├── .gitignore ├── README.md ├── __init__.py ├── basic_usage.ipynb ├── polygonization.py ├── sameo.py ├── segment_anything ├── LICENSE ├── __init__.py ├── automatic_mask_generator.py ├── build_sam.py ├── modeling │ ├── __init__.py │ ├── common.py │ ├── image_encoder.py │ ├── mask_decoder.py │ ├── prompt_encoder.py │ ├── sam.py │ └── transformer.py ├── predictor.py └── utils │ ├── __init__.py │ ├── amg.py │ ├── onnx.py │ └── transforms.py ├── sliding_window.py ├── title_sameo.png └── tms2geotiff ├── .gitignore ├── LICENSE ├── README.md ├── _.git ├── HEAD ├── config ├── description ├── hooks │ ├── applypatch-msg.sample │ ├── commit-msg.sample │ ├── fsmonitor-watchman.sample │ ├── post-update.sample │ ├── pre-applypatch.sample │ ├── pre-commit.sample │ ├── pre-merge-commit.sample │ ├── pre-push.sample │ ├── pre-rebase.sample │ ├── pre-receive.sample │ ├── prepare-commit-msg.sample │ ├── push-to-checkout.sample │ └── update.sample ├── index ├── info │ └── exclude ├── logs │ ├── HEAD │ └── refs │ │ ├── heads │ │ └── master │ │ └── remotes │ │ └── origin │ │ └── HEAD ├── objects │ └── pack │ │ ├── pack-66bf8968b6af3c5470882dc8a09fd50bee45e557.idx │ │ └── pack-66bf8968b6af3c5470882dc8a09fd50bee45e557.pack ├── packed-refs └── refs │ ├── heads │ └── master │ └── remotes │ └── origin │ └── HEAD ├── __init__.py ├── requirements.txt ├── tms2geotiff.py └── tmssplit.py /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ***I suggest you use https://github.com/opengeos/segment-geospatial instead of this repo. It was the first successful attempt to join SAM and EO data, but now there are much better documented and much better maintained options.*** 2 | 3 | ![Automatic segmentation example](title_sameo.png?raw=true "Automatic segmentation example") 4 | 5 | # Segment Anything EO tools 6 | Earth observation tools for Meta AI Segment Anything 7 | 8 | # Licensing 9 | 10 | [Facebook Research Segment Anything](https://github.com/facebookresearch/segment-anything) — Apache-2.0 license 11 | 12 | [Gumblex tms2geotiff](https://github.com/gumblex/tms2geotiff) — BSD-2-Clause license 13 | 14 | [aeronetlib](https://github.com/Geoalert/aeronetlib) — MIT licence 15 | 16 | 17 | 18 | Other code — MIT license 19 | 20 | ***Segment Anything and tms2geotiff were copied to this repo 9 Apr 2022, you can update them to more recent versions if needed*** 21 | 22 | ## This tools are developed to ease the processing of spatial data (GeoTIFF and TMS) with Meta AI Segment Anything models using sliding window algorithm for big files 23 | 24 | ### You can: 25 | - download TMS data (including OpenAerialMap and Mapbox Maxar) as GeoTIFF files 26 | - process GeoTIFF files with Meta AI Segment Anything models 27 | - save predicted segments as GeoTIFF raster data and GPKG vector data 28 | 29 | and a little bit more 30 | 31 | ### Usage: 32 | - jupyter notebook in the repo https://github.com/aliaksandr960/segment-anything-eo/blob/main/basic_usage.ipynb 33 | 34 | ### Technical details: 35 | - Using a sliding window algorithm to process large images 36 | - In order to separate instances, every instance gets surrounded by 1px width spare space, so it is not the same as how original Segment Anything works 37 | 38 | ***Segment Anything was released less than a week ago, and these are the first experiments with it. I don't know how paramters affect perfomance — feel free to change everything.*** 39 | -------------------------------------------------------------------------------- /__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aliaksandr960/segment-anything-eo/2f00b6fc4b8218e0f02c70114bbeeaa3fcc70712/__init__.py -------------------------------------------------------------------------------- /polygonization.py: -------------------------------------------------------------------------------- 1 | import shapely 2 | import geopandas as gpd 3 | import rasterio 4 | from rasterio import features 5 | 6 | 7 | def tiff_to_shapes(tiff_path, simplify_tolerance=None): 8 | with rasterio.open(tiff_path) as src: 9 | band=src.read() 10 | 11 | mask = band!= 0 12 | shapes = features.shapes(band, mask=mask, transform=src.transform) 13 | result = [shapely.geometry.shape(shape) for shape, _ in shapes] 14 | if simplify_tolerance is not None: 15 | result = [shape.simplify(tolerance=simplify_tolerance) for shape in result] 16 | return result 17 | 18 | 19 | def tiff_to_gpkg(tiff_path, gpkg_path, simplify_tolerance=None): 20 | with rasterio.open(tiff_path) as src: 21 | band=src.read() 22 | 23 | mask = band!= 0 24 | shapes = features.shapes(band, mask=mask, transform=src.transform) 25 | 26 | fc = [{"geometry": shapely.geometry.shape(shape), "properties": {"value": value}} for shape, value in shapes] 27 | if simplify_tolerance is not None: 28 | for i in fc: 29 | i["geometry"] = i["geometry"].simplify(tolerance=simplify_tolerance) 30 | 31 | gdf = gpd.GeoDataFrame.from_features(fc) 32 | gdf.set_crs(epsg=src.crs.to_epsg(), inplace=True) 33 | gdf.to_file(gpkg_path, driver='GPKG') -------------------------------------------------------------------------------- /sameo.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import cv2 3 | import sliding_window 4 | import polygonization 5 | from tms2geotiff.tms2geotiff import draw_tile 6 | from segment_anything import sam_model_registry, SamAutomaticMaskGenerator 7 | 8 | 9 | # Availble sam_kwargs: 10 | 11 | # points_per_side: Optional[int] = 32, 12 | # points_per_batch: int = 64, 13 | # pred_iou_thresh: float = 0.88, 14 | # stability_score_thresh: float = 0.95, 15 | # stability_score_offset: float = 1.0, 16 | # box_nms_thresh: float = 0.7, 17 | # crop_n_layers: int = 0, 18 | # crop_nms_thresh: float = 0.7, 19 | # crop_overlap_ratio: float = 512 / 1500, 20 | # crop_n_points_downscale_factor: int = 1, 21 | # point_grids: Optional[List[np.ndarray]] = None, 22 | # min_mask_region_area: int = 0, 23 | # output_mode: str = "binary_mask", 24 | 25 | class SamEO: 26 | def __init__(self, checkpoint="sam_vit_h_4b8939.pth", 27 | model_type='vit_h', 28 | device='cpu', 29 | erosion_kernel=(3, 3), 30 | mask_multiplier=255, 31 | sam_kwargs=None): 32 | 33 | self.checkpoint = checkpoint 34 | self.model_type = model_type 35 | self.device = device 36 | self.sam_kwargs = sam_kwargs 37 | self.reinit_sam() 38 | 39 | self.erosion_kernel = erosion_kernel 40 | if self.erosion_kernel is not None: 41 | self.erosion_kernel = np.ones(erosion_kernel, np.uint8) 42 | 43 | self.mask_multiplier = mask_multiplier 44 | 45 | def reinit_sam(self): 46 | self.sam = sam_model_registry[self.model_type](checkpoint=self.checkpoint) 47 | self.sam.to(device=self.device) 48 | 49 | sam_kwargs = self.sam_kwargs if self.sam_kwargs is not None else {} 50 | self.mask_generator = SamAutomaticMaskGenerator(self.sam, **sam_kwargs) 51 | 52 | def __call__(self, image): 53 | h, w, _ = image.shape 54 | 55 | resulting_mask = np.zeros((h, w), dtype=np.uint8) 56 | resulting_borders = np.zeros((h, w), dtype=np.uint8) 57 | 58 | masks = self.mask_generator.generate(image) 59 | for m in masks: 60 | mask = (m['segmentation'] > 0).astype(np.uint8) 61 | resulting_mask += mask 62 | 63 | if self.erosion_kernel is not None: 64 | mask_erode = cv2.erode(mask, self.erosion_kernel, iterations=1) 65 | mask_erode = (mask_erode > 0).astype(np.uint8) 66 | edge_mask = mask - mask_erode 67 | resulting_borders += edge_mask 68 | 69 | resulting_mask = (resulting_mask > 0).astype(np.uint8) 70 | resulting_borders = (resulting_borders > 0).astype(np.uint8) 71 | resulting_mask_with_borders = resulting_mask - resulting_borders 72 | return resulting_mask_with_borders *self.mask_multiplier 73 | 74 | def tiff_to_tiff(self, in_path, out_path, **kwargs): 75 | return sliding_window.tiff_to_tiff(in_path, out_path, self, **kwargs) 76 | 77 | def image_to_image(self, image, **kwargs): 78 | return sliding_window.image_to_image(image, self, **kwargs) 79 | 80 | def download_tms_as_tiff(self, source, pt1, pt2, zoom, dist): 81 | image = draw_tile(source, pt1[0], pt1[1], pt2[0], pt2[1], 82 | zoom, dist) 83 | return image 84 | 85 | def tiff_to_gpkg(self, tiff_path, gpkg_path, simplify_tolerance=None): 86 | polygonization.tiff_to_gpkg(tiff_path, gpkg_path, simplify_tolerance) -------------------------------------------------------------------------------- /segment_anything/LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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We also recommend that a 185 | file or class name and description of purpose be included on the 186 | same "printed page" as the copyright notice for easier 187 | identification within third-party archives. 