Changed examples

Makefile

@@ -1,6 +1,6 @@
 video:
 	manim -pqh src/vae.py VAEScene --media_dir media
-	cp media/videos/vae/1080p60/VAEScene.mp4 final_videos
+	cp media/videos/vae/720p60/VAEScene.mp4 examples
 train:
 	cd src/autoencoder_models
 	python vanilla_autoencoder.py

src/autoencoder_models/generate_interpolation.py

@@ -37,15 +37,14 @@ save_object["interpolation_path"] = interpolation_path
 for i in range(num_images):
     # Generate 
     z = torch.Tensor(interpolation_path[i]).unsqueeze(0)
-    gen_image = vae.decode(z)
+    gen_image = vae.decode(z).detach().numpy()
+    gen_image = np.reshape(gen_image, (28, 28)) * 255
     save_object["interpolation_images"].append(gen_image)
 
 fig, axs = plt.subplots(num_images, 1, figsize=(1, num_images))
 image_pairs = []
 for i in range(num_images):
-    im = save_object["interpolation_images"][i]
-    im = im.detach().numpy()
-    recon_image = np.reshape(im, (28, 28)) * 255
+    recon_image = save_object["interpolation_images"][i]
     # Add to plot
     axs[i].imshow(recon_image)
 

src/vae.py

@@ -5,11 +5,13 @@ and Traditional Autoencoders.
 
 """
 from configparser import Interpolation
+from random import sample
 from typing_extensions import runtime
 from manim import *
 import pickle
 import numpy as np
 import neural_network
+from scipy.interpolate import make_interp_spline
 
 class VariationalAutoencoder(Group):
     """Variational Autoencoder Manim Visualization"""
@@ -76,24 +78,27 @@ class VariationalAutoencoder(Group):
         embedding.add(self.point_dots)
         return embedding
 
+    def _construct_image_mobject(self, input_image, height=2):
+        """Constructs an ImageMobject from a numpy grayscale image"""
+        # Convert image to rgb
+        input_image = np.repeat(input_image, 3, axis=0)
+        input_image = np.rollaxis(input_image, 0, start=3)
+        #  Make the ImageMobject
+        image_mobject = ImageMobject(input_image, image_mode="RGB")
+        image_mobject.set_resampling_algorithm(RESAMPLING_ALGORITHMS["nearest"])
+        image_mobject.height = height
+
+        return image_mobject
+
     def _construct_input_output_images(self, image_pair):
         """Places the input and output images for the AE"""
         # Takes the image pair
         # image_pair is assumed to be [2, x, y]
         input_image = image_pair[0][None, :, :]
         recon_image = image_pair[1][None, :, :]
-        # Convert images to rgb
-        input_image = np.repeat(input_image, 3, axis=0)
-        input_image = np.rollaxis(input_image, 0, start=3)
-        recon_image = np.repeat(recon_image, 3, axis=0)
-        recon_image = np.rollaxis(recon_image, 0, start=3)
-        # Make an image objects
-        input_image_object = ImageMobject(input_image, image_mode="RGB")
-        input_image_object.set_resampling_algorithm(RESAMPLING_ALGORITHMS["nearest"])
-        input_image_object.height = 2
-        recon_image_object = ImageMobject(recon_image, image_mode="RGB")
-        recon_image_object.set_resampling_algorithm(RESAMPLING_ALGORITHMS["nearest"])
-        recon_image_object.height = 2
+        # Make the image mobjects
+        input_image_object = self._construct_image_mobject(input_image)
+        recon_image_object = self._construct_image_mobject(recon_image)
 
         return input_image_object, recon_image_object
 
@@ -129,6 +134,17 @@ class VariationalAutoencoder(Group):
         animation_group = AnimationGroup(*animations)
         return animation_group
 
+    def make_reset_vae_animation(self):
+        """Resets the VAE to just the neural network"""
+        animation_group = AnimationGroup(
+            FadeOut(self.input_image),
+            FadeOut(self.output_image),
+            FadeOut(self.distribution_objects),
+            run_time=1.0
+        )
+
+        return animation_group
+        
     def make_forward_pass_animation(self, image_pair, run_time=1.5):
         """Overriden forward pass animation specific to a VAE"""
         per_unit_runtime = run_time
@@ -151,15 +167,22 @@ class VariationalAutoencoder(Group):
             dot_convergence_animation
         )
         # Make an ellipse centered at mean_point witAnimationGraph std outline
-        center_dot = Dot(mean_point, radius=self.dot_radius, color=GREEN)
-        ellipse = Ellipse(width=std[0], height=std[1], color=RED, fill_opacity=0.5, stroke_width=self.ellipse_stroke_width)
+        center_dot = Dot(mean_point, radius=self.dot_radius, color=RED)
+        ellipse = Ellipse(width=std[0], height=std[1], color=RED, fill_opacity=0.3, stroke_width=self.ellipse_stroke_width)
         ellipse.move_to(mean_point)
+        self.distribution_objects = VGroup(
+            center_dot, 
+            ellipse
+        )
+        # Make ellipse animation
         ellipse_animation = AnimationGroup(
             GrowFromCenter(center_dot), 
             GrowFromCenter(ellipse),
         )
         # Make the dot divergence animation
-        dot_divergence_animation = self.make_dot_divergence_animation(mean_point, run_time=per_unit_runtime)
+        sampled_point = [0.51, 1.0]
+        divergence_point = self.embedding.axes.coords_to_point(*sampled_point)
+        dot_divergence_animation = self.make_dot_divergence_animation(divergence_point, run_time=per_unit_runtime)
         # Make decoder foward pass
         decoder_forward_pass = self.decoder.make_forward_propagation_animation(run_time=per_unit_runtime)
         # Add the animations to the group
@@ -175,34 +198,77 @@ class VariationalAutoencoder(Group):
 
         return animation_group
 
+    def make_interpolation_animation(self, interpolation_images, frame_rate=5):
+        """Makes an animation interpolation"""
+        num_images = len(interpolation_images)
+        # Make madeup path
+        interpolation_latent_path = np.linspace([-0.5, -1], [1, 1.3], num=num_images)
+        # Make the path animation
+        first_dot_location = self.embedding.axes.coords_to_point(*interpolation_latent_path[0])
+        moving_dot = Dot(first_dot_location, radius=self.dot_radius, color=RED)
+        animation_list = [GrowFromCenter(moving_dot)]
+        for image_index in range(num_images - 1):
+            next_index = image_index + 1
+            # Get path
+            next_point = interpolation_latent_path[next_index]
+            next_position = self.embedding.axes.coords_to_point(*next_point)
+            # Draw path from current point to next point
+            move_animation = moving_dot.animate.move_to(next_position)
+            animation_list.append(move_animation)
+
+        interpolation_animation = Succession(*animation_list, run_time=0.1*num_images)
+        # Make the images animation
+        animation_list = []
+        for numpy_image in interpolation_images:
+            numpy_image = interpolation_images[0]
+            numpy_image = numpy_image[None, :, :]
+            manim_image = self._construct_image_mobject(numpy_image)
+            # Move the image to the correct location
+            manim_image.move_to(self.output_image)
+            # Add the image
+            animation_list.append(FadeIn(manim_image, run_time=0.1))
+            # Wait
+            animation_list.append(Wait(1 / frame_rate))
+            # Remove the image
+            animation_list.append(FadeOut(manim_image, run_time=0.1))
+        images_animation = Succession(*animation_list)
+        # Combine the two into an AnimationGroup
+        animation_group = AnimationGroup(
+            interpolation_animation,
+            images_animation
+        )
+
+        return animation_group
 
 class MNISTImageHandler():
     """Deals with loading serialized VAE mnist images from "autoencoder_models" """
 
     def __init__(
         self, 
-        image_pairs_path="src/autoencoder_models/image_pairs.pkl", 
-        interpolations_path="src/autoencoder_models/interpolations.pkl"
+        image_pairs_file_path="src/autoencoder_models/image_pairs.pkl", 
+        interpolations_file_path="src/autoencoder_models/interpolations.pkl"
     ):
-        self.image_pairs_path = image_pairs_path
-        self.interpolations_path = interpolations_path
+        self.image_pairs_file_path = image_pairs_file_path
+        self.interpolations_file_path = interpolations_file_path
 
         self.image_pairs = []
-        self.interpolations = []
+        self.interpolation_images = []
+        self.interpolation_latent_path = []
 
         self.load_serialized_data()
 
     def load_serialized_data(self):
-        with open(self.image_pairs_path, "rb") as f:
+        with open(self.image_pairs_file_path, "rb") as f:
             self.image_pairs = pickle.load(f)
 
-        with open(self.interpolations_path, "rb") as f:
-            self.interpolations_path = pickle.load(f)
+        with open(self.interpolations_file_path, "rb") as f:
+            self.interpolation_dict = pickle.load(f)
+            self.interpolation_images = self.interpolation_dict["interpolation_images"]
+            self.interpolation_latent_path = self.interpolation_dict["interpolation_path"]
 
 """
     The VAE Scene for the twitter video. 
 """
-
 config.pixel_height = 720 
 config.pixel_width = 1280 
 config.frame_height = 10.0
@@ -217,17 +283,17 @@ class VAEScene(Scene):
         # Set Scene config
         vae = VariationalAutoencoder()
         mnist_image_handler = MNISTImageHandler()
-        image_pair = mnist_image_handler.image_pairs[2]
+        image_pair = mnist_image_handler.image_pairs[3]
         vae.move_to(ORIGIN)
         vae.scale(1.2)
         self.add(vae)
+        # Make a forward pass animation
         forward_pass_animation = vae.make_forward_pass_animation(image_pair)
         self.play(forward_pass_animation)
-        """
-        autoencoder = Autoencoder()
-        autoencoder.move_to(ORIGIN)
-        # Make a forward pass animation
-        self.add(autoencoder)
-        forward_pass_animation = autoencoder.make_forward_pass_animation(run_time=1.5)
-        self.play(forward_pass_animation)
-        """
+        # Remove the input and output images
+        reset_animation = vae.make_reset_vae_animation()
+        self.play(reset_animation)
+        # Interpolation animation
+        interpolation_images = mnist_image_handler.interpolation_images
+        interpolation_animation = vae.make_interpolation_animation(interpolation_images)
+        self.play(interpolation_animation)