391 lines
15 KiB
Python
391 lines
15 KiB
Python
"""
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使用SAM3模型分割超大遥感影像中的水体（分区域处理，优化内存）
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流程：
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1. 将影像划分为若干有重叠的子区域。
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2. 对每个子区域独立执行粗分割、边缘带构建、精修分割。
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3. 合并所有子区域的掩码（重叠区域取最大值）。
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4. 后处理：填充内部NoData空洞、移除小面积碎片、可选保留最大连通域。
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5. 保存结果。
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优点：避免在全尺寸概率图上进行GPU操作，显著降低显存占用。
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"""
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import torch
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import torch.nn.functional as F
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import matplotlib
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import matplotlib.pyplot as plt
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import numpy as np
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from PIL import Image
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import rasterio
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from rasterio.windows import Window
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from rasterio.io import MemoryFile
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from tqdm import tqdm
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from scipy import ndimage
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from sam3.model_builder import build_sam3_image_model
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from sam3.model.sam3_image_processor import Sam3Processor
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# ---------- 工具函数（保持不变） ----------
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def binary_dilate(mask, radius):
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if radius <= 0:
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return mask
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kernel = 2 * radius + 1
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return F.max_pool2d(mask.float(), kernel_size=kernel, stride=1, padding=radius) > 0.5
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def binary_erode(mask, radius):
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if radius <= 0:
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return mask
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return ~binary_dilate(~mask, radius)
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def combine_masks_logits(masks_logits):
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if masks_logits.numel() == 0:
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return None
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probs = masks_logits.squeeze(1)
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if probs.dim() == 2:
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return probs
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return torch.amax(probs, dim=0)
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def upsample_prob(prob, size):
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return F.interpolate(prob[None, None, ...], size=size, mode="bilinear", align_corners=False).squeeze(0).squeeze(0)
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def build_band(mask, radius):
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"""mask: 2D tensor (CPU or GPU)"""
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mask_4d = mask[None, None, ...] # (1,1,H,W)
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dil = binary_dilate(mask_4d, radius)
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ero = binary_erode(mask_4d, radius)
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band = torch.logical_xor(dil, ero).squeeze(0).squeeze(0)
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return band
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def tile_slices(height, width, tile_size, overlap):
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stride = max(tile_size - overlap, 1)
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for top in range(0, height, stride):
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for left in range(0, width, stride):
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bottom = min(top + tile_size, height)
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right = min(left + tile_size, width)
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yield top, left, bottom, right
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def _to_uint8(arr):
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if arr.dtype == np.uint8:
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return arr
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arr = arr.astype(np.float32)
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vmin = np.percentile(arr, 2.0)
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vmax = np.percentile(arr, 98.0)
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if vmax <= vmin:
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return np.zeros_like(arr, dtype=np.uint8)
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arr = (arr - vmin) / (vmax - vmin)
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arr = np.clip(arr, 0.0, 1.0)
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return (arr * 255.0).astype(np.uint8)
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def _bands_to_pil(bands):
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if bands.ndim != 3:
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raise ValueError("bands must be (C,H,W)")
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c, h, w = bands.shape
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if c == 1:
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rgb = np.repeat(bands, 3, axis=0)
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else:
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rgb = bands[:3]
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rgb = _to_uint8(rgb)
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rgb = np.transpose(rgb, (1, 2, 0))
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return Image.fromarray(rgb, mode="RGB")
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def _read_pil_window(src, window):
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bands = src.read(window=window, boundless=True, fill_value=0)
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return _bands_to_pil(bands)
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def _coarse_shape(height, width, max_side):
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scale = max_side / float(max(height, width))
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h = max(int(round(height * scale)), 1)
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w = max(int(round(width * scale)), 1)
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return h, w
