深度图数据增强-形态学腐蚀操作

形态学腐蚀，膨胀的介绍，见：

图像形态学操作-腐蚀与膨胀，开运算和闭运算（含简单代码演示）-CSDN博客https://blog.csdn.net/qq_54708219/article/details/148215779?spm=1001.2014.3001.5502在深度图中，有或多或少的孔洞，我们可以对孔洞进行腐蚀或膨胀操作以扩充数据集。

首先，要用连通分量标记算法对孔洞进行标记。以下展示用scipy库进行标记的代码，其底层原理见200. 岛屿数量 - 力扣（LeetCode）：

import numpy as np
from scipy.ndimage import label

region = [
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 1, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 1, 1, 1, 0, 0, 0, 0],
    [0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
    [0, 0, 0, 1, 1, 1, 1, 0, 0, 0],
    [0, 0, 0, 0, 1, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, 1, 1, 0, 0, 0, 0, 0, 1, 1],
    [0, 1, 0, 0, 0, 0, 0, 0, 0, 0]
]

region_np = np.array(region)
structure = np.ones((3, 3), dtype=int)
labeled_array, num_components = label(region_np, structure=structure)

components = []
for i in range(1, num_components + 1):
    y, x = np.where(labeled_array == i)
    # 将 np.int64 转换为普通整数
    components.append([(int(y[idx]), int(x[idx])) for idx in range(len(y))])

print(f"共有 {num_components} 个连通分量")
for i, comp in enumerate(components, 1):
    print(f"连通分量 {i} 的位置：")
    print(comp)
    
'''
共有 3 个连通分量
连通分量 1 的位置：
[(2, 2), (2, 3), (3, 2), (3, 3), (3, 4), (3, 5), (4, 2), (4, 3), (4, 4), (4, 5), (4, 6), (5, 3), (5, 4), (5, 5), (5, 6), (6, 4), (6, 5)]
连通分量 2 的位置：
[(8, 1), (8, 2), (9, 1)]
连通分量 3 的位置：
[(8, 8), (8, 9)]
'''

计算出连通分量以后，我们可以对每个连通分量单独进行腐蚀操作：

import numpy as np
from scipy.ndimage import label, binary_dilation

region = [
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 1, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 1, 1, 1, 0, 0, 0, 0],
    [0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
    [0, 0, 0, 1, 1, 1, 1, 0, 0, 0],
    [0, 0, 0, 0, 1, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, 1, 1, 0, 0, 0, 0, 0, 1, 1],
    [0, 1, 0, 0, 0, 0, 0, 0, 0, 0]
]

region_np = np.array(region)
structure = np.ones((3, 3), dtype=int)  # 8-连通结构元素

# 标记连通分量
labeled, num_components = label(region_np, structure=structure)

# 对每个连通分量单独膨胀，并收集结果
components = []
dilated_combined = np.zeros_like(region_np)  # 合并膨胀结果

for i in range(1, num_components + 1):
    # 提取当前连通分量的掩膜
    mask = (labeled == i).astype(int)
    
    # 执行膨胀
    dilated = binary_dilation(mask, structure=structure).astype(int)
    
    # 记录膨胀后的坐标
    y, x = np.where(dilated)
    components.append([(int(yi), int(xi)) for yi, xi in zip(y, x)])
    
    # 合并到总结果中
    dilated_combined = np.logical_or(dilated_combined, dilated).astype(int)

# 打印每个分量的膨胀结果
print(f"共有 {num_components} 个连通分量")
for i, coords in enumerate(components, 1):
    print(f"膨胀后连通分量 {i} 的坐标：")
    print(coords)

# 打印合并后的膨胀区域
print("\n合并后的膨胀区域矩阵：")
for row in dilated_combined:
    print(row.tolist())
    
# 结果
'''
共有 3 个连通分量
膨胀后连通分量 1 的坐标：
[(1, 1), (1, 2), (1, 3), (1, 4), (2, 1), (2, 2), (2, 3), (2, 4), (2, 5), (2, 6), (3, 1), (3, 2), (3, 3), (3, 4), (3, 5), (3, 6), (3, 7), (4, 1), (4, 2), (4, 3), (4, 4), (4, 5), (4, 6), (4, 7), (5, 1), (5, 2), (5, 3), (5, 4), (5, 5), (5, 6), (5, 7), (6, 2), (6, 3), (6, 4), (6, 5), (6, 6), (6, 7), (7, 3), (7, 4), (7, 5), (7, 6)]
膨胀后连通分量 2 的坐标：
[(7, 0), (7, 1), (7, 2), (7, 3), (8, 0), (8, 1), (8, 2), (8, 3), (9, 0), (9, 1), (9, 2), (9, 3)]
膨胀后连通分量 3 的坐标：
[(7, 7), (7, 8), (7, 9), (8, 7), (8, 8), (8, 9), (9, 7), (9, 8), (9, 9)]

