class FigureOptions:
def __init__(self, should_save: bool, dpi: int, size_w: float, size_h: float) -> None:
self.bn_should_save = BoxBindable(should_save)
self.bn_dpi = BoxBindable(dpi)
self.bn_size_w = BoxBindable(size_w)
self.bn_size_h = BoxBindable(size_h)
# Validation
self.dpi_err = validate(
value=self.bn_dpi,
checks=(check_is_not_empty,
check_is_positive),
enabled=self.bn_should_save)
self.size_w_err = validate(
value=self.bn_size_w,
checks=(check_is_not_empty,
check_is_positive),
enabled=self.bn_should_save)
self.size_h_err = validate(
value=self.bn_size_h,
checks=(check_is_not_empty,
check_is_positive),
enabled=self.bn_should_save)
self.errors = bn_apply(lambda *args: any(args), self.dpi_err, self.size_w_err, self.size_h_err)
@property
def size(self) -> Tuple[float, float]:
return self.bn_size_w.get(), self.bn_size_h.get()
class CameraAcquirer(ImageAcquirer):
def __init__(self) -> None:
self._loop = asyncio.get_event_loop()
self.bn_camera = BoxBindable(None) # type: Bindable[Optional[Camera]]
self.bn_num_frames = BoxBindable(1)
self.bn_frame_interval = BoxBindable(
None) # type: Bindable[Optional[float]]
def acquire_images(self) -> Sequence[InputImage]:
camera = self.bn_camera.get()
if camera is None:
raise ValueError("'camera' can't be None")
num_frames = self.bn_num_frames.get()
if num_frames is None or num_frames <= 0:
raise ValueError(
"'num_frames' must be > 0 and not None, currently: '{}'".
format(num_frames))
frame_interval = self.bn_frame_interval.get()
if frame_interval is None or frame_interval <= 0:
if num_frames == 1:
frame_interval = 0
else:
raise ValueError(
"'frame_interval' must be > 0 and not None, currently: '{}'"
.format(frame_interval))
input_images = []
for i in range(num_frames):
capture_delay = i * frame_interval
input_image = _BaseCameraInputImage(
camera=camera,
delay=capture_delay,
first_image=input_images[0] if input_images else None,
loop=self._loop,
)
input_images.append(input_image)
return input_images
def get_image_size_hint(self) -> Optional[Tuple[int, int]]:
camera = self.bn_camera.get()
if camera is None:
return
return camera.get_image_size_hint()
class ConanAnalysisSaverOptions:
def __init__(self) -> None:
self.bn_save_dir_parent = BoxBindable(None) # type: Bindable[Optional[Path]]
self.bn_save_dir_name = BoxBindable('')
self.angle_figure_opts = FigureOptions(
should_save=True,
dpi=300,
size_w=15,
size_h=9
)
@property
def save_root_dir(self) -> Path:
return self.bn_save_dir_parent.get() / self.bn_save_dir_name.get()
class WizardController:
def __init__(self, pages: Iterable) -> None:
self._pages = tuple(pages)
self.bn_current_page = BoxBindable(self._pages[0])
def next_page(self) -> None:
current_page = self.bn_current_page.get()
next_page_idx = self._pages.index(current_page) + 1
next_page = self._pages[next_page_idx]
self.bn_current_page.set(next_page)
def prev_page(self) -> None:
current_page = self.bn_current_page.get()
prev_page_idx = self._pages.index(current_page) - 1
prev_page = self._pages[prev_page_idx]
self.bn_current_page.set(prev_page)
class ImageSequenceAcquirer(ImageAcquirer):
IS_REPLICATED = False
def __init__(self) -> None:
self.bn_images = BoxBindable(
tuple()) # type: Bindable[Sequence[np.ndarray]]
self.bn_frame_interval = BoxBindable(
None) # type: Bindable[Optional[int]]
def acquire_images(self) -> Sequence[InputImage]:
images = self.bn_images.get()
if len(images) == 0:
raise ValueError("'_images' can't be empty")
frame_interval = self.bn_frame_interval.get()
if frame_interval is None or frame_interval <= 0:
if len(images) == 1:
