Merging

.pylintrc

@@ -67,8 +67,8 @@ ENABLED:
 # R0801: similar lines
 
 [IMPORTS]
-ignored-modules=aiohttp,cv2,defusedxml,imgaug,google,gym,fetch,librosa,matplotlib,
+ignored-modules=aiohttp,defusedxml,imgaug,google,gym,fetch,librosa,matplotlib,
     memory_profiler,numpy,oef,opacus,
-    openapi_core,pandas,psutil,pydantic,pytest,tensorflow,tensorflow_datasets,tensorflow_core,
+    openapi_core,pandas,PIL,psutil,pydantic,pytest,tensorflow,tensorflow_datasets,tensorflow_core,
     tensorflow_privacy,temper,torch,torchvision,torchsummary,tqdm,typing_extensions,
     scipy,skimage,sklearn,vyper,web3

colearn_pytorch/pytorch_xray.py

@@ -5,14 +5,14 @@
 from glob import glob
 from pathlib import Path
 from typing import Tuple, Optional, List
-from typing_extensions import TypedDict
 
-import cv2
 import numpy as np
 import torch
 import torch.nn as nn
 import torch.nn.functional as nn_func
+from PIL import Image
 from torch.utils.data import Dataset, DataLoader
+from typing_extensions import TypedDict
 
 from colearn_pytorch.pytorch_learner import PytorchLearner
 from .utils import auc_from_logits
@@ -276,10 +276,10 @@ def to_rgb_normalize_and_resize(
         :param height: Output height
         :return: Resized and normalizes image as np.array
         """
-        img = cv2.imread(str(filename))
-        img = cv2.resize(img, (width, height))
-        img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-        img = img.astype(np.float32) / 255.
+        img = Image.open(str(filename))
+        img = img.resize((width, height))
+        img = img.convert('L')  # convert to greyscale
+        img = np.array(img.getdata()).reshape((1, img.size[0], img.size[1])) / 255
 
         return img
 

examples/pytorch_xray.py

@@ -5,21 +5,21 @@
 import tempfile
 from glob import glob
 from pathlib import Path
-from typing_extensions import TypedDict
 
 import numpy as np
-import cv2
 import torch.nn as nn
 import torch.nn.functional as nn_func
 import torch.utils.data
+from PIL import Image
 from torch.utils.data import Dataset
 from torchsummary import summary
+from typing_extensions import TypedDict
 
 from colearn.training import initial_result, collective_learning_round, set_equal_weights
 from colearn.utils.plot import ColearnPlot
 from colearn.utils.results import Results, print_results
-from colearn_pytorch.utils import auc_from_logits
 from colearn_pytorch.pytorch_learner import PytorchLearner
+from colearn_pytorch.utils import auc_from_logits
 
 """
 Xray training example using Pytorch
@@ -39,7 +39,6 @@
 
 """
 
-
 # define some constants
 n_learners = 5
 batch_size = 8
@@ -179,10 +178,10 @@ def __getitem__(self, idx):
 
     @staticmethod
     def to_rgb_normalize_and_resize(filename, width, height):
-        img = cv2.imread(str(filename))
-        img = cv2.resize(img, (width, height))
-        img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-        img = img.astype(np.float32) / 255.
+        img = Image.open(str(filename))
+        img = img.resize((width, height))
+        img = img.convert('L')  # convert to greyscale
+        img = np.array(img.getdata()).reshape((1, img.size[0], img.size[1])) / 255
 
         return img
 

setup.py

@@ -7,7 +7,7 @@
 ]
 pytorch_deps = [
     'opacus~=0.10.0',
-    'opencv-python~=4.4.0',
+    'Pillow~=8.0.1',
     'scipy~=1.5.0',
     'torch~=1.7.0',
     'torchsummary~=1.5.0',