188 | 189 | Copyright [yyyy] [name of copyright owner] 190 | 191 | Licensed under the Apache License, Version 2.0 (the "License"); 192 | you may not use this file except in compliance with the License. 193 | You may obtain a copy of the License at 194 | 195 | http://www.apache.org/licenses/LICENSE-2.0 196 | 197 | Unless required by applicable law or agreed to in writing, software 198 | distributed under the License is distributed on an "AS IS" BASIS, 199 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 200 | See the License for the specific language governing permissions and 201 | limitations under the License. 202 | -------------------------------------------------------------------------------- /segment_anything/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # All rights reserved. 3 | 4 | # This source code is licensed under the license found in the 5 | # LICENSE file in the root directory of this source tree. 6 | 7 | from .build_sam import ( 8 | build_sam, 9 | build_sam_vit_h, 10 | build_sam_vit_l, 11 | build_sam_vit_b, 12 | sam_model_registry, 13 | ) 14 | from .predictor import SamPredictor 15 | from .automatic_mask_generator import SamAutomaticMaskGenerator 16 | -------------------------------------------------------------------------------- /segment_anything/automatic_mask_generator.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # All rights reserved. 3 | 4 | # This source code is licensed under the license found in the 5 | # LICENSE file in the root directory of this source tree. 6 | 7 | import numpy as np 8 | import torch 9 | from torchvision.ops.boxes import batched_nms, box_area # type: ignore 10 | 11 | from typing import Any, Dict, List, Optional, Tuple 12 | 13 | from .modeling import Sam 14 | from .predictor import SamPredictor 15 | from .utils.amg import ( 16 | MaskData, 17 | area_from_rle, 18 | batch_iterator, 19 | batched_mask_to_box, 20 | box_xyxy_to_xywh, 21 | build_all_layer_point_grids, 22 | calculate_stability_score, 23 | coco_encode_rle, 24 | generate_crop_boxes, 25 | is_box_near_crop_edge, 26 | mask_to_rle_pytorch, 27 | remove_small_regions, 28 | rle_to_mask, 29 | uncrop_boxes_xyxy, 30 | uncrop_masks, 31 | uncrop_points, 32 | ) 33 | 34 | 35 | class SamAutomaticMaskGenerator: 36 | def __init__( 37 | self, 38 | model: Sam, 39 | points_per_side: Optional[int] = 32, 40 | points_per_batch: int = 64, 41 | pred_iou_thresh: float = 0.88, 42 | stability_score_thresh: float = 0.95, 43 | stability_score_offset: float = 1.0, 44 | box_nms_thresh: float = 0.7, 45 | crop_n_layers: int = 0, 46 | crop_nms_thresh: float = 0.7, 47 | crop_overlap_ratio: float = 512 / 1500, 48 | crop_n_points_downscale_factor: int = 1, 49 | point_grids: Optional[List[np.ndarray]] = None, 50 | min_mask_region_area: int = 0, 51 | output_mode: str = "binary_mask", 52 | ) -> None: 53 | """ 54 | Using a SAM model, generates masks for the entire image. 55 | Generates a grid of point prompts over the image, then filters 56 | low quality and duplicate masks. The default settings are chosen 57 | for SAM with a ViT-H backbone. 58 | 59 | Arguments: 60 | model (Sam): The SAM model to use for mask prediction. 61 | points_per_side (int or None): The number of points to be sampled 62 | along one side of the image. The total number of points is 63 | points_per_side**2. If None, 'point_grids' must provide explicit 64 | point sampling. 65 | points_per_batch (int): Sets the number of points run simultaneously 66 | by the model. Higher numbers may be faster but use more GPU memory. 67 | pred_iou_thresh (float): A filtering threshold in [0,1], using the 68 | model's predicted mask quality. 69 | stability_score_thresh (float): A filtering threshold in [0,1], using 70 | the stability of the mask under changes to the cutoff used to binarize 71 | the model's mask predictions. 72 | stability_score_offset (float): The amount to shift the cutoff when 73 | calculated the stability score. 74 | box_nms_thresh (float): The box IoU cutoff used by non-maximal 75 | suppression to filter duplicate masks. 76 | crops_n_layers (int): If >0, mask prediction will be run again on 77 | crops of the image. Sets the number of layers to run, where each 78 | layer has 2**i_layer number of image crops. 79 | crops_nms_thresh (float): The box IoU cutoff used by non-maximal 80 | suppression to filter duplicate masks between different crops. 81 | crop_overlap_ratio (float): Sets the degree to which crops overlap. 82 | In the first crop layer, crops will overlap by this fraction of 83 | the image length. Later layers with more crops scale down this overlap. 84 | crop_n_points_downscale_factor (int): The number of points-per-side 85 | sampled in layer n is scaled down by crop_n_points_downscale_factor**n. 86 | point_grids (list(np.ndarray) or None): A list over explicit grids 87 | of points used for sampling, normalized to [0,1]. The nth grid in the 88 | list is used in the nth crop layer. Exclusive with points_per_side. 89 | min_mask_region_area (int): If >0, postprocessing will be applied 90 | to remove disconnected regions and holes in masks with area smaller 91 | than min_mask_region_area. Requires opencv. 92 | output_mode (str): The form masks are returned in. Can be 'binary_mask', 93 | 'uncompressed_rle', or 'coco_rle'. 'coco_rle' requires pycocotools. 94 | For large resolutions, 'binary_mask' may consume large amounts of 95 | memory. 96 | """ 97 | 98 | assert (points_per_side is None) != ( 99 | point_grids is None 100 | ), "Exactly one of points_per_side or point_grid must be provided." 101 | if points_per_side is not None: 102 | self.point_grids = build_all_layer_point_grids( 103 | points_per_side, 104 | crop_n_layers, 105 | crop_n_points_downscale_factor, 106 | ) 107 | elif point_grids is not None: 108 | self.point_grids = point_grids 109 | else: 110 | raise ValueError("Can't have both points_per_side and point_grid be None.") 111 | 112 | assert output_mode in [ 113 | "binary_mask", 114 | "uncompressed_rle", 115 | "coco_rle", 116 | ], f"Unknown output_mode {output_mode}." 117 | if output_mode == "coco_rle": 118 | from pycocotools import mask as mask_utils # type: ignore # noqa: F401 119 | 120 | if min_mask_region_area > 0: 121 | import cv2 # type: ignore # noqa: F401 122 | 123 | self.predictor = SamPredictor(model) 124 | self.points_per_batch = points_per_batch 125 | self.pred_iou_thresh = pred_iou_thresh 126 | self.stability_score_thresh = stability_score_thresh 127 | self.stability_score_offset = stability_score_offset 128 | self.box_nms_thresh = box_nms_thresh 129 | self.crop_n_layers = crop_n_layers 130 | self.crop_nms_thresh = crop_nms_thresh 131 | self.crop_overlap_ratio = crop_overlap_ratio 132 | self.crop_n_points_downscale_factor = crop_n_points_downscale_factor 133 | self.min_mask_region_area = min_mask_region_area 134 | self.output_mode = output_mode 135 | 136 | @torch.no_grad() 137 | def generate(self, image: np.ndarray) -> List[Dict[str, Any]]: 138 | """ 139 | Generates masks for the given image. 140 | 141 | Arguments: 142 | image (np.ndarray): The image to generate masks for, in HWC uint8 format. 143 | 144 | Returns: 145 | list(dict(str, any)): A list over records for masks. Each record is 146 | a dict containing the following keys: 147 | segmentation (dict(str, any) or np.ndarray): The mask. If 148 | output_mode='binary_mask', is an array of shape HW. Otherwise, 149 | is a dictionary containing the RLE. 150 | bbox (list(float)): The box around the mask, in XYWH format. 151 | area (int): The area in pixels of the mask. 152 | predicted_iou (float): The model's own prediction of the mask's 153 | quality. This is filtered by the pred_iou_thresh parameter. 154 | point_coords (list(list(float))): The point coordinates input 155 | to the model to generate this mask. 156 | stability_score (float): A measure of the mask's quality. This 157 | is filtered on using the stability_score_thresh parameter. 158 | crop_box (list(float)): The crop of the image used to generate 159 | the mask, given in XYWH format. 