Update soybean_dataset.py

soybean_dataset.py

@@ -121,184 +121,58 @@ class SoybeanDataset(datasets.GeneratorBasedBuilder):
                 name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["valid"]}),
         ]
 
-# 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
-from typing import List
-import datasets
-import logging
-import csv
-import numpy as np
-from PIL import Image
-import os
-import io
-import pandas as pd
-import matplotlib.pyplot as plt
-from numpy import asarray
-import requests
-from io import BytesIO
-from numpy import asarray
-
-
-# TODO: Add BibTeX citation
-# Find for instance the citation on arxiv or on the dataset repo/website
-_CITATION = """\
-@article{chen2023dataset,
-  title={A dataset of the quality of soybean harvested by mechanization for deep-learning-based monitoring and analysis},
-  author={Chen, M and Jin, C and Ni, Y and Yang, T and Xu, J},
-  journal={Data in Brief},
-  volume={52},
-  pages={109833},
-  year={2023},
-  publisher={Elsevier},
-  doi={10.1016/j.dib.2023.109833}
-}
 
-"""
+        def __init__(self, max_workers=5):
+        # Initialize a ThreadPoolExecutor with the desired number of workers
+        self.executor = ThreadPoolExecutor(max_workers=max_workers)
 
-# TODO: Add description of the dataset here
-# You can copy an official description
-_DESCRIPTION = """\
-This dataset contains images captured during the mechanized harvesting of soybeans, aimed at facilitating the development of machine vision and deep learning models for quality analysis. It contains information of original soybean pictures in different forms, labels of whether the soybean belongs to training, validation, or testing datasets, segmentation class of soybean pictures in one dataset.
-"""
+    def process_image(self, image_url):
+        # This function is now a static method that doesn't need self
+        response = requests.get(image_url)
+        response.raise_for_status()  # This will raise an exception if there is a download error
+        img = Image.open(BytesIO(response.content))
+        return img
 
-# TODO: Add a link to an official homepage for the dataset here
-_HOMEPAGE = "https://huggingface.co/datasets/lisawen/soybean_dataset"
-
-# TODO: Add the licence for the dataset here if you can find it
-_LICENSE = "Under a Creative Commons 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)
-_URL = "/content/drive/MyDrive/sta_663/soybean/dataset.csv"
-_URLs = {
-    "train" : "https://raw.githubusercontent.com/lisawen0707/soybean/main/train_dataset.csv",
-    "test": "https://raw.githubusercontent.com/lisawen0707/soybean/main/test_dataset.csv",
-    "valid": "https://raw.githubusercontent.com/lisawen0707/soybean/main/valid_dataset.csv"
-}
-
-# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
-class SoybeanDataset(datasets.GeneratorBasedBuilder):
-    """TODO: Short description of my dataset."""
-
-    _URLs = _URLs
-    VERSION = datasets.Version("1.1.0")
-
-    def _info(self):
-      # raise ValueError('woops!')
-      return datasets.DatasetInfo(
-          description=_DESCRIPTION,
-          features=datasets.Features(
-              {
-                  "unique_id": datasets.Value("string"),
-                  "sets": datasets.Value("string"),
-                  "original_image": datasets.Image(),
-                  "segmentation_image": datasets.Image(),
-                  
-              }
-          ),
-          # No default supervised_keys (as we have to pass both question
-          # and context as input).
-          supervised_keys=("original_image","segmentation_image"),
-          homepage="https://github.com/lisawen0707/soybean/tree/main",
-          citation=_CITATION,
-      )
-
-    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
-        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
-        # Since the dataset is on Google Drive, you need to implement a way to download it using the Google Drive API.
-
-        # The path to the dataset file in Google Drive
-        urls_to_download = self._URLs
-        downloaded_files = dl_manager.download_and_extract(urls_to_download)
-
-        # Since we're using a local file, we don't need to download it, so we just return the path.
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),    
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["valid"]}),
-        ]
-
-    def download_image(self, image_url):
-        try:
-            response = requests.get(image_url)
-            response.raise_for_status()  # This will raise an exception for HTTP errors
-            img = Image.open(BytesIO(response.content))
-            return img
-        except requests.RequestException as e:
-            logging.error(f"Error downloading {image_url}: {e}")
-            return None
-
-    def download_images_in_batch(self, image_urls):
-        images = {}
-        with ThreadPoolExecutor() as executor:
-            future_to_url = {executor.submit(self.download_image, url): url for url in image_urls}
-            for future in as_completed(future_to_url):
-                url = future_to_url[future]
-                try:
-                    image = future.result()
-                    if image:
-                        images[url] = image
-                except Exception as e:
-                    logging.error(f"Error processing {url}: {e}")
-        return images
+    def download_images(self, image_urls):
+        # Use the executor to download images concurrently
+        # and return a future to image map
+        future_to_url = {self.executor.submit(self.process_image, url): url for url in image_urls}
+        return future_to_url
 
     def _generate_examples(self, filepath):
-        logging.info(f"Generating examples from = {filepath}")
+        logging.info("generating examples from = %s", filepath)
 
         with open(filepath, encoding="utf-8") as f:
             data = csv.DictReader(f)
-            image_urls = [row['original_image'] for row in data] + [row['segmentation_image'] for row in data]
-            # Remove duplicates and None values
-            image_urls = list(set(filter(None, image_urls)))
 
-            # Download images in batch
-            images = self.download_images_in_batch(image_urls)
+            # Create a set to collect all unique image URLs to download
+            image_urls = {row['original_image'] for row in data}
+            image_urls.update(row['segmentation_image'] for row in data)
+            
+            # Start the batch download
+            future_to_url = self.download_images(image_urls)
 
-            # Reset file pointer to the beginning to iterate again
+            # Reset the file pointer to the start for the second pass
             f.seek(0)
-            data = csv.DictReader(f)
+            next(data)  # Skip header
 
             for row in data:
                 unique_id = row['unique_id']
                 original_image_url = row['original_image']
                 segmentation_image_url = row['segmentation_image']
+                sets = row['sets']
 
-                original_image = images.get(original_image_url)
-                segmentation_image = images.get(segmentation_image_url)
-
-                if not original_image or not segmentation_image:
-                    logging.warning(f"Missing image for {unique_id}, skipping example.")
-                    continue
+                # Wait for the individual image futures to complete and get the result
+                original_image = future_to_url[self.executor.submit(self.process_image, original_image_url)].result()
+                segmentation_image = future_to_url[self.executor.submit(self.process_image, segmentation_image_url)].result()
 
                 yield unique_id, {
                     "unique_id": unique_id,
-                    "sets": row['sets'],
+                    "sets": sets,
                     "original_image": original_image,
                     "segmentation_image": segmentation_image,
                     # ... add other features if necessary
-                } 
+                }