Update EMT.py

EMT.py

@@ -1,212 +1,4 @@
 
-# import os
-# import datasets
-# import tarfile
-
-# _HOMEPAGE = "https://github.com/AV-Lab/emt-dataset"
-# _LICENSE = "CC-BY-SA 4.0"
-# _CITATION = """  
-# @article{EMTdataset2025,  
-#       title={EMT: A Visual Multi-Task Benchmark Dataset for Autonomous Driving in the Arab Gulf Region},  
-#       author={Nadya Abdel Madjid and Murad Mebrahtu and Abdelmoamen Nasser and Bilal Hassan and Naoufel Werghi and Jorge Dias and Majid Khonji},  
-#       year={2025},  
-#       eprint={2502.19260},  
-#       archivePrefix={arXiv},  
-#       primaryClass={cs.CV},  
-#       url={https://arxiv.org/abs/2502.19260}  
-# }  
-# """ 
-
-# _DESCRIPTION = """\
-# A multi-task dataset for detection, tracking, prediction, and intention prediction. 
-# This dataset includes 34,386 annotated frames collected over 57 minutes of driving, with annotations for detection and tracking.
-# """
-
-# _TRAIN_IMAGE_ARCHIVE_URL = "https://huggingface.co/datasets/KuAvLab/EMT/resolve/main/train_images.tar.gz"
-# _TEST_IMAGE_ARCHIVE_URL = "https://huggingface.co/datasets/KuAvLab/EMT/resolve/main/test_images.tar.gz"
-
-# _TRAIN_ANNOTATION_ARCHIVE_URL = "https://huggingface.co/datasets/KuAvLab/EMT/resolve/main/train_annotation.tar.gz"
-# _TEST_ANNOTATION_ARCHIVE_URL = "https://huggingface.co/datasets/KuAvLab/EMT/resolve/main/test_annotation.tar.gz"
-
-
-# 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,
-#             features=datasets.Features(
-#                 {
-#                     "image": datasets.Image(),
-#                     "objects": datasets.Sequence(
-#                         {
-#                             "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
-#                             "class_id": datasets.Value("int32"),
-#                             "track_id": datasets.Value("int32"),
-#                             "class_name": datasets.Value("string"),
-#                         }
-#                     ),
-#                 }
-#             ),
-#             supervised_keys=None,
-#             homepage=_HOMEPAGE,
-#             license=_LICENSE,
-#             citation=_CITATION,
-#         )
-
-#     def _split_generators(self, dl_manager):
-#         """Download (if not cached) and prepare dataset splits."""
-        
-#         image_urls = {
-#             "train": _TRAIN_IMAGE_ARCHIVE_URL,
-#             "test": _TEST_IMAGE_ARCHIVE_URL,
-#         }
-        
-#         annotation_urls = {
-#             "train": _TRAIN_ANNOTATION_ARCHIVE_URL,
-#             "test": _TEST_ANNOTATION_ARCHIVE_URL,
-#         }
-        
-#         # Based on the requested split, we only download the relevant data
-#         split = self.config.name  # Determine the requested split (train or test)
-    
-#         # Ensure paths are correctly resolved for the requested split
-#         extracted_paths = dl_manager.download_and_extract({split: annotation_urls[split]})
-#         image_archives = dl_manager.download_and_extract({split: image_urls[split]})
-    
-#         # Ensure annotation paths point to the correct subdirectory
-#         annotation_path = os.path.join(extracted_paths[split], "annotations", split)
-#         image_path = image_archives[split]
-        
-#         return [
-#             datasets.SplitGenerator(
-#                 name=datasets.Split.TRAIN if split == "train" else datasets.Split.TEST,
-#                 gen_kwargs={
-#                     "images": dl_manager.iter_archive(image_path),
-#                     "annotation_path": annotation_path,
-#                 },
-#             ),
-#         ]
-
-
-#     # def _split_generators(self, dl_manager):
-#     #     """Download (if not cached) and prepare dataset splits."""
-        
-#     #     image_urls = {
-#     #         "train": _TRAIN_IMAGE_ARCHIVE_URL,
-#     #         "test": _TEST_IMAGE_ARCHIVE_URL,
-#     #     }
-    
-#     #     annotation_urls = {
-#     #         "train": _TRAIN_ANNOTATION_ARCHIVE_URL,