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# ---------- 粗分割分块推理 ----------
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def coarse_tiles(processor, src, prompt, tile_size, overlap):
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"""
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对数据集src进行分块粗分割，返回全尺寸概率图（GPU张量）
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src: rasterio数据集（可以是内存文件或原始文件）
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"""
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height, width = src.height, src.width
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device = processor.device
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full_probs = torch.zeros((height, width), dtype=torch.float32, device=device)
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stride = max(tile_size - overlap, 1)
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num_tiles_y = (height + stride - 1) // stride
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num_tiles_x = (width + stride - 1) // stride
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total_tiles = num_tiles_y * num_tiles_x
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# 内部进度条，leave=True 以便保留历史记录
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with tqdm(total=total_tiles, desc="粗分割分块", unit="块", leave=True) as pbar:
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for top, left, bottom, right in tile_slices(height, width, tile_size, overlap):
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window = Window(left, top, right - left, bottom - top)
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crop = _read_pil_window(src, window)
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state = processor.set_image(crop)
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state = processor.set_text_prompt(prompt=prompt, state=state)
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tile_prob = combine_masks_logits(state["masks_logits"])
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if tile_prob is None:
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pbar.update(1)
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continue
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if tile_prob.shape[-2:] != (bottom - top, right - left):
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tile_prob = upsample_prob(tile_prob, (bottom - top, right - left))
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full_probs[top:bottom, left:right] = torch.maximum(
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full_probs[top:bottom, left:right], tile_prob
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)
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pbar.update(1)
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return full_probs
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# ---------- 精修分块 ----------
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def refine_tiles(processor, src, prompt, band_coarse, tile_size, overlap):
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"""
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对边缘带进行精修分割
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band_coarse: 2D GPU张量，指示需要精修的区域
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"""
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height, width = src.height, src.width
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device = processor.device
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full_probs = torch.zeros((height, width), dtype=torch.float32, device=device)
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band_h, band_w = band_coarse.shape
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scale_y = band_h / float(height)
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scale_x = band_w / float(width)
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stride = max(tile_size - overlap, 1)
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num_tiles_y = (height + stride - 1) // stride
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num_tiles_x = (width + stride - 1) // stride
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total_tiles = num_tiles_y * num_tiles_x
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# 内部进度条，leave=True 以便保留历史记录
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with tqdm(total=total_tiles, desc="精修分块", unit="块", leave=True) as pbar:
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for top, left, bottom, right in tile_slices(height, width, tile_size, overlap):
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c_top = int(top * scale_y)
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c_left = int(left * scale_x)
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c_bottom = max(int(np.ceil(bottom * scale_y)), c_top + 1)
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c_right = max(int(np.ceil(right * scale_x)), c_left + 1)
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if not band_coarse[c_top:c_bottom, c_left:c_right].any():
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pbar.update(1)
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continue
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window = Window(left, top, right - left, bottom - top)
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crop = _read_pil_window(src, window)
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state = processor.set_image(crop)
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state = processor.set_text_prompt(prompt=prompt, state=state)
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tile_prob = combine_masks_logits(state["masks_logits"])
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if tile_prob is None:
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pbar.update(1)
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continue
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if tile_prob.shape[-2:] != (bottom - top, right - left):
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tile_prob = upsample_prob(tile_prob, (bottom - top, right - left))
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full_probs[top:bottom, left:right] = torch.maximum(
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full_probs[top:bottom, left:right], tile_prob
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)
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pbar.update(1)
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return full_probs
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# ---------- 后处理函数（面积过滤） ----------
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def filter_by_area(mask, min_area=None, keep_largest_only=False):
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"""
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对二值掩码进行连通域面积过滤。
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mask: np.ndarray, dtype=bool or uint8, shape (H,W)
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min_area: int, 最小面积阈值（像素），小于此值的连通域移除。若为None则不过滤。
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keep_largest_only: bool, 若True，只保留面积最大的连通域（同时受min_area约束）。