160 | """ 161 | 162 | # Generate masks 163 | mask_data = self._generate_masks(image) 164 | 165 | # Filter small disconnected regions and holes in masks 166 | if self.min_mask_region_area > 0: 167 | mask_data = self.postprocess_small_regions( 168 | mask_data, 169 | self.min_mask_region_area, 170 | max(self.box_nms_thresh, self.crop_nms_thresh), 171 | ) 172 | 173 | # Encode masks 174 | if self.output_mode == "coco_rle": 175 | mask_data["segmentations"] = [coco_encode_rle(rle) for rle in mask_data["rles"]] 176 | elif self.output_mode == "binary_mask": 177 | mask_data["segmentations"] = [rle_to_mask(rle) for rle in mask_data["rles"]] 178 | else: 179 | mask_data["segmentations"] = mask_data["rles"] 180 | 181 | # Write mask records 182 | curr_anns = [] 183 | for idx in range(len(mask_data["segmentations"])): 184 | ann = { 185 | "segmentation": mask_data["segmentations"][idx], 186 | "area": area_from_rle(mask_data["rles"][idx]), 187 | "bbox": box_xyxy_to_xywh(mask_data["boxes"][idx]).tolist(), 188 | "predicted_iou": mask_data["iou_preds"][idx].item(), 189 | "point_coords": [mask_data["points"][idx].tolist()], 190 | "stability_score": mask_data["stability_score"][idx].item(), 191 | "crop_box": box_xyxy_to_xywh(mask_data["crop_boxes"][idx]).tolist(), 192 | } 193 | curr_anns.append(ann) 194 | 195 | return curr_anns 196 | 197 | def _generate_masks(self, image: np.ndarray) -> MaskData: 198 | orig_size = image.shape[:2] 199 | crop_boxes, layer_idxs = generate_crop_boxes( 200 | orig_size, self.crop_n_layers, self.crop_overlap_ratio 201 | ) 202 | 203 | # Iterate over image crops 204 | data = MaskData() 205 | for crop_box, layer_idx in zip(crop_boxes, layer_idxs): 206 | crop_data = self._process_crop(image, crop_box, layer_idx, orig_size) 207 | data.cat(crop_data) 208 | 209 | # Remove duplicate masks between crops 210 | if len(crop_boxes) > 1: 211 | # Prefer masks from smaller crops 212 | scores = 1 / box_area(data["crop_boxes"]) 213 | scores = scores.to(data["boxes"].device) 214 | keep_by_nms = batched_nms( 215 | data["boxes"].float(), 216 | scores, 217 | torch.zeros(len(data["boxes"])), # categories 218 | iou_threshold=self.crop_nms_thresh, 219 | ) 220 | data.filter(keep_by_nms) 221 | 222 | data.to_numpy() 223 | return data 224 | 225 | def _process_crop( 226 | self, 227 | image: np.ndarray, 228 | crop_box: List[int], 229 | crop_layer_idx: int, 230 | orig_size: Tuple[int, ...], 231 | ) -> MaskData: 232 | # Crop the image and calculate embeddings 233 | x0, y0, x1, y1 = crop_box 234 | cropped_im = image[y0:y1, x0:x1, :] 235 | cropped_im_size = cropped_im.shape[:2] 236 | self.predictor.set_image(cropped_im) 237 | 238 | # Get points for this crop 239 | points_scale = np.array(cropped_im_size)[None, ::-1] 240 | points_for_image = self.point_grids[crop_layer_idx] * points_scale 241 | 242 | # Generate masks for this crop in batches 243 | data = MaskData() 244 | for (points,) in batch_iterator(self.points_per_batch, points_for_image): 245 | batch_data = self._process_batch(points, cropped_im_size, crop_box, orig_size) 246 | data.cat(batch_data) 247 | del batch_data 248 | self.predictor.reset_image() 249 | 250 | # Remove duplicates within this crop. 251 | keep_by_nms = batched_nms( 252 | data["boxes"].float(), 253 | data["iou_preds"], 254 | torch.zeros(len(data["boxes"])), # categories 255 | iou_threshold=self.box_nms_thresh, 256 | ) 257 | data.filter(keep_by_nms) 258 | 259 | # Return to the original image frame 260 | data["boxes"] = uncrop_boxes_xyxy(data["boxes"], crop_box) 261 | data["points"] = uncrop_points(data["points"], crop_box) 262 | data["crop_boxes"] = torch.tensor([crop_box for _ in range(len(data["rles"]))]) 263 | 264 | return data 265 | 266 | def _process_batch( 267 | self, 268 | points: np.ndarray, 269 | im_size: Tuple[int, ...], 270 | crop_box: List[int], 271 | orig_size: Tuple[int, ...], 272 | ) -> MaskData: 273 | orig_h, orig_w = orig_size 274 | 275 | # Run model on this batch 276 | transformed_points = self.predictor.transform.apply_coords(points, im_size) 277 | in_points = torch.as_tensor(transformed_points, device=self.predictor.device) 278 | in_labels = torch.ones(in_points.shape[0], dtype=torch.int, device=in_points.device) 279 | masks, iou_preds, _ = self.predictor.predict_torch( 280 | in_points[:, None, :], 281 | in_labels[:, None], 282 | multimask_output=True, 283 | return_logits=True, 284 | ) 285 | 286 | # Serialize predictions and store in MaskData 287 | data = MaskData( 288 | masks=masks.flatten(0, 1), 289 | iou_preds=iou_preds.flatten(0, 1), 290 | points=torch.as_tensor(points.repeat(masks.shape[1], axis=0)), 291 | ) 292 | del masks 293 | 294 | # Filter by predicted IoU 295 | if self.pred_iou_thresh > 0.0: 296 | keep_mask = data["iou_preds"] > self.pred_iou_thresh 297 | data.filter(keep_mask) 298 | 299 | # Calculate stability score 300 | data["stability_score"] = calculate_stability_score( 301 | data["masks"], self.predictor.model.mask_threshold, self.stability_score_offset 302 | ) 303 | if self.stability_score_thresh > 0.0: 304 | keep_mask = data["stability_score"] >= self.stability_score_thresh 305 | data.filter(keep_mask) 306 | 307 | # Threshold masks and calculate boxes 308 | data["masks"] = data["masks"] > self.predictor.model.mask_threshold 309 | data["boxes"] = batched_mask_to_box(data["masks"]) 310 | 311 | # Filter boxes that touch crop boundaries 312 | keep_mask = ~is_box_near_crop_edge(data["boxes"], crop_box, [0, 0, orig_w, orig_h]) 313 | if not torch.all(keep_mask): 314 | data.filter(keep_mask) 315 | 316 | # Compress to RLE 317 | data["masks"] = uncrop_masks(data["masks"], crop_box, orig_h, orig_w) 318 | data["rles"] = mask_to_rle_pytorch(data["masks"]) 319 | del data["masks"] 320 | 321 | return data 322 | 323 | @staticmethod 324 | def postprocess_small_regions( 325 | mask_data: MaskData, min_area: int, nms_thresh: float 326 | ) -> MaskData: 327 | """ 328 | Removes small disconnected regions and holes in masks, then reruns 329 | box NMS to remove any new duplicates. 330 | 331 | Edits mask_data in place. 332 | 333 | Requires open-cv as a dependency. 334 | """ 335 | if len(mask_data["rles"]) == 0: 336 | return mask_data 337 | 338 | # Filter small disconnected regions and holes 339 | new_masks = [] 340 | scores = [] 341 | for rle in mask_data["rles"]: 342 | mask = rle_to_mask(rle) 343 | 344 | mask, changed = remove_small_regions(mask, min_area, mode="holes") 345 | unchanged = not changed 346 | mask, changed = remove_small_regions(mask, min_area, mode="islands") 347 | unchanged = unchanged and not changed 348 | 349 | new_masks.append(torch.as_tensor(mask).unsqueeze(0)) 350 | # Give score=0 to changed masks and score=1 to unchanged masks 351 | # so NMS will prefer ones that didn't need postprocessing 352 | scores.append(float(unchanged)) 353 | 354 | # Recalculate boxes and remove any new duplicates 355 | masks = torch.cat(new_masks, dim=0) 356 | boxes = batched_mask_to_box(masks) 357 | keep_by_nms = batched_nms( 358 | boxes.float(), 359 | torch.as_tensor(scores), 360 | torch.zeros(len(boxes)), # categories 361 | iou_threshold=nms_thresh, 362 | ) 363 | 364 | # Only recalculate RLEs for masks that have changed 365 | for i_mask in keep_by_nms: 366 | if scores[i_mask] == 0.0: 367 | mask_torch = masks[i_mask].unsqueeze(0) 368 | mask_data["rles"][i_mask] = mask_to_rle_pytorch(mask_torch)[0] 369 | mask_data["boxes"][i_mask] = boxes[i_mask] # update res directly 370 | mask_data.filter(keep_by_nms) 371 | 372 | return mask_data 373 | -------------------------------------------------------------------------------- /segment_anything/build_sam.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # All rights reserved. 3 | 4 | # This source code is licensed under the license found in the 5 | # LICENSE file in the root directory of this source tree. 6 | 7 | import torch 8 | 9 | from functools import partial 10 | 11 | from .modeling import ImageEncoderViT, MaskDecoder, PromptEncoder, Sam, TwoWayTransformer 12 | 13 | 14 | def build_sam_vit_h(checkpoint=None): 15 | return _build_sam( 16 | encoder_embed_dim=1280, 17 | encoder_depth=32, 18 | encoder_num_heads=16, 19 | encoder_global_attn_indexes=[7, 15, 23, 31], 20 | checkpoint=checkpoint, 21 | ) 22 | 23 | 24 | build_sam = build_sam_vit_h 25 | 26 | 27 | def build_sam_vit_l(checkpoint=None): 28 | return _build_sam( 29 | encoder_embed_dim=1024, 30 | encoder_depth=24, 31 | encoder_num_heads=16, 32 | encoder_global_attn_indexes=[5, 11, 17, 23], 33 | checkpoint=checkpoint, 34 | ) 35 | 36 | 37 | def build_sam_vit_b(checkpoint=None): 38 | return _build_sam( 39 | encoder_embed_dim=768, 40 | encoder_depth=12, 41 | encoder_num_heads=12, 42 | encoder_global_attn_indexes=[2, 5, 8, 11], 43 | checkpoint=checkpoint, 44 | ) 45 | 46 | 47 | sam_model_registry = { 48 | "default": build_sam_vit_h, 49 | "vit_h": build_sam_vit_h, 50 | "vit_l": build_sam_vit_l, 51 | "vit_b": build_sam_vit_b, 52 | } 53 | 54 | 55 | def _build_sam( 56 | encoder_embed_dim, 57 | encoder_depth, 58 | encoder_num_heads, 59 | encoder_global_attn_indexes, 60 | checkpoint=None, 61 | ): 62 | prompt_embed_dim = 256 63 | image_size = 1024 64 | vit_patch_size = 16 65 | image_embedding_size = image_size // vit_patch_size 66 | sam = Sam( 67 | image_encoder=ImageEncoderViT( 