Update new_dataset_script.py
Browse files- new_dataset_script.py +65 -82
new_dataset_script.py
CHANGED
@@ -79,40 +79,33 @@ class NewDataset(datasets.GeneratorBasedBuilder):
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DEFAULT_CONFIG_NAME = "first_domain" # It's not mandatory to have a default configuration. Just use one if it make sense.
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def _info(self):
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# Homepage of the dataset for documentation
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homepage=_HOMEPAGE,
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# License for the dataset if available
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license=_LICENSE,
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# Citation for the dataset
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citation=_CITATION,
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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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@@ -173,64 +166,54 @@ class NewDataset(datasets.GeneratorBasedBuilder):
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]
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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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annotations.append({
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"category_id": int(class_id),
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"bounding_box": {
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"
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}
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})
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return annotations
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DEFAULT_CONFIG_NAME = "first_domain" # It's not mandatory to have a default configuration. Just use one if it make sense.
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def _info(self):
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features = datasets.Features({
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"image_id": datasets.Value("string"),
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"species": datasets.Value("string"),
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"scientific_name": datasets.Value("string"),
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"pics_array": datasets.Array3D(dtype="uint8", shape=(3, 768, 1024)), # Assuming images are RGB with shape 768x1024
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"image_resolution": {
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"width": datasets.Value("int32"),
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"height": datasets.Value("int32"),
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},
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"annotations": datasets.Sequence({
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"category_id": datasets.Value("int32"),
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"bounding_box": {
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"x_min": datasets.Value("float32"),
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"y_min": datasets.Value("float32"),
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"x_max": datasets.Value("float32"),
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"y_max": datasets.Value("float32"),
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},
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}),
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})
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=features, # Here we define them because they are different between the two configurations
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homepage=_HOMEPAGE,
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license=_LICENSE,
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citation=_CITATION,
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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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),
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]
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# ... other necessary imports and class definitions
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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_min = (x_center_rel - width_rel / 2) * width
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y_min = (y_center_rel - height_rel / 2) * height
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x_max = (x_center_rel + width_rel / 2) * width
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y_max = (y_center_rel + height_rel / 2) * 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_min": x_min,
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"y_min": y_min,
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"x_max": x_max,
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"y_max": y_max
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}
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})
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return annotations
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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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image_id = os.path.splitext(file_name)[0] # Extract the base name without the file extension
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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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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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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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annotations = self._parse_yolo_labels(label_path, width, height)
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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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