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images.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ad6d6d6cb582aeea7843f4e33d7e8d767252d32a2ff16cb72b39e7cc63bfaad3
+size 135662992

labels.tarz.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:cfa8ff3b95accf47303c7276c2d6da2cb00f4b544f2ba217399b5eaa7275911c
+size 3681280

pidray-semantics.py

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+
+import json
+
+import datasets
+from datasets.tasks import QuestionAnsweringExtractive
+
+
+logger = datasets.logging.get_logger(__name__)
+
+_URL = "https://huggingface.co/datasets/jaradat/pidray-semantics/resolve/main/pixel_values.tar.gz"
+_URL2 = "https://huggingface.co/datasets/jaradat/pidray-semantics/resolve/main/label.tar.gz"
+                      
+class pidraySemantics(datasets.GeneratorBasedBuilder):
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            features=datasets.Features(
+                {
+                    #"text": datasets.Value("string"),
+                    "pixel_values": datasets.Image(),
+                    "label": datasets.Image(),
+                }
+            ),
+            # No default supervised_keys (as we have to pass both question
+            # and context as input).
+            supervised_keys=None,
+            homepage="https://huggingface.co/datasets/jaradat/pidray-semantics",
+
+        )
+
+    def _split_generators(self, dl_manager):
+        path = dl_manager.download(_URL)
+        image_iters = dl_manager.iter_archive(path)
+
+        
+        path2 = dl_manager.download(_URL2)
+        label_iters = dl_manager.iter_archive(path2)
+
+        return [
+            datasets.SplitGenerator(
+            name=datasets.Split.TRAIN, 
+            gen_kwargs={
+                "images": image_iters,
+                "label": label_iters
+                }
+            ),
+            
+        ]
+
+    def _generate_examples(self, images, label):
+        idx = 0
+        # iterate through images
+        
+        for (filepath, image), (filepath2, image2) in zip(images, label):
+                        
+            yield idx, {
+                "pixel_values": {"path": filepath, "bytes": image.read()},
+                "label": {"path": filepath2, "bytes": image2.read()},
+            }
+            idx += 1

pidray-targz.py

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+# coding=utf-8
+# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Lint as: python3
+
+
+import json
+
+import datasets
+from datasets.tasks import QuestionAnsweringExtractive
+
+
+logger = datasets.logging.get_logger(__name__)
+
+_URL = "https://huggingface.co/datasets/jaradat/pidray-semantics/resolve/main/pixel_values.tar.gz"
+_URL2 = "https://huggingface.co/datasets/jaradat/pidray-semantics/resolve/main/label.tar.gz"
+
+
+
+class PIDrayTargz(datasets.GeneratorBasedBuilder):
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            features=datasets.Features(
+                {
+                    #"text": datasets.Value("string"),
+                    "pixel_values": datasets.Image(),
+                    "label": datasets.Image(),
+                }
+            ),
+            # No default supervised_keys (as we have to pass both question
+            # and context as input).
+            supervised_keys=None,
+            homepage="https://huggingface.co/datasets/jaradat/pidray-semantics",
+
+        )
+
+    def _split_generators(self, dl_manager):
+        path = dl_manager.download(_URL)
+        image_iters = dl_manager.iter_archive(path)
+
+        
+        path2 = dl_manager.download(_URL2)
+        label_iters = dl_manager.iter_archive(path2)
+
+        return [
+            datasets.SplitGenerator(
+            name=datasets.Split.TRAIN, 
+            gen_kwargs={
+                "images": image_iters,
+                "label": label_iters
+                }
+            ),
+            
+        ]
+
+    def _generate_examples(self, images, label):
+        """This function returns the examples in the raw (text) form."""
+        idx = 0
+        # iterate through images
+        for filepath, image in images
+            
+            text = filepath.split
+            yield idx, {
+                "pixel_values": {"filepath": filepath, "image": image.read()},
+                "label": {"filepath": label[idx]['filepath'], "label": label[idx]['image'].read()},
+            }
+            idx += 1