-#     #         "test": _TEST_ANNOTATION_ARCHIVE_URL,
-#     #     }
-    
-#     #     # Ensure paths are correctly resolved
-#     #     extracted_paths = dl_manager.download_and_extract(annotation_urls)
-#     #     image_archives = dl_manager.download_and_extract(image_urls)
-    
-#     #     # ✅ Ensure annotation paths point to the correct subdirectory
-#     #     train_annotation_path = os.path.join(extracted_paths["train"], "annotations", "train")
-#     #     test_annotation_path = os.path.join(extracted_paths["test"], "annotations", "test")
-    
-#     #     return [
-#     #         datasets.SplitGenerator(
-#     #             name=datasets.Split.TRAIN,
-#     #             gen_kwargs={
-#     #                 "images": dl_manager.iter_archive(image_archives["train"]),
-#     #                 "annotation_path": train_annotation_path,  # ✅ Corrected path
-#     #             },
-#     #         ),
-#     #         datasets.SplitGenerator(
-#     #             name=datasets.Split.TEST,
-#     #             gen_kwargs={
-#     #                 "images": dl_manager.iter_archive(image_archives["test"]),
-#     #                 "annotation_path": test_annotation_path,  # ✅ Corrected path
-#     #             },
-#     #         ),
-#     # ]
-
-#     def _generate_examples(self, images, annotation_path):
-#         """Generate dataset examples by matching images to their corresponding annotations."""
-    
-#         annotations = {}
-    
-#         # Determine whether we're processing train or test split
-#         if "train" in annotation_path:
-#             annotation_split = "train"
-#         elif "test" in annotation_path:
-#             annotation_split = "test"
-#         else:
-#             raise ValueError(f"Unknown annotation path: {annotation_path}")
-    
-#         ann_dir = annotation_path
-    
-#         print(f"Extracted annotations path: {annotation_path}")
-#         print(f"Looking for annotations in: {ann_dir}")
-    
-#         # Check if annotation directory exists
-#         if not os.path.exists(ann_dir):
-#             raise FileNotFoundError(f"Annotation directory does not exist: {ann_dir}")
-    
-#         # Extract annotation files and read their contents
-#         for ann_file in os.listdir(ann_dir):
-#             video_name = os.path.splitext(ann_file)[0]  # Extract video folder name from file
-#             ann_path = os.path.join(ann_dir, ann_file)
-            
-#             if os.path.isdir(ann_path):
-#                 continue  # Skip directories
-    
-#             print("Processing annotation file:", ann_path)
-    
-#             with open(ann_path, "r", encoding="utf-8") as f:
-#                 for line in f:
-#                     parts = line.strip().split()
-#                     if len(parts) < 8:
-#                         continue
-    
-#                     frame_id, track_id, class_name = parts[:3]
-#                     bbox = list(map(float, parts[4:8]))
-#                     class_id = _GT_OBJECT_CLASSES.get(class_name, -1)
-#                     img_name = f"{frame_id}.jpg"
-    
-#                     # Store annotation in a dictionary
-#                     key = f"{video_name}/{img_name}"
-#                     if key not in annotations:
-#                         annotations[key] = []
-    
-#                     annotations[key].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)
-#             video_name = os.path.basename(os.path.dirname(file_path))  # Match the video folder
-#             key = f"{video_name}/{img_name}"
-    
-#             if key in annotations:
-#                 yield idx, {
-#                     "image": {"path": file_path, "bytes": file_obj.read()},
-#                     "objects": annotations[key],
-#                 }
-#                 idx += 1
-
-
 import os
 import datasets
 import tarfile
@@ -364,8 +156,7 @@ class EMT(datasets.GeneratorBasedBuilder):
         ]
 