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返回过滤后的掩码 (uint8, 0/1)。
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"""
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labeled, num = ndimage.label(mask)
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if num == 0:
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return mask.astype(np.uint8)
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sizes = ndimage.sum(mask, labeled, range(1, num+1))
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keep_labels = []
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if keep_largest_only:
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max_idx = np.argmax(sizes)
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if min_area is None or sizes[max_idx] >= min_area:
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keep_labels = [max_idx + 1]
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else:
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for i, size in enumerate(sizes, start=1):
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if min_area is None or size >= min_area:
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keep_labels.append(i)
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if not keep_labels:
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return np.zeros_like(mask, dtype=np.uint8)
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keep_mask = np.isin(labeled, keep_labels)
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return keep_mask.astype(np.uint8)
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# ---------- 划分子区域函数 ----------
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def split_into_regions(width, height, num_splits_y=2, num_splits_x=3, overlap=256):
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"""
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将图像划分为若干有重叠的子区域，返回每个子区域的 (left, top, right, bottom)
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坐标（像素坐标，相对于原图）。
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"""
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regions = []
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tile_w = (width + num_splits_x - 1) // num_splits_x
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tile_h = (height + num_splits_y - 1) // num_splits_y
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for i in range(num_splits_y):
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for j in range(num_splits_x):
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left = max(j * tile_w - overlap, 0)
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top = max(i * tile_h - overlap, 0)
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right = min((j + 1) * tile_w + overlap, width)
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bottom = min((i + 1) * tile_h + overlap, height)
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regions.append((left, top, right, bottom))
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return regions
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# ---------- 主程序 ----------
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matplotlib.use("TkAgg")
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# 参数设置
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image_path = r"E:\is2\dingshanhu\result_caijian.tif"
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mask_output_path = r"E:\is2\dingshanhu\result_maskV1.tif"
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prompt = "water body"
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coarse_read_max_side = 768 # 不再使用，保留以防万一
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coarse_resolution = 1008
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fine_resolution = 1008
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coarse_threshold = 0.5
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final_threshold = 0.5
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band_radius = 64
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tile_size = 2048 # 精修分块大小
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overlap = 256 # 精修重叠
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coarse_tile_size = 4096 # 粗分割分块大小
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coarse_overlap = 256 # 粗分割重叠
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# ========== 分区域参数 ==========
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num_splits_y = 2 # 纵向切分数
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num_splits_x = 3 # 横向切分数（共 2x3=6 份）
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region_overlap = 256 # 子区域之间的重叠像素数
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# ===============================
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# ========== 后处理参数 ==========
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min_area = 1000 # 最小面积阈值（像素），小于此值的连通域将被移除；设为0或None表示不进行面积过滤
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keep_largest_only = False # 是否只保留最大的连通域（True/False）
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# ===============================
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device = "cuda" if torch.cuda.is_available() else "cpu"
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print(f"使用设备: {device}")
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# 加载模型（所有子区域共享同一个模型）
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model = build_sam3_image_model().to(device).eval()
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coarse_processor = Sam3Processor(model, resolution=coarse_resolution, device=device)
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fine_processor = Sam3Processor(model, resolution=fine_resolution, device=device)
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# 打开原始影像
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with rasterio.open(image_path) as src:
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nodata = src.nodata
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print(f"原始影像NoData值: {nodata}")
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height, width = src.height, src.width
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# 划分区域
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regions = split_into_regions(width, height, num_splits_y, num_splits_x, region_overlap)
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print(f"共划分 {len(regions)} 个子区域")
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# 创建全尺寸掩码数组（CPU内存）
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full_mask = np.zeros((height, width), dtype=np.uint8)
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# 子区域总进度条
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with tqdm(total=len(regions), desc="处理子区域", unit="子区域") as region_pbar:
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for idx, (left, top, right, bottom) in enumerate(regions):
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print(f"\n子区域 {idx+1}/{len(regions)}: 坐标范围 ({left},{top}) -> ({right},{bottom})")
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region_w = right - left
