Update RDD_2020.py

RDD_2020.py

@@ -36,9 +36,6 @@ _LICENSE = ""
 # TODO: Add link to the official dataset URLs here
 # The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
 # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
-_URLS = {
-    "dataset": "https://prod-dcd-datasets-cache-zipfiles.s3.eu-west-1.amazonaws.com/5ty2wb6gvg-1.zip"
-}
 
 
 # TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
@@ -73,70 +70,69 @@ class RDD2020_Dataset(datasets.GeneratorBasedBuilder):
             homepage='https://data.mendeley.com/datasets/5ty2wb6gvg/1',
             citation=_CITATION,
         )
-        
+
     def _split_generators(self, dl_manager):
-        # The direct links to the zipped files on Hugging Face
+
         urls_to_download = {
-            "test2": "https://huggingface.co/datasets/ShixuanAn/RDD_2020/raw/main/test2.zip",
-            "test1": "https://huggingface.co/datasets/ShixuanAn/RDD_2020/raw/main/test1.zip",
-            "train": "https://huggingface.co/datasets/ShixuanAn/RDD_2020/raw/main/train.zip"
+        "train": "https://huggingface.co/datasets/ShixuanAn/RDD_2020/blob/main/train.zip",
+        "test1": "https://huggingface.co/datasets/ShixuanAn/RDD_2020/blob/main/test1.zip",
+        "test2": "https://huggingface.co/datasets/ShixuanAn/RDD_2020/blob/main/test2.zip",
         }
 
-        # Download and extract the dataset using the dl_manager
         downloaded_files = {
-            key: dl_manager.download_and_extract(url) for key, url in urls_to_download.items()
+        name: dl_manager.download_and_extract(url) 
+        for name, url in urls_to_download.items()
         }
 
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 gen_kwargs={
-                    "images_dir": os.path.join(downloaded_files["train"], "images"),
-                    "annotations_dir": os.path.join(downloaded_files["train"], "annotations", "xmls"),
+                    "filepath": downloaded_files["train"],
                     "split": "train",
-                },
+                }
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
                 gen_kwargs={
-                    "images_dir": os.path.join(downloaded_files["test1"], "images"),
-                    "annotations_dir": None,  # No annotations for test1
+                    "filepath": downloaded_files["test1"],
                     "split": "test1",
-                },
+                }
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
                 gen_kwargs={
-                    "images_dir": os.path.join(downloaded_files["test2"], "images"),
-                    "annotations_dir": None,  # No annotations for test2
+                    "filepath": downloaded_files["test2"],
                     "split": "test2",
-                },
+                }
             ),
         ]
 
-    def _generate_examples(self, images_dir, annotations_dir, split):
-        # Loop over each country directory in the images_dir
-        for country_dir in os.listdir(images_dir):
-            country_images_dir = os.path.join(images_dir, country_dir)
-            country_annotations_dir = os.path.join(annotations_dir, country_dir, "xmls") if annotations_dir else None
 
-            # Now loop over each image in the country's image directory
-            for image_file in os.listdir(country_images_dir):
-                if not image_file.endswith('.jpg'):
-                    continue
-                image_id = image_file.split('.')[0]
-                annotation_file = image_id + '.xml'
-                annotation_path = os.path.join(country_annotations_dir, annotation_file) if country_annotations_dir else None
+    def _generate_examples(self, filepath, split):
+        
+        # Iterate over each country directory
+        for country_dir in ['Czech', 'India', 'Japan']:
+            images_dir = f"{filepath}/{country_dir}/images"
+            annotations_dir = f"{filepath}/{country_dir}/annotations/xmls" if split == "train" else None
 
-                if annotation_path and not os.path.exists(annotation_path):
+            # Iterate over each image in the country's image directory
+            for image_file in os.listdir(images_dir):
+                if not image_file.endswith('.jpg'):
                     continue
 
-                # Parse the XML file for annotations if it exists
-                crack_type = []
-                crack_coordinates = []
-                if annotation_path:
+                image_id = f"{image_file.split('.')[0]}"
+            
+                image_path = os.path.join(images_dir, image_file)
+                if annotations_dir:
+                    annotation_file = image_id + '.xml'
+                    annotation_path = os.path.join(annotations_dir, annotation_file)
+                    if not os.path.exists(annotation_path):
+                        continue
                     tree = ET.parse(annotation_path)
                     root = tree.getroot()
+                    crack_type = []
+                    crack_coordinates = []
                     for obj in root.findall('object'):
                         crack_type.append(obj.find('name').text)
                         bndbox = obj.find('bndbox')
@@ -147,19 +143,15 @@ class RDD2020_Dataset(datasets.GeneratorBasedBuilder):
                             "y_max": int(bndbox.find('ymax').text),
                         }
                         crack_coordinates.append(coordinates)
+                else:
+                    crack_type = []
+                    crack_coordinates = []
 
-                # Assuming images are of uniform size, you might want to adjust this or extract from image directly
-                image_resolution = {"width": 600, "height": 600, "depth": 3} if country_dir != "India" else {"width": 720, "height": 720, "depth": 3}
-
-                # Yield the example as a key, value pair
                 yield image_id, {
                     "image_id": image_id,
                     "country": country_dir,
                     "type": split,
-                    "image_resolution": image_resolution,
-                    "image_path": os.path.join(country_images_dir, image_file),
+                    "image_path": image_path,
                     "crack_type": crack_type,
                     "crack_coordinates": crack_coordinates,
-                }
-
-    
+                }