Update Populus_Stomatal_Images_Datasets.py

Populus_Stomatal_Images_Datasets.py

@@ -1,20 +1,3 @@
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# 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.
-# TODO: Address all TODOs and remove all explanatory comments
-"""TODO: Add a description here."""
-
-
 import csv
 import json
 import os
@@ -24,62 +7,31 @@ import pandas as pd
 import datasets
 
 
-# TODO: Add BibTeX citation
-# Find for instance the citation on arxiv or on the dataset repo/website
+
 _CITATION = """\
-@InProceedings{huggingface:dataset,
-title = {A great new dataset},
-author={huggingface, Inc.
-},
-year={2020}
-}
+@article{nature},
+  title={Labeled temperate hardwood tree stomatal image datasets from seven taxa of Populus and 17 hardwood species},
+  author={Jiaxin Wang, Heidi J. Renninger and Qin Ma},
+  journal={Sci Data 11, 1 (2024)},
+  year={2024}
 """
 
-# TODO: Add description of the dataset here
-# You can copy an official description
 _DESCRIPTION = """\
-This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
+This new dataset is designed to solve image classification and segmentation tasks and is crafted with a lot of care.
 """
 
-# TODO: Add a link to an official homepage for the dataset here
-_HOMEPAGE = ""
+_HOMEPAGE = "https://zenodo.org/records/8271253"
 
-# TODO: Add the licence for the dataset here if you can find it
-_LICENSE = ""
 
-# TODO: Add link to the official dataset URLs here
-# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
-# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
-# _URLS = {
-#     "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
-#     "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
-# }
+_LICENSE = "https://creativecommons.org/licenses/by/4.0/"
 
 
-# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
 class NewDataset(datasets.GeneratorBasedBuilder):
     """TODO: Short description of my dataset."""
 
     VERSION = datasets.Version("1.1.0")
 
-    # This is an example of a dataset with multiple configurations.
-    # If you don't want/need to define several sub-sets in your dataset,
-    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
-
-    # If you need to make complex sub-parts in the datasets with configurable options
-    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
-    # BUILDER_CONFIG_CLASS = MyBuilderConfig
-
-    # You will be able to load one or the other configurations in the following list with
-    # data = datasets.load_dataset('my_dataset', 'first_domain')
-    # data = datasets.load_dataset('my_dataset', 'second_domain')
-    # BUILDER_CONFIGS = [
-    #     datasets.BuilderConfig(name="first_domain", version=VERSION, description="This part of my dataset covers a first domain"),
-    #     datasets.BuilderConfig(name="second_domain", version=VERSION, description="This part of my dataset covers a second domain"),
-    # ]
-
-    # DEFAULT_CONFIG_NAME = "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.
-
+    
     def _info(self):
         features = datasets.Features({
             "image_id": datasets.Value("string"),
@@ -125,7 +77,6 @@ class NewDataset(datasets.GeneratorBasedBuilder):
         # Get all image filenames
         all_image_filenames = species_info['FileName'].apply(lambda x: x + '.jpg').tolist()
         
-        # No longer need to randomize and split the dataset
         return [datasets.SplitGenerator(
             name=datasets.Split.TRAIN,
             gen_kwargs={
@@ -136,8 +87,6 @@ class NewDataset(datasets.GeneratorBasedBuilder):
         )]
     
 
-
-
     def _parse_yolo_labels(self, label_path, width, height):
         annotations = []
         with open(label_path, 'r') as file:
@@ -178,13 +127,8 @@ class NewDataset(datasets.GeneratorBasedBuilder):
                 # Default values if not found
                 species = None
                 scientific_name = None
-                width = 1024  # Default value
-                height = 768   # Default value
-    
-            # pics_array = None
-            # with Image.open(image_path) as img:
-            #     pics_array = np.array(img)# Convert the PIL image to a numpy array and then to a list
-            #     # print(pics_array.shape)
+                width = 1024 
+                height = 768   
             
             annotations = self._parse_yolo_labels(label_path, width, height)
             
@@ -198,50 +142,4 @@ class NewDataset(datasets.GeneratorBasedBuilder):
                 "image": img,
                 "image_resolution": {"width": width, "height": height},
                 "annotations": annotations
-            }
-
-
-      
-
-    # def _generate_examples(self, filepaths, species_info, data_dir, annotations_file):
-    #     """Yields examples as (key, example) tuples."""
-    #     # Load annotations from JSON file
-    #     with open(annotations_file, 'r') as file:
-    #         annotations_dict = json.load(file)
-
-    #     for file_name in filepaths:
-    #         image_id = os.path.splitext(file_name)[0]  # Extract the base name without the file extension
-    #         image_path = os.path.join(data_dir, f"{image_id}.jpg")
-            
-    #         # Find the corresponding row in the CSV for the current image
-    #         species_row = species_info.loc[species_info['FileName'] == image_id]
-    #         if not species_row.empty:
-    #             species = species_row['Species'].values[0]
-    #             scientific_name = species_row['ScientificName'].values[0]
-    #             width = species_row['Witdh'].values[0]  # Corrected field name from 'Witdh'
-    #             height = species_row['Heigth'].values[0]  # Corrected field name from 'Heigth'
-    #         else:
-    #             # Default values if not found
-    #             species = None
-    #             scientific_name = None
-    #             width = 1024  # Default value
-    #             height = 768   # Default value
-
-    #         # pics_array = None
-    #         # with Image.open(image_path) as img:
-    #         #     pics_array = np.array(img)  # Convert the PIL image to a numpy array
-
-    #         # Retrieve annotations for the current image from the dictionary
-    #         annotations = annotations_dict.get(image_id, [])
-
-    #         # Yield the dataset example
-    #         yield image_id, {
-    #             "image_id": image_id,
-    #             "species": species,
-    #             "scientific_name": scientific_name,
-    #             #"pics_array": pics_array.tolist(),  # Convert numpy array to list for JSON serializability
-    #             "image_path": image_path,
-    #             "image": img,
-    #             "image_resolution": {"width": width, "height": height},
-    #             "annotations": annotations
-    #         }
+            }