Change image readers from mmcv to cv2 in example usage and test

opendr-eu · Jun 28, 2021 · fbc562a · fbc562a
1 parent e266b40
commit fbc562a
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Showing 4 changed files with 16 additions and 15 deletions.
diff --git a/src/opendr/perception/panoptic_segmentation/datasets/cityscapes.py b/src/opendr/perception/panoptic_segmentation/datasets/cityscapes.py
@@ -19,7 +19,7 @@
 from pathlib import Path
 from typing import Tuple, Any, Dict, Union, List
 
-import mmcv
+import cv2
 import numpy as np
 from PIL import Image as PilImage
 from cityscapesscripts.evaluation.evalPanopticSemanticLabeling import pq_compute_multi_core, average_pq
@@ -213,7 +213,7 @@ def __getitem__(self, idx: int) -> Tuple[Image, None]:
         :rtype: Tuple of (Image, None)
         """
         image_filename = self._image_filenames[idx]
-        image = Image(mmcv.imread(image_filename), image_filename.name)
+        image = Image(cv2.imread(image_filename), image_filename.name)
 
         return image, None
 
@@ -258,7 +258,8 @@ def prepare_data(input_path: Union[str, bytes, os.PathLike], output_path: Union[
         if not (input_path / 'leftImg8bit').exists():
             raise ValueError('Please download and extract the image files first: leftImg8bit_trainvaltest.zip')
         if not (input_path / 'gtFine').exists():
-            raise ValueError('Please download and extract the ground truth fine annotations first: gtFine_trainvaltest.zip')
+            raise ValueError(
+                'Please download and extract the ground truth fine annotations first: gtFine_trainvaltest.zip')
 
         # COCO-style category list
         coco_categories = []

diff --git a/src/opendr/perception/panoptic_segmentation/datasets/kitti.py b/src/opendr/perception/panoptic_segmentation/datasets/kitti.py
@@ -20,7 +20,6 @@
 from typing import Tuple, Any, Dict, Union, List, Optional
 
 import cv2
-import mmcv
 import numpy as np
 from PIL import Image as PilImage
 from cityscapesscripts.evaluation.evalPanopticSemanticLabeling import pq_compute_multi_core, average_pq
@@ -215,7 +214,7 @@ def __getitem__(self, idx: int) -> Tuple[Image, None]:
         :rtype: Tuple of (Image, None)
         """
         image_filename = self._image_filenames[idx]
-        image = Image(mmcv.imread(image_filename), image_filename.name)
+        image = Image(cv2.imread(image_filename), image_filename.name)
 
         return image, None
 
@@ -258,7 +257,8 @@ def prepare_data(input_path: Union[str, bytes, os.PathLike], output_path: Union[
         if output_path.exists():
             raise ValueError('The specified output path already exists.')
         if not (input_path / 'training').exists() or not (input_path / 'validation').exists():
-            raise ValueError('Please download and extract the KITTI panoptic segmentation dataset first: http://panoptic.cs.uni-freiburg.de/')
+            raise ValueError(
+                'Please download and extract the KITTI panoptic segmentation dataset first: http://panoptic.cs.uni-freiburg.de/')
 
         # COCO-style category list
         coco_categories = []

diff --git a/src/opendr/perception/panoptic_segmentation/efficient_ps/example_usage.py b/src/opendr/perception/panoptic_segmentation/efficient_ps/example_usage.py
@@ -14,7 +14,7 @@
 from pathlib import Path
 from typing import List, Tuple
 
-import mmcv
+import cv2
 
 from opendr.engine.data import Image
 from opendr.engine.target import Heatmap
@@ -64,11 +64,11 @@ def evaluate():
 
 def inference():
     image_filenames = [
-        f'{CITYSCAPES_ROOT}/test/images/lindau_000001_000019.png',
-        f'{CITYSCAPES_ROOT}/test/images/lindau_000002_000019.png',
-        f'{CITYSCAPES_ROOT}/test/images/lindau_000003_000019.png',
+        f'{CITYSCAPES_ROOT}/val/images/lindau_000001_000019.png',
+        f'{CITYSCAPES_ROOT}/val/images/lindau_000002_000019.png',
+        f'{CITYSCAPES_ROOT}/val/images/lindau_000003_000019.png',
     ]
-    images = [Image(mmcv.imread(f)) for f in image_filenames]
+    images = [Image(cv2.imread(f)) for f in image_filenames]
 
     learner = EfficientPsLearner(
         device='cuda:0',

diff --git a/tests/sources/tools/perception/panoptic_segmentation/efficient_ps/test_efficient_ps.py b/tests/sources/tools/perception/panoptic_segmentation/efficient_ps/test_efficient_ps.py
@@ -18,7 +18,7 @@
 import zipfile
 from typing import List, Tuple
 
-import mmcv
+import cv2
 
 from opendr.engine.data import Image
 from opendr.engine.target import Heatmap
@@ -83,14 +83,14 @@ def test_eval(self):
 
     def test_infer_single_image(self):
         image_filename = os.path.join(self.test_data, 'infer_data', 'lindau_000001_000019.png')
-        image = Image(mmcv.imread(image_filename))
+        image = Image(cv2.imread(image_filename))
         learner = EfficientPsLearner()
         learner.load(self.model_weights)
         prediction: Tuple[Heatmap, Heatmap] = learner.infer(image)
         for heatmap in prediction:
             self.assertIsInstance(heatmap, Heatmap)
 
-        image_with_filename = ImageWithFilename(mmcv.imread(image_filename), filename='lindau_000001_000019.png')
+        image_with_filename = ImageWithFilename(cv2.imread(image_filename), filename='lindau_000001_000019.png')
         prediction: Tuple[Heatmap, Heatmap] = learner.infer(image_with_filename)
         for heatmap in prediction:
             self.assertIsInstance(heatmap, Heatmap)
@@ -100,7 +100,7 @@ def test_infer_batch_images(self):
             os.path.join(self.test_data, 'infer_data', 'lindau_000001_000019.png'),
             os.path.join(self.test_data, 'infer_data', 'lindau_000003_000019.png'),
         ]
-        images = [Image(mmcv.imread(f)) for f in image_filenames]
+        images = [Image(cv2.imread(f)) for f in image_filenames]
         learner = EfficientPsLearner()
         learner.load(self.model_weights)
         predictions: List[Tuple[Heatmap, Heatmap]] = learner.infer(images)