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region_h = bottom - top
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# 读取子区域数据
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window = Window(left, top, region_w, region_h)
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sub_bands = src.read(window=window) # shape: (bands, h, w)
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# 构建子区域元数据
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sub_profile = src.profile.copy()
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if not src.is_tiled:
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sub_profile.pop('blockxsize', None)
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sub_profile.pop('blockysize', None)
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sub_profile['tiled'] = False
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else:
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sub_profile['tiled'] = True
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sub_profile.update({
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'height': region_h,
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'width': region_w,
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'transform': rasterio.windows.transform(window, src.transform)
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})
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# 将子区域数据包装成内存中的 rasterio 数据集
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with MemoryFile() as memfile:
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with memfile.open(**sub_profile) as sub_dst:
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sub_dst.write(sub_bands)
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with memfile.open() as sub_src:
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# ---------- 粗分割 ----------
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coarse_prob = coarse_tiles(
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coarse_processor, sub_src, prompt,
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tile_size=coarse_tile_size, overlap=coarse_overlap
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)
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coarse_mask = coarse_prob > coarse_threshold
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# ---------- 构建边缘带（直接在 GPU 上进行） ----------
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band = build_band(coarse_mask, band_radius)
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# ---------- 精修分割 ----------
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fine_probs = refine_tiles(
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fine_processor, sub_src, prompt, band,
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tile_size=tile_size, overlap=overlap
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)
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# ---------- 合并粗/细结果 ----------
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final_prob = torch.maximum(fine_probs, coarse_prob)
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final_mask = final_prob > final_threshold
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# ---------- 获取子区域掩码（numpy） ----------
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sub_mask_np = final_mask.cpu().numpy().astype(np.uint8)
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# 合并到全尺寸掩码（重叠区域取最大值）
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full_mask[top:bottom, left:right] = np.maximum(
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full_mask[top:bottom, left:right], sub_mask_np
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)
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# 更新子区域进度条
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region_pbar.update(1)
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# ========== 后处理 ==========
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print("\n后处理：填充内部NoData空洞...")
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if nodata is not None:
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band1 = src.read(1)
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if np.issubdtype(band1.dtype, np.floating):
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nodata_mask = np.isclose(band1, float(nodata))
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else:
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nodata_mask = (band1 == nodata)
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labeled_mask, num_features = ndimage.label(nodata_mask)
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boundary_mask = np.zeros_like(nodata_mask, dtype=bool)
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boundary_mask[0, :] = True
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boundary_mask[-1, :] = True
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boundary_mask[:, 0] = True
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boundary_mask[:, -1] = True
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boundary_labels = set(labeled_mask[boundary_mask])
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internal_nodata_mask = np.isin(labeled_mask, list(boundary_labels), invert=True) & nodata_mask
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if internal_nodata_mask.any():
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struct = ndimage.generate_binary_structure(2, 2)
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internal_dilated = ndimage.binary_dilation(internal_nodata_mask, structure=struct, iterations=7)
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full_mask[internal_dilated] = 1
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print(f" 内部NoData原始像素数: {np.sum(internal_nodata_mask)}，膨胀后像素数: {np.sum(internal_dilated)}")
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else:
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print(" 无内部NoData区域")
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print("后处理：面积过滤...")
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if min_area is not None or keep_largest_only:
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original_count = np.sum(full_mask)
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full_mask = filter_by_area(full_mask, min_area=min_area, keep_largest_only=keep_largest_only)
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filtered_count = np.sum(full_mask)
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print(f" 后处理前水体像素数: {original_count}，后处理后: {filtered_count}")
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# ========== 保存结果 ==========
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profile = src.profile.copy()
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profile.update(count=1, dtype="uint8", compress='lzw')
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with rasterio.open(mask_output_path, "w", **profile) as dst:
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dst.write(full_mask, 1)
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print(f"分割完成，结果已保存至：{mask_output_path}") |