90 lines
3.4 KiB
Python
Executable File
90 lines
3.4 KiB
Python
Executable File
import os
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import json
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import uuid
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import shutil
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import argparse
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import tqdm
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from functools import partial
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from concurrent.futures import ThreadPoolExecutor
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from helper import category2yaml2json
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from lmm_utils.predict_garmentcode_picture import Predictor
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def search_picture_files(directory):
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"""Search for all image files in the directory"""
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picture_files = []
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for root, _, files in os.walk(directory):
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for file in files:
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if file.lower().endswith(('.jpg', '.png', '.jpeg', '.gif')):
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picture_files.append(os.path.join(root, file))
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return picture_files
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def main(input_folder_path, output_folder_path,sim_bool=False):
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dsl_ga = Predictor()
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all_picture_files = search_picture_files(input_folder_path)
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input_output_list = []
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uuid_list = []
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for input_picture_path in all_picture_files:
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output_json_path = input_picture_path.replace(input_folder_path, output_folder_path)
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output_json_dir = os.path.dirname(output_json_path)
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json_file_name = os.path.splitext(os.path.basename(input_picture_path))[0]
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output_json_path = os.path.join(output_json_dir, json_file_name, json_file_name + '.json')
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input_output_list.append((input_picture_path, output_json_path))
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threadPool = ThreadPoolExecutor(max_workers=5, thread_name_prefix="img_thread")
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futures = []
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for i, (input_picture_path, output_json_path) in tqdm.tqdm(enumerate(input_output_list)):
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if os.path.exists(output_json_path):
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continue
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item_id = str(uuid.uuid4())
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uuid_list.append(item_id)
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task = partial(
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category2yaml2json,
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category='picture',
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category_data=input_picture_path,
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final_json_path=output_json_path,
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id=item_id,
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model='Qwen/Qwen2.5-VL-72B-Instruct',
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base_url='https://api-inference.modelscope.cn/v1/',
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api_key='108a28f0-de01-4c43-b189-6cad25d32990',
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dsl_ga=dsl_ga,
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sim_bool=sim_bool
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)
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future = threadPool.submit(task)
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futures.append((future, input_picture_path))
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print(i, input_picture_path, output_json_path)
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fail_picture_list = []
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for future, input_picture_path in futures:
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try:
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result = future.result()
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print(f"Task result: {result}")
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except Exception as e:
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fail_picture_list.append(input_picture_path)
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print(f"Task failed: {e}")
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# Clean up the temporary folder
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for _uuid in uuid_list:
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try:
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shutil.rmtree(f"user_data/temp_user_folder_for{_uuid}gpt")
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except Exception as e:
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print(f"Error when deleting temp folder: {e}")
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pass
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threadPool.shutdown(wait=True)
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# Save the list of failures
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fail_dir = f"user_data/fail_{os.path.basename(input_folder_path)}"
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os.makedirs(fail_dir, exist_ok=True)
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with open(os.path.join(fail_dir, "fail_picture_list.json"), "w") as file:
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json.dump(fail_picture_list, file, indent=4)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Image-to-GarmentCode generation script")
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parser.add_argument('--input', type=str, required=True, help='Input image folder path')
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parser.add_argument('--output', type=str, required=True, help='Output folder path')
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parser.add_argument('--sim', type=bool, default=False, help='Enable simulation mode (default: False)')
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args = parser.parse_args()
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main(args.input, args.output,args.sim)
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