Update new_dataset_script.py
Browse files- new_dataset_script.py +113 -106
new_dataset_script.py
CHANGED
@@ -115,115 +115,122 @@ class NewDataset(datasets.GeneratorBasedBuilder):
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def _split_generators(self, dl_manager):
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# Download and extract the dataset using Hugging Face's datasets library
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data_files = dl_manager.download_and_extract({
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"csv": "https://huggingface.co/datasets/XintongHe/Populus_Stomatal_Images_Datasets/resolve/main/Labeled Stomatal Images.csv",
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"zip": "https://huggingface.co/datasets/XintongHe/Populus_Stomatal_Images_Datasets/resolve/main/Labeled Stomatal Images.zip"
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})
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# Load the CSV file containing species and scientific names
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species_info = pd.read_csv(data_files["csv"])
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all_files = species_info['FileName'].tolist()
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#
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val_split_end = train_split_end + int(num_files * 0.15)
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image_path = os.path.join(data_dir, f"{image_id}.jpg")
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# Construct the full label file path
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label_path = os.path.join(data_dir, f"{image_id}.txt")
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# Open image and convert to array
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with Image.open(image_path) as img:
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pics_array = np.array(img)
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width, height = img.size
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# Extract species and scientific name from CSV file
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species_row = species_info.loc[species_info['FileName'] == image_id]
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species = species_row['Species'].values[0]
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scientific_name = species_row['ScientificName'].values[0]
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# Parse YOLO annotations
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annotations = self._parse_yolo_labels(label_path, width, height)
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# Yield the final structured example
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yield image_id, {
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"image_id": image_id,
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"species": species,
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"scientific_name": scientific_name,
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"pics_array": pics_array,
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"image_resolution": {"width": width, "height": height},
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"annotations": annotations
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}
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annotations = []
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with open(label_path, 'r') as file:
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yolo_data = file.readlines()
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for line in yolo_data:
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class_id, x_center_rel, y_center_rel, width_rel, height_rel = map(float, line.split())
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annotations.append({
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"category_id": int(class_id),
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"bounding_box": {
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"x_center": x_center_rel * width,
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"y_center": y_center_rel * height,
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"width": width_rel * width,
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"height": height_rel * height
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}
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})
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return annotatio
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)
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def _split_generators(self, dl_manager):
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# Download and extract the dataset using Hugging Face's datasets library
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data_files = dl_manager.download_and_extract({
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"csv": "https://huggingface.co/datasets/XintongHe/Populus_Stomatal_Images_Datasets/resolve/main/Labeled Stomatal Images.csv",
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"zip": "https://huggingface.co/datasets/XintongHe/Populus_Stomatal_Images_Datasets/resolve/main/Labeled Stomatal Images.zip"
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})
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# Load the CSV file containing species and scientific names
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species_info = pd.read_csv(data_files["csv"])
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# The directory 'Labeled Stomatal Images' is where the images and labels are stored after extraction
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extracted_images_path = os.path.join(data_files["zip"], "Labeled Stomatal Images")
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# Get the list of image filenames from the CSV
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all_image_filenames = species_info['FileName'].apply(lambda x: x + '.jpg').tolist()
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# Shuffle the list for random split
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random.seed(42) # Set a random seed for reproducibility
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random.shuffle(all_image_filenames)
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# Split the files into train/validation/test
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num_files = len(all_image_filenames)
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train_split_end = int(num_files * 0.7)
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val_split_end = train_split_end + int(num_files * 0.15)
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train_files = all_image_filenames[:train_split_end]
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val_files = all_image_filenames[train_split_end:val_split_end]
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test_files = all_image_filenames[val_split_end:]
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return [
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SplitGenerator(
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name=Split.TRAIN,
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gen_kwargs={
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"filepaths": train_files,
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"species_info": species_info,
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"data_dir": extracted_images_path,
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"split": "train",
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},
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),
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SplitGenerator(
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name=Split.VALIDATION,
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gen_kwargs={
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"filepaths": val_files,
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"species_info": species_info,
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"data_dir": extracted_images_path,
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"split": "validation",
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},
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),
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SplitGenerator(
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name=Split.TEST,
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gen_kwargs={
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"filepaths": test_files,
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"species_info": species_info,
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"data_dir": extracted_images_path,
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"split": "test",
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},
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),
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]
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import os
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from PIL import Image
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import numpy as np
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import pandas as pd
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# ... other necessary imports and class definitions
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def _generate_examples(self, filepaths, species_info, data_dir, split):
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"""Yields examples as (key, example) tuples."""
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for file_name in filepaths:
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# Extract the base name without the file extension for image_id
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image_id = os.path.splitext(file_name)[0]
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# Construct the full image file path and label file path
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image_path = os.path.join(data_dir, f"{image_id}.jpg")
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label_path = os.path.join(data_dir, f"{image_id}.txt")
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# Open image and convert to array
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with Image.open(image_path) as img:
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pics_array = np.array(img)
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width, height = img.size
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# Extract species and scientific name from CSV file
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species_row = species_info.loc[species_info['FileName'] == file_name]
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species = species_row['Species'].values[0] if not species_row.empty else None
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scientific_name = species_row['ScientificName'].values[0] if not species_row.empty else None
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# Parse YOLO annotations
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annotations = self._parse_yolo_labels(label_path, width, height)
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# Yield the final structured example
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yield image_id, {
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"image_id": image_id,
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"species": species,
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"scientific_name": scientific_name,
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"pics_array": pics_array,
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"image_resolution": {"width": width, "height": height},
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"annotations": annotations
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}
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def _parse_yolo_labels(self, label_path, width, height):
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annotations = []
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with open(label_path, 'r') as file:
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yolo_data = file.readlines()
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for line in yolo_data:
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class_id, x_center_rel, y_center_rel, width_rel, height_rel = map(float, line.split())
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x_center_abs = x_center_rel * width
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y_center_abs = y_center_rel * height
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bbox_width = width_rel * width
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bbox_height = height_rel * height
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annotations.append({
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"category_id": int(class_id),
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"bounding_box": {
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"x_center": x_center_abs,
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"y_center": y_center_abs,
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"width": bbox_width,
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"height": bbox_height
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}
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})
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return annotations
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