speed
Browse files- soybean_dataset.py +22 -48
soybean_dataset.py
CHANGED
@@ -117,69 +117,43 @@ class SoybeanDataset(datasets.GeneratorBasedBuilder):
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name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["valid"]}),
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]
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def process_image(self,
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# Open the image from the downloaded bytes and return the PIL Image
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img = Image.open(BytesIO(response.content))
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return img
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def _generate_examples(self, filepath):
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#"""Yields examples as (key, example) tuples."""
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logging.info("generating examples from = %s", filepath)
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with open(filepath, encoding="utf-8") as f:
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data = csv.DictReader(f)
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for row in data:
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# Assuming the 'original_image' column has the full path to the image file
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unique_id = row['unique_id']
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original_image_path = row['original_image']
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segmentation_image_path = row['segmentation_image']
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"unique_id": unique_id,
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"sets": sets,
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"original_image": original_image,
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"segmentation_image": segmentation_image,
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# ... add other features if necessary
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}
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# for row in data:
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# # Assuming the 'original_image' column has the full path to the image file
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# unique_id = row['unique_id']
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# original_image_path = row['original_image']
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# segmentation_image_path = row['segmentation_image']
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# sets = row['sets']
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# original_image_array = self.process_image(original_image_path)
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# segmentation_image_array = self.process_image(segmentation_image_path)
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# # Here you need to replace 'initial_radius', 'final_radius', 'initial_angle', 'final_angle', 'target'
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# # with actual columns from your CSV or additional processing you need to do
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# yield row['unique_id'], {
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# "unique_id": unique_id,
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# "sets": sets,
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# "original_image": original_image_array,
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# "segmentation_image": segmentation_image_array,
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# # ... add other features if necessary
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# }
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name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["valid"]}),
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]
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def process_image(self, image_path):
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# Load the image from the local filesystem
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img = Image.open(image_path)
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return img
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def _generate_examples(self, filepath):
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logging.info("generating examples from = %s", filepath)
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with open(filepath, encoding="utf-8") as f:
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data = csv.DictReader(f)
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for row in data:
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unique_id = row['unique_id']
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original_image_path = row['original_image'] # Adjust this path if necessary
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segmentation_image_path = row['segmentation_image'] # Adjust this path if necessary
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# Check if image exists locally before loading
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if os.path.exists(original_image_path):
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original_image = self.process_image(original_image_path)
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else:
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logging.error(f"Original image not found: {original_image_path}")
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continue # or handle missing image appropriately
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if os.path.exists(segmentation_image_path):
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segmentation_image = self.process_image(segmentation_image_path)
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else:
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logging.error(f"Segmentation image not found: {segmentation_image_path}")
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continue # or handle missing image appropriately
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yield unique_id, {
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"unique_id": unique_id,
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"sets": row['sets'],
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"original_image": original_image,
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"segmentation_image": segmentation_image,
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# ... add other features if necessary
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}
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