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Overall working 3D convolution visualization.

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This commit is contained in:
Alec Helbling
2022-12-30 21:41:50 -05:00
parent ff4c1ffded
commit b1a85ea782
9 changed files with 305 additions and 151 deletions
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41
manim_ml/neural_network/layers/image_to_convolutional3d.py
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@ -48,6 +48,14 @@ class ImageToConvolutional3DLayer(VGroupNeuralNetworkLayer, ThreeDLayer):
target_feature_map = self.output_layer.feature_maps[0]
# Map image mobject to feature map
# Make rotation of image
rotation = ApplyMethod(
image_mobject.rotate,
ThreeDLayer.rotation_angle,
ThreeDLayer.rotation_axis,
image_mobject.get_center(),
run_time=0.5
)
"""
x_rotation = ApplyMethod(
image_mobject.rotate,
ThreeDLayer.three_d_x_rotation,
@ -62,6 +70,7 @@ class ImageToConvolutional3DLayer(VGroupNeuralNetworkLayer, ThreeDLayer):
image_mobject.get_center(),
run_time=0.5
)
"""
# Set opacity
set_opacity = ApplyMethod(
image_mobject.set_opacity,
@ -84,43 +93,13 @@ class ImageToConvolutional3DLayer(VGroupNeuralNetworkLayer, ThreeDLayer):
)
# Compose the animations
animation = Succession(
x_rotation,
y_rotation,
rotation,
scale_image,
set_opacity,
move_image,
)
return animation
"""
# Make the object 3D by adding it back into camera frame
def remove_fixed_func(image_mobject):
# self.camera.remove_fixed_orientation_mobjects(image_mobject)
# self.camera.remove_fixed_in_frame_mobjects(image_mobject)
return image_mobject
remove_fixed = ApplyFunction(
remove_fixed_func,
image_mobject
)
animations.append(remove_fixed)
# Make a transformation of the image_mobject to the first feature map
input_to_feature_map_transformation = Transform(image_mobject, target_feature_map)
animations.append(input_to_feature_map_transformation)
# Make the object fixed in 2D again
def make_fixed_func(image_mobject):
# self.camera.add_fixed_orientation_mobjects(image_mobject)
# self.camera.add_fixed_in_frame_mobjects(image_mobject)
return image_mobject
make_fixed = ApplyFunction(
make_fixed_func,
image_mobject
)
animations.append(make_fixed)
return AnimationGroup(*animations)
"""
def scale(self, scale_factor, **kwargs):
super().scale(scale_factor, **kwargs)