dataset/recover.py

@@ -1,4 +1,3 @@
-from concurrent.futures import ThreadPoolExecutor
 import random
 import cv2
 import numpy as np
@@ -149,53 +148,10 @@ def process_images(input_folder, background_image_path, output_base):
             except Exception as e:
                 print(f"Error processing {input_path}: {str(e)}")
 
-def process_single_file(input_path, output_path):
-    """处理单个文件的独立函数"""
-    try:
-        result = remove_background(input_path)
-        # result = edge_fill2(result)  # 按需启用
-        cv2.imwrite(output_path, result)
-        print(f"Processed: {input_path} -> {output_path}")
-    except Exception as e:
-        print(f"Error processing {input_path}: {str(e)}")
-
-def process_imageswithpool(input_folder, background_image_path, output_base):
-    """
-    多线程版本的处理函数
-    使用ThreadPoolExecutor并行处理文件
-    """
-    with ThreadPoolExecutor(max_workers=os.cpu_count()*2) as executor:
-        futures = []
-        for root, dirs, files in os.walk(input_folder):
-            # 创建输出目录（主线程保证目录创建顺序）
-            relative_path = os.path.relpath(root, input_folder)
-            output_dir = os.path.join(output_base, relative_path)
-            os.makedirs(output_dir, exist_ok=True)
-
-            # 提交任务到线程池
-            for filename in files:
-                if not filename.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp')):
-                    continue
-                
-                input_path = os.path.join(root, filename)
-                output_path = os.path.join(output_dir, filename)
-                futures.append(executor.submit(
-                    process_single_file, 
-                    input_path,
-                    output_path
-                ))
-
-        # 可选：等待所有任务完成并处理异常
-        for future in futures:
-            try:
-                future.result()
-            except Exception as e:
-                print(f"Unhandled error in thread: {str(e)}")
-
 
 # 使用示例
 input_directory = 'L:/Grade_datasets/JY_A'
 background_image_path = 'F:/dataset/02.TA_EC/rundata/BACKGROUND/ZY_B'
 output_directory = 'L:/Grade_datasets/MOVE_BACKGROUND'
 
-process_imageswithpool(input_directory, background_image_path, output_directory)
+process_images(input_directory, background_image_path, output_directory)

dataset/splitdataset.py

@@ -1,42 +0,0 @@
-import os
-import random
-import shutil
-
-def split_dataset(input_folder, output_folder):
-    # 创建输出子目录
-    subdatasets = [os.path.join(output_folder, f'subdataset_{i+1}') for i in range(3)]
-    for subdataset in subdatasets:
-        os.makedirs(subdataset, exist_ok=True)
-
-    # 遍历每个类别文件夹
-    for root, dirs, _ in os.walk(input_folder):
-        for category in dirs:
-            category_folder = os.path.join(root, category)
-            images = [os.path.join(category_folder, f) for f in os.listdir(category_folder)
-                      if f.endswith(('.png', '.jpg', '.jpeg', '.bmp', '.tiff'))]
-
-            # 如果图像数量少于 1200，进行补充
-            if len(images) < 1200:
-                images.extend(random.choices(images, k=1200 - len(images)))
-
-            # 打乱图像顺序
-            random.shuffle(images)
-
-            # 为每个子数据集创建类别子文件夹
-            for subdataset_path in subdatasets:
-                category_subfolder = os.path.join(subdataset_path, category)
-                os.makedirs(category_subfolder, exist_ok=True)
-
-            # 将图像分配到子数据集中，每个子数据集 400 张
-            for i, image_path in enumerate(images):
-                subdataset_index = i // 400
-                subdataset_path = subdatasets[subdataset_index]
-                category_subfolder = os.path.join(subdataset_path, category)
-                shutil.copy(image_path, category_subfolder)
-
-    print(f'Dataset split into 3 subdatasets with 400 images per category at {output_folder}')
-
-if __name__ == "__main__":
-    input_folder = 'L:/Grade_datasets/MOVE_BACKGROUND'
-    output_folder = 'L:/Grade_datasets/SPLIT'
-    split_dataset(input_folder, output_folder)