def run_paikin_tal_driver(img_files, puzzle_type, piece_width):
"""
Runs the Paikin and Tal image solver.
Args:
img_files ([str]): An array of one or more image file path(s).
puzzle_type (Optional PuzzleType): Type of the puzzle to solve
piece_width (Optional int): Width of a puzzle piece in pixels.
"""
image_filenames = config.add_image_folder_path(img_files)
# Print the names of the images being solved:
logging.info("Standard Paikin & Tal Driver")
puzzle_importer.log_puzzle_filenames(image_filenames)
# When skipping placement, simply import the solved results.
if SKIP_PLACEMENT:
paikin_tal_solver = PaikinTalSolver.pickle_import_after_standard_run_placement(image_filenames, puzzle_type)
else:
paikin_tal_solver = run_paikin_tal_solver(image_filenames, puzzle_type, piece_width)
# Get the results
paikin_tal_solver.segment(color_segments=True, perform_segment_cleaning=True)
(pieces_partitioned_by_puzzle_id, _) = paikin_tal_solver.get_solved_puzzles()
timestamp = time.time()
Puzzle.output_results_information_and_puzzles(
PuzzleSolver.PaikinTal, image_filenames, paikin_tal_solver, pieces_partitioned_by_puzzle_id, timestamp
)
def run_multipuzzle_solver_driver(image_files, puzzle_type, piece_width):
"""
Runs the multipuzzle solver on a set of images.
Args:
image_files (List[str]): List of puzzle file paths.
puzzle_type (PuzzleType): Type of the puzzle to solve.
piece_width (int): Puzzle piece width in number of pixels
"""
image_filenames = config.add_image_folder_path(image_files)
logging.info("Starting Multipuzzle Solver Driver.")
puzzle_importer.log_puzzle_filenames(image_filenames)
multipuzzle_solver = build_multipuzzle_solver(image_filenames, puzzle_type, piece_width)
# Run the solver
multipuzzle_solver.run()
def _build_similarity_matrix(self):
"""
Creates the asymmetric overlap and segment similarity matrices for use in the hierarchical clustering.
"""
logging.info("Reminder on the image files:")
puzzle_importer.log_puzzle_filenames(self._image_filenames)
# Build the similarity matrix. Worst similarity is 0
numb_segments = len(self._segments)
self._asymmetric_overlap_matrix = np.full((numb_segments, numb_segments), fill_value=-1, dtype=np.float)
# Calculate asymmetric overlap for each segment
numb_pieces_in_each_segment = [segment.numb_pieces for segment in self._segments]
for segment_i in xrange(0, numb_segments):
# Iterate through all the stitching pieces in this segment and calculate the asymmetric overlap
for stitching_piece_info in self._stitching_pieces[segment_i]:
overlap = stitching_piece_info.calculate_overlap_coefficient(numb_pieces_in_each_segment)
# Get the max overlap between pairs of segments
for segment_j in xrange(0, numb_segments):
if segment_i == segment_j:
continue
# Update if the new value is greater
if self._asymmetric_overlap_matrix[segment_i, segment_j] < overlap[segment_j]:
self._asymmetric_overlap_matrix[segment_i, segment_j] = overlap[segment_j]
MultiPuzzleSolver._log_numpy_matrix("Asymmetric Segment Overlap Matrix:", self._asymmetric_overlap_matrix)
# Calculate the similarity matrix
self._segment_similarity_matrix = np.full((numb_segments, numb_segments), fill_value=-1, dtype=np.float)
for segment_i in xrange(0, numb_segments):
for segment_j in xrange(segment_i + 1, numb_segments):
similarity = self._asymmetric_overlap_matrix[segment_i, segment_j] \
+ self._asymmetric_overlap_matrix[segment_j, segment_i]
similarity /= 2
self._segment_similarity_matrix[segment_i, segment_j] = similarity
MultiPuzzleSolver._log_numpy_matrix("Segment Similarity Matrix", self._segment_similarity_matrix)
if MultiPuzzleSolver._ALLOW_POST_SIMILARITY_MATRIX_CALCULATION_PICKLE_EXPORT:
self._pickle_export_after_similarity_matrix_calculation()