合并后的膨胀区域矩阵：
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 1, 1, 1, 1, 0, 0, 0, 0, 0]
[0, 1, 1, 1, 1, 1, 1, 0, 0, 0]
[0, 1, 1, 1, 1, 1, 1, 1, 0, 0]
[0, 1, 1, 1, 1, 1, 1, 1, 0, 0]
[0, 1, 1, 1, 1, 1, 1, 1, 0, 0]
[0, 0, 1, 1, 1, 1, 1, 1, 0, 0]
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
[1, 1, 1, 1, 0, 0, 0, 1, 1, 1]
[1, 1, 1, 1, 0, 0, 0, 1, 1, 1]
'''

综上，我们可以写出对图片中某个ROI区域中随机膨胀一部分空洞的过程：

# 本数据集生成的代码
# 数据增强方法：随机膨胀一部分ROI区域的空洞区域

import cv2
import os
import numpy as np
import shutil
from scipy.ndimage import label, binary_dilation
import random

image_file_dir = "C:/pyprojects/yolo11/fheiaunjk/images"
output_dir = "C:/pyprojects/yolo11/output"

# 正确拼接输出路径
img_output_path = os.path.join(output_dir, "images")  # 正确写法
label_output_path = os.path.join(output_dir, "labels")  # 正确写法

# 直接创建目标目录（无需使用 os.path.dirname）
os.makedirs(img_output_path, exist_ok=True)    # 创建 output/images
os.makedirs(label_output_path, exist_ok=True)  # 创建 output/labels

for filename in os.listdir(image_file_dir):
    # 1. 读取原图并检查（移到外层循环，只读一次）
    image_file_path = os.path.join(image_file_dir, filename)
    image = cv2.imread(image_file_path, cv2.IMREAD_UNCHANGED)
    if image is None:
        print(f"错误：无法读取图片 {image_file_path}")
        continue
    
    # 2. 提前获取图像尺寸
    if len(image.shape) != 2:
        print(f"跳过非深度图：{filename}")
        continue
    orig_h, orig_w = image.shape

    # 3. 处理标签文件
    labels_dir = image_file_dir.replace("images", "labels")
    base_name = os.path.splitext(filename)[0]
    label_file_path = os.path.join(labels_dir, f"{base_name}.txt")
    if not os.path.exists(label_file_path):
        print(f"警告：标签文件 {label_file_path} 不存在")
        continue

    # 4. 读取并解析标签
    with open(label_file_path, 'r') as f:
        lines = [line.strip().split() for line in f.readlines()]

    # 5. 对每个检测框生成增强数据
    for k, line in enumerate(lines):
        if len(line) < 5: continue
        
        # 解析检测框坐标（添加边界保护）
        class_id, xc, yc, w, h = line[0], *map(float, line[1:5])
        x_center, y_center = xc * orig_w, yc * orig_h
        width, height = w * orig_w, h * orig_h
        
        # 计算并修正ROI边界
        x1 = max(0, int(x_center - width/2))
        y1 = max(0, int(y_center - height/2))
        x2 = min(orig_w, int(x_center + width/2))
        y2 = min(orig_h, int(y_center + height/2))
        
        # 跳过无效ROI
        if x1 >= x2 or y1 >= y2:
            print(f"跳过无效ROI：{filename} Box{k}")
            continue

        # 6. 每个ROI生成10个增强样本
        for m in range(10):
            # 创建副本避免污染原图
            modified_image = image.copy()
            
            # --- 处理当前ROI区域 ---
            roi = modified_image[y1:y2, x1:x2]
            
            # 生成掩膜及连通域
            mask = np.where(roi == 0, 1, 0).astype(np.uint8)
            labeled_array, num_components = label(mask, structure=np.ones((3,3)))
            
            if num_components == 0:
                dilated_combined = mask  # 无连通分量则保持原状
            else:
                # 随机选择并膨胀
                selected = random.sample(range(1, num_components+1), random.randint(1, num_components))
                dilated_combined = mask.copy()  # 基于原始mask初始化

                for i in selected:
                    component = (labeled_array == i).astype(int)
                    dilated = binary_dilation(component, structure=np.ones((3,3)))
                    dilated_combined = np.logical_or(dilated_combined, dilated)
            
            # 应用修改到ROI
            modified_roi = np.where(dilated_combined, 0, roi)
            modified_image[y1:y2, x1:x2] = modified_roi
            
            # 7. 生成唯一文件名（添加检测框索引和增强次数）
            unique_id = f"{base_name}_box{k}_aug{m}"
            img_save_path = os.path.join(img_output_path, f"{unique_id}.png")
            cv2.imwrite(img_save_path, modified_image)
            
            # 8. 生成对应标签（仅保留当前检测框）
            label_save_path = os.path.join(label_output_path, f"{unique_id}.txt")
            shutil.copy2(label_file_path, label_save_path)

效果展示：

提请读者注意：本代码实现是随机选取一部分孔洞进行一次腐蚀操作，读者可尝试将腐蚀操作次数更改为多次。