Update EMT.py

EMT.py

@@ -3,7 +3,9 @@
 import os
 import datasets
 
+
 _HOMEPAGE = "https://github.com/AV-Lab/emt-dataset"
+
 _LICENSE = "CC-BY-SA 4.0"
 
 _CITATION = """  
@@ -23,31 +25,25 @@ A multi-task dataset for detection, tracking, prediction, and intention predicti
 This dataset includes 34,386 annotated frames collected over 57 minutes of driving, with annotations for detection + tracking.
 """
 
-# Image archive URL
-_IMAGE_ARCHIVE_URL = "https://huggingface.co/datasets/KuAvLab/EMT/resolve/main/emt_images.tar.gz"
+# Annotation repository
+_ANNOTATION_REPO = "https://huggingface.co/datasets/Murdism/EMT/resolve/main/labels"
 
-# Annotations URL (organized in train/test subfolders)
-_ANNOTATION_REPO = "https://huggingface.co/datasets/KuAvLab/EMT/resolve/main/annotations"
-# "https://huggingface.co/datasets/Murdism/EMT/resolve/main/annotations"
+# Tar file URLs for images
+_TRAIN_IMAGE_ARCHIVE_URL = "https://huggingface.co/datasets/KuAvLab/EMT/blob/main/train_images.tar.gz"
+_TEST_IMAGE_ARCHIVE_URL = "https://huggingface.co/datasets/KuAvLab/EMT/blob/main/test_images.tar.gz"
 
-_GT_OBJECT_CLASSES = {
-    0: "Pedestrian",
-    1: "Cyclist",
-    2: "Motorbike",
-    3: "Small_motorised_vehicle",
-    4: "Car",
-    5: "Medium_vehicle",
-    6: "Large_vehicle",
-    7: "Bus",
-    8: "Emergency_vehicle",
-}
-
-# Update: Consider using a predefined set of object classes for easier filtering
-OBJECT_CLASSES = {v: k for k, v in _GT_OBJECT_CLASSES.items()}
 
 class EMT(datasets.GeneratorBasedBuilder):
     """EMT dataset."""
 
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="full_size",
+            description="All images are in their original size.",
+            version=datasets.Version("1.0.0"),
+        )
+    ]
+
     def _info(self):
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
@@ -56,7 +52,7 @@ class EMT(datasets.GeneratorBasedBuilder):
                     "image": datasets.Image(),
                     "objects": datasets.Sequence(
                         {
-                            "bbox": datasets.Sequence(datasets.Value("float32")),
+                            "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
                             "class_id": datasets.Value("int32"),
                             "track_id": datasets.Value("int32"),
                             "class_name": datasets.Value("string"),
@@ -71,7 +67,12 @@ class EMT(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        archive_path = dl_manager.download(_IMAGE_ARCHIVE_URL)
+        """Download and extract train/test images and annotations."""
+        image_paths = {
+            "train": dl_manager.download_and_extract(_TRAIN_IMAGE_ARCHIVE_URL),
+            "test": dl_manager.download_and_extract(_TEST_IMAGE_ARCHIVE_URL),
+        }
+
         annotation_paths = {
             "train": dl_manager.download_and_extract(f"{_ANNOTATION_REPO}/train/"),
             "test": dl_manager.download_and_extract(f"{_ANNOTATION_REPO}/test/"),
@@ -81,49 +82,48 @@ class EMT(datasets.GeneratorBasedBuilder):
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 gen_kwargs={
-                    "images": dl_manager.iter_archive(archive_path),
+                    "images": dl_manager.iter_archive(image_paths["train"]),
                     "annotation_path": annotation_paths["train"],
                 },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
                 gen_kwargs={
-                    "images": dl_manager.iter_archive(archive_path),
+                    "images": dl_manager.iter_archive(image_paths["test"]),
                     "annotation_path": annotation_paths["test"],
                 },
             ),
-            ]
-    def _generate_examples(self, images, annotation_path):
-        """Generate examples from annotations and image archive."""
+        ]
 
