options for minimum recursion value

3 files changed, 96 insertions, 13 deletions

diff --git a/options/dataset_options.py b/options/dataset_options.py
index e1ef153..368f2b2 100644
--- a/options/dataset_options.py
+++ b/options/dataset_options.py
@@ -77,6 +77,20 @@ class DatasetOptions(BaseOptions):
         ### RECURSION
 
         self.parser.add_argument(
+          '--transition-period',
+          default=3000,
+          type=int,
+          help='period of sine wave transition'
+        )
+
+        self.parser.add_argument(
+          '--transition-min',
+          default=1e-3,
+          type=float,
+          help='minimum amount of stabilization to apply'
+        )
+
+        self.parser.add_argument(
           '--recursive',
           action='store_true',
           help='recurse on previous output'
diff --git a/test-mogrify-recursive.sh b/test-mogrify-recursive.sh
index 0986fb0..17e36af 100644
--- a/test-mogrify-recursive.sh
+++ b/test-mogrify-recursive.sh
@@ -30,34 +30,99 @@ how_many=2000
   #   --blur --canny \
   #   --norm batch
 
+  # python test-mogrify.py \
+  #   --dataroot "/home/lens/Desktop/messi_ronaldo_256" \
+  #   --start_img "/home/lens/Desktop/messi_ronaldo_256/frame_00001.png" \
+  #   --experiment "messi_ronaldo" \
+  #   --name woodscaled_4 \
+  #   --recursive --recursive-frac 0.1 \
+  #   --sequence --sequence-frac 0.3 \
+  #   --process-frac 0.5 \
+  #   --how_many 10000 --transition-period 1000 \
+  #   --loadSize 256 --fineSize 256 \
+  #   --model test --which_model_netG unet_256  \
+  #   --which_direction AtoB --dataset_mode recursive  \
+  #   --which_epoch latest \
+  #   --norm batch
+
+  python test-mogrify.py \
+    --dataroot "/home/lens/Desktop/venice" \
+    --start_img "/home/lens/Desktop/venice/frame_00001.png" \
+    --experiment "venice" \
+    --name venice \
+    --recursive --recursive-frac 0.1 \
+    --sequence --sequence-frac 0.3 \
+    --process-frac 0.5 \
+    --transition-min 0.001 \
+    --how_many 10000 --transition-period 1000 \
+    --loadSize 256 --fineSize 256 \
+    --model test --which_model_netG unet_256  \
+    --which_direction AtoB --dataset_mode recursive  \
+    --which_epoch latest \
+    --norm batch
+
   python test-mogrify.py \
-    --dataroot "/home/lens/Desktop/venice_256" \
-    --start_img "/home/lens/Desktop/venice_256/frame_00001.png" \
-    --experiment "messi_ronaldo" \
-    --name pcfade \
-    --recursive --recursive-frac 0.2 \
+    --dataroot "/home/lens/Desktop/konigsheide" \
+    --start_img "/home/lens/Desktop/konigsheide/frame_00001.png" \
+    --experiment "konigsheide" \
+    --name konigsheide \
+    --recursive --recursive-frac 0.1 \
     --sequence --sequence-frac 0.3 \
     --process-frac 0.5 \
-    --loadSize 256 --fineSize 256 --how_many 18000 \
+    --transition-min 0.01 \
+    --how_many 10000 --transition-period 1000 \
+    --loadSize 256 --fineSize 256 \
     --model test --which_model_netG unet_256  \
     --which_direction AtoB --dataset_mode recursive  \
     --which_epoch latest \
     --norm batch
 
   python test-mogrify.py \
-    --dataroot "/home/lens/Desktop/walkermodified/walker5_256/" \
-    --start_img "/home/lens/Desktop/walkermodified/walker5_256/frame_00001.png" \
-    --experiment "messi_woodscaled4_r2_s5" \
-    --name woodscaled_4 \
+    --dataroot "/home/lens/Desktop/venice" \
+    --start_img "/home/lens/Desktop/venice/frame_00001.png" \
+    --experiment "konigsheide" \
+    --name konigsheide \
     --recursive --recursive-frac 0.1 \
     --sequence --sequence-frac 0.3 \
     --process-frac 0.5 \
-    --loadSize 256 --fineSize 256 --how_many 2000 \
+    --transition-min 0.05 \
+    --how_many 10000 --transition-period 1000 \
+    --loadSize 256 --fineSize 256 \
     --model test --which_model_netG unet_256  \
     --which_direction AtoB --dataset_mode recursive  \
     --which_epoch latest \
     --norm batch
 
+  python test-mogrify.py \
+    --dataroot "/home/lens/Desktop/konigsheide" \
+    --start_img "/home/lens/Desktop/konigsheide/frame_00001.png" \
+    --experiment "venice" \
+    --name venice \
+    --recursive --recursive-frac 0.1 \
+    --sequence --sequence-frac 0.3 \
+    --process-frac 0.5 \
+    --transition-min 0.01 \
+    --how_many 10000 --transition-period 1000 \
+    --loadSize 256 --fineSize 256 \
+    --model test --which_model_netG unet_256  \
+    --which_direction AtoB --dataset_mode recursive  \
+    --which_epoch latest \
+    --norm batch
+
+  # python test-mogrify.py \
+  #   --dataroot "/home/lens/Desktop/walkermodified/walker5_256/" \
+  #   --start_img "/home/lens/Desktop/walkermodified/walker5_256/frame_00001.png" \
+  #   --experiment "messi_woodscaled4_r2_s5" \
+  #   --name woodscaled_4 \
+  #   --recursive --recursive-frac 0.1 \
+  #   --sequence --sequence-frac 0.3 \
+  #   --process-frac 0.5 \
+  #   --loadSize 256 --fineSize 256 --how_many 2000 \
+  #   --model test --which_model_netG unet_256  \
+  #   --which_direction AtoB --dataset_mode recursive  \
+  #   --which_epoch latest \
+  #   --norm batch
+
 # python test-mogrify.py \
 #   --dataroot "$dataset" \
 #   --start_img "$dataset/frame_00003.png" \
diff --git a/test-mogrify.py b/test-mogrify.py
index c1e0c71..68f279d 100644
--- a/test-mogrify.py
+++ b/test-mogrify.py
@@ -131,7 +131,6 @@ if __name__ == '__main__':
 
     print("https://asdf.us/neural/" + video_fn)
 
-
   if data_opt.mov:
     render_video()
     sys.exit(1)
@@ -194,6 +193,11 @@ if __name__ == '__main__':
     image_pil.save(tmp_path)
     os.rename(tmp_path, render_path)
 
+    def clamp(n,a,b):
+      return max(a, min(n, b))
+    def lerp(n,a,b):
+      return (b-a)*n+a
+
     if dataset.name() == 'RecursiveDatasetDataLoader':
       if data_opt.recursive and last_im is not None:
         tmp_im = im.copy()
@@ -201,7 +205,7 @@ if __name__ == '__main__':
         if data_opt.sequence and A_dir is not None:
           A_img = Image.open(sequence_path).convert('RGB')
           A_im = np.asarray(A_img)
-          t = math.sin(i / 3000 * math.pi * 2 ) / 2 + 1
+          t = lerp(math.sin(i / data_opt.transition_period * math.pi * 2 ) / 2 + 1, data_opt.transition_min, 1.0)
           frac_a = data_opt.recursive_frac * (1 - t)
           frac_b = data_opt.sequence_frac * (1 - t)
           frac_c = 1.0 - frac_a - frac_b