# Since only one image, we don't care about the frame_interval.
frame_interval = 0
else:
raise ValueError(
"'frame_interval' must be > 0 and not None, currently: '{}'"
.format(frame_interval))
input_images = []
for i, img in enumerate(images):
input_image = _BaseImageSequenceInputImage(image=img,
timestamp=i *
frame_interval)
input_image.is_replicated = self.IS_REPLICATED
input_images.append(input_image)
return input_images
def get_image_size_hint(self) -> Optional[Tuple[int, int]]:
images = self.bn_images.get()
if images is None or len(images) == 0:
return None
first_image = images[0]
return first_image.shape[1::-1]
class WizardModel:
def __init__(self, pages: Iterable[Any]) -> None:
self._pages = tuple(pages)
self._interpage_actions = {}
self.bn_current_page = BoxBindable(self._pages[0])
def next_page(self) -> None:
current_page = self.bn_current_page.get()
next_page_idx = self._pages.index(current_page) + 1
next_page = self._pages[next_page_idx]
self.perform_interpage_action(current_page, next_page)
self.bn_current_page.set(next_page)
def prev_page(self) -> None:
current_page = self.bn_current_page.get()
prev_page_idx = self._pages.index(current_page) - 1
prev_page = self._pages[prev_page_idx]
self.perform_interpage_action(current_page, prev_page)
self.bn_current_page.set(prev_page)
def perform_interpage_action(self, start_page: Any, end_page: Any) -> None:
if (start_page, end_page) not in self._interpage_actions:
return
callback = self._interpage_actions[(start_page, end_page)]
callback()
def register_interpage_action(self, start_page: Any, end_page: Any,
callback: Callable[[], Any]) -> None:
self._interpage_actions[(start_page, end_page)] = callback
class IFTAnalysisSaverOptions:
def __init__(self) -> None:
self.bn_save_dir_parent = BoxBindable(None) # type: Bindable[Optional[Path]]
self.bn_save_dir_name = BoxBindable('')
self.drop_residuals_figure_opts = FigureOptions(
should_save=True,
dpi=300,
size_w=10,
size_h=10
)
self.ift_figure_opts = FigureOptions(
should_save=True,
dpi=300,
size_w=15,
size_h=9
)
self.volume_figure_opts = FigureOptions(
should_save=True,
dpi=300,
size_w=15,
size_h=9
)
self.surface_area_figure_opts = FigureOptions(
should_save=True,
dpi=300,
size_w=15,
size_h=9
)
@property
def save_root_dir(self) -> Path:
return self.bn_save_dir_parent.get() / self.bn_save_dir_name.get()
class ConanSession:
def __init__(self, do_exit: Callable[[], Any], *,
loop: asyncio.AbstractEventLoop) -> None:
self._loop = loop
self._do_exit = do_exit
self._feature_extractor_params = FeatureExtractorParams()
self._conancalc_params = ContactAngleCalculatorParams()
self._bn_analyses = BoxBindable(
tuple()) # type: Bindable[Sequence[ConanAnalysis]]
self._analyses_saved = False
self.image_acquisition = ImageAcquisitionModel()
self.image_acquisition.use_acquirer_type(AcquirerType.LOCAL_STORAGE)
self.image_processing = ConanImageProcessingModel(
image_acquisition=self.image_acquisition,
feature_extractor_params=self._feature_extractor_params,
conancalc_params=self._conancalc_params,
do_extract_features=self.extract_features,
)
self.results = ConanResultsModel(
in_analyses=self._bn_analyses,
do_cancel_analyses=self.cancel_analyses,
do_save_analyses=self.save_analyses,
create_save_options=self._create_save_options,
check_if_safe_to_discard=self.check_if_safe_to_discard_analyses,
)
def start_analyses(self) -> None:
assert len(self._bn_analyses.get()) == 0
new_analyses = []
input_images = self.image_acquisition.acquire_images()
for input_image in input_images:
new_analysis = ConanAnalysis(
input_image=input_image,
do_extract_features=self.extract_features,
do_calculate_conan=self.calculate_contact_angle,
)
new_analyses.append(new_analysis)
self._bn_analyses.set(new_analyses)
self._analyses_saved = False
def cancel_analyses(self) -> None:
analyses = self._bn_analyses.get()
for analysis in analyses:
analysis.cancel()
def clear_analyses(self) -> None:
self.cancel_analyses()
self._bn_analyses.set(tuple())
def save_analyses(self, options: ConanAnalysisSaverOptions) -> None:
analyses = self._bn_analyses.get()
if len(analyses) == 0:
return
save_drops(analyses, options)
self._analyses_saved = True
def _create_save_options(self) -> ConanAnalysisSaverOptions:
return ConanAnalysisSaverOptions()
def check_if_safe_to_discard_analyses(self) -> bool:
if self._analyses_saved:
return True
else:
analyses = self._bn_analyses.get()
if len(analyses) == 0:
return True
all_images_replicated = all(analysis.is_image_replicated
for analysis in analyses)
if not all_images_replicated:
return False
return True
def extract_features(self,
image: Bindable[np.ndarray]) -> FeatureExtractor:
return FeatureExtractor(
image=image,
params=self._feature_extractor_params,
loop=self._loop,
)
def calculate_contact_angle(
self,
extracted_features: FeatureExtractor) -> ContactAngleCalculator:
return ContactAngleCalculator(
features=extracted_features,
params=self._conancalc_params,
)
def exit(self) -> None:
self.clear_analyses()
self.image_acquisition.destroy()
self._do_exit()
class IFTSession:
def __init__(self, do_exit: Callable[[], Any], *,
loop: asyncio.AbstractEventLoop) -> None:
self._loop = loop
self._do_exit = do_exit
self._feature_extractor_params = FeatureExtractorParams()
self._physprops_calculator_params = PhysicalPropertiesCalculatorParams(
)
self._bn_analyses = BoxBindable(
tuple()) # type: Bindable[Sequence[IFTDropAnalysis]]
self._analyses_saved = False
self.image_acquisition = ImageAcquisitionModel()
self.image_acquisition.use_acquirer_type(AcquirerType.LOCAL_STORAGE)
self.physical_parameters = PhysicalParametersModel(
physprops_calculator_params=self._physprops_calculator_params, )
self.image_processing = IFTImageProcessingModel(
image_acquisition=self.image_acquisition,
feature_extractor_params=self._feature_extractor_params,
do_extract_features=self.extract_features,
)
self.results = IFTResultsModel(
in_analyses=self._bn_analyses,
do_cancel_analyses=self.cancel_analyses,
do_save_analyses=self.save_analyses,
create_save_options=self._create_save_options,
check_if_safe_to_discard=self.check_if_safe_to_discard_analyses,
)
def start_analyses(self) -> None:
assert len(self._bn_analyses.get()) == 0
new_analyses = []
input_images = self.image_acquisition.acquire_images()
for input_image in input_images:
new_analysis = IFTDropAnalysis(
input_image=input_image,
do_extract_features=self.extract_features,
do_young_laplace_fit=self.young_laplace_fit,
do_calculate_physprops=self.calculate_physprops)
new_analyses.append(new_analysis)
self._bn_analyses.set(new_analyses)
self._analyses_saved = False
def cancel_analyses(self) -> None:
analyses = self._bn_analyses.get()
for analysis in analyses:
analysis.cancel()
def clear_analyses(self) -> None:
self.cancel_analyses()
self._bn_analyses.set(tuple())
def save_analyses(self, options: IFTAnalysisSaverOptions) -> None:
analyses = self._bn_analyses.get()
if len(analyses) == 0:
return
save_drops(analyses, options)
self._analyses_saved = True
def _create_save_options(self) -> IFTAnalysisSaverOptions:
return IFTAnalysisSaverOptions()
def check_if_safe_to_discard_analyses(self) -> bool:
if self._analyses_saved:
return True
else:
analyses = self._bn_analyses.get()
if len(analyses) == 0:
return True
all_images_replicated = all(analysis.is_image_replicated
for analysis in analyses)
if not all_images_replicated:
return False
return True
def extract_features(self,
image: Bindable[np.ndarray]) -> FeatureExtractor:
return FeatureExtractor(
image=image,
params=self._feature_extractor_params,
loop=self._loop,
)
def young_laplace_fit(
self, extracted_features: FeatureExtractor) -> YoungLaplaceFitter:
return YoungLaplaceFitter(features=extracted_features, loop=self._loop)
def calculate_physprops(
self, extracted_features: FeatureExtractor,
young_laplace_fit: YoungLaplaceFitter
) -> PhysicalPropertiesCalculator:
return PhysicalPropertiesCalculator(
features=extracted_features,
young_laplace_fit=young_laplace_fit,
params=self._physprops_calculator_params,
)
def exit(self) -> None:
self.clear_analyses()
self.image_acquisition.destroy()
self._do_exit()