def signal_to_symbol(self, values):
thresholds = { # Well, nine (we add one for "black").
'0': 235,
'1': 207,
'2': 179,
'3': 151,
'4': 123,
'5': 95,
'6': 67,
'7': 39,
'8': 11,
}
_inv_thresholds = dict((v,k) for k, v in thresholds.iteritems())
_keys = sorted(_inv_thresholds.keys())
ret = []
for sym_i in values:
sym_values = values.get(sym_i)
_values = []
for coord in sym_values:
_values.append(sym_values.get(coord))
avg_value = average(_values)
_bs = bsearch(_keys, avg_value)
_key = _keys[_bs]
base8 = _inv_thresholds[_key]
ret.append(base8)
return ret
def signal_to_symbol(self, values):
thresholds = { # Well, nine (we add one for "black").
'0': 235,
'1': 207,
'2': 179,
'3': 151,
'4': 123,
'5': 95,
'6': 67,
'7': 39,
'8': 11,
}
_inv_thresholds = dict((v, k) for k, v in thresholds.iteritems())
_keys = sorted(_inv_thresholds.keys())
ret = []
for sym_i in values:
sym_values = values.get(sym_i)
_values = []
for coord in sym_values:
_values.append(sym_values.get(coord))
avg_value = average(_values)
_bs = bsearch(_keys, avg_value)
_key = _keys[_bs]
base8 = _inv_thresholds[_key]
ret.append(base8)
return ret
def signal_to_symbol(self, values):
ret = []
for sym_i in values:
sym_values = values.get(sym_i)
avg_value = average([average(sym_values.get(coord))
for coord in sym_values])
keys = sorted(self._inv_thresholds.keys())
ret.append(int(self._inv_thresholds[keys[bsearch(keys, avg_value)]]))
return ret
def signal_to_symbol(self, values):
ret = []
for sym_i in values:
sym_values = values.get(sym_i)
avg_value = average(
[average(sym_values.get(coord)) for coord in sym_values])
keys = sorted(self._inv_thresholds.keys())
ret.append(
int(self._inv_thresholds[keys[bsearch(keys, avg_value)]]))
return ret
def test_float(self): self.assertEqual(6, bsearch(self.array, self.to_find_float))
def test_int(self): self.assertEqual(2, bsearch(self.array, self.to_find_int))
def test_float(self):
self.assertEqual(6, bsearch(self.array, self.to_find_float))
def test_int(self):
self.assertEqual(2, bsearch(self.array, self.to_find_int))
print '%4d' % round(idct_mat[row, col]),
print ''
print
recovered_mat = numpy.zeros((8,8))
idct_mat += 128
from util import bsearch
_inv_keys = sorted(_inv_thresholds.keys())
ret = []
print 'Recovered Matrix'
for row in range(8):
for col in range(8):
recovered_val = round(idct_mat[row, col])
ret.append(int(_inv_thresholds[
_inv_keys[bsearch(_inv_keys, recovered_val)]]))
print '%4d' % recovered_val,
recovered_mat[row, col] = recovered_val
print ''
print
# Recover the real image that was written.
recovered_image = Image.open('test.jpg')
recovered_pixels = recovered_image.load()
recovered_img_ret = []
print
print 'Recovered Image Real'
# for i in range(0, 16, 2):
# for j in range(0, 16, 2):
# avg = 0.
