def load_homography(self,
storage=None,
perspective=None,
image=None,
file=None):
"""
Args:
storage (tuple): ((x, y), [upper-left, upper-right, bottom-left, bottom-right])
perspective (Perspective):
image (np.ndarray):
file (str): File path of image
"""
if storage is not None:
self.find_homography(*storage)
elif perspective is not None:
hori = perspective.horizontal[0].add(perspective.horizontal[-1])
vert = perspective.vertical[0].add(perspective.vertical[-1])
src_pts = hori.cross(vert).points
x = len(perspective.vertical) - 1
y = len(perspective.horizontal) - 1
self.find_homography(size=(x, y), src_pts=src_pts)
elif image is not None:
perspective_ = Perspective(self.config)
perspective_.load(image)
self.load_homography(perspective=perspective_)
elif file is not None:
image_ = np.array(Image.open(file).convert('RGB'))
perspective_ = Perspective(self.config)
perspective_.load(image_)
self.load_homography(perspective=perspective_)
else:
raise MapDetectionError(
'No data feed to load_homography, please input at least one.')
class MapDetector:
"""
Map detector wrapper
"""
image: np.ndarray
config: AzurLaneConfig
left_edge: bool
right_edge: bool
lower_edge: bool
upper_edge: bool
generate: callable
def __init__(self, config):
"""
Args:
config (AzurLaneConfig):
"""
self.config = config
self.backend = None
self.detector_set_backend()
def detector_set_backend(self, name=''):
"""
Args:
name (str): 'homography' or 'perspective'
"""
if not name:
name = self.config.DETECTION_BACKEND
if name == 'homography':
self.backend = Homography(config=self.config)
else:
self.backend = Perspective(config=self.config)
def load(self, image):
"""
Args:
image: Shape (720, 1280, 3)
"""
self.backend.load(image)
self.left_edge = bool(self.backend.left_edge)
self.right_edge = bool(self.backend.right_edge)
self.lower_edge = bool(self.backend.lower_edge)
self.upper_edge = bool(self.backend.upper_edge)
self.generate = self.backend.generate