def flatten_image(image, piece_size, indexed=False):
"""Converts image into list of square pieces.
Input image is divided into square pieces of specified size and than
flattened into list. Each list element is PIECE_SIZE x PIECE_SIZE x 3
:params image: Input image.
:params piece_size: Size of single square piece. Each piece is PIECE_SIZE x PIECE_SIZE
:params indexed: If True list of Pieces with IDs will be returned, otherwise just plain list of ndarray pieces
Usage::
>>> from gaps.image_helpers import flatten_image
>>> flat_image = flatten_image(image, 32)
"""
rows, columns = image.shape[0] // piece_size, image.shape[1] // piece_size
pieces = []
# Crop pieces from original image
for y in range(rows):
for x in range(columns):
left, top, w, h = x * piece_size, y * piece_size, (
x + 1) * piece_size, (y + 1) * piece_size
piece = np.empty((piece_size, piece_size, image.shape[2]))
piece[:piece_size, :piece_size, :] = image[top:h, left:w, :]
pieces.append(piece)
if indexed:
pieces = [Piece(value, index) for index, value in enumerate(pieces)]
return pieces, rows, columns
print(rows, columns)
def flatten_image_stripe(image, piece_size, indexed=False):
rows, columns = 1, image.shape[1] // piece_size
pieces = []
# Crop pieces from original image
for y in range(rows):
for x in range(columns):
left, top, w, h = x * piece_size, 0, (
x + 1) * piece_size, image.shape[0]
piece = np.empty((image.shape[0], piece_size, image.shape[2]))
piece[:, :, :] = image[top:h, left:w, :]
pieces.append(piece)
if indexed:
pieces = [Piece(value, index) for index, value in enumerate(pieces)]
return pieces, rows, columns
def best_adjoin(self, piece_size):
pieces = np.reshape(self.pieces, (self.rows, self.columns))
empty_image = np.zeros(
(piece_size, piece_size, pieces[0][0].shape()[2]))
empty_piece = Piece(empty_image, -1)
for row in range(self.rows):
for col in range(self.columns):
if row == 0:
if col == 0 and ImageAnalysis.in_range(pieces[row][col].id, "D", pieces[row + 1][col].id) \
and ImageAnalysis.in_range(pieces[row][col].id, "R", pieces[row][col + 1].id):
continue
if col < self.columns - 1 and ImageAnalysis.in_range(pieces[row][col].id, "D",
pieces[row + 1][col].id) \
and ImageAnalysis.in_range(pieces[row][col].id, "R", pieces[row][col + 1].id):
continue
if col == self.columns - 1 and ImageAnalysis.in_range(pieces[row][col].id, "D",
pieces[row + 1][col].id) \
and ImageAnalysis.in_range(pieces[row][col].id, "L", pieces[row][col - 1].id):
continue
if 0 < row < self.rows - 1:
if col == 0 and ImageAnalysis.in_range(pieces[row][col].id, "D", pieces[row + 1][col].id) \
and ImageAnalysis.in_range(pieces[row][col].id, "R", pieces[row][col + 1].id):
continue
if col < self.columns - 1 and ImageAnalysis.in_range(pieces[row][col].id, "D",
pieces[row + 1][col].id) \
and ImageAnalysis.in_range(pieces[row][col].id, "R", pieces[row][col + 1].id):
continue
if col == self.columns - 1 and ImageAnalysis.in_range(pieces[row][col].id, "D",
pieces[row + 1][col].id) \
and ImageAnalysis.in_range(pieces[row][col].id, "L", pieces[row][col - 1].id):
continue
if row == self.rows - 1:
if col == 0 and ImageAnalysis.in_range(pieces[row][col].id, "T", pieces[row - 1][col].id) \
and ImageAnalysis.in_range(pieces[row][col].id, "R", pieces[row][col + 1].id):
continue
if col < self.columns - 1 and ImageAnalysis.in_range(pieces[row][col].id, "R",
pieces[row][col + 1].id) \
and ImageAnalysis.in_range(pieces[row][col].id, "T", pieces[row - 1][col].id):
continue
if col == self.columns - 1 and ImageAnalysis.in_range(pieces[row][col].id, "L",
pieces[row][col - 1].id) \
and ImageAnalysis.in_range(pieces[row][col].id, "T", pieces[row - 1][col].id):
continue
# if row == 0:
# if col == 0 and ImageAnalysis.best_match(pieces[row][col].id, "D") == pieces[row + 1][col].id \
# and ImageAnalysis.best_match(pieces[row][col].id, "R") == pieces[row][col + 1].id:
# continue
# if col < self.columns - 1 and ImageAnalysis.best_match(pieces[row][col].id, "D") == pieces[row + 1][col].id \
# and ImageAnalysis.best_match(pieces[row][col].id, "R") == pieces[row][col + 1].id \
# and ImageAnalysis.best_match(pieces[row][col].id, "L") == pieces[row][col - 1].id:
# continue
# if col == self.columns - 1 and ImageAnalysis.best_match(pieces[row][col].id, "D") == pieces[row + 1][col].id \
# and ImageAnalysis.best_match(pieces[row][col].id, "L") == pieces[row][col - 1].id:
# continue
# if 0 < row < self.rows - 1:
# if col == 0 and ImageAnalysis.best_match(pieces[row][col].id, "D") == pieces[row + 1][col].id \
# and ImageAnalysis.best_match(pieces[row][col].id, "T") == pieces[row - 1][col].id \
# and ImageAnalysis.best_match(pieces[row][col].id, "R") == pieces[row][col + 1].id:
# continue
# if col < self.columns - 1 and ImageAnalysis.best_match(pieces[row][col].id, "T") == pieces[row - 1][col].id \
# and ImageAnalysis.best_match(pieces[row][col].id, "D") == pieces[row + 1][col].id \
# and ImageAnalysis.best_match(pieces[row][col].id, "L") == pieces[row][col - 1].id \
# and ImageAnalysis.best_match(pieces[row][col].id, "R") == pieces[row][col + 1].id:
# continue
# if col == self.columns - 1 and ImageAnalysis.best_match(pieces[row][col].id, "T") == pieces[row - 1][col].id \
# and ImageAnalysis.best_match(pieces[row][col].id, "D") == pieces[row + 1][col].id \
# and ImageAnalysis.best_match(pieces[row][col].id, "L") == pieces[row][col - 1].id:
# continue
# if row == self.rows - 1:
# if col == 0 and ImageAnalysis.best_match(pieces[row][col].id, "T") == pieces[row - 1][col].id \
# and ImageAnalysis.best_match(pieces[row][col].id, "R") == pieces[row][col + 1].id:
# continue
# if col < self.columns - 1 and ImageAnalysis.best_match(pieces[row][col].id, "R") == pieces[row][col + 1].id \
# and ImageAnalysis.best_match(pieces[row][col].id, "L") == pieces[row][col - 1].id \
# and ImageAnalysis.best_match(pieces[row][col].id, "T") == pieces[row - 1][col].id:
# continue
# if col == self.columns - 1 and ImageAnalysis.best_match(pieces[row][col].id, "L") == pieces[row][col - 1].id \
# and ImageAnalysis.best_match(pieces[row][col].id, "T") == pieces[row - 1][col].id:
# continue
pieces[row][col] = empty_piece
self.penalize_image = pieces
return image_helpers.assemble_image(
[each.image for each in pieces.flatten()], self.rows, self.columns)