Upload WaterFlowCountersRecognition.py

WaterFlowCountersRecognition.py

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+import json
+import os
+import collections
+from PIL import Image
+
+import datasets
+
+
+_CITATION = """\
+@SIA86{huggingface:dataset,
+title = {WaterFlowCountersRecognition dataset},
+author={SIA86},
+year={2023}
+}
+"""
+
+_DESCRIPTION = """\
+This dataset is designed to detect digital data from water flow counters photos.
+"""
+_HOMEPAGE = "https://github.com/SIA86/WaterFlowRecognition"
+
+_REGION_NAME = ['value_a', 'value_b', 'serial']
+
+_REGION_ROTETION = ['0', '90', '180', '270']
+
+_ANNOTATION_FILENAME = '_annotations.json'
+
+
+class WaterFlowCounterConfig(datasets.BuilderConfig):
+    """Builder Config for WaterFlowCounter"""
+
+    def __init__(self, data_url, **kwargs):
+        """BuilderConfig for WaterFlowCounter.
+        Args:
+          data_url: `string`, url to download the photos.
+          metadata_urls: instance segmentation regions and description 
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(WaterFlowCounterConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
+        self.data_url = data_url
+
+
+class WaterFlowCounter(datasets.GeneratorBasedBuilder):
+    """WaterFlowCounter Images dataset"""
+
+    BUILDER_CONFIGS = [
+        WaterFlowCounterConfig(
+            name="WFCR_full",
+            description="Full dataset which contains coordinates and names of regions and information about rotation",
+            data_url={
+                "train": "https://huggingface.co/datasets/SIA86/WaterFlowCountersRecognition/blob/main/data/train_photos.zip",
+                "test": "https://huggingface.co/datasets/SIA86/WaterFlowCountersRecognition/blob/main/data/test_photos.zip",
+            }
+        )
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                
+                "image": datasets.Image(),
+                "regions": datasets.Sequence(
+                    {                        
+                        "all_points_x": datasets.Sequence(datasets.Value("int64")),
+                        "all_points_y": datasets.Sequence(datasets.Value("int64")),                                                                      
+                        "name": datasets.ClassLabel(names=_REGION_NAME, num_classes=3),
+                        "rotated": datasets.ClassLabel(names=_REGION_ROTETION, num_classes=4)
+                    }         
+                )
+            }
+        )
+
+        return datasets.DatasetInfo(
+            features=features,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_files = dl_manager.download_and_extract(self.config.data_urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "folder_dir": data_files["train"],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "folder_dir": data_files["test"],
+                },
+            )
+        ]
+
+    def generate_examples(self, folder_dir):
+        name_to_id = {}
+        rotation_to_id = {}
+
+        for indx, name in enumerate(_REGION_NAME):
+            name_to_id[name] = indx
+
+        for indx, name in enumerate(_REGION_ROTETION):
+            rotation_to_id[name] = indx
+
+        with open('_annotations.json', 'r', encoding='utf-8') as fr:
+            json_file = json.load(fr)
+
+        for file in os.listdir(folder_dir):
+            filepath = os.path.join(folder_dir, file)
+
+            with open(filepath, "rb") as f:
+                image_bytes = f.read()
+
+            all_x = []
+            all_y = []
+            names = []
+            
+            for el in json_file['_via_img_metadata']:
+                if json_file['_via_img_metadata'][el]['filename'] == file:
+                    for region in json_file['_via_img_metadata'][el]['regions']:
+                        all_x.append(region['shape_attributes']['all_points_x'])
+                        all_y.append(region['shape_attributes']['all_points_y'])
+                        names.append(name_to_id[list(region['region_attributes']['name'].keys())[0]])
+                        rotated = rotation_to_id[list(region['region_attributes']['rotated'].keys())[0]]
+
+                    yield idx, {       
+                        "image": {"path": filepath, "bytes": image_bytes},
+                        "regions": {
+                            "all_points_x": all_x,
+                            "all_points_y": all_y,                                                                      
+                            "name":names,
+                            "rotated": rotated
+                        }
+                    }
+                    idx += 1
+