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_overflow(self): size = 256 MAT = np.zeros([size, size]) # print(MAT) dfs_iterative(MAT, 0, 0)
def test_dfs_iterative(self): MAT = np.array([[0, 0, 0, 1], [0, 2, 0, 3], [0, 0, 0, 7], [1, 0, 2, 0]]) dfs_iterative(MAT, 0, 3) dfs_iterative(MAT, 0, 2)
stdscr = curses.initscr()
curses.cbreak()
curses.noecho()
curses.curs_set(False)
curses.start_color()
utils.initialize_colors()
else:
stdscr = None
arguments = (stdscr, *utils.extract_all(sys.argv[1]), animated_flag)
result = choice
if choice == 'bfs':
result = result, *bfs.bfs_iterative(*arguments)
elif choice == 'dfs':
result = result, *dfs.dfs_iterative(*arguments)
elif choice == 'random':
result = result, *random_search.random_search(*arguments)
elif choice == 'dijkstra':
result = result, *dijkstra.dijkstra(*arguments)
elif choice == 'greedy':
result = result, *greedy_search.greedy_search(*arguments)
elif choice == 'a-star':
result = result, *a_star.a_star(*arguments)
if animated_flag:
curses.curs_set(True)
curses.echo()
curses.nocbreak()
curses.endwin()
def test_overflow(self):
size = 256
MAT = np.zeros([size, size])
# print(MAT)
dfs_iterative(MAT, 0, 0)
def test_dfs_iterative(self):
MAT = np.array([[0, 0, 0, 1], [0, 2, 0, 3], [0, 0, 0, 7], [1, 0, 2,
0]])
dfs_iterative(MAT, 0, 3)
dfs_iterative(MAT, 0, 2)
