Added Sintel demo

.gitignore

@@ -10,3 +10,4 @@ __pycache__
 /data
 cmake*
 export
+*.zip

config_example.py

@@ -1,5 +1,6 @@
 from artist import ArtistDataProvider
 from fivek import FiveKDataProvider
+from folder_data_provider import FolderDataProvider
 from critics import critic
 from agent import agent_generator
 from util import Dict
@@ -177,22 +178,26 @@ def c_lr_callback(t):
 ###########################################################################
 
 # Input dataset (train)
+cfg.fake_data_provider = lambda: FolderDataProvider(folder='data/sintel/inputs', default_batch_size=cfg.batch_size)
+'''
 cfg.fake_data_provider = lambda: FiveKDataProvider(
     raw=True,
     bnw=cfg.bnw,
     output_size=64,
     default_batch_size=cfg.batch_size,
     augmentation=0.3,
     set_name='2k_train',)
+'''
 
 # Input dataset (test)
+'''
 cfg.fake_data_provider_test = lambda: FiveKDataProvider(set_name='u_test', raw=True, bnw=cfg.bnw,
                                                         output_size=64,
                                                         default_batch_size=cfg.batch_size,
                                                         augmentation=0.0)
+'''
+cfg.fake_data_provider_test = lambda: FolderDataProvider(folder='data/sintel/inputs', default_batch_size=cfg.batch_size)
 
 # Target dataset
-cfg.real_data_provider = lambda: ArtistDataProvider(augmentation=1.0, name='FiveK_C',
-                                                    output_size=64, bnw=cfg.bnw,
-                                                    default_batch_size=cfg.batch_size, target=None,
-                                                    set_name='2k_target')
+cfg.real_data_provider = lambda: FolderDataProvider(folder='data/sintel/outputs', default_batch_size=cfg.batch_size)
+

folder_data_provider.py

@@ -0,0 +1,57 @@
+import numpy as np
+import os
+import cv2
+import random
+from util import get_image_center
+from data_provider import DataProvider
+
+class FolderDataProvider(DataProvider):
+
+  def __init__(self,
+               folder,
+               read_limit=-1,
+               main_size=80,
+               crop_size=64,
+               augmentation_factor=4,
+               *args,
+               **kwargs):
+    files = os.listdir(folder)
+    files = sorted(files)
+
+    if read_limit != -1:
+      files = files[:read_limit]
+    data = []
+    files.sort()
+    for f in files:
+      image = (cv2.imread(os.path.join(folder, f))[:, :, ::-1] /
+               255.0).astype(np.float32)
+      image = get_image_center(image)
+      # image = cv2.resize(image, (64, 64), interpolation=cv2.INTER_AREA)
+      # data.append(image)
+      image = cv2.resize(
+          image, (main_size, main_size), interpolation=cv2.INTER_AREA)
+      for i in range(augmentation_factor):
+        new_image = image
+        if random.random() < 0.5:
+          new_image = new_image[:, ::-1, :]
+        sx, sy = random.randrange(main_size - crop_size + 1), random.randrange(
+            main_size - crop_size + 1)
+        data.append(new_image[sx:sx + crop_size, sy:sy + crop_size])
+    data = np.stack(data, axis=0)
+    print("# image after augmentation =", len(data))
+    super(FolderDataProvider, self).__init__(data, *args, bnw=False,
+                                             augmentation=1.0,
+                                             output_size=crop_size,
+                                             **kwargs)
+
+def test():
+  dp = FolderDataProvider('data/sintel/outputs')
+  while True:
+    d = dp.get_next_batch(64)
+    cv2.imshow('img', d[0][0, :, :, ::-1])
+    cv2.waitKey(0)
+
+
+if __name__ == '__main__':
+  test()
+  # preprocess()