud.WordCloud(width = 1000,height = 700,\
background_color = "white",
font_path = "msyh.ttc",mask = mask
)
w.generate(txt)
w.to_file("grwordcloudm.png")
# 文本的平均列数
f = open("latex.log")
