make ImageDataGenerator behaviour fully seedable/repeatable

docs/templates/preprocessing/image.md

@@ -56,12 +56,14 @@ Generate batches of tensor image data with real-time data augmentation. The data
             - __X__: sample data.
             - __augment__: Boolean (default: False). Whether to fit on randomly augmented samples.
             - __rounds__: int (default: 1). If augment, how many augmentation passes over the data to use.
+            - __seed__: int (default: None). Random seed.
     - __flow(X, y)__: Takes numpy data & label arrays, and generates batches of augmented/normalized data. Yields batches indefinitely, in an infinite loop.
         - __Arguments__:
             - __X__: data.
             - __y__: labels.
             - __batch_size__: int (default: 32).
             - __shuffle__: boolean (defaut: True).
+            - __seed__: int (default: None).
             - __save_to_dir__: None or str (default: None). This allows you to optimally specify a directory to which to save the augmented pictures being generated (useful for visualizing what you are doing).
             - __save_prefix__: str (default: `''`). Prefix to use for filenames of saved pictures (only relevant if `save_to_dir` is set).
             - __save_format__: one of "png", "jpeg" (only relevant if `save_to_dir` is set). Default: "jpeg".
@@ -77,7 +79,7 @@ Generate batches of tensor image data with real-time data augmentation. The data
             - __class_mode__: one of "categorical", "binary", "sparse" or None. Default: "categorical". Determines the type of label arrays that are returned: "categorical" will be 2D one-hot encoded labels, "binary" will be 1D binary labels, "sparse" will be 1D integer labels. If None, no labels are returned (the generator will only yield batches of image data, which is useful to use `model.predict_generator()`, `model.evaluate_generator()`, etc.).
             - __batch_size__: size of the batches of data (default: 32).
             - __shuffle__: whether to shuffle the data (default: True)
-            - __seed__: optional random seed for shuffling.
+            - __seed__: optional random seed for shuffling and transformations.
             - __save_to_dir__: None or str (default: None). This allows you to optimally specify a directory to which to save the augmented pictures being generated (useful for visualizing what you are doing).
             - __save_prefix__: str. Prefix to use for filenames of saved pictures (only relevant if `save_to_dir` is set).
             - __save_format__: one of "png", "jpeg" (only relevant if `save_to_dir` is set). Default: "jpeg".
@@ -151,3 +153,38 @@ model.fit_generator(
         validation_data=validation_generator,
         nb_val_samples=800)
 ```
+
+Example of using it to transform images and masks together.
+
+```python
+
+# we create two instances with the same arguments
+data_gen_args = dict(
+    featurewise_center=True,
+    featurewise_std_normalization=True,
+    rotation_range=90.,
+    width_shift_range=0.1,
+    height_shift_range=0.1,
+    zoom_range=0.2
+)
+generator_i = ImageDataGenerator(**data_gen_args)
+generator_m = ImageDataGenerator(**data_gen_args)
+
+# We need to provide the same seed to the fit and flow methods
+seed = 1
+fake_y = np.zeroes(images.shape)
+generator_i.fit(images, augment=True, seed=seed)
+generator_m.fit(masks, augment=True, seed=seed)
+flow_images = generator_i.flow(images, fake_y, batch_size=2, seed=seed)
+flow_masks = generator_m.flow(masks, fake_y, batch_size=2, seed=seed)
+
+# combine the two generators into one which gives the first from each
+def dual_gen(flow_images,flow_masks):
+    for [X,_],[y,_] in zip(flow_images,flow_masks):
+        yield X,y
+
+model.fit_generator(
+        dual_gen(flow_images,flow_masks),
+        samples_per_epoch=2000,
+        nb_epoch=50)
+```

keras/preprocessing/image.py

@@ -390,6 +390,9 @@ def fit(self, X,
                 how many augmentation passes to do over the data
             seed: random seed.
         '''
+        if seed is not None:
+            np.random.seed(seed)
+
         X = np.copy(X)
         if augment:
             aX = np.zeros(tuple([rounds * X.shape[0]] + list(X.shape)[1:]))
@@ -431,11 +434,11 @@ def _flow_index(self, N, batch_size=32, shuffle=False, seed=None):
         # ensure self.batch_index is 0
         self.reset()
         while 1:
+            if seed is not None:
+                np.random.seed(seed + self.total_batches_seen)
             if self.batch_index == 0:
                 index_array = np.arange(N)
                 if shuffle:
-                    if seed is not None:
-                        np.random.seed(seed + self.total_batches_seen)
                     index_array = np.random.permutation(N)
 
             current_index = (self.batch_index * batch_size) % N

tests/keras/preprocessing/test_image.py

@@ -90,6 +90,55 @@ def test_img_flip(self):
                 assert ((potentially_flipped_x == x).all() or
                         (potentially_flipped_x == flip_axis(x, col_index)).all())
 
+    def test_dual_generators(self):
+        '''
+        Should be able to run ImageDataGenerator on multiple arrays of images
+        and get identical transforms (as long as we provide the same seed).
+        This lets us transform images and masks together or other use cases.
+        '''
+        for test_images in self.all_test_images:
+            img_list = []
+            for im in test_images:
+                img_list.append(img_to_array(im)[None, ...])
+
+            X = np.vstack(img_list)
+            y = np.arange(X.shape[0])
+
+            seed = 1
+            batch_size = 2
+            data_gen_args = dict(
+                featurewise_center=True,
+                samplewise_center=True,
+                featurewise_std_normalization=True,
+                samplewise_std_normalization=True,
+                zca_whitening=True,
+                rotation_range=90.,
+                width_shift_range=0.1,
+                height_shift_range=0.1,
+                shear_range=0.5,
+                zoom_range=0.2,
+                channel_shift_range=0.01,
+                fill_mode='nearest',
+                cval=0.5,
+                horizontal_flip=True,
+                vertical_flip=True)
+
+            generator1 = ImageDataGenerator(**data_gen_args)
+            generator2 = ImageDataGenerator(**data_gen_args)
+
+            generator1.fit(X, augment=True, seed=seed)
+            generator2.fit(X, augment=True, seed=seed)
+
+            flow1 = generator1.flow(X, y, batch_size=batch_size, seed=seed)
+            flow2 = generator2.flow(X, y, batch_size=batch_size, seed=seed)
+
+            for i in range(10):
+                X1, y1 = next(flow1)
+                X2, y2 = next(flow2)
+                for b in range(batch_size):
+                    assert (X1 == X2).all()
+                    assert (y1 == y2).all()
+
 
 if __name__ == '__main__':
     pytest.main([__file__])