5600 door flow exs

.gitattributes

@@ -53,3 +53,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+*zip filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,17 @@
+---
+dataset_info:
+  config_name: video_01
+  features:
+  - name: original_image
+    dtype: image
+  - name: edit_prompt
+    dtype: string
+  - name: edited_image
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 1955100
+    num_examples: 5600
+  download_size: 2794614827
+  dataset_size: 1955100
+---

data/video_01.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:991eb80477f78928ccda866d469a59baa12dbb72092894480a90fbcd7f87fb48
+size 2794614827

flow_data.py

@@ -0,0 +1,73 @@
+import os
+from xml.etree import ElementTree as ET
+import datasets
+
+_DESCRIPTION = """\
+The dataset img2flow data.
+"""
+_NAME = "flow_data"
+
+_HOMEPAGE = f"https://huggingface.co/datasets/pranjalipathre/{_NAME}"
+
+_LICENSE = ""
+
+_DATA = f"https://huggingface.co/datasets/pranjalipathre/{_NAME}/resolve/main/data/"
+# _DATA = f"./data/"
+
+class img2flowDataset(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="video_01", data_dir=f"{_DATA}video_01.zip"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "video_01"
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "original_image": datasets.Image(),
+                    "edit_prompt": datasets.Value("string"),
+                    "edited_image": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation=None,
+        )
+
+    def _split_generators(self, dl_manager):
+        data = dl_manager.download_and_extract(self.config.data_dir)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "data": data,
+                },
+            ),
+        ]
+
+    @staticmethod
+    def parse_text(root: ET.Element, file: str, index: int) -> dict:
+        idx = str(index).zfill(6)
+        ele = root.find(f".//*[@frame='{idx}']")
+        dt = {
+            "text": ele.get("text")
+        }
+        return dt
+
+    def _generate_examples(self, data):
+        treePath = os.path.join(data, "annotations.xml")
+
+        tree = ET.parse(treePath)
+        root = tree.getroot()
+
+        for idx, file in enumerate(sorted(os.listdir(os.path.join(data, "original_images")))):
+            dat = self.parse_text(root, file, idx)
+            txt = dat["text"]
+
+            yield idx, {
+                "original_image": os.path.join(data, "original_images", file),
+                "edit_prompt": txt,
+                "edited_image": os.path.join(data, "edited_images", file).replace(".png", ".flo"),
+            }