68 | depth=encoder_depth, 69 | embed_dim=encoder_embed_dim, 70 | img_size=image_size, 71 | mlp_ratio=4, 72 | norm_layer=partial(torch.nn.LayerNorm, eps=1e-6), 73 | num_heads=encoder_num_heads, 74 | patch_size=vit_patch_size, 75 | qkv_bias=True, 76 | use_rel_pos=True, 77 | global_attn_indexes=encoder_global_attn_indexes, 78 | window_size=14, 79 | out_chans=prompt_embed_dim, 80 | ), 81 | prompt_encoder=PromptEncoder( 82 | embed_dim=prompt_embed_dim, 83 | image_embedding_size=(image_embedding_size, image_embedding_size), 84 | input_image_size=(image_size, image_size), 85 | mask_in_chans=16, 86 | ), 87 | mask_decoder=MaskDecoder( 88 | num_multimask_outputs=3, 89 | transformer=TwoWayTransformer( 90 | depth=2, 91 | embedding_dim=prompt_embed_dim, 92 | mlp_dim=2048, 93 | num_heads=8, 94 | ), 95 | transformer_dim=prompt_embed_dim, 96 | iou_head_depth=3, 97 | iou_head_hidden_dim=256, 98 | ), 99 | pixel_mean=[123.675, 116.28, 103.53], 100 | pixel_std=[58.395, 57.12, 57.375], 101 | ) 102 | sam.eval() 103 | if checkpoint is not None: 104 | with open(checkpoint, "rb") as f: 105 | state_dict = torch.load(f) 106 | sam.load_state_dict(state_dict) 107 | return sam 108 | -------------------------------------------------------------------------------- /segment_anything/modeling/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # All rights reserved. 3 | 4 | # This source code is licensed under the license found in the 5 | # LICENSE file in the root directory of this source tree. 6 | 7 | from .sam import Sam 8 | from .image_encoder import ImageEncoderViT 9 | from .mask_decoder import MaskDecoder 10 | from .prompt_encoder import PromptEncoder 11 | from .transformer import TwoWayTransformer 12 | -------------------------------------------------------------------------------- /segment_anything/modeling/common.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # All rights reserved. 3 | 4 | # This source code is licensed under the license found in the 5 | # LICENSE file in the root directory of this source tree. 6 | 7 | import torch 8 | import torch.nn as nn 9 | 10 | from typing import Type 11 | 12 | 13 | class MLPBlock(nn.Module): 14 | def __init__( 15 | self, 16 | embedding_dim: int, 17 | mlp_dim: int, 18 | act: Type[nn.Module] = nn.GELU, 19 | ) -> None: 20 | super().__init__() 21 | self.lin1 = nn.Linear(embedding_dim, mlp_dim) 22 | self.lin2 = nn.Linear(mlp_dim, embedding_dim) 23 | self.act = act() 24 | 25 | def forward(self, x: torch.Tensor) -> torch.Tensor: 26 | return self.lin2(self.act(self.lin1(x))) 27 | 28 | 29 | # From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa 30 | # Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119 # noqa 31 | class LayerNorm2d(nn.Module): 32 | def __init__(self, num_channels: int, eps: float = 1e-6) -> None: 33 | super().__init__() 34 | self.weight = nn.Parameter(torch.ones(num_channels)) 35 | self.bias = nn.Parameter(torch.zeros(num_channels)) 36 | self.eps = eps 37 | 38 | def forward(self, x: torch.Tensor) -> torch.Tensor: 39 | u = x.mean(1, keepdim=True) 40 | s = (x - u).pow(2).mean(1, keepdim=True) 41 | x = (x - u) / torch.sqrt(s + self.eps) 42 | x = self.weight[:, None, None] * x + self.bias[:, None, None] 43 | return x 44 | -------------------------------------------------------------------------------- /segment_anything/modeling/image_encoder.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # All rights reserved. 3 | 4 | # This source code is licensed under the license found in the 5 | # LICENSE file in the root directory of this source tree. 6 | 7 | import torch 8 | import torch.nn as nn 9 | import torch.nn.functional as F 10 | 11 | from typing import Optional, Tuple, Type 12 | 13 | from .common import LayerNorm2d, MLPBlock 14 | 15 | 16 | # This class and its supporting functions below lightly adapted from the ViTDet backbone available at: https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/vit.py # noqa 17 | class ImageEncoderViT(nn.Module): 18 | def __init__( 19 | self, 20 | img_size: int = 1024, 21 | patch_size: int = 16, 22 | in_chans: int = 3, 23 | embed_dim: int = 768, 24 | depth: int = 12, 25 | num_heads: int = 12, 26 | mlp_ratio: float = 4.0, 27 | out_chans: int = 256, 28 | qkv_bias: bool = True, 29 | norm_layer: Type[nn.Module] = nn.LayerNorm, 30 | act_layer: Type[nn.Module] = nn.GELU, 31 | use_abs_pos: bool = True, 32 | use_rel_pos: bool = False, 33 | rel_pos_zero_init: bool = True, 34 | window_size: int = 0, 35 | global_attn_indexes: Tuple[int, ...] = (), 36 | ) -> None: 37 | """ 38 | Args: 39 | img_size (int): Input image size. 40 | patch_size (int): Patch size. 41 | in_chans (int): Number of input image channels. 42 | embed_dim (int): Patch embedding dimension. 43 | depth (int): Depth of ViT. 44 | num_heads (int): Number of attention heads in each ViT block. 45 | mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. 46 | qkv_bias (bool): If True, add a learnable bias to query, key, value. 47 | norm_layer (nn.Module): Normalization layer. 48 | act_layer (nn.Module): Activation layer. 49 | use_abs_pos (bool): If True, use absolute positional embeddings. 50 | use_rel_pos (bool): If True, add relative positional embeddings to the attention map. 51 | rel_pos_zero_init (bool): If True, zero initialize relative positional parameters. 52 | window_size (int): Window size for window attention blocks. 53 | global_attn_indexes (list): Indexes for blocks using global attention. 54 | """ 55 | super().__init__() 56 | self.img_size = img_size 57 | 58 | self.patch_embed = PatchEmbed( 59 | kernel_size=(patch_size, patch_size), 60 | stride=(patch_size, patch_size), 61 | in_chans=in_chans, 62 | embed_dim=embed_dim, 63 | ) 64 | 65 | self.pos_embed: Optional[nn.Parameter] = None 66 | if use_abs_pos: 67 | # Initialize absolute positional embedding with pretrain image size. 68 | self.pos_embed = nn.Parameter( 69 | torch.zeros(1, img_size // patch_size, img_size // patch_size, embed_dim) 70 | ) 71 | 72 | self.blocks = nn.ModuleList() 73 | for i in range(depth): 74 | block = Block( 75 | dim=embed_dim, 76 | num_heads=num_heads, 77 | mlp_ratio=mlp_ratio, 78 | qkv_bias=qkv_bias, 79 | norm_layer=norm_layer, 80 | act_layer=act_layer, 81 | use_rel_pos=use_rel_pos, 82 | rel_pos_zero_init=rel_pos_zero_init, 83 | window_size=window_size if i not in global_attn_indexes else 0, 84 | input_size=(img_size // patch_size, img_size // patch_size), 85 | ) 86 | self.blocks.append(block) 87 | 88 | self.neck = nn.Sequential( 89 | nn.Conv2d( 90 | embed_dim, 91 | out_chans, 92 | kernel_size=1, 93 | bias=False, 94 | ), 95 | LayerNorm2d(out_chans), 96 | nn.Conv2d( 97 | out_chans, 98 | out_chans, 99 | kernel_size=3, 100 | padding=1, 101 | bias=False, 102 | ), 103 | LayerNorm2d(out_chans), 104 | ) 105 | 106 | def forward(self, x: torch.Tensor) -> torch.Tensor: 107 | x = self.patch_embed(x) 108 | if self.pos_embed is not None: 109 | x = x + self.pos_embed 110 | 111 | for blk in self.blocks: 112 | x = blk(x) 113 | 114 | x = self.neck(x.permute(0, 3, 1, 2)) 115 | 116 | return x 117 | 118 | 119 | class Block(nn.Module): 120 | """Transformer blocks with support of window attention and residual propagation blocks""" 121 | 122 | def __init__( 123 | self, 124 | dim: int, 125 | num_heads: int, 126 | mlp_ratio: float = 4.0, 127 | qkv_bias: bool = True, 128 | norm_layer: Type[nn.Module] = nn.LayerNorm, 129 | act_layer: Type[nn.Module] = nn.GELU, 130 | use_rel_pos: bool = False, 131 | rel_pos_zero_init: bool = True, 132 | window_size: int = 0, 133 | input_size: Optional[Tuple[int, int]] = None, 134 | ) -> None: 135 | """ 136 | Args: 137 | dim (int): Number of input channels. 138 | num_heads (int): Number of attention heads in each ViT block. 139 | mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. 140 | qkv_bias (bool): If True, add a learnable bias to query, key, value. 141 | norm_layer (nn.Module): Normalization layer. 142 | act_layer (nn.Module): Activation layer. 143 | use_rel_pos (bool): If True, add relative positional embeddings to the attention map. 144 | rel_pos_zero_init (bool): If True, zero initialize relative positional parameters. 145 | window_size (int): Window size for window attention blocks. If it equals 0, then 146 | use global attention. 147 | input_size (int or None): Input resolution for calculating the relative positional 148 | parameter size. 149 | """ 150 | super().