210 lines
7.6 KiB
Python
210 lines
7.6 KiB
Python
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import os
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import numpy as np
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import torch
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import torch.nn.functional as F
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from tqdm import tqdm
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import rasterio
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from rasterio.windows import Window
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from .models import get_model
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from .data import TileDataset, get_predict_transforms
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class UAVSegPredictor:
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"""大尺度遥感图像语义分割预测器"""
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def __init__(self,
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model_path,
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model_type='deeplabv3plus',
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num_classes=6,
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tile_size=512,
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stride=None,
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device=None,
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overlap_weights=True):
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"""
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Args:
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model_path (str): 模型权重文件路径
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model_type (str): 模型类型，可选 'deeplabv3plus' 或 'unetpp'
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num_classes (int): 类别数量
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tile_size (int): 分块大小
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stride (int): 滑动窗口步长，如果为None则等于tile_size的一半
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device (str): 设备，如果为None则自动选择
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overlap_weights (bool): 是否使用重叠区域加权融合
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"""
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self.model_path = model_path
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self.model_type = model_type
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self.num_classes = num_classes
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self.tile_size = tile_size
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self.stride = stride if stride is not None else tile_size // 2
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self.overlap_weights = overlap_weights
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# 设置设备
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if device is None:
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self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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else:
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self.device = torch.device(device)
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print(f"使用设备: {self.device}")
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# 加载模型
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self._load_model()
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# 数据预处理
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self.transform = get_predict_transforms()
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def _load_model(self):
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"""加载模型"""
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# 初始化模型
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self.model = get_model(self.model_type, self.num_classes)
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# 加载权重
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checkpoint = torch.load(self.model_path, map_location=self.device)
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# 检查模型类型
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if 'model_type' in checkpoint and checkpoint['model_type'] != self.model_type:
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print(f"警告: 加载的模型类型 ({checkpoint['model_type']}) 与当前模型类型 ({self.model_type}) 不匹配")
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# 加载模型参数
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if 'model_state_dict' in checkpoint:
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self.model.load_state_dict(checkpoint['model_state_dict'])
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else:
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self.model.load_state_dict(checkpoint)
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# 设置为评估模式
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self.model.to(self.device)
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self.model.eval()
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print(f"模型已从 {self.model_path} 加载")
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def predict(self, image_path, output_path=None,color_map=None):
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"""对单张图像进行预测
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Args:
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image_path (str): 输入图像路径
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output_path (str): 输出掩码路径，如果为None则不保存
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Returns:
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numpy.ndarray: 预测的分割掩码
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"""
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# 创建分块数据集
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dataset = TileDataset(
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image_path=image_path,
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tile_size=self.tile_size,
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stride=self.stride,
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transform=self.transform
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)
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# 获取图像信息
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with rasterio.open(image_path) as src:
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height, width = src.height, src.width
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profile = src.profile.copy()
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# 初始化结果和权重图
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result = np.zeros((self.num_classes, height, width), dtype=np.float32)
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weights = np.zeros((height, width), dtype=np.float32)
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# 生成权重图（如果使用重叠区域加权融合）
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if self.overlap_weights:
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weight_kernel = self._get_weight_kernel()
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# 预测每个分块
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with torch.no_grad():
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for i, (image, tile_info) in enumerate(tqdm(dataset, desc="预测分块")):
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# 获取分块位置信息
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x, y = tile_info['x'], tile_info['y']
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# 将图像移动到设备
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image = image.unsqueeze(0).to(self.device)
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# 预测
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pred = self.model(image)
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# 将预测结果转换为概率
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pred = F.softmax(pred, dim=1)
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# 将预测结果移动到CPU并转换为NumPy数组
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pred = pred.squeeze(0).cpu().numpy()
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# 更新结果和权重图
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if self.overlap_weights:
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# 使用权重核
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for c in range(self.num_classes):
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result[c, y:y+self.tile_size, x:x+self.tile_size] += pred[c] * weight_kernel
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weights[y:y+self.tile_size, x:x+self.tile_size] += weight_kernel
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else:
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# 简单累加
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for c in range(self.num_classes):
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result[c, y:y+self.tile_size, x:x+self.tile_size] += pred[c]
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weights[y:y+self.tile_size, x:x+self.tile_size] += 1
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# 归一化结果
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for c in range(self.num_classes):
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result[c] = np.divide(result[c], weights, out=np.zeros_like(result[c]), where=weights > 0)
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# 获取最终分割掩码（类别索引）
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mask = np.argmax(result, axis=0).astype(np.uint8)
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# 保存结果
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if output_path is not None:
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# 更新profile
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profile.update({
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'driver': 'GTiff',
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'height': height,
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'width': width,
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'count': 1,
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'dtype': 'uint8',
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'nodata': None
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})
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color_map = color_map
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# 生成彩色图像
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colored_mask = np.zeros((height, width, 3), dtype=np.uint8)
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for i in range(self.num_classes):
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colored_mask[mask == i] = color_map[i]
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# 保存彩色图像
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from PIL import Image
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colored_path = output_path.replace('.png', '_colored.png')
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Image.fromarray(colored_mask).save(colored_path)
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print(f"彩色预测图已保存到 {colored_path}")
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# 保存原始掩码
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with rasterio.open(output_path, 'w', **profile) as dst:
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dst.write(mask, 1)
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print(f"预测结果已保存到 {output_path}")
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return mask
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def predict_folder(self, input_dir, output_dir,color_map=None):
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"""对文件夹中的所有图像进行预测
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Args:
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input_dir (str): 输入图像目录
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output_dir (str): 输出掩码目录
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"""
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# 创建输出目录
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os.makedirs(output_dir, exist_ok=True)
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# 获取所有图像文件
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image_files = [f for f in os.listdir(input_dir) if f.endswith(('.tif', '.png', '.jpg'))]
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# 预测每张图像
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for image_file in tqdm(image_files, desc="预测图像"):
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# 构建路径
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image_path = os.path.join(input_dir, image_file)
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output_path = os.path.join(output_dir, image_file)
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# 预测
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self.predict(image_path, output_path,color_map)
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def _get_weight_kernel(self):
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"""生成权重核，用于重叠区域加权融合"""
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# 创建二维高斯核
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y, x = np.mgrid[0:self.tile_size, 0:self.tile_size]
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x = x.astype(np.float32) - self.tile_size / 2
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y = y.astype(np.float32) - self.tile_size / 2
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sigma = self.tile_size / 4
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weight = np.exp(-(x**2 + y**2) / (2 * sigma**2))
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return weight
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