Merge pull request #76 from juglab/issue-73

Fix for bug # 73

Author: turekg

n2v/internals/N2V_DataGenerator.py

@@ -183,8 +183,8 @@ def __extract_patches__(self, data, num_patches=None, shape=(256, 256), n_dims=2
             patches = []
             if n_dims == 2:
                 if data.shape[1] > shape[0] and data.shape[2] > shape[1]:
-                    for y in range(0, data.shape[1] - shape[0], shape[0]):
-                        for x in range(0, data.shape[2] - shape[1], shape[1]):
+                    for y in range(0, data.shape[1] - shape[0] + 1, shape[0]):
+                        for x in range(0, data.shape[2] - shape[1] + 1, shape[1]):
                             patches.append(data[:, y:y + shape[0], x:x + shape[1]])
 
                     return np.concatenate(patches)
@@ -194,14 +194,13 @@ def __extract_patches__(self, data, num_patches=None, shape=(256, 256), n_dims=2
                     print("'shape' is too big.")
             elif n_dims == 3:
                 if data.shape[1] > shape[0] and data.shape[2] > shape[1] and data.shape[3] > shape[2]:
-                    for z in range(0, data.shape[1] - shape[0],  shape[0]):
-                        for y in range(0, data.shape[2] - shape[1], shape[1]):
-                            for x in range(0, data.shape[3] - shape[2], shape[2]):
+                    for z in range(0, data.shape[1] - shape[0] + 1,  shape[0]):
+                        for y in range(0, data.shape[2] - shape[1] + 1, shape[1]):
+                            for x in range(0, data.shape[3] - shape[2] + 1, shape[2]):
                                 patches.append(data[:, z:z + shape[0], y:y + shape[1], x:x + shape[2]])
 
                     return np.concatenate(patches)
-                elif data.shape[1] == shape[0] and data.shape[2] == shape[1] and data.shape[3] == shape[
-                    2]:
+                elif data.shape[1] == shape[0] and data.shape[2] == shape[1] and data.shape[3] == shape[2]:
                     return data
                 else:
                     print("'shape' is too big.")

n2v/version.py

@@ -1 +1 @@
-__version__ = '0.1.10'
+__version__ = '0.1.11'

tests/functional/test_training2D_RGB.py

@@ -7,20 +7,20 @@
 from n2v.utils.n2v_utils import manipulate_val_data
 from n2v.internals.N2V_DataGenerator import N2V_DataGenerator
 from matplotlib import pyplot as plt
-import urllib
+import urllib.request
 import os
 import zipfile
 
 # create a folder for our data
 if not os.path.isdir('./data'):
     os.mkdir('data')
-    # check if data has been downloaded already
-    zipPath="data/RGB.zip"
-    if not os.path.exists(zipPath):
-        # download and unzip data
-        data = urllib.request.urlretrieve('https://cloud.mpi-cbg.de/index.php/s/Frru2hsjjAljpfW/download', zipPath)
-        with zipfile.ZipFile(zipPath, 'r') as zip_ref:
-            zip_ref.extractall("data")
+# check if data has been downloaded already
+zipPath = "data/RGB.zip"
+if not os.path.exists(zipPath):
+    # download and unzip data
+    data = urllib.request.urlretrieve('https://cloud.mpi-cbg.de/index.php/s/Frru2hsjjAljpfW/download', zipPath)
+    with zipfile.ZipFile(zipPath, 'r') as zip_ref:
+        zip_ref.extractall("data")
 
 # For training, we will load __one__ low-SNR RGB image and use the <code>N2V_DataGenerator</code> to extract non-overlapping patches
 datagen = N2V_DataGenerator()
@@ -29,7 +29,7 @@
 # The function will return a list of images (numpy arrays).
 # In the 'dims' parameter we specify the order of dimensions in the image files we are reading:
 # 'C' stands for channels (color)
-imgs = datagen.load_imgs_from_directory(directory="data/", filter='*.png', dims='YXC')
+imgs = datagen.load_imgs_from_directory(directory="./data", filter='*.png', dims='YXC')
 
 print('shape of loaded images: ',imgs[0].shape)
 # Remove alpha channel

tests/functional/test_training2D_SEM.py

@@ -13,7 +13,7 @@
 # create a folder for our data
 if not os.path.isdir('./data'):
     os.mkdir('./data')
-zipPath="data/SEM.zip"
+zipPath = "data/SEM.zip"
 if not os.path.exists(zipPath):
     # download and unzip data
     data = urllib.request.urlretrieve('https://cloud.mpi-cbg.de/index.php/s/pXgfbobntrw06lC/download', zipPath)

tests/test_Noise2VoidDataGenerator.py

@@ -0,0 +1,47 @@
+from n2v.internals.N2V_DataGenerator import N2V_DataGenerator
+import urllib.request
+import os
+import zipfile
+
+
+def test_generate_patches_2D():
+
+    if not os.path.isdir('data'):
+        os.mkdir('data')
+    zip_path = "data/RGB.zip"
+    if not os.path.exists(zip_path):
+        data = urllib.request.urlretrieve('https://cloud.mpi-cbg.de/index.php/s/Frru2hsjjAljpfW/download', zip_path)
+        with zipfile.ZipFile(zip_path, 'r') as zip_ref:
+            zip_ref.extractall('data')
+
+    datagen = N2V_DataGenerator()
+
+    imgs = datagen.load_imgs_from_directory(directory="data", filter='*.png', dims='YXC')
+    imgs[0] = imgs[0][..., :3]
+    patches = datagen.generate_patches_from_list(imgs, shape=(1100, 2800))
+    assert len(patches) == 1
+    patches = datagen.generate_patches_from_list(imgs, shape=(550, 1400))
+    assert len(patches) == 4
+    patches = datagen.generate_patches_from_list(imgs, shape=(110, 280))
+    assert len(patches) == 100
+
+def test_generate_patches_3D():
+
+    if not os.path.isdir('data'):
+        os.mkdir('data')
+    zip_path = 'data/flywing-data.zip'
+    if not os.path.exists(zip_path):
+        # download and unzip data
+        data = urllib.request.urlretrieve('https://cloud.mpi-cbg.de/index.php/s/RKStdwKo4FlFrxE/download', zip_path)
+        with zipfile.ZipFile(zip_path, 'r') as zip_ref:
+            zip_ref.extractall('data')
+
+    datagen = N2V_DataGenerator()
+
+    imgs = datagen.load_imgs_from_directory(directory="data", filter='*.tif', dims='ZYX')
+    print(imgs[0].shape)
+    patches = datagen.generate_patches_from_list(imgs[:1], shape=(35, 520, 692))
+    assert len(patches) == 1
+    patches = datagen.generate_patches_from_list(imgs[:1], shape=(5, 52, 174))
+    assert len(patches) == 210
+

tests/test_Noise2VoidDataWrapper.py

@@ -1,4 +1,4 @@
-from n2v.internals.N2V_DataWrapper import  N2V_DataWrapper
+from n2v.internals.N2V_DataWrapper import N2V_DataWrapper
 
 import numpy as np