__init__() 151 | self.norm1 = norm_layer(dim) 152 | self.attn = Attention( 153 | dim, 154 | num_heads=num_heads, 155 | qkv_bias=qkv_bias, 156 | use_rel_pos=use_rel_pos, 157 | rel_pos_zero_init=rel_pos_zero_init, 158 | input_size=input_size if window_size == 0 else (window_size, window_size), 159 | ) 160 | 161 | self.norm2 = norm_layer(dim) 162 | self.mlp = MLPBlock(embedding_dim=dim, mlp_dim=int(dim * mlp_ratio), act=act_layer) 163 | 164 | self.window_size = window_size 165 | 166 | def forward(self, x: torch.Tensor) -> torch.Tensor: 167 | shortcut = x 168 | x = self.norm1(x) 169 | # Window partition 170 | if self.window_size > 0: 171 | H, W = x.shape[1], x.shape[2] 172 | x, pad_hw = window_partition(x, self.window_size) 173 | 174 | x = self.attn(x) 175 | # Reverse window partition 176 | if self.window_size > 0: 177 | x = window_unpartition(x, self.window_size, pad_hw, (H, W)) 178 | 179 | x = shortcut + x 180 | x = x + self.mlp(self.norm2(x)) 181 | 182 | return x 183 | 184 | 185 | class Attention(nn.Module): 186 | """Multi-head Attention block with relative position embeddings.""" 187 | 188 | def __init__( 189 | self, 190 | dim: int, 191 | num_heads: int = 8, 192 | qkv_bias: bool = True, 193 | use_rel_pos: bool = False, 194 | rel_pos_zero_init: bool = True, 195 | input_size: Optional[Tuple[int, int]] = None, 196 | ) -> None: 197 | """ 198 | Args: 199 | dim (int): Number of input channels. 200 | num_heads (int): Number of attention heads. 201 | qkv_bias (bool: If True, add a learnable bias to query, key, value. 202 | rel_pos (bool): If True, add relative positional embeddings to the attention map. 203 | rel_pos_zero_init (bool): If True, zero initialize relative positional parameters. 204 | input_size (int or None): Input resolution for calculating the relative positional 205 | parameter size. 206 | """ 207 | super().__init__() 208 | self.num_heads = num_heads 209 | head_dim = dim // num_heads 210 | self.scale = head_dim**-0.5 211 | 212 | self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) 213 | self.proj = nn.Linear(dim, dim) 214 | 215 | self.use_rel_pos = use_rel_pos 216 | if self.use_rel_pos: 217 | assert ( 218 | input_size is not None 219 | ), "Input size must be provided if using relative positional encoding." 220 | # initialize relative positional embeddings 221 | self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim)) 222 | self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim)) 223 | 224 | def forward(self, x: torch.Tensor) -> torch.Tensor: 225 | B, H, W, _ = x.shape 226 | # qkv with shape (3, B, nHead, H * W, C) 227 | qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) 228 | # q, k, v with shape (B * nHead, H * W, C) 229 | q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0) 230 | 231 | attn = (q * self.scale) @ k.transpose(-2, -1) 232 | 233 | if self.use_rel_pos: 234 | attn = add_decomposed_rel_pos(attn, q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W)) 235 | 236 | attn = attn.softmax(dim=-1) 237 | x = (attn @ v).view(B, self.num_heads, H, W, -1).permute(0, 2, 3, 1, 4).reshape(B, H, W, -1) 238 | x = self.proj(x) 239 | 240 | return x 241 | 242 | 243 | def window_partition(x: torch.Tensor, window_size: int) -> Tuple[torch.Tensor, Tuple[int, int]]: 244 | """ 245 | Partition into non-overlapping windows with padding if needed. 246 | Args: 247 | x (tensor): input tokens with [B, H, W, C]. 248 | window_size (int): window size. 249 | 250 | Returns: 251 | windows: windows after partition with [B * num_windows, window_size, window_size, C]. 252 | (Hp, Wp): padded height and width before partition 253 | """ 254 | B, H, W, C = x.shape 255 | 256 | pad_h = (window_size - H % window_size) % window_size 257 | pad_w = (window_size - W % window_size) % window_size 258 | if pad_h > 0 or pad_w > 0: 259 | x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h)) 260 | Hp, Wp = H + pad_h, W + pad_w 261 | 262 | x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C) 263 | windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) 264 | return windows, (Hp, Wp) 265 | 266 | 267 | def window_unpartition( 268 | windows: torch.Tensor, window_size: int, pad_hw: Tuple[int, int], hw: Tuple[int, int] 269 | ) -> torch.Tensor: 270 | """ 271 | Window unpartition into original sequences and removing padding. 272 | Args: 273 | x (tensor): input tokens with [B * num_windows, window_size, window_size, C]. 274 | window_size (int): window size. 275 | pad_hw (Tuple): padded height and width (Hp, Wp). 276 | hw (Tuple): original height and width (H, W) before padding. 277 | 278 | Returns: 279 | x: unpartitioned sequences with [B, H, W, C]. 280 | """ 281 | Hp, Wp = pad_hw 282 | H, W = hw 283 | B = windows.shape[0] // (Hp * Wp // window_size // window_size) 284 | x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1) 285 | x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1) 286 | 287 | if Hp > H or Wp > W: 288 | x = x[:, :H, :W, :].contiguous() 289 | return x 290 | 291 | 292 | def get_rel_pos(q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor: 293 | """ 294 | Get relative positional embeddings according to the relative positions of 295 | query and key sizes. 296 | Args: 297 | q_size (int): size of query q. 298 | k_size (int): size of key k. 299 | rel_pos (Tensor): relative position embeddings (L, C). 300 | 301 | Returns: 302 | Extracted positional embeddings according to relative positions. 303 | """ 304 | max_rel_dist = int(2 * max(q_size, k_size) - 1) 305 | # Interpolate rel pos if needed. 306 | if rel_pos.shape[0] != max_rel_dist: 307 | # Interpolate rel pos. 308 | rel_pos_resized = F.interpolate( 309 | rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1), 310 | size=max_rel_dist, 311 | mode="linear", 312 | ) 313 | rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0) 314 | else: 315 | rel_pos_resized = rel_pos 316 | 317 | # Scale the coords with short length if shapes for q and k are different. 318 | q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0) 319 | k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0) 320 | relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0) 321 | 322 | return rel_pos_resized[relative_coords.long()] 323 | 324 | 325 | def add_decomposed_rel_pos( 326 | attn: torch.Tensor, 327 | q: torch.Tensor, 328 | rel_pos_h: torch.Tensor, 329 | rel_pos_w: torch.Tensor, 330 | q_size: Tuple[int, int], 331 | k_size: Tuple[int, int], 332 | ) -> torch.Tensor: 333 | """ 334 | Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`. 335 | https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py # noqa B950 336 | Args: 337 | attn (Tensor): attention map. 338 | q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C). 339 | rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis. 340 | rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis. 341 | q_size (Tuple): spatial sequence size of query q with (q_h, q_w). 342 | k_size (Tuple): spatial sequence size of key k with (k_h, k_w). 343 | 344 | Returns: 345 | attn (Tensor): attention map with added relative positional embeddings. 346 | """ 347 | q_h, q_w = q_size 348 | k_h, k_w = k_size 349 | Rh = get_rel_pos(q_h, k_h, rel_pos_h) 350 | Rw = get_rel_pos(q_w, k_w, rel_pos_w) 351 | 352 | B, _, dim = q.shape 353 | r_q = q.reshape(B, q_h, q_w, dim) 354 | rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh) 355 | rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw) 356 | 357 | attn = ( 358 | attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :] 359 | ).view(B, q_h * q_w, k_h * k_w) 360 | 361 | return attn 362 | 363 | 364 | class PatchEmbed(nn.Module): 365 | """ 366 | Image to Patch Embedding. 367 | """ 368 | 369 | def __init__( 370 | self, 371 | kernel_size: Tuple[int, int] = (16, 16), 372 | stride: Tuple[int, int] = (16, 16), 373 | padding: Tuple[int, int] = (0, 0), 374 | in_chans: int = 3, 375 | embed_dim: int = 768, 376 | ) -> None: 377 | """ 378 | Args: 379 | kernel_size (Tuple): kernel size of the projection layer. 380 | stride (Tuple): stride of the projection layer. 381 | padding (Tuple): padding size of the projection layer. 382 | in_chans (int): Number of input image channels. 383 | embed_dim (int): embed_dim (int): Patch embedding dimension. 384 | """ 385 | super().__init__() 386 | 387 | self.proj = nn.Conv2d( 388 | in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding 389 | ) 390 | 391 | def forward(self, x: torch.Tensor) -> torch.Tensor: 392 | x = self.proj(x) 393 | # B C H W -> B H W C 394 | x = x.permute(0, 2, 3, 1) 395 | return x 396 | -------------------------------------------------------------------------------- /segment_anything/modeling/mask_decoder.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # All rights reserved. 