geohot

# extract ILSVRC2012 without killing your SSD

import tarfile
import os
import sys

def mkdir(x):
  try:
    os.makedirs(x)
  except OSError, e:

geohot

from keras.models import Sequential, Model
from keras.layers import Dense, Input, BatchNormalization as BN

input_img1 = Input(shape=(4,), name="input_img1")

vision_model = Sequential()
vision_model.add(Dense(4, input_shape=(4,)))
vision_model.add(BN(axis=1))
vision_model.add(Dense(4))

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guess = np.concatenate([np.array(map(lambda x: np.array(x[:,3]).ravel(), rts)), fpts[:, 0:3]], axis=0)
selects = []
xys  = []
for i, trk in enumerate(good_good_tracks):
  for shot, kp in trk:
    xy = G.node[(shot,kp)]['kp']/0.81412059
    xys.append(xy)
    select = np.zeros((len(frms)+len(good_good_tracks),))
    select[shot] = 1
    select[len(frms)+i] = 1

geohot

#!/usr/bin/env python
def msg(x):
  print "S:",x.encode("hex")
  if len(x) <= 7:
    ret = chr(len(x)) + x
  else:
    assert False
  return ret.ljust(8, "\x00")

def isotp_send(panda, x, addr, bus=0):

geohot

import cv2
import numpy as np
from tqdm import tqdm

def test_frame_gen(limit=None):
  fq = []
  cap = cv2.VideoCapture("data/test.mp4")
  cnt = 0 
  while 1:
    ret, frame = cap.read()

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def ransac_polyfit(x, y, order=3, n=20, k=100, t=0.1, d=100, f=0.8):
  # Thanks https://en.wikipedia.org/wiki/Random_sample_consensus
  
  # n – minimum number of data points required to fit the model
  # k – maximum number of iterations allowed in the algorithm
  # t – threshold value to determine when a data point fits a model
  # d – number of close data points required to assert that a model fits well to data
  # f – fraction of close data points required
  
  besterr = np.inf

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def getFundamentalMatrix(pts1, pts2):
  vvec = []
  for p1, p2 in zip(pts1, pts2):
    vvec.append([p2[0] * p1[0], p2[0] * p1[1], p2[0], p2[1] * p1[0], p2[1] * p1[1], p2[1], p1[0], p1[1], 1]) 
  vvec = np.array(vvec)

  U, S, Vt = np.linalg.svd(vvec)
  Fvl = Vt[-1]
  
  om = Fvl.reshape(3,3)

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real_colors = [[115,82,68],[194,150,130],[98,122,157],[87,108,67],[133,128,177],[103,189,170],[214,126,44],[80,91,166],[193,90,99],[94,60,108],[157,188,64],[224,163,46],[56,61,150],[70,148,73],[175,54,60],[231,199,31],[187,86,149],[8,133,161],[243,243,242],[200,200,200],[160,160,160],[122,122,121],[85,85,85],[52,52,52]]

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# homogenous line from two points (this isn't on the internet...)
line = [0,0,0]
line[0] = (f1[1] - f2[1]) * (f1[1] * f2[0] - f1[0] * f2[1])
line[1] = (f1[0] - f2[0]) * (f1[0] * f2[1] - f1[1] * f2[0])
line[2] = (f1[0] * f2[1] - f1[1] * f2[0]) * (f1[1] * f2[0] - f1[0] * f2[1])
line = np.array(line)

# line intersection (https://en.wikipedia.org/wiki/Line%E2%80%93line_intersection#Using_homogeneous_coordinates)
p = np.array([l1[1] * l2[2] - l2[1] * l1[2], l2[0] * l1[2] - l1[0] * l2[2], l1[0] * l2[1] - l2[0] * l1[1]])
p /= p[2]

geohot

#!/usr/bin/env python
import os
import sys
import argparse
import zmq
import json
import cv2
import numpy as np
from hexdump import hexdump
import scipy.misc