View index.html
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<style>
html, body { width: 100%; height: 100%; background: #000; }
body { margin: 0; overflow: hidden; }
canvas { width: 100%; height: 100%; }
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<script src='https://cdnjs.cloudflare.com/ajax/libs/three.js/92/three.min.js'></script>
View helvetiker_bold.typeface.json
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View 000630070000650-words.json
[{"y1": 0.1001984126984127, "y0": 0.07787698412698413, "x0": 0.0875, "x1": 0.20625}, {"y1": 0.09871031746031746, "y0": 0.07539682539682539, "x0": 0.2294642857142857, "x1": 0.3580357142857143}, {"y1": 0.09226190476190477, "y0": 0.07539682539682539, "x0": 0.3794642857142857, "x1": 0.4544642857142857}, {"y1": 0.09176587301587301, "y0": 0.07490079365079365, "x0": 0.47946428571428573, "x1": 0.5303571428571429}, {"y1": 0.09722222222222222, "y0": 0.07490079365079365, "x0": 0.5553571428571429, "x1": 0.6455357142857143}, {"y1": 0.09077380952380952, "y0": 0.07341269841269842, "x0": 0.66875, "x1": 0.7026785714285714}, {"y1": 0.09077380952380952, "y0": 0.07242063492063493, "x0": 0.7223214285714286, "x1": 0.775}, {"y1": 0.09077380952380952, "y0": 0.07291666666666667, "x0": 0.7973214285714286, "x1": 0.8366071428571429}, {"y1": 0.12202380952380952, "y0": 0.10119047619047619, "x0": 0.08839285714285715, "x1": 0.15982142857142856}, {"y1": 0.11656746031746032, "y0": 0.10119047619047619, "x0": 0.1732142857142857, "x1": 0.1866071
View find_similar.py
from nltk import ngrams
from glob import glob
from datasketch import MinHash, MinHashLSHForest
import json
##
# Config
##
perm = 256
View nest.py
import os
import glob
for i in range(10):
for j in range(10):
for k in range(10):
outdir = '/'.join([str(l) for l in [i,j,k]])
if not os.path.exists(outdir):
os.makedirs(outdir)
View c3.min.css
.c3 svg{font:10px sans-serif;-webkit-tap-highlight-color:transparent}.c3 line,.c3 path{fill:none;stroke:#000}.c3 text{-webkit-user-select:none;-moz-user-select:none;user-select:none}.c3-bars path,.c3-event-rect,.c3-legend-item-tile,.c3-xgrid-focus,.c3-ygrid{shape-rendering:crispEdges}.c3-chart-arc path{stroke:#fff}.c3-chart-arc rect{stroke:#fff;stroke-width:1}.c3-chart-arc text{fill:#fff;font-size:13px}.c3-grid line{stroke:#aaa}.c3-grid text{fill:#aaa}.c3-xgrid,.c3-ygrid{stroke-dasharray:3 3}.c3-text.c3-empty{fill:grey;font-size:2em}.c3-line{stroke-width:1px}.c3-circle._expanded_{stroke-width:1px;stroke:#fff}.c3-selected-circle{fill:#fff;stroke-width:2px}.c3-bar{stroke-width:0}.c3-bar._expanded_{fill-opacity:1;fill-opacity:.75}.c3-target.c3-focused{opacity:1}.c3-target.c3-focused path.c3-line,.c3-target.c3-focused path.c3-step{stroke-width:2px}.c3-target.c3-defocused{opacity:.3!important}.c3-region{fill:#4682b4;fill-opacity:.1}.c3-brush .extent{fill-opacity:.1}.c3-legend-item{font-size:12px}.c3-legend-item-hi
View slide.py
import numpy as np
def get_windows(arr, window_size=64, step=32):
windows = []
row = 0
col = 0
max_row, max_col = arr.shape
while row < max_row:
while col < max_col:
windows.append(arr[row:row+window_size, col:col+window_size])
View measure_img_similarity.py
import warnings
from skimage.measure import compare_ssim
from skimage.transform import resize
from scipy.stats import wasserstein_distance
from scipy.misc import imsave
from scipy.ndimage import imread
import numpy as np
import cv2
##