3 | 4 | # This source code is licensed under the license found in the 5 | # LICENSE file in the root directory of this source tree. 6 | 7 | import torch 8 | from torch import nn 9 | from torch.nn import functional as F 10 | 11 | from typing import List, Tuple, Type 12 | 13 | from .common import LayerNorm2d 14 | 15 | 16 | class MaskDecoder(nn.Module): 17 | def __init__( 18 | self, 19 | *, 20 | transformer_dim: int, 21 | transformer: nn.Module, 22 | num_multimask_outputs: int = 3, 23 | activation: Type[nn.Module] = nn.GELU, 24 | iou_head_depth: int = 3, 25 | iou_head_hidden_dim: int = 256, 26 | ) -> None: 27 | """ 28 | Predicts masks given an image and prompt embeddings, using a 29 | tranformer architecture. 30 | 31 | Arguments: 32 | transformer_dim (int): the channel dimension of the transformer 33 | transformer (nn.Module): the transformer used to predict masks 34 | num_multimask_outputs (int): the number of masks to predict 35 | when disambiguating masks 36 | activation (nn.Module): the type of activation to use when 37 | upscaling masks 38 | iou_head_depth (int): the depth of the MLP used to predict 39 | mask quality 40 | iou_head_hidden_dim (int): the hidden dimension of the MLP 41 | used to predict mask quality 42 | """ 43 | super().__init__() 44 | self.transformer_dim = transformer_dim 45 | self.transformer = transformer 46 | 47 | self.num_multimask_outputs = num_multimask_outputs 48 | 49 | self.iou_token = nn.Embedding(1, transformer_dim) 50 | self.num_mask_tokens = num_multimask_outputs + 1 51 | self.mask_tokens = nn.Embedding(self.num_mask_tokens, transformer_dim) 52 | 53 | self.output_upscaling = nn.Sequential( 54 | nn.ConvTranspose2d(transformer_dim, transformer_dim // 4, kernel_size=2, stride=2), 55 | LayerNorm2d(transformer_dim // 4), 56 | activation(), 57 | nn.ConvTranspose2d(transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2), 58 | activation(), 59 | ) 60 | self.output_hypernetworks_mlps = nn.ModuleList( 61 | [ 62 | MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3) 63 | for i in range(self.num_mask_tokens) 64 | ] 65 | ) 66 | 67 | self.iou_prediction_head = MLP( 68 | transformer_dim, iou_head_hidden_dim, self.num_mask_tokens, iou_head_depth 69 | ) 70 | 71 | def forward( 72 | self, 73 | image_embeddings: torch.Tensor, 74 | image_pe: torch.Tensor, 75 | sparse_prompt_embeddings: torch.Tensor, 76 | dense_prompt_embeddings: torch.Tensor, 77 | multimask_output: bool, 78 | ) -> Tuple[torch.Tensor, torch.Tensor]: 79 | """ 80 | Predict masks given image and prompt embeddings. 81 | 82 | Arguments: 83 | image_embeddings (torch.Tensor): the embeddings from the image encoder 84 | image_pe (torch.Tensor): positional encoding with the shape of image_embeddings 85 | sparse_prompt_embeddings (torch.Tensor): the embeddings of the points and boxes 86 | dense_prompt_embeddings (torch.Tensor): the embeddings of the mask inputs 87 | multimask_output (bool): Whether to return multiple masks or a single 88 | mask. 89 | 90 | Returns: 91 | torch.Tensor: batched predicted masks 92 | torch.Tensor: batched predictions of mask quality 93 | """ 94 | masks, iou_pred = self.predict_masks( 95 | image_embeddings=image_embeddings, 96 | image_pe=image_pe, 97 | sparse_prompt_embeddings=sparse_prompt_embeddings, 98 | dense_prompt_embeddings=dense_prompt_embeddings, 99 | ) 100 | 101 | # Select the correct mask or masks for outptu 102 | if multimask_output: 103 | mask_slice = slice(1, None) 104 | else: 105 | mask_slice = slice(0, 1) 106 | masks = masks[:, mask_slice, :, :] 107 | iou_pred = iou_pred[:, mask_slice] 108 | 109 | # Prepare output 110 | return masks, iou_pred 111 | 112 | def predict_masks( 113 | self, 114 | image_embeddings: torch.Tensor, 115 | image_pe: torch.Tensor, 116 | sparse_prompt_embeddings: torch.Tensor, 117 | dense_prompt_embeddings: torch.Tensor, 118 | ) -> Tuple[torch.Tensor, torch.Tensor]: 119 | """Predicts masks. See 'forward' for more details.""" 120 | # Concatenate output tokens 121 | output_tokens = torch.cat([self.iou_token.weight, self.mask_tokens.weight], dim=0) 122 | output_tokens = output_tokens.unsqueeze(0).expand(sparse_prompt_embeddings.size(0), -1, -1) 123 | tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1) 124 | 125 | # Expand per-image data in batch direction to be per-mask 126 | src = torch.repeat_interleave(image_embeddings, tokens.shape[0], dim=0) 127 | src = src + dense_prompt_embeddings 128 | pos_src = torch.repeat_interleave(image_pe, tokens.shape[0], dim=0) 129 | b, c, h, w = src.shape 130 | 131 | # Run the transformer 132 | hs, src = self.transformer(src, pos_src, tokens) 133 | iou_token_out = hs[:, 0, :] 134 | mask_tokens_out = hs[:, 1 : (1 + self.num_mask_tokens), :] 135 | 136 | # Upscale mask embeddings and predict masks using the mask tokens 137 | src = src.transpose(1, 2).view(b, c, h, w) 138 | upscaled_embedding = self.output_upscaling(src) 139 | hyper_in_list: List[torch.Tensor] = [] 140 | for i in range(self.num_mask_tokens): 141 | hyper_in_list.append(self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :])) 142 | hyper_in = torch.stack(hyper_in_list, dim=1) 143 | b, c, h, w = upscaled_embedding.shape 144 | masks = (hyper_in @ upscaled_embedding.view(b, c, h * w)).view(b, -1, h, w) 145 | 146 | # Generate mask quality predictions 147 | iou_pred = self.iou_prediction_head(iou_token_out) 148 | 149 | return masks, iou_pred 150 | 151 | 152 | # Lightly adapted from 153 | # https://github.com/facebookresearch/MaskFormer/blob/main/mask_former/modeling/transformer/transformer_predictor.py # noqa 154 | class MLP(nn.Module): 155 | def __init__( 156 | self, 157 | input_dim: int, 158 | hidden_dim: int, 159 | output_dim: int, 160 | num_layers: int, 161 | sigmoid_output: bool = False, 162 | ) -> None: 163 | super().__init__() 164 | self.num_layers = num_layers 165 | h = [hidden_dim] * (num_layers - 1) 166 | self.layers = nn.ModuleList( 167 | nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]) 168 | ) 169 | self.sigmoid_output = sigmoid_output 170 | 171 | def forward(self, x): 172 | for i, layer in enumerate(self.layers): 173 | x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x) 174 | if self.sigmoid_output: 175 | x = F.sigmoid(x) 176 | return x 177 | -------------------------------------------------------------------------------- /segment_anything/modeling/prompt_encoder.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # All rights reserved. 3 | 4 | # This source code is licensed under the license found in the 5 | # LICENSE file in the root directory of this source tree. 6 | 7 | import numpy as np 8 | import torch 9 | from torch import nn 10 | 11 | from typing import Any, Optional, Tuple, Type 12 | 13 | from .common import LayerNorm2d 14 | 15 | 16 | class PromptEncoder(nn.Module): 17 | def __init__( 18 | self, 19 | embed_dim: int, 20 | image_embedding_size: Tuple[int, int], 21 | input_image_size: Tuple[int, int], 22 | mask_in_chans: int, 23 | activation: Type[nn.Module] = nn.GELU, 24 | ) -> None: 25 | """ 26 | Encodes prompts for input to SAM's mask decoder. 27 | 28 | Arguments: 29 | embed_dim (int): The prompts' embedding dimension 30 | image_embedding_size (tuple(int, int)): The spatial size of the 31 | image embedding, as (H, W). 32 | input_image_size (int): The padded size of the image as input 33 | to the image encoder, as (H, W). 34 | mask_in_chans (int): The number of hidden channels used for 35 | encoding input masks. 36 | activation (nn.Module): The activation to use when encoding 37 | input masks. 38 | """ 39 | super().__init__() 40 | self.embed_dim = embed_dim 41 | self.input_image_size = input_image_size 42 | self.image_embedding_size = image_embedding_size 43 | self.pe_layer = PositionEmbeddingRandom(embed_dim // 2) 44 | 45 | self.num_point_embeddings: int = 4 # pos/neg point + 2 box corners 46 | point_embeddings = [nn.Embedding(1, embed_dim) for i in range(self.num_point_embeddings)] 47 | self.point_embeddings = nn.ModuleList(point_embeddings) 48 | self.not_a_point_embed = nn.Embedding(1, embed_dim) 49 | 50 | self.mask_input_size = (4 * image_embedding_size[0], 4 * image_embedding_size[1]) 51 | self.mask_downscaling = nn.Sequential( 52 | nn.Conv2d(1, mask_in_chans // 4, kernel_size=2, stride=2), 53 | LayerNorm2d(mask_in_chans // 4), 54 | activation(), 55 | nn.Conv2d(mask_in_chans // 4, mask_in_chans, kernel_size=2, stride=2), 56 | LayerNorm2d(mask_in_chans), 57 | activation(), 58 | nn.Conv2d(mask_in_chans, embed_dim, kernel_size=1), 59 | ) 60 | self.no_mask_embed = nn.Embedding(1, embed_dim) 61 | 62 | def get_dense_pe(self) -> torch.Tensor: 63 | """ 64 | Returns the positional encoding used to encode point prompts, 65 | applied to a dense set of points the shape of the image encoding. 