     
-
-    def _generate_examples(self, images, annotation_path):
+    def _generate_examples(self, image_dir, annotation_path):
         """Generate dataset examples by matching images to their corresponding annotations."""
     
         annotations = {}
@@ -424,14 +215,90 @@ class EMT(datasets.GeneratorBasedBuilder):
     
         # Yield dataset entries
         idx = 0
-        for file_path, file_obj in images:
-            img_name = os.path.basename(file_path)
-            video_name = os.path.basename(os.path.dirname(file_path))  # Match the video folder
-            key = f"{video_name}/{img_name}"
+        for root, _, files in os.walk(image_dir):
+            for file_name in files:
+                if not file_name.endswith((".jpg", ".png")):
+                    continue
+    
+                file_path = os.path.join(root, file_name)
+                video_name = os.path.basename(root)  # Match the video folder
+                key = f"{video_name}/{file_name}"
+    
+                if key in annotations:
+                    with open(file_path, "rb") as img_file:
+                        yield idx, {
+                            "image": {"path": file_path, "bytes": img_file.read()},
+                            "objects": annotations[key],
+                        }
+                        idx += 1
+
+    # def _generate_examples(self, images, annotation_path):
+    #     """Generate dataset examples by matching images to their corresponding annotations."""
     
-            if key in annotations:
-                yield idx, {
-                    "image": {"path": file_path, "bytes": file_obj.read()},
-                    "objects": annotations[key],
-                }
-                idx += 1
+    #     annotations = {}
+    
+    #     # Determine whether we're processing train or test split
+    #     if "train" in annotation_path:
+    #         annotation_split = "train"
+    #     elif "test" in annotation_path:
+    #         annotation_split = "test"
+    #     else:
+    #         raise ValueError(f"Unknown annotation path: {annotation_path}")
+    
+    #     ann_dir = annotation_path
+    
+    #     print(f"Extracted annotations path: {annotation_path}")
+    #     print(f"Looking for annotations in: {ann_dir}")
+    
+    #     # Check if annotation directory exists
+    #     if not os.path.exists(ann_dir):
+    #         raise FileNotFoundError(f"Annotation directory does not exist: {ann_dir}")
+    
+    #     # Extract annotation files and read their contents
+    #     for ann_file in os.listdir(ann_dir):
+    #         video_name = os.path.splitext(ann_file)[0]  # Extract video folder name from file
+    #         ann_path = os.path.join(ann_dir, ann_file)
+            
+    #         if os.path.isdir(ann_path):
+    #             continue  # Skip directories
+    
+    #         print("Processing annotation file:", ann_path)
+    
+    #         with open(ann_path, "r", encoding="utf-8") as f:
+    #             for line in f:
+    #                 parts = line.strip().split()
+    #                 if len(parts) < 8:
+    #                     continue
+    
+    #                 frame_id, track_id, class_name = parts[:3]
+    #                 bbox = list(map(float, parts[4:8]))
+    #                 class_id = _GT_OBJECT_CLASSES.get(class_name, -1)
+    #                 img_name = f"{frame_id}.jpg"
+    
+    #                 # Store annotation in a dictionary
+    #                 key = f"{video_name}/{img_name}"
+    #                 if key not in annotations:
+    #                     annotations[key] = []
+    
+    #                 annotations[key].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)
+    #         video_name = os.path.basename(os.path.dirname(file_path))  # Match the video folder
+    #         key = f"{video_name}/{img_name}"
+    
+    #         if key in annotations:
+    #             yield idx, {
+    #                 "image": {"path": file_path, "bytes": file_obj.read()},
+    #                 "objects": annotations[key],
+    #             }
+    #             idx += 1