Initial commit.

hugging_face_dataset/README.md

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+---
+language:
+  - en
+dataset_info:
+  features:
+  - name: image_id
+    dtype: int64
+  - name: image
+    dtype: image
+  - name: width
+    dtype: int32
+  - name: height
+    dtype: int32
+  - name: objects
+    list:
+    - name: category_id
+      dtype:
+        class_label:
+          names:
+            0: boxed
+            1: grid
+            2: spread
+            3: stack
+    - name: image_id
+      dtype: string
+    - name: id
+      dtype: int64
+    - name: area
+      dtype: int64
+    - name: bbox
+      sequence: float32
+      length: 4
+    - name: iscrowd
+      dtype: bool
+  config_name: card-detection
+  splits:
+  - name: train
+  download_size: 96890427
+  dataset_size: 0
+---

hugging_face_dataset/card_detector_dataset.py

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+# Copyright 2022 Daniel van Strien.
+#
+# 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.
+"""NLS Chapbook Illustrations"""
+
+import collections
+import json
+import os
+from typing import Any, Dict, List
+import pandas as pd
+import datasets
+
+_CITATION = """Connor Hoehn"""
+
+_DESCRIPTION = "This dataset comprises of card display images from the public domain"
+
+_HOMEPAGE = "https://www.connorhoehn.com"
+
+_LICENSE = "Public Domain Mark 1.0"
+
+_DATASET_URL = "https://www.connorhoehn.com/object_detection_dataset_v1.zip"
+
+_CATEGORIES = ["boxed","grid","spread","stack"]
+
+class CardDisplayDetectorConfig(datasets.BuilderConfig):
+    """BuilderConfig for card display dataset."""
+
+    def __init__(self, name, **kwargs):
+        
+        super(CardDisplayDetectorConfig, self).__init__(
+            version=datasets.Version("1.0.0"),
+            name=name,
+            description="Card Display Detector",
+            **kwargs,
+        )
+
+
+class CardDisplayDetector(datasets.GeneratorBasedBuilder):
+    """Card Display dataset."""
+
+    BUILDER_CONFIGS = [
+        CardDisplayDetectorConfig("card-detection"),
+    ]
+
+    def _info(self):
+    
+        if self.config.name == "display-detection":
+            features = datasets.Features(
+                {
+                    "image_id": datasets.Value("int64"),
+                    "image": datasets.Image(),
+                    "width": datasets.Value("int32"),
+                    "height": datasets.Value("int32"),
+                }
+            )
+            object_dict = {
+                "category_id": datasets.ClassLabel(names=_CATEGORIES),
+                "image_id": datasets.Value("string"),
+                "id": datasets.Value("int64"),
+                "area": datasets.Value("int64"),
+                "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
+                "iscrowd": datasets.Value("bool"),
+            }
+            features["objects"] = [object_dict]
+            
+            return datasets.DatasetInfo(
+                description=_DESCRIPTION,
+                features=features,
+                homepage=_HOMEPAGE,
+                license=_LICENSE,
+                citation=_CITATION,
+            )
+        else:
+            features = datasets.Features(
+                {
+                    "image_id": datasets.Value("int64"),
+                    "image": datasets.Image(),
+                    "width": datasets.Value("int32"),
+                    "height": datasets.Value("int32"),
+                }
+            )
+            
+            object_dict = {
+                "category_id": datasets.ClassLabel(names=_CATEGORIES),
+                "image_id": datasets.Value("string"),
+                "id": datasets.Value("int64"),
+                "area": datasets.Value("int64"),
+                "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
+                "iscrowd": datasets.Value("bool"),
+            }
+            
+            features["objects"] = [object_dict]
+            
+            return datasets.DatasetInfo(
+                description=_DESCRIPTION,
+                features=features,
+                homepage=_HOMEPAGE,
+                license=_LICENSE,
+                citation=_CITATION,
+            )
+
+
+    def _split_generators(self, dl_manager):
+        
+        dataset_zip = dl_manager.download_and_extract(_DATASET_URL)
+        
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # COCO -> x.json, images/
+                gen_kwargs={
+                    "annotations_file": os.path.join(dataset_zip, "result.json"),
+                    "image_dir": os.path.join(dataset_zip, "images"),
+                },
+            )
+        ]
+
+    # Return dictionary of unique image_ids that have multiple nested annotations
+    def _get_image_id_to_annotations_mapping(self, annotations: List[Dict]) -> Dict[int, List[Dict[Any, Any]]]:
+        """
+        A helper function to build a mapping from image ids to annotations.
+        """
+        image_id_to_annotations = collections.defaultdict(list)
+        
+        for annotation in annotations:
+            
+            image_id_to_annotations[annotation["image_id"]].append(annotation)
+            
+        return image_id_to_annotations
+    
+
+    def _generate_examples(self, annotations_file, image_dir):
+        
+        def _image_info_to_example(image_info, image_dir):
+            
+            # from the annotation file
+            image = image_info["file_name"]
+            
+            return {
+                "image_id": image_info["id"],
+                "image": os.path.join(image_dir, image),
+                "width": image_info["width"],
+                "height": image_info["height"],
+            }
+        
+        with open(annotations_file, encoding="utf8") as annotation_json:
+            
+            annotation_data = json.load(annotation_json)
+            
+            images = annotation_data["images"]
+            
+            annotations = annotation_data["annotations"]
+            
+            # dictionary of image_ids with all related annotations (bbox)
+            image_id_to_annotations = self._get_image_id_to_annotations_mapping(
+                annotations
+            )
+
+            if self.config.name == "illustration-detection":
+                
+                # yield image_id, features
+                for image_id, image_info in enumerate(images):
+                    #image_info -> (w,h,id,filename)
+                    
+                    image_details = _image_info_to_example(image_info, image_dir)
+                    
+                    # Get images unit id
+                    annotations = image_id_to_annotations[image_info["id"]]
+                    
+                    objects = []
+                    
+                    # Add the annotation information to the image details
+                    for annotation in annotations:                  
+                        objects.append(annotation)
+                    
+                    # nested dictionary
+                    image_details["objects"] = objects
+                    
+                    yield (image_id, image_details)

hugging_face_dataset/trainer_example.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5a6c5d60-6e65-4a51-a0cf-81f96d540d05",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "instance_type": "ml.t3.medium",
+  "kernelspec": {
+   "display_name": "Python 3 (PyTorch 1.6 Python 3.6 CPU Optimized)",
+   "language": "python",
+   "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-east-1:081325390199:image/pytorch-1.6-cpu-py36-ubuntu16.04-v1"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}