66 | 67 | Returns: 68 | torch.Tensor: Positional encoding with shape 69 | 1x(embed_dim)x(embedding_h)x(embedding_w) 70 | """ 71 | return self.pe_layer(self.image_embedding_size).unsqueeze(0) 72 | 73 | def _embed_points( 74 | self, 75 | points: torch.Tensor, 76 | labels: torch.Tensor, 77 | pad: bool, 78 | ) -> torch.Tensor: 79 | """Embeds point prompts.""" 80 | points = points + 0.5 # Shift to center of pixel 81 | if pad: 82 | padding_point = torch.zeros((points.shape[0], 1, 2), device=points.device) 83 | padding_label = -torch.ones((labels.shape[0], 1), device=labels.device) 84 | points = torch.cat([points, padding_point], dim=1) 85 | labels = torch.cat([labels, padding_label], dim=1) 86 | point_embedding = self.pe_layer.forward_with_coords(points, self.input_image_size) 87 | point_embedding[labels == -1] = 0.0 88 | point_embedding[labels == -1] += self.not_a_point_embed.weight 89 | point_embedding[labels == 0] += self.point_embeddings[0].weight 90 | point_embedding[labels == 1] += self.point_embeddings[1].weight 91 | return point_embedding 92 | 93 | def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor: 94 | """Embeds box prompts.""" 95 | boxes = boxes + 0.5 # Shift to center of pixel 96 | coords = boxes.reshape(-1, 2, 2) 97 | corner_embedding = self.pe_layer.forward_with_coords(coords, self.input_image_size) 98 | corner_embedding[:, 0, :] += self.point_embeddings[2].weight 99 | corner_embedding[:, 1, :] += self.point_embeddings[3].weight 100 | return corner_embedding 101 | 102 | def _embed_masks(self, masks: torch.Tensor) -> torch.Tensor: 103 | """Embeds mask inputs.""" 104 | mask_embedding = self.mask_downscaling(masks) 105 | return mask_embedding 106 | 107 | def _get_batch_size( 108 | self, 109 | points: Optional[Tuple[torch.Tensor, torch.Tensor]], 110 | boxes: Optional[torch.Tensor], 111 | masks: Optional[torch.Tensor], 112 | ) -> int: 113 | """ 114 | Gets the batch size of the output given the batch size of the input prompts. 115 | """ 116 | if points is not None: 117 | return points[0].shape[0] 118 | elif boxes is not None: 119 | return boxes.shape[0] 120 | elif masks is not None: 121 | return masks.shape[0] 122 | else: 123 | return 1 124 | 125 | def _get_device(self) -> torch.device: 126 | return self.point_embeddings[0].weight.device 127 | 128 | def forward( 129 | self, 130 | points: Optional[Tuple[torch.Tensor, torch.Tensor]], 131 | boxes: Optional[torch.Tensor], 132 | masks: Optional[torch.Tensor], 133 | ) -> Tuple[torch.Tensor, torch.Tensor]: 134 | """ 135 | Embeds different types of prompts, returning both sparse and dense 136 | embeddings. 137 | 138 | Arguments: 139 | points (tuple(torch.Tensor, torch.Tensor) or none): point coordinates 140 | and labels to embed. 141 | boxes (torch.Tensor or none): boxes to embed 142 | masks (torch.Tensor or none): masks to embed 143 | 144 | Returns: 145 | torch.Tensor: sparse embeddings for the points and boxes, with shape 146 | BxNx(embed_dim), where N is determined by the number of input points 147 | and boxes. 148 | torch.Tensor: dense embeddings for the masks, in the shape 149 | Bx(embed_dim)x(embed_H)x(embed_W) 150 | """ 151 | bs = self._get_batch_size(points, boxes, masks) 152 | sparse_embeddings = torch.empty((bs, 0, self.embed_dim), device=self._get_device()) 153 | if points is not None: 154 | coords, labels = points 155 | point_embeddings = self._embed_points(coords, labels, pad=(boxes is None)) 156 | sparse_embeddings = torch.cat([sparse_embeddings, point_embeddings], dim=1) 157 | if boxes is not None: 158 | box_embeddings = self._embed_boxes(boxes) 159 | sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=1) 160 | 161 | if masks is not None: 162 | dense_embeddings = self._embed_masks(masks) 163 | else: 164 | dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1).expand( 165 | bs, -1, self.image_embedding_size[0], self.image_embedding_size[1] 166 | ) 167 | 168 | return sparse_embeddings, dense_embeddings 169 | 170 | 171 | class PositionEmbeddingRandom(nn.Module): 172 | """ 173 | Positional encoding using random spatial frequencies. 174 | """ 175 | 176 | def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = None) -> None: 177 | super().__init__() 178 | if scale is None or scale <= 0.0: 179 | scale = 1.0 180 | self.register_buffer( 181 | "positional_encoding_gaussian_matrix", 182 | scale * torch.randn((2, num_pos_feats)), 183 | ) 184 | 185 | def _pe_encoding(self, coords: torch.Tensor) -> torch.Tensor: 186 | """Positionally encode points that are normalized to [0,1].""" 187 | # assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape 188 | coords = 2 * coords - 1 189 | coords = coords @ self.positional_encoding_gaussian_matrix 190 | coords = 2 * np.pi * coords 191 | # outputs d_1 x ... x d_n x C shape 192 | return torch.cat([torch.sin(coords), torch.cos(coords)], dim=-1) 193 | 194 | def forward(self, size: Tuple[int, int]) -> torch.Tensor: 195 | """Generate positional encoding for a grid of the specified size.""" 196 | h, w = size 197 | device: Any = self.positional_encoding_gaussian_matrix.device 198 | grid = torch.ones((h, w), device=device, dtype=torch.float32) 199 | y_embed = grid.cumsum(dim=0) - 0.5 200 | x_embed = grid.cumsum(dim=1) - 0.5 201 | y_embed = y_embed / h 202 | x_embed = x_embed / w 203 | 204 | pe = self._pe_encoding(torch.stack([x_embed, y_embed], dim=-1)) 205 | return pe.permute(2, 0, 1) # C x H x W 206 | 207 | def forward_with_coords( 208 | self, coords_input: torch.Tensor, image_size: Tuple[int, int] 209 | ) -> torch.Tensor: 210 | """Positionally encode points that are not normalized to [0,1].""" 211 | coords = coords_input.clone() 212 | coords[:, :, 0] = coords[:, :, 0] / image_size[1] 213 | coords[:, :, 1] = coords[:, :, 1] / image_size[0] 214 | return self._pe_encoding(coords.to(torch.float)) # B x N x C 215 | -------------------------------------------------------------------------------- /segment_anything/modeling/sam.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # All rights reserved. 3 | 4 | # This source code is licensed under the license found in the 5 | # LICENSE file in the root directory of this source tree. 6 | 7 | import torch 8 | from torch import nn 9 | from torch.nn import functional as F 10 | 11 | from typing import Any, Dict, List, Tuple 12 | 13 | from .image_encoder import ImageEncoderViT 14 | from .mask_decoder import MaskDecoder 15 | from .prompt_encoder import PromptEncoder 16 | 17 | 18 | class Sam(nn.Module): 19 | mask_threshold: float = 0.0 20 | image_format: str = "RGB" 21 | 22 | def __init__( 23 | self, 24 | image_encoder: ImageEncoderViT, 25 | prompt_encoder: PromptEncoder, 26 | mask_decoder: MaskDecoder, 27 | pixel_mean: List[float] = [123.675, 116.28, 103.53], 28 | pixel_std: List[float] = [58.395, 57.12, 57.375], 29 | ) -> None: 30 | """ 31 | SAM predicts object masks from an image and input prompts. 32 | 33 | Arguments: 34 | image_encoder (ImageEncoderViT): The backbone used to encode the 35 | image into image embeddings that allow for efficient mask prediction. 36 | prompt_encoder (PromptEncoder): Encodes various types of input prompts. 37 | mask_decoder (MaskDecoder): Predicts masks from the image embeddings 38 | and encoded prompts. 39 | pixel_mean (list(float)): Mean values for normalizing pixels in the input image. 40 | pixel_std (list(float)): Std values for normalizing pixels in the input image. 41 | """ 42 | super().__init__() 43 | self.image_encoder = image_encoder 44 | self.prompt_encoder = prompt_encoder 45 | self.mask_decoder = mask_decoder 46 | self.register_buffer("pixel_mean", torch.Tensor(pixel_mean).view(-1, 1, 1), False) 47 | self.register_buffer("pixel_std", torch.Tensor(pixel_std).view(-1, 1, 1), False) 48 | 49 | @property 50 | def device(self) -> Any: 51 | return self.pixel_mean.device 52 | 53 | @torch.no_grad() 54 | def forward( 55 | self, 56 | batched_input: List[Dict[str, Any]], 57 | multimask_output: bool, 58 | ) -> List[Dict[str, torch.Tensor]]: 59 | """ 60 | Predicts masks end-to-end from provided images and prompts. 61 | If prompts are not known in advance, using SamPredictor is 62 | recommended over calling the model directly. 63 | 64 | Arguments: 65 | batched_input (list(dict)): A list over input images, each a 66 | dictionary with the following keys. A prompt key can be 67 | excluded if it is not present. 68 | 'image': The image as a torch tensor in 3xHxW format, 69 | already transformed for input to the model. 70 | 'original_size': (tuple(int, int)) The original size of 71 | the image before transformation, as (H, W). 72 | 'point_coords': (torch.Tensor) Batched point prompts for 73 | this image, with shape BxNx2. Already transformed to the 74 | input frame of the model. 75 | 'point_labels': (torch.Tensor) Batched labels for point prompts, 76 | with shape BxN. 77 | 'boxes': (torch.Tensor) Batched box inputs, with shape Bx4. 78 | Already transformed to the input frame of the model. 79 | 'mask_inputs': (torch.Tensor) Batched mask inputs to the model, 80 | in the form Bx1xHxW. 81 | multimask_output (bool): Whether the model should predict multiple 82 | disambiguating masks, or return a single mask. 83 | 84 | Returns: 85 | (list(dict)): A list over input images, where each element is 86 | as dictionary with the following keys. 87 | 'masks': (torch.Tensor) Batched binary mask predictions, 88 | with shape BxCxHxW, where B is the number of input promts, 89 | C is determiend by multimask_output, and (H, W) is the 90 | original size of the image. 91 | 'iou_predictions': (torch.Tensor) The model's predictions 92 | of mask quality, in shape BxC. 93 | 'low_res_logits': (torch.Tensor) Low resolution logits with 94 | shape BxCxHxW, where H=W=256. Can be passed as mask input 95 | to subsequent iterations of prediction. 96 | """ 97 | input_images = torch.stack([self.preprocess(x["image"]) for x in batched_input], dim=0) 98 | image_embeddings = self.image_encoder(input_images) 99 | 100 | outputs = [] 101 | for image_record, curr_embedding in zip(batched_input, image_embeddings): 102 | if "point_coords" in image_record: 103 | points = (image_record["point_coords"], image_record["point_labels"]) 104 | else: 105 | points = None 106 | sparse_embeddings, dense_embeddings = self.prompt_encoder( 107 | points=points, 108 | boxes=image_record.get("boxes", None), 109 | masks=image_record.get("mask_inputs", None), 110 | ) 111 | low_res_masks, iou_predictions = self.mask_decoder( 112 | image_embeddings=curr_embedding.unsqueeze(0), 113 | image_pe=self.prompt_encoder.get_dense_pe(), 114 | sparse_prompt_embeddings=sparse_embeddings, 115 | dense_prompt_embeddings=dense_embeddings, 116 | multimask_output=multimask_output, 117 | ) 118 | masks = self.postprocess_masks( 119 | low_res_masks, 120 | input_size=image_record["image"].shape[-2:], 121 | original_size=image_record["original_size"], 122 | ) 123 | masks = masks > self.mask_threshold 124 | outputs.append( 125 | { 126 | "masks": masks, 127 | "iou_predictions": iou_predictions, 128 | "low_res_logits": low_res_masks, 129 | } 130 | ) 131 | return outputs 132 | 133 | def postprocess_masks( 134 | self, 135 | masks: torch.Tensor, 136 | input_size: Tuple[int, ...], 137 | original_size: Tuple[int, ...], 138 | ) -> torch.Tensor: 139 | """ 140 | Remove padding and upscale masks to the original image size. 141 | 142 | Arguments: 143 | masks (torch.Tensor): Batched masks from the mask_decoder, 144 | in BxCxHxW format. 145 | input_size (tuple(int, int)): The size of the image input to the 146 | model, in (H, W) format. Used to remove padding. 147 | original_size (tuple(int, int)): The original size of the image 148 | before resizing for input to the model, in (H, W) format. 149 | 150 | Returns: 151 | (torch.Tensor): Batched masks in BxCxHxW format, where (H, W) 152 | is given by original_size. 153 | """ 154 | masks = F.interpolate( 155 | masks, 156 | (self.image_encoder.img_size, self.image_encoder.img_size), 157 | mode="bilinear", 158 | align_corners=False, 159 | ) 160 | masks = masks[..., : input_size[0], : input_size[1]] 161 | masks = F.interpolate(masks, original_size, mode="bilinear", align_corners=False) 162 | return masks 163 | 164 | def preprocess(self, x: torch.Tensor) -> torch.Tensor: 165 | """Normalize pixel values and pad to a square input.""" 166 | # Normalize colors 167 | x = (x - self.pixel_mean) / self.pixel_std 168 | 169 | # Pad 170 | h, w = x.shape[-2:] 171 | padh = self.image_encoder.img_size - h 172 | padw = self.image_encoder.img_size - w 173 | x = F.pad(x, (0, padw, 0, padh)) 174 | return x 175 | -------------------------------------------------------------------------------- /segment_anything/modeling/transformer.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # All rights reserved. 3 | 4 | # This source code is licensed under the license found in the 5 | # LICENSE file in the root directory of this source tree. 6 | 7 | import torch 8 | from torch import Tensor, nn 9 | 10 | import math 11 | from typing import Tuple, Type 12 | 13 | from .common import MLPBlock 14 | 15 | 16 | class TwoWayTransformer(nn.Module): 17 | def __init__( 18 | self, 19 | depth: int, 20 | embedding_dim: int, 21 | num_heads: int, 22 | mlp_dim: int, 23 | activation: Type[nn.Module] = nn.ReLU, 24 | attention_downsample_rate: int = 2, 25 | ) -> None: 26 | """ 27 | A transformer decoder that attends to an input image using 28 | queries whose positional embedding is supplied. 29 | 30 | Args: 31 | depth (int): number of layers in the transformer 32 | embedding_dim (int): the channel dimension for the input embeddings 33 | num_heads (int): the number of heads for multihead attention. Must 34 | divide embedding_dim 35 | mlp_dim (int): the channel dimension internal to the MLP block 36 | activation (nn.Module): the activation to use in the MLP block 37 | """ 38 | super().__init__() 39 | self.depth = depth 40 | self.embedding_dim = embedding_dim 41 | self.num_heads = num_heads 42 | self.mlp_dim = mlp_dim 43 | self.layers = nn.ModuleList() 44 | 45 | for i in range(depth): 46 | self.layers.append( 47 | TwoWayAttentionBlock( 48 | embedding_dim=embedding_dim, 49 | num_heads=num_heads, 50 | mlp_dim=mlp_dim, 51 | activation=activation, 52 | attention_downsample_rate=attention_downsample_rate, 53 | skip_first_layer_pe=(i == 0), 54 | ) 55 | ) 56 | 57 | self.final_attn_token_to_image = Attention( 58 | embedding_dim, num_heads, downsample_rate=attention_downsample_rate 59 | ) 60 | self.norm_final_attn = nn.LayerNorm(embedding_dim) 61 | 62 | def forward( 63 | self, 64 | image_embedding: Tensor, 65 | image_pe: Tensor, 66 | point_embedding: Tensor, 67 | ) -> Tuple[Tensor, Tensor]: 68 | """ 69 | Args: 70 | image_embedding (torch.Tensor): image to attend to. Should be shape 71 | B x embedding_dim x h x w for any h and w. 72 | image_pe (torch.Tensor): the positional encoding to add to the image. Must 73 | have the same shape as image_embedding. 74 | point_embedding (torch.Tensor): the embedding to add to the query points. 75 | Must have shape B x N_points x embedding_dim for any N_points. 76 | 77 | Returns: 78 | torch.Tensor: the processed point_embedding 79 | torch.Tensor: the processed image_embedding 80 | """ 81 | # BxCxHxW -> BxHWxC == B x N_image_tokens x C 82 | bs, c, h, w = image_embedding.shape 83 | image_embedding = image_embedding.flatten(2).permute(0, 2, 1) 84 | image_pe = image_pe.flatten(2).permute(0, 2, 1) 85 | 86 | # Prepare queries 87 | queries = point_embedding 88 | keys = image_embedding 89 | 90 | # Apply transformer blocks and final layernorm 91 | for layer in self.layers: 92 | queries, keys = layer( 93 | queries=queries, 94 | keys=keys, 95 | query_pe=point_embedding, 96 | key_pe=image_pe, 97 | ) 98 | 99 | # Apply the final attenion layer from the points to the image 100 | q = queries + point_embedding 101 | k = keys + image_pe 102 | attn_out = self.final_attn_token_to_image(q=q, k=k, v=keys) 103 | queries = queries + attn_out 104 | queries = self.norm_final_attn(queries) 105 | 106 | return queries, keys 107 | 108 | 109 | class TwoWayAttentionBlock(nn.Module): 110 | def __init__( 111 | self, 112 | embedding_dim: int, 113 | num_heads: int, 114 | mlp_dim: int = 2048, 115 | activation: Type[nn.Module] = nn.ReLU, 116 | attention_downsample_rate: int = 2, 117 | skip_first_layer_pe: bool = False, 118 | ) -> None: 119 | """ 120 | A transformer block with four layers: (1) self-attention of sparse 121 | inputs, (2) cross attention of sparse inputs to dense inputs, (3) mlp 122 | block on sparse inputs, and (4) cross attention of dense inputs to sparse 123 | inputs. 124 | 125 | Arguments: 126 | embedding_dim (int): the channel dimension of the embeddings 127 | num_heads (int): the number of heads in the attention layers 128 | mlp_dim (int): the hidden dimension of the mlp block 129 | activation (nn.Module): the activation of the mlp block 130 | skip_first_layer_pe (bool): skip the PE on the first layer 131 | """ 132 | super().__init__() 133 | self.self_attn = Attention(embedding_dim, num_heads) 134 | self.norm1 = nn.LayerNorm(embedding_dim) 135 | 136 | self.cross_attn_token_to_image = Attention( 137 | embedding_dim, num_heads, downsample_rate=attention_downsample_rate 138 | ) 139 | self.norm2 = nn.LayerNorm(embedding_dim) 140 | 141 | self.mlp = MLPBlock(embedding_dim, mlp_dim, activation) 142 | self.norm3 = nn.LayerