Initial commit

water.py

@@ -0,0 +1,182 @@
+import torch
+import torch.nn.functional as F
+import matplotlib
+import matplotlib.pyplot as plt
+import numpy as np
+from PIL import Image
+import rasterio
+from rasterio.windows import Window
+
+from sam3.model_builder import build_sam3_image_model
+from sam3.model.sam3_image_processor import Sam3Processor
+
+
+def binary_dilate(mask, radius):
+    if radius <= 0:
+        return mask
+    kernel = 2 * radius + 1
+    return F.max_pool2d(mask.float(), kernel_size=kernel, stride=1, padding=radius) > 0.5
+
+
+def binary_erode(mask, radius):
+    if radius <= 0:
+        return mask
+    return ~binary_dilate(~mask, radius)
+
+
+def combine_masks_logits(masks_logits):
+    if masks_logits.numel() == 0:
+        return None
+    probs = masks_logits.squeeze(1)
+    if probs.dim() == 2:
+        return probs
+    return torch.amax(probs, dim=0)
+
+
+def upsample_prob(prob, size):
+    return (
+        F.interpolate(prob[None, None, ...], size=size, mode="bilinear", align_corners=False)
+        .squeeze(0)
+        .squeeze(0)
+    )
+
+
+def infer_coarse(processor, image, prompt, threshold, target_size=None):
+    state = processor.set_image(image)
+    state = processor.set_text_prompt(prompt=prompt, state=state)
+    prob = combine_masks_logits(state["masks_logits"])
+    if prob is None:
+        prob = torch.zeros((image.height, image.width), device=processor.device)
+    if target_size is not None:
+        prob = upsample_prob(prob, target_size)
+    else:
+        prob = upsample_prob(prob, (image.height, image.width))
+    prob_cpu = prob.detach().cpu()
+    mask_cpu = prob_cpu > threshold
+    return prob_cpu, mask_cpu
+
+
+def build_band(mask_cpu, radius):
+    mask = mask_cpu[None, None, ...]
+    dil = binary_dilate(mask, radius)
+    ero = binary_erode(mask, radius)
+    band = torch.logical_xor(dil, ero).squeeze(0).squeeze(0)
+    return band
+
+
+def tile_slices(height, width, tile_size, overlap):
+    stride = max(tile_size - overlap, 1)
+    for top in range(0, height, stride):
+        for left in range(0, width, stride):
+            bottom = min(top + tile_size, height)
+            right = min(left + tile_size, width)
+            yield top, left, bottom, right
+
+
+def _to_uint8(arr):
+    if arr.dtype == np.uint8:
+        return arr
+    arr = arr.astype(np.float32)
+    vmin = np.percentile(arr, 2.0)
+    vmax = np.percentile(arr, 98.0)
+    if vmax <= vmin:
+        return np.zeros_like(arr, dtype=np.uint8)
+    arr = (arr - vmin) / (vmax - vmin)
+    arr = np.clip(arr, 0.0, 1.0)
+    return (arr * 255.0).astype(np.uint8)
+
+
+def _bands_to_pil(bands):
+    if bands.ndim != 3:
+        raise ValueError("bands must be (C,H,W)")
+    c, h, w = bands.shape
+    if c == 1:
+        rgb = np.repeat(bands, 3, axis=0)
+    else:
+        rgb = bands[:3]
+    rgb = _to_uint8(rgb)
+    rgb = np.transpose(rgb, (1, 2, 0))
+    return Image.fromarray(rgb, mode="RGB")
+
+
+def _read_pil_window(src, window):
+    bands = src.read(window=window, boundless=True, fill_value=0)
+    return _bands_to_pil(bands)
+
+
+def _coarse_shape(height, width, max_side):
+    scale = max_side / float(max(height, width))
+    h = max(int(round(height * scale)), 1)
+    w = max(int(round(width * scale)), 1)
+    return h, w
+
+
+def refine_tiles(processor, src, prompt, band_coarse, tile_size, overlap):
+    height, width = src.height, src.width
+    full_probs = torch.zeros((height, width), dtype=torch.float16)
+    band_h, band_w = band_coarse.shape
+    scale_y = band_h / float(height)
+    scale_x = band_w / float(width)
+    for top, left, bottom, right in tile_slices(height, width, tile_size, overlap):
+        c_top = int(top * scale_y)
+        c_left = int(left * scale_x)
+        c_bottom = max(int(np.ceil(bottom * scale_y)), c_top + 1)
+        c_right = max(int(np.ceil(right * scale_x)), c_left + 1)
+        if not band_coarse[c_top:c_bottom, c_left:c_right].any():
+            continue
+        window = Window(left, top, right - left, bottom - top)
+        crop = _read_pil_window(src, window)
+        state = processor.set_image(crop)
+        state = processor.set_text_prompt(prompt=prompt, state=state)
+        tile_prob = combine_masks_logits(state["masks_logits"])
+        if tile_prob is None:
+            continue
+        if tile_prob.shape[-2:] != (bottom - top, right - left):
+            tile_prob = upsample_prob(tile_prob, (bottom - top, right - left))
+        tile_prob = tile_prob.detach().cpu().to(full_probs.dtype)
+        region = full_probs[top:bottom, left:right]
+        full_probs[top:bottom, left:right] = torch.maximum(region, tile_prob)
+    return full_probs
+
+
+matplotlib.use("TkAgg")
+
+image_path = r"E:\is2\yaopu\result.tif"
+mask_output_path = r"E:\is2\yaopu\result_mask.tif"
+prompt = "water body"
+coarse_read_max_side = 768
+coarse_resolution = 1008
+fine_resolution = 1008
+coarse_threshold = 0.5
+final_threshold = 0.5
+band_radius = 64
+tile_size = 2048
+overlap = 256
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = build_sam3_image_model().to(device).eval()
+coarse_processor = Sam3Processor(model, resolution=coarse_resolution, device=device)
+fine_processor = Sam3Processor(model, resolution=fine_resolution, device=device)
+
+with rasterio.open(image_path) as src:
+    coarse_h, coarse_w = _coarse_shape(src.height, src.width, coarse_read_max_side)
+    coarse_bands = src.read(out_shape=(src.count, coarse_h, coarse_w))
+    coarse_image = _bands_to_pil(coarse_bands)
+    coarse_prob, coarse_mask = infer_coarse(
+        coarse_processor,
+        coarse_image,
+        prompt,
+        coarse_threshold,
+        target_size=(src.height, src.width),
+    )
+    band = build_band(coarse_mask, band_radius)
+    fine_probs = refine_tiles(
+        fine_processor, src, prompt, band, tile_size, overlap
+    )
+    final_prob = torch.maximum(fine_probs.float(), coarse_prob.float())
+    final_mask = final_prob > final_threshold
+    mask_np = final_mask.numpy().astype(np.uint8)
+    profile = src.profile.copy()
+    profile.update(count=1, dtype="uint8")
+    with rasterio.open(mask_output_path, "w", **profile) as dst:
+        dst.write(mask_np, 1)