def retrain(im1, im2):
fp1 = open('im1.png', 'wb')
fp1.write(base64.b64decode(im1))
fp1.close()
fp2 = open('im2.png', 'wb')
fp2.write(base64.b64decode(im2))
fp2.close()
img1 = Image.open('im1.png')
img1.load()
data1 = np.asarray(img1, dtype="int32")
img2 = Image.open('im2.png')
img2.load()
data2 = np.asarray(img2, dtype="int32")
data1 = np.array(data1)
data2 = np.array(data1)
RESULT = np.copy(data2)
ORIGINAL = np.copy(data1)
[numRow, numCol] = RESULT.shape
for x in range(numRow):
dfs_iterative(RESULT, x, 0)
dfs_iterative(RESULT, x, numCol - 1)
for y in range(numCol):
dfs_iterative(RESULT, 0, y)
dfs_iterative(RESULT, numRow - 1, y)
train = []
target = []
tile_size = 16
# send this to configuration the 16
for x in range(0, numRow - tile_size + 1, tile_size):
for y in range(0, numCol - tile_size + 1, tile_size):
temp = RESULT[x:x + tile_size, y:y + tile_size]
if -1 not in temp:
train.append(
(normalize(ORIGINAL[x:x + tile_size, y:y +
tile_size])).transpose().flatten())
if 0 in temp and 1 in temp:
target.append(1)
else:
target.append(0)
print(np.array(train).shape)
print(np.array(target).shape)
np.savetxt("fooI.csv", np.array(train), delimiter=",")
np.savetxt("fooO.csv", np.array(target), delimiter=",")
def retrain(im1, im2):
fp1 = open('im1.png', 'wb')
fp1.write(base64.b64decode(im1))
fp1.close()
fp2 = open('im2.png', 'wb')
fp2.write(base64.b64decode(im2))
fp2.close()
img1 = Image.open('im1.png')
img1.load()
data1 = np.asarray( img1, dtype="int32")
img2 = Image.open('im2.png')
img2.load()
data2 = np.asarray( img2, dtype="int32")
data1 = np.array(data1)
data2 = np.array(data1)
RESULT = np.copy(data2)
ORIGINAL = np.copy(data1)
[numRow, numCol] = RESULT.shape
for x in range(numRow):
dfs_iterative(RESULT, x, 0)
dfs_iterative(RESULT, x, numCol - 1)
for y in range(numCol):
dfs_iterative(RESULT, 0, y)
dfs_iterative(RESULT, numRow - 1, y)
train = []
target = []
tile_size = 16
# send this to configuration the 16
for x in range(0, numRow - tile_size + 1, tile_size):
for y in range(0, numCol - tile_size + 1, tile_size):
temp = RESULT[x:x + tile_size, y:y + tile_size]
if -1 not in temp:
train.append((normalize(ORIGINAL[x:x + tile_size, y:y + tile_size])).transpose().flatten())
if 0 in temp and 1 in temp:
target.append(1)
else:
target.append(0)
print(np.array(train).shape)
print(np.array(target).shape)
np.savetxt("fooI.csv", np.array(train), delimiter=",")
np.savetxt("fooO.csv", np.array(target), delimiter=",")
def test_normalize(self):
test = [[4, 59, 102, 85, 49, 65, 48, 85],
[52, 58, 69, 70, 50, 97, 85, 63],
[35, 46, 57, 84, 67, 42, 77, 67],
[23, 62, 46, 79, 63, 84, 72, 88],
[5, 16, 46, 56, 72, 57, 93, 80],
[0, 15, 8, 22, 60, 54, 50, 52], [0, 0, 0, 16, 27, 15, 37, 33],
[0, 0, 14, 2, 12, 1, 8, 19]]
answer = [[
-0.04613047, 0.01521417, 0.06317451, 0.04421345, 0.0040606,
0.02190631, 0.00294524, 0.04421345
],
[
0.00740667, 0.01409881, 0.02636774, 0.02748309,
0.00517595, 0.05759773, 0.04421345, 0.01967559
],
[
-0.0115544, 0.00071453, 0.01298345, 0.04309809,
0.02413702, -0.0037469, 0.03529059, 0.02413702
],
[
-0.02493868, 0.01856024, 0.00071453, 0.0375213,
0.01967559, 0.04309809, 0.02971381, 0.04755952
],
[
-0.04501511, -0.03274618, 0.00071453, 0.01186809,
0.02971381, 0.01298345, 0.0531363, 0.03863666
],
[
-0.05059189, -0.03386154, -0.04166904, -0.02605404,
0.01632952, 0.00963738, 0.00517595, 0.00740667
],
[
-0.05059189, -0.05059189, -0.05059189, -0.03274618,
-0.02047726, -0.03386154, -0.00932369, -0.01378511
],
[
-0.05059189, -0.05059189, -0.0349769, -0.04836118,
-0.03720761, -0.04947654, -0.04166904, -0.02940011
]]
np.testing.assert_array_almost_equal(normalize(np.array(test)),
np.array(answer))
def test_normalize(self): test = [[4, 59, 102, 85, 49, 65, 48, 85], [52, 58, 69, 70, 50, 97, 85, 63], [35, 46, 57, 84, 67, 42, 77, 67], [23, 62, 46, 79, 63, 84, 72, 88], [5, 16, 46, 56, 72, 57, 93, 80], [0, 15, 8, 22, 60, 54, 50, 52], [0, 0, 0, 16, 27, 15, 37, 33], [0, 0, 14, 2, 12, 1, 8, 19]] answer = [[-0.04613047, 0.01521417, 0.06317451, 0.04421345, 0.0040606, 0.02190631, 0.00294524, 0.04421345], [0.00740667, 0.01409881, 0.02636774, 0.02748309, 0.00517595, 0.05759773, 0.04421345, 0.01967559], [-0.0115544, 0.00071453, 0.01298345, 0.04309809, 0.02413702, -0.0037469, 0.03529059, 0.02413702], [-0.02493868, 0.01856024, 0.00071453, 0.0375213, 0.01967559, 0.04309809, 0.02971381, 0.04755952], [-0.04501511, -0.03274618, 0.00071453, 0.01186809, 0.02971381, 0.01298345, 0.0531363, 0.03863666], [-0.05059189, -0.03386154, -0.04166904, -0.02605404, 0.01632952, 0.00963738, 0.00517595, 0.00740667], [-0.05059189, -0.05059189, -0.05059189, -0.03274618, -0.02047726, -0.03386154, -0.00932369, -0.01378511], [-0.05059189, -0.05059189, -0.0349769, -0.04836118, -0.03720761, -0.04947654, -0.04166904, -0.02940011]] np.testing.assert_array_almost_equal(normalize(np.array(test)), np.array(answer))
