trial Images

SkyScenes.py

@@ -821,7 +821,9 @@ class SKYSCENES(datasets.GeneratorBasedBuilder):
             name="trial",
             description="Semseg maps for all variations of Height = 60m and Pitch=90",
             data_urls={
-            "images":"https://huggingface.co/datasets/hoffman-lab/SkyScenes/resolve/main/Segment/H_60_P_90/ClearNoon/Town01/Town01.tar.gz"
+            "images":["https://huggingface.co/datasets/hoffman-lab/SkyScenes/resolve/main/Images/H_60_P_45/ClearNoon/Town01/Town01.tar.gz","https://huggingface.co/datasets/hoffman-lab/SkyScenes/resolve/main/Images/H_60_P_45/ClearNoon/Town02/Town02.tar.gz","https://huggingface.co/datasets/hoffman-lab/SkyScenes/resolve/main/Images/H_60_P_45/ClearNoon/Town03/Town03.tar.gz","https://huggingface.co/datasets/hoffman-lab/SkyScenes/resolve/main/Images/H_60_P_45/ClearNoon/Town04/Town04.tar.gz","https://huggingface.co/datasets/hoffman-lab/SkyScenes/resolve/main/Images/H_60_P_45/ClearNoon/Town05/Town05.tar.gz","https://huggingface.co/datasets/hoffman-lab/SkyScenes/resolve/main/Images/H_60_P_45/ClearNoon/Town06/Town06.tar.gz","https://huggingface.co/datasets/hoffman-lab/SkyScenes/resolve/main/Images/H_60_P_45/ClearNoon/Town07/Town07.tar.gz","https://huggingface.co/datasets/hoffman-lab/SkyScenes/resolve/main/Images/H_60_P_45/ClearNoon/Town10HD/Town10HD.tar.gz",
+                      "https://huggingface.co/datasets/hoffman-lab/SkyScenes/resolve/main/Images/H_60_P_45/ClearSunset/Town01/Town01.tar.gz","https://huggingface.co/datasets/hoffman-lab/SkyScenes/resolve/main/Images/H_60_P_45/ClearSunset/Town02/Town02.tar.gz","https://huggingface.co/datasets/hoffman-lab/SkyScenes/resolve/main/Images/H_60_P_45/ClearSunset/Town03/Town03.tar.gz","https://huggingface.co/datasets/hoffman-lab/SkyScenes/resolve/main/Images/H_60_P_45/ClearSunset/Town04/Town04.tar.gz","https://huggingface.co/datasets/hoffman-lab/SkyScenes/resolve/main/Images/H_60_P_45/ClearSunset/Town05/Town05.tar.gz","https://huggingface.co/datasets/hoffman-lab/SkyScenes/resolve/main/Images/H_60_P_45/ClearSunset/Town06/Town06.tar.gz","https://huggingface.co/datasets/hoffman-lab/SkyScenes/resolve/main/Images/H_60_P_45/ClearSunset/Town07/Town07.tar.gz","https://huggingface.co/datasets/hoffman-lab/SkyScenes/resolve/main/Images/H_60_P_45/ClearSunset/Town10HD/Town10HD.tar.gz",
+                     ],
             
             }
         ),
@@ -901,34 +903,27 @@ class SKYSCENES(datasets.GeneratorBasedBuilder):
         # final_kwargs = {}
         # print(len(self.config.data_urls)
         # for key,_ in self.config.data_urls.items():
-        # data_files = dl_manager.download_and_extract(self.config.data_urls)
-        data_files = dl_manager.download(self.config.data_urls["images"])
+        data_files = dl_manager.download_and_extract(self.config.data_urls["images"])
+        # data_files = dl_manager.download(self.config.data_urls["images"])
         # data_files.append(down)
         # final_kwargs[key] = down
-        print()
+        
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 gen_kwargs={
-                    "images":dl_manager.iter_archive(data_files)
-                    # "images": data_files["images"],
+                    "images": data_files,
                 },
             ),
         ]
 
     def _generate_examples(self, images=None):
         """Generate images and labels for splits."""
-        print('TYPE',type(images))
-        # for f in images:
-        for file_path, file_obj in images:
-            yield file_path, {
-                "image": {"path": file_path, "bytes": file_obj.read()},
-                }
-        # for j in range(len(images)):
-        #     dict_final = {}
-        #     filepath_img = os.listdir(images[j])
-        #     for i in range(len(os.listdir(images[j]))):
-        #         with open(os.path.join(images[j], filepath_img[i]), "rb") as f:
-        #             image_bytes = f.read()
-        #         dict_final["files"] = {"path":filepath_img[i],"bytes":image_bytes}
-        #         yield filepath_img[i], dict_final
+        for j in range(len(images)):
+            dict_final = {}
+            filepath_img = os.listdir(images[j])
+            for i in range(len(os.listdir(images[j]))):
+                with open(os.path.join(images[j], filepath_img[i]), "rb") as f:
+                    image_bytes = f.read()
+                dict_final["files"] = {"path":filepath_img[i],"bytes":image_bytes}
+                yield filepath_img[i], dict_final