137 lines
4.8 KiB
Python
137 lines
4.8 KiB
Python
import cv2
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import numpy as np
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import matplotlib.pyplot as plt
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from PIL import Image
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import os
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from znzh_x import PredictionVisualizer
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class SimpleChangeVisualizer:
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def __init__(self):
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self.fig_size = (15, 10)
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def visualize_change_detection(self, img1_path, img2_path, mask_path, save_path=None):
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"""
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简单的变化检测可视化
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Args:
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img1_path: 第一期影像路径
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img2_path: 第二期影像路径
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mask_path: 预测掩膜路径
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save_path: 保存路径（可选）
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"""
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# 读取图像
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img1 = cv2.imread(img1_path)
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img2 = cv2.imread(img2_path)
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mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)
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if img1 is None or img2 is None or mask is None:
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print("错误：无法读取图像文件")
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return
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# 转换颜色空间
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img1_rgb = cv2.cvtColor(img1, cv2.COLOR_BGR2RGB)
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img2_rgb = cv2.cvtColor(img2, cv2.COLOR_BGR2RGB)
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# 创建变化区域的彩色掩膜
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change_mask = np.zeros_like(img2_rgb)
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change_mask[mask > 127] = [255, 0, 0] # 红色表示变化
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# 创建叠加图像
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overlay1 = cv2.addWeighted(img1_rgb, 0.7, change_mask, 0.3, 0)
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overlay2 = cv2.addWeighted(img2_rgb, 0.7, change_mask, 0.3, 0)
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# 创建subplot
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fig, axes = plt.subplots(2, 3, figsize=self.fig_size)
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fig.suptitle('变化检测结果可视化', fontsize=16, fontweight='bold')
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# 第一行：原始图像
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axes[0, 0].imshow(img1_rgb)
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axes[0, 0].set_title('第一期影像', fontsize=12)
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axes[0, 0].axis('off')
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axes[0, 1].imshow(img2_rgb)
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axes[0, 1].set_title('第二期影像', fontsize=12)
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axes[0, 1].axis('off')
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axes[0, 2].imshow(mask, cmap='gray')
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axes[0, 2].set_title('变化检测掩膜', fontsize=12)
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axes[0, 2].axis('off')
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# 第二行：叠加结果
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axes[1, 0].imshow(overlay1)
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axes[1, 0].set_title('第一期影像+变化区域', fontsize=12)
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axes[1, 0].axis('off')
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axes[1, 1].imshow(overlay2)
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axes[1, 1].set_title('第二期影像+变化区域', fontsize=12)
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axes[1, 1].axis('off')
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# 变化统计
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total_pixels = mask.size
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changed_pixels = np.sum(mask > 127)
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change_ratio = (changed_pixels / total_pixels) * 100
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stats_text = f"""变化统计信息：
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总像素数: {total_pixels:,}
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变化像素数: {changed_pixels:,}
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变化比例: {change_ratio:.2f}%
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"""
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axes[1, 2].text(0.1, 0.5, stats_text, fontsize=11,
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verticalalignment='center', transform=axes[1, 2].transAxes,
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bbox=dict(boxstyle="round,pad=0.3", facecolor="lightblue"))
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axes[1, 2].set_title('统计信息', fontsize=12)
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axes[1, 2].axis('off')
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plt.tight_layout()
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if save_path:
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plt.savefig(save_path, dpi=300, bbox_inches='tight')
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print(f"可视化结果已保存到: {save_path}")
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plt.show()
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return fig
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def batch_visualize(self, img1_dir, img2_dir, mask_dir, output_dir):
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"""
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批量可视化处理
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"""
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os.makedirs(output_dir, exist_ok=True)
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mask_files = [f for f in os.listdir(mask_dir) if f.endswith('.png')]
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for mask_file in mask_files:
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base_name = os.path.splitext(mask_file)[0]
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img1_path = os.path.join(img1_dir, base_name + '.png')
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img2_path = os.path.join(img2_dir, base_name + '.png')
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mask_path = os.path.join(mask_dir, mask_file)
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save_path = os.path.join(output_dir, f'{base_name}_visualization.png')
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if os.path.exists(img1_path) and os.path.exists(img2_path):
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print(f"处理: {base_name}")
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self.visualize_change_detection(img1_path, img2_path, mask_path, save_path)
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else:
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print(f"跳过: {base_name} (缺少对应的影像文件)")
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# 使用示例
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if __name__ == "__main__":
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visualizer = SimpleChangeVisualizer()
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# 单个文件可视化示例
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# visualizer.visualize_change_detection(
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# img1_path="path/to/image1.png",
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# img2_path="path/to/image2.png",
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# mask_path="path/to/mask.png",
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# save_path="path/to/result.png"
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# )
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# 批量处理示例
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img1_dir = "/media/data0/HL/2025-7-6/val/image1"
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img2_dir = "/media/data0/HL/2025-7-6/val/image2"
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mask_dir = "/media/data0/HL/CropLand-CD-main_3/CropLand-CD-main/save"
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output_dir = "/media/data0/HL/CropLand-CD-main_3/CropLand-CD-main/visualizations"
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visualizer.batch_visualize(img1_dir, img2_dir, mask_dir, output_dir) |