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import json
from datasets import Dataset, load_dataset
import math, os
# Dataset info structure:
# - task_type: string - Type of the task
# - key: string - Unique identifier for the sample
# - instruction: string - Task instruction/prompt
# - instruction_language: string - Language of the instruction
# - input_image: Image - Original input image
# - input_image_raw: Image - Raw/unprocessed input image
# - Intersection_exist: bool - Whether intersection exists
def calculate_dimensions(target_area, ratio):
width = math.sqrt(target_area * ratio)
height = width / ratio
width = round(width / 32) * 32
height = round(height / 32) * 32
new_area = width * height
if new_area < target_area:
width += 32
new_area = width * height
elif new_area > target_area:
width -= 32
new_area = width * height
return width, height, new_area
# Load dataset
dataset = load_dataset("stepfun-ai/GEdit-Bench")
save_path = "/path/to/save/directory"
# Dictionary to store instruction and image paths
instruction_image_paths = {}
for item in dataset['train']:
task_type = item['task_type']
key = item['key']
instruction = item['instruction']
instruction_language = item['instruction_language']
input_image = item['input_image']
input_image_raw = item['input_image_raw']
intersection_exist = item['Intersection_exist']
target_width, target_height, new_area = calculate_dimensions(512 * 512, input_image_raw.width / input_image_raw.height)
resize_input_image = input_image_raw.resize((target_width, target_height))
save_path_fullset_source_image = f"{save_path}/fullset/{task_type}/{instruction_language}/{key}_SRCIMG.png"
save_path_fullset = f"{save_path}/fullset/{task_type}/{instruction_language}/{key}.png"
relative_path = f"fullset/{task_type}/{instruction_language}/{key}.png"
# Create directories if they don't exist
os.makedirs(os.path.dirname(save_path_fullset_source_image), exist_ok=True)
os.makedirs(os.path.dirname(save_path_fullset), exist_ok=True)
# Save the images
input_image.save(save_path_fullset_source_image)
resize_input_image.save(save_path_fullset)
# Store instruction and corresponding image path in the dictionary
instruction_image_paths[key] = {
'prompt': instruction,
'id': relative_path,
'edit_type': task_type,
}
# Save the dictionary to a JSON file
json_file_path = "/path/to/save/instruction_image_paths.json"
with open(json_file_path, 'w') as json_file:
json.dump(instruction_image_paths, json_file, indent=4)
print(f"Instruction and image paths saved to {json_file_path}")
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