Update Graptolodiea-Speciemens-Imaging.py

Graptolodiea-Speciemens-Imaging.py

@@ -6,63 +6,115 @@ from typing import List
 import datasets
 import pandas as pd
 import numpy as np
+import csv
+import logging
+from PIL import Image
+import ast
 
 _CITATION = """\
-
-"""
-
-
+111
+""" 
 
 _DESCRIPTION = """\
-Dataset Description:
-The Graptoloidea Specimens Imaging dataset is a curated collection of over 1,300 image-text pairs, focusing on Graptoloidea specimens. It encompasses detailed attributes such as species classification, geological stages, and specific locality information (with coordinates), complemented by high-quality images of each specimen. This dataset serves as a valuable resource for paleontological research, offering insights into the morphological diversity and geological distribution of Graptoloidea.
-
-Highlights:
-- Comprehensive Collection: Over 1,300 unique specimens, each with a corresponding high-quality image and descriptive text.
-- Diverse Geological Coverage: Specimens span different geological stages, offering a timeline of the Graptoloidea evolution.
-- Rich Annotations: Apart from visual data, the dataset includes detailed taxonomic classification, geological context, and precise locality information.
-- Research-Ready: Ideal for tasks like paleontological classification, morphological analysis, age estimation, and geographical distribution studies.
-- Educational Value: Serves as an invaluable resource for educational and outreach programs, providing tangible insights into paleontology.
+[Your dataset description here...]
 """
 
-
 _HOMEPAGE = "https://zenodo.org/records/6194943"
-
-
-_license = ""
-
-
-_URLS = {
-    "part1": "https://zenodo.org/records/6194943/files/graptolite%20specimens%20with%20scale.zip.001?download=1",
-    "part2": "https://zenodo.org/records/6194943/files/graptolite%20specimens%20with%20scale.zip.002?download=1",
-}
-
+_license = "111"
 
 class GraptoloideaSpecimensDataset(datasets.GeneratorBasedBuilder):
-    """Imaging for graptoloidea specimens with extra information"""
-
+    _URL = "https://raw.githubusercontent.com/LeoZhangzaolin/photos/main/Final_GS_with_Images.csv"
 
     def _info(self):
-    return datasets.DatasetInfo(
-        description=_DESCRIPTION,
-        features=datasets.Features(
-            {
-                "Suborder": datasets.Value("string"),
-                "Infraorder": datasets.Value("string"),
-                "Family (Subfamily)": datasets.Value("string"),
-                "Genus": datasets.Value("string"),
-                "Tagged Species Name": datasets.Value("string"),
-                "Image": datasets.Value("string"),
-                "Stage": datasets.Value("string"),
-                "Mean Age Value": datasets.Value("float64"),
-                "Locality (Longitude, Latitude, Horizon)": datasets.Value("string"),
-                "Reference (Specimens Firstly Published)": datasets.Value("string"),
-            }
-        ),
-        supervised_keys=None,
-        homepage=_HOMEPAGE,
-        citation=_CITATION,
-    )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "Suborder": datasets.Value("string"),
+                    "Infraorder": datasets.Value("string"),
+                    "Family (Subfamily)": datasets.Value("string"),
+                    "Genus": datasets.Value("string"),
+                    "Tagged Species Name": datasets.Value("string"),
+                    "Image": datasets.Value("string"),
+                    "Stage": datasets.Value("string"),
+                    "Mean Age Value": datasets.Value("float64"),
+                    "Locality (Longitude, Latitude, Horizon)": datasets.Value("string"),
+                    "Reference (Specimens Firstly Published)": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        downloaded_file = dl_manager.download_and_extract(self._URL)
+        
+        # Read the CSV file
+        df = pd.read_csv(downloaded_file)
+        df = df.sample(frac=1).reset_index(drop=True)  # Shuffle the dataset
+
+        # Splitting the dataset
+        train_size = int(0.7 * len(df))
+        test_size = int(0.15 * len(df))
+
+        train_df = df[:train_size]
+        test_df = df[train_size:train_size + test_size]
+        validation_df = df[train_size + test_size:]
+
+        # Save split dataframes to temporary CSV files
+        train_file = '/tmp/train_split.csv'
+        test_file = '/tmp/test_split.csv'
+        validation_file = '/tmp/validation_split.csv'
+        
+        train_df.to_csv(train_file, index=False)
+        test_df.to_csv(test_file, index=False)
+        validation_df.to_csv(validation_file, index=False)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_file}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_file}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": validation_file}),
+        ]
+
+    def _generate_examples(self, filepath):
+        """This function returns the examples from the CSV file."""
+        logging.info("generating examples from = %s", filepath)
+        with open(filepath, encoding='utf-8') as f:
+            reader = csv.DictReader(f)
+            key = 0
+            for row in reader:
+                key += 1
+                # Extracting data from each column
+                suborder = row['Suborder'].strip()
+                infraorder = row['Infraorder'].strip()
+                family_subfamily = row['Family (Subfamily)'].strip()
+                genus = row['Genus'].strip()
+                species_name = row['tagged species name'].strip()
+                image = row['image'].strip()
+                stage = row['Stage'].strip()
+                mean_age = row['mean age value']
+                locality = row['Locality (Longitude, Latitude, Horizon)'].strip()
+                reference = row['Reference (specimens firstly published)'].strip()
+
+                # Constructing the example
+                yield key, {
+                    "Suborder": suborder,
+                    "Infraorder": infraorder,
+                    "Family (Subfamily)": family_subfamily,
+                    "Genus": genus,
+                    "Tagged Species Name": species_name,
+                    "Image": image,
+                    "Stage": stage,
+                    "Mean Age Value": mean_age,
+                    "Locality (Longitude, Latitude, Horizon)": locality,
+                    "Reference (Specimens Firstly Published)": reference,
+                }
+
+
+        
+    
+