From 5c9df03c214ae75a641be1fa7cf333754ab37260 Mon Sep 17 00:00:00 2001
From: wrz1-zzzzz <914921336@qq.com>
Date: Mon, 18 Nov 2024 16:42:35 +0800
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---
 DPL/yolov5/mask_11.18.py | 137 +++++++++++++++++++++++++++++++++++++++
 1 file changed, 137 insertions(+)
 create mode 100644 DPL/yolov5/mask_11.18.py

diff --git a/DPL/yolov5/mask_11.18.py b/DPL/yolov5/mask_11.18.py
new file mode 100644
index 0000000..7aa7691
--- /dev/null
+++ b/DPL/yolov5/mask_11.18.py
@@ -0,0 +1,137 @@
+import os
+import cv2
+import numpy as np
+import time
+
+def read_yolov5_labels(label_folder):
+    """
+    读取YOLOv5标签文件夹中的标签文件，提取每个框的位置。
+    :param label_folder: YOLOv5标签文件夹路径
+    :return: 返回一个字典，格式为 {image_name: [(x_center, y_center, width, height), ...]}
+    """
+    labels = {}
+
+    # 遍历YOLOv5结果文件夹中的所有txt文件
+    for filename in os.listdir(label_folder):
+        if filename.endswith('.txt'):
+            image_name = filename.replace('.txt', '')
+            file_path = os.path.join(label_folder, filename)
+
+            # 读取文件并解析检测框
+            with open(file_path, 'r') as f:
+                boxes = []
+                for line in f:
+                    parts = line.strip().split()
+                    if len(parts) < 5:  # 检查标签格式是否完整
+                        continue
+
+                    # 从YOLOv5标签格式解析出数据
+                    class_id = int(parts[0])
+                    x_center = float(parts[1])
+                    y_center = float(parts[2])
+                    width = float(parts[3])
+                    height = float(parts[4])
+
+                    # 计算框的绝对坐标
+                    boxes.append([x_center, y_center, width, height])
+
+                labels[image_name] = boxes
+
+    return labels
+
+def generate_mask(image_shape, boxes, width_blocks=24, height_blocks=24):
+    """
+    根据检测框信息生成掩膜，返回True和False的矩阵
+    :param image_shape: 图像的shape（height, width）
+    :param boxes: 检测框信息，格式为 [(x_center, y_center, width, height), ...]
+    :param width_blocks: 图像宽度分块数
+    :param height_blocks: 图像高度分块数
+    :return: 掩膜矩阵 (height, width)，True表示框内区域，False表示其他区域
+    """
+    height, width = image_shape
+
+    # 创建一个与图像大小相同的全False矩阵
+    mask = np.zeros((height, width), dtype=bool)
+
+    # 遍历每个框，更新掩膜矩阵
+    for box in boxes:
+        x_center, y_center, width_box, height_box = box
+
+        # 转换为绝对坐标
+        x1 = int((x_center - width_box / 2) * width)
+        y1 = int((y_center - height_box / 2) * height)
+        x2 = int((x_center + width_box / 2) * width)
+        y2 = int((y_center + height_box / 2) * height)
+
+        # 确保框不超出图像边界
+        x1 = max(0, x1)
+        y1 = max(0, y1)
+        x2 = min(width, x2)
+        y2 = min(height, y2)
+
+        # 将检测框区域标记为True
+        mask[y1:y2, x1:x2] = True
+
+    return mask
+
+def process_images_and_generate_masks(image_folder, label_folder, output_folder, width_blocks=24, height_blocks=24):
+    """
+    处理图像文件夹，生成掩膜并保存为True和False的矩阵
+    :param image_folder: 输入图像文件夹路径
+    :param label_folder: YOLOv5标签文件夹路径
+    :param output_folder: 输出掩膜矩阵保存文件夹路径
+    :param width_blocks: 图像宽度分块数
+    :param height_blocks: 图像高度分块数
+    :return: None
+    """
+    # 确保输出文件夹存在
+    if not os.path.exists(output_folder):
+        os.makedirs(output_folder)
+
+    # 读取YOLOv5标签文件
+    labels = read_yolov5_labels(label_folder)
+
+    # 遍历图像文件夹中的图片
+    for filename in os.listdir(image_folder):
+        if filename.endswith(('.jpg', '.png', '.bmp')):
+            image_path = os.path.join(image_folder, filename)
+            image_name = filename.replace('.jpg', '').replace('.png', '').replace('.bmp', '')
+
+            # 获取对应的标签框
+            boxes = labels.get(image_name, [])
+            if not boxes:
+                print(f"未找到检测框信息：{image_name}")
+                continue
+
+            # 读取图片
+            image = cv2.imread(image_path)
+            if image is None:
+                print(f"无法读取图片: {image_path}")
+                continue
+
+            # 获取图像的尺寸 (height, width)
+            height, width = image.shape[:2]
+
+            # 记录处理时间
+            start_time = time.time()
+
+            # 生成掩膜
+            mask = generate_mask((height, width), boxes, width_blocks, height_blocks)
+
+            # 计算处理时间
+            processing_time = time.time() - start_time
+            print(f"处理图片 {image_name} 耗时: {processing_time:.4f}秒")
+
+            # 保存掩膜矩阵到文件
+            mask_filename = f"{image_name}_mask.npy"
+            mask_path = os.path.join(output_folder, mask_filename)
+            np.save(mask_path, mask)
+            print(f"保存掩膜: {mask_filename}")
+
+# 测试代码
+if __name__ == "__main__":
+    image_folder = "runs/detect/exp6"  # 输入图像文件夹路径
+    label_folder = "runs/detect/labels"  # YOLOv5标签文件夹路径
+    output_folder = "datasets/mask"  # 输出掩膜矩阵保存文件夹路径
+
+    process_images_and_generate_masks(image_folder, label_folder, output_folder)