def capture_mobjects(self, mobjects, **kwargs):
mobjects = self.get_mobjects_to_display(mobjects, **kwargs)
# Organize this list into batches of the same type, and
# apply corresponding function to those batches
type_func_pairs = [
(VMobject, self.display_multiple_vectorized_mobjects),
(PMobject, self.display_multiple_point_cloud_mobjects),
(AbstractImageMobject, self.display_multiple_image_mobjects),
(Mobject, lambda batch, pa: batch), # Do nothing
]
def get_mobject_type(mobject):
for mobject_type, func in type_func_pairs:
if isinstance(mobject, mobject_type):
return mobject_type
raise Exception(
"Trying to display something which is not of type Mobject")
batch_type_pairs = batch_by_property(mobjects, get_mobject_type)
# Display in these batches
for batch, batch_type in batch_type_pairs:
# check what the type is, and call the appropriate function
for mobject_type, func in type_func_pairs:
if batch_type == mobject_type:
func(batch, self.pixel_array)
def capture_mobjects(self, mobjects, **kwargs):
mobjects = self.get_mobjects_to_display(mobjects, **kwargs)
# Organize this list into batches of the same type, and
# apply corresponding function to those batches
type_func_pairs = [
(VMobject, self.display_multiple_vectorized_mobjects),
(PMobject, self.display_multiple_point_cloud_mobjects),
(AbstractImageMobject, self.display_multiple_image_mobjects),
(Mobject, lambda batch: batch), # Do nothing
]
def get_mobject_type(mobject):
for mobject_type, func in type_func_pairs:
if isinstance(mobject, mobject_type):
return mobject_type
raise Exception(
"Trying to display something which is not of type Mobject"
)
batch_type_pairs = batch_by_property(mobjects, get_mobject_type)
# Display in these batches
for batch, batch_type in batch_type_pairs:
# check what the type is, and call the appropriate function
for mobject_type, func in type_func_pairs:
if batch_type == mobject_type:
func(batch)
def display_multiple_vectorized_mobjects(self, vmobjects):
if len(vmobjects) == 0:
return
batch_file_pairs = batch_by_property(
vmobjects, lambda vm: vm.get_background_image_file())
for batch, file_name in batch_file_pairs:
if file_name:
self.display_multiple_background_colored_vmobject(batch)
else:
self.display_multiple_non_background_colored_vmobjects(batch)
def display_multiple_vectorized_mobjects(self, vmobjects):
if len(vmobjects) == 0:
return
batch_file_pairs = batch_by_property(
vmobjects,
lambda vm: vm.get_background_image_file()
)
for batch, file_name in batch_file_pairs:
if file_name:
self.display_multiple_background_colored_vmobject(batch)
else:
self.display_multiple_non_background_colored_vmobjects(batch)
def display(self, *cvmobjects):
batch_image_file_pairs = batch_by_property(
cvmobjects, lambda cv: cv.get_background_image_file())
curr_array = None
for batch, image_file in batch_image_file_pairs:
background_array = self.get_background_array(image_file)
pixel_array = self.pixel_array
self.camera.display_multiple_non_background_colored_vmobjects(
batch, pixel_array)
new_array = np.array(
(background_array * pixel_array.astype('float') / 255),
dtype=self.camera.pixel_array_dtype)
if curr_array is None:
curr_array = new_array
else:
curr_array = np.maximum(curr_array, new_array)
self.reset_pixel_array()
return curr_array
def display(self, *cvmobjects):
batch_image_file_pairs = batch_by_property(
cvmobjects, lambda cv: cv.get_background_image_file())
curr_array = None
for batch, image_file in batch_image_file_pairs:
background_array = self.get_background_array(image_file)
for cvmobject in batch:
self.camera.display_vectorized(cvmobject, self.canvas)
self.canvas.flush()
new_array = np.array(
(background_array * self.pixel_array.astype('float') / 255),
dtype=self.camera.pixel_array_dtype)
if curr_array is None:
curr_array = new_array
else:
curr_array = np.maximum(curr_array, new_array)
self.pixel_array[:, :] = 0
self.reset_canvas()
return curr_array
def display(self, *cvmobjects):
batch_image_file_pairs = batch_by_property(
cvmobjects, lambda cv: cv.get_background_image_file()
)
curr_array = None
for batch, image_file in batch_image_file_pairs:
background_array = self.get_background_array(image_file)
for cvmobject in batch:
self.camera.display_vectorized(cvmobject, self.canvas)
self.canvas.flush()
new_array = np.array(
(background_array * self.pixel_array.astype('float') / 255),
dtype=self.camera.pixel_array_dtype
)
if curr_array is None:
curr_array = new_array
else:
curr_array = np.maximum(curr_array, new_array)
self.pixel_array[:, :] = 0
self.reset_canvas()
return curr_array