-        # Dictionary to store annotations
+    def _generate_examples(self, images, annotation_path):
+        """Generate dataset examples by matching images to their corresponding annotations."""
         annotations = {}
 
-        # Process each image in the dataset
         for file_path, file_obj in images:
             img_name = os.path.basename(file_path)  # e.g., "000001.jpg"
             video_name = os.path.basename(os.path.dirname(file_path))  # e.g., "video_1112"
 
-            # Expected annotation file
             ann_file = os.path.join(annotation_path, f"{video_name}.txt")
 
-            # Read annotations only for the current video
             if os.path.exists(ann_file):
+                if ann_file not in annotations:
+                    annotations[ann_file] = {}
+
+                if img_name not in annotations[ann_file]:
+                    annotations[ann_file][img_name] = []
+
                 with open(ann_file, "r", encoding="utf-8") as f:
                     for line in f:
                         parts = line.strip().split()
-                        if len(parts) < 8:  # Ensure there are enough elements
+                        if len(parts) < 8:
                             continue
-                        
+
                         frame_id, track_id, class_name = parts[:3]
-                        bbox = list(map(float, parts[4:8]))  # Extract bounding box
-                        class_id = _GT_OBJECT_CLASSES.get(class_name, -1)  # Convert class_name to numeric ID
+                        bbox = list(map(float, parts[4:8]))
+                        class_id = _GT_OBJECT_CLASSES.get(class_name, -1)
 
-                        # Match annotations to the correct image
                         if f"{frame_id}.jpg" == img_name:
-                            if img_name not in annotations:
-                                annotations[img_name] = []
-                            annotations[img_name].append(
+                            annotations[ann_file][img_name].append(
                                 {
                                     "bbox": bbox,
                                     "class_id": class_id,
@@ -136,46 +136,12 @@ class EMT(datasets.GeneratorBasedBuilder):
         idx = 0
         for file_path, file_obj in images:
             img_name = os.path.basename(file_path)
-            if img_name in annotations:
+            video_name = os.path.basename(os.path.dirname(file_path))
+            ann_file = os.path.join(annotation_path, f"{video_name}.txt")
+
+            if ann_file in annotations and img_name in annotations[ann_file]:
                 yield idx, {
                     "image": {"path": file_path, "bytes": file_obj.read()},
-                    "objects": annotations[img_name],
+                    "objects": annotations[ann_file][img_name],
                 }
                 idx += 1
-
-    # def _generate_examples(self, images, annotation_path):
-    #     """Generate examples from annotations and image archive."""
-
-    #     # Load annotation files
-    #     annotations = {}
-    #     for root, _, files in os.walk(annotation_path):
-    #         for file in files:
-    #             with open(os.path.join(root, file), "r", encoding="utf-8") as f:
-    #                 for line in f:
-    #                     parts = line.strip().split()
-    #                     frame_id, track_id, class_name = parts[:3]
-    #                     bbox = list(map(float, parts[4:8]))  # Extract bounding box
-    #                     class_id = _GT_OBJECT_CLASSES.get(class_name, -1)  # Convert class_name to numeric ID, default to -1 if not found
-
-    #                     img_path = f"{frame_id}.jpg"
-    #                     if img_path not in annotations:
-    #                         annotations[img_path] = []
-    #                     annotations[img_path].append(
-    #                         {
-    #                             "bbox": bbox,
-    #                             "class_id": class_id,
-    #                             "track_id": int(track_id),
-    #                             "class_name": class_name,
-    #                         }
-    #                     )
-
-    #     # Yield dataset entries
-    #     idx = 0
-    #     for file_path, file_obj in images:
-    #         img_name = os.path.basename(file_path)
-    #         if img_name in annotations:
-    #             yield idx, {
-    #                 "image": {"path": file_path, "bytes": file_obj.read()},
-    #                 "objects": annotations[img_name],
-    #             }
-    #             idx += 1