From 64307dc3dbe633d4ff8bfb80a7f5895f00019356 Mon Sep 17 00:00:00 2001
From: Vee9ahd1 <Vee9ahd1@arson.club>
Date: Mon, 3 Jun 2019 16:15:53 -0400
Subject: implemented selectable interpolation and loop settings

---
 gantools/latent_space.py | 67 +++++++++++++++++++-----------------------------
 1 file changed, 27 insertions(+), 40 deletions(-)

(limited to 'gantools/latent_space.py')

diff --git a/gantools/latent_space.py b/gantools/latent_space.py
index e8e997a..44c64a3 100644
--- a/gantools/latent_space.py
+++ b/gantools/latent_space.py
@@ -7,12 +7,13 @@ def one_hot(index, dim):
         y[0, index] = 1.0
     return y
 
-# linear interpolation
-def linear_interp(begin, end, step_count):
-    initial = np.tile(begin, (step_count, 1))
-    delta = np.tile((end - begin)/step_count, (step_count, 1))
-    g = np.tile(np.arange(step_count), (begin.size, 1)).transpose()
-    return (delta * g) + initial
+# interpolation methods
+def linear_interp(points, step_count):
+    def linear_interp1d(y):
+        x = np.linspace(0., 1., len(y))
+        xnew = np.linspace(0., 1., step_count)
+        return interpolate.interp1d(x, y)(xnew)
+    return np.apply_along_axis(linear_interp1d, 0, points)
 
 def cubic_spline_interp(points, step_count):
     def cubic_spline_interp1d(y):
@@ -20,49 +21,35 @@ def cubic_spline_interp(points, step_count):
         tck = interpolate.splrep(x, y, s=0)
         xnew = np.linspace(0., 1., step_count)
         return interpolate.splev(xnew, tck, der=0)
-    p = np.asarray(points)
     return np.apply_along_axis(cubic_spline_interp1d, 0, points)
 
 # TODO: the math in this function is embarrasingly bad. fix at some point.
-def sequence_keyframes(keyframes, num_frames, batch_size=1):
+def sequence_keyframes(keyframes, num_frames, batch_size=1, interp_method='linear', loop=False):
+    interp_fn = {
+            'linear': linear_interp,
+            'cubic': cubic_spline_interp,
+            }[interp_method]
     div = int(num_frames // len(keyframes))
     rem = int(num_frames - (div * len(keyframes)))
-    frame_counts = np.full((len(keyframes),), div) + \
-            np.append(np.ones((rem,), dtype=int), np.zeros((len(keyframes) - rem,), dtype=int))
-    batch_div = int(num_frames//batch_size)
-    batch_rem = 1 if int(num_frames%batch_size) > 0 else 0
+    frame_counts = np.full((len(keyframes), ), div) + \
+            np.append(np.ones((rem,), dtype=int), np.zeros((len(keyframes) - rem, ), dtype=int))
+    batch_div = int(num_frames // batch_size)
+    batch_rem = 1 if int(num_frames % batch_size) > 0 else 0
     batch_count = batch_div + batch_rem
 
-    keyframes.append(keyframes[0])# seq returns to start
-    readahead = iter(keyframes)
-    next(readahead)
-    '''
-    z_seq, label_seq, truncation_seq = [], [], []
-    for (begin, end, frame_count) in zip(keyframes, readahead, frame_counts):
-        z_begin = np.asarray(begin['vector']) * begin['truncation']
-        z_end = np.asarray(end['vector']) * end['truncation']
-        z_seq.extend(linear_interp(z_begin, z_end, frame_count))
-        label_begin = np.asarray(begin['label'])
-        label_end = np.asarray(end['label'])
-        label_seq.extend(linear_interp(label_begin, label_end, frame_count))
-        truncation_seq.extend(np.linspace(begin['truncation'], end['truncation'], frame_count))
-    '''
-    # cubic interp
+    if loop is True:
+        keyframes.append(keyframes[0])# seq returns to start
+
     truncation_keys = np.asarray([keyframe['truncation'] for keyframe in keyframes])
     z_keys = np.asarray([np.asarray(keyframe['vector']) * keyframe['truncation'] for keyframe in keyframes])
     label_keys = np.asarray([keyframe['label'] for keyframe in keyframes])
-    z_seq = cubic_spline_interp(z_keys, num_frames)
-    label_seq = cubic_spline_interp(label_keys, num_frames)
-    truncation_seq = []
-    for (begin, end, frame_count) in zip(keyframes, readahead, frame_counts):
-        truncation_seq.extend(np.linspace(begin['truncation'], end['truncation'], frame_count))
-    """
-    truncation_seq = np.reshape(\
-                [np.linspace(trunc_begin, trunc_end, frame_count)\
-                for (trunc_begin, trunc_end, frame_count)\
-                in zip(truncation_keys[:-1], truncation_keys[1:], frame_counts)],\
-            (-1,))
-    """
+
+    z_seq = interp_fn(z_keys, num_frames)
+    label_seq = interp_fn(label_keys, num_frames)
+    truncation_seq = interp_fn(truncation_keys, num_frames)
+
     # you can only change trunc once per batch
-    truncation_seq_resampled = signal.resample(truncation_seq, batch_count)
+    truncation_seq_resampled = np.full((1),truncation_seq[0])\
+            if batch_count is 1\
+            else signal.resample(truncation_seq, batch_count)
     return np.asarray(z_seq), np.asarray(label_seq), truncation_seq_resampled
-- 
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