style(opendataset): use "stem" to represent files without suffix

tensorbay/opendataset/CADC/loader.py

@@ -123,7 +123,7 @@ def _load_frame(
         # The data file name is a string of length 10 with each digit being a number:
         # 0000000000.jpg
         # 0000000001.bin
-        data_file_name = f"{frame_index:010}"
+        stem = f"{frame_index:010}"
 
         # Each line of the timestamps file looks like:
         # 2018-03-06 15:02:33.000000000
@@ -134,7 +134,7 @@ def _load_frame(
             # The image folder corresponds to different cameras, whose name is likes "CAM00".
             # The image folder looks like "image_00".
             camera_folder = f"image_{sensor_name[-2:]}"
-            image_file = f"{data_file_name}.png"
+            image_file = f"{stem}.png"
 
             data = Data(
                 os.path.join(data_path, camera_folder, "data", image_file),
@@ -143,7 +143,7 @@ def _load_frame(
             )
         else:
             data = Data(
-                os.path.join(data_path, "lidar_points", "data", f"{data_file_name}.bin"),
+                os.path.join(data_path, "lidar_points", "data", f"{stem}.bin"),
                 timestamp=timestamp,
             )
             data.label.box3d = _load_labels(annotation["cuboids"])

tensorbay/opendataset/CIHP/loader.py

@@ -71,16 +71,16 @@ def CIHP(path: str) -> Dataset:
             os.path.join(segment_abspath, f"{segment_name}_id.txt"), "r", encoding="utf-8"
         ) as fp:
             if segment_name == "test":
-                for filename in fp:
-                    segment.append(Data(os.path.join(image_path, f"{filename.rstrip()}.jpg")))
+                for stem in fp:
+                    segment.append(Data(os.path.join(image_path, f"{stem.rstrip()}.jpg")))
             else:
                 category_path = os.path.join(segment_abspath, "Category_ids")
                 instance_path = os.path.join(segment_abspath, "Instance_ids")
-                for filename in fp:
-                    filename = filename.rstrip()
-                    data = Data(os.path.join(image_path, f"{filename}.jpg"))
+                for stem in fp:
+                    stem = stem.rstrip()
+                    data = Data(os.path.join(image_path, f"{stem}.jpg"))
                     label = data.label
-                    png_filename = f"{filename}.png"
+                    png_filename = f"{stem}.png"
                     label.semantic_mask = SemanticMask(os.path.join(category_path, png_filename))
                     label.instance_mask = InstanceMask(os.path.join(instance_path, png_filename))
                     segment.append(data)

tensorbay/opendataset/LIP/loader.py

@@ -76,8 +76,8 @@ def LIP(path: str) -> Dataset:
         if segment_name == "test":
             image_path = os.path.join(test_path, "testing_images")
             with open(os.path.join(test_path, "test_id.txt"), "r", encoding="utf-8") as fp:
-                for filename in fp:
-                    segment.append(Data(os.path.join(image_path, f"{filename.rstrip()}.jpg")))
+                for stem in fp:
+                    segment.append(Data(os.path.join(image_path, f"{stem.rstrip()}.jpg")))
         else:
             image_path = os.path.join(trainval_image_path, f"{segment_name}_images")
             parsing_path = os.path.join(trainval_parsing_path, f"{segment_name}_segmentations")
@@ -90,10 +90,10 @@ def LIP(path: str) -> Dataset:
 
 
 def _get_data(keypoints_info: List[str], image_path: str, parsing_path: str) -> Data:
-    filename = os.path.splitext(keypoints_info[0])[0]
-    data = Data(os.path.join(image_path, f"{filename}.jpg"))
+    stem = os.path.splitext(keypoints_info[0])[0]
+    data = Data(os.path.join(image_path, f"{stem}.jpg"))
     label = data.label
-    label.semantic_mask = SemanticMask(os.path.join(parsing_path, f"{filename}.png"))
+    label.semantic_mask = SemanticMask(os.path.join(parsing_path, f"{stem}.png"))
     keypoints = LabeledKeypoints2D()
     for x, y, v in chunked(islice(keypoints_info, 1, None), 3):
         keypoints.append(

tensorbay/opendataset/OxfordIIITPet/loader.py

@@ -59,17 +59,14 @@ def OxfordIIITPet(path: str) -> Dataset:
     test_segment = dataset.create_segment("test")
     annotation_path = os.path.join(root_path, "annotations")
     for image_path in glob(os.path.join(root_path, "images", "*.jpg")):
-        image_name = os.path.splitext(os.path.basename(image_path))[0]
-        name = "Cat" if image_name.istitle() else "Dog"
-        category, num = image_name.rsplit("_", 1)
-
+        stem = os.path.splitext(os.path.basename(image_path))[0]
+        name = "Cat" if stem.istitle() else "Dog"
+        category, num = stem.rsplit("_", 1)
         data = Data(image_path, target_remote_path=f"{category}_{num.zfill(3)}.jpg")
         label = data.label
         label.classification = Classification(category=f"{name}.{category}")
-        label.semantic_mask = SemanticMask(
-            os.path.join(annotation_path, "trimaps", f"{image_name}.png")
-        )
-        xml_path = os.path.join(annotation_path, "xmls", f"{image_name}.xml")
+        label.semantic_mask = SemanticMask(os.path.join(annotation_path, "trimaps", f"{stem}.png"))
+        xml_path = os.path.join(annotation_path, "xmls", f"{stem}.xml")
         if os.path.exists(xml_path):
             label.box2d = _get_box_label(xml_path)
             trainval_segment.append(data)

tensorbay/opendataset/VOC2012ActionClassification/loader.py

@@ -55,24 +55,23 @@ def VOC2012ActionClassification(path: str) -> Dataset:
     for segment_name in _SEGMENT_NAMES:
         segment = dataset.create_segment(segment_name)
         with open(os.path.join(action_path, f"{segment_name}.txt"), encoding="utf-8") as fp:
-            for filename in fp:
-                filename = filename.strip()
-                segment.append(_get_data(filename, image_path, annotation_path))
+            for stem in fp:
+                stem = stem.strip()
+                segment.append(_get_data(stem, image_path, annotation_path))
     return dataset
 
 
-def _get_data(filename: str, image_path: str, annotation_path: str) -> Data:
+def _get_data(stem: str, image_path: str, annotation_path: str) -> Data:
     try:
         import xmltodict  # pylint: disable=import-outside-toplevel
     except ModuleNotFoundError as error:
         raise ModuleImportError(module_name=error.name) from error
-
-    data = Data(os.path.join(image_path, f"{filename}.jpg"))
+    data = Data(os.path.join(image_path, f"{stem}.jpg"))
     box2d = []
-    with open(os.path.join(annotation_path, f"{filename}.xml"), "r", encoding="utf-8") as fp:
+    with open(os.path.join(annotation_path, f"{stem}.xml"), "r", encoding="utf-8") as fp:
         labels: Any = xmltodict.parse(fp.read())
-
     objects = labels["annotation"]["object"]
+
     if not isinstance(objects, list):
         objects = [objects]
     for item in objects:

tensorbay/opendataset/VOC2012Detection/loader.py

@@ -60,29 +60,29 @@ def VOC2012Detection(path: str) -> Dataset:
     for segment_name in _SEGMENT_NAMES:
         segment = dataset.create_segment(segment_name)
         with open(os.path.join(main_path, f"{segment_name}.txt"), encoding="utf-8") as fp:
-            for filename in fp:
-                segment.append(_get_data(filename.rstrip(), image_path, annotation_path))
+            for stem in fp:
+                segment.append(_get_data(stem.rstrip(), image_path, annotation_path))
     return dataset
 
 
-def _get_data(filename: str, image_path: str, annotation_path: str) -> Data:
+def _get_data(stem: str, image_path: str, annotation_path: str) -> Data:
     """Get all information of the datum corresponding to filename.
 
     Arguments:
-        filename: The filename of the data.
+        stem: The stem of the data.
         image_path: The path of the image directory.
         annotation_path: The path of the annotation directory.
 
     Returns:
         Data: class: `~tensorbay.dataset.data.Data` instance.
 
     """
-    data = Data(os.path.join(image_path, f"{filename}.jpg"))
+    data = Data(os.path.join(image_path, f"{stem}.jpg"))
     box2d = []
-    with open(os.path.join(annotation_path, f"{filename}.xml"), "r", encoding="utf-8") as fp:
+    with open(os.path.join(annotation_path, f"{stem}.xml"), "r", encoding="utf-8") as fp:
         labels: Any = xmltodict.parse(fp.read())
-
     objects = labels["annotation"]["object"]
+
     if not isinstance(objects, list):
         objects = [objects]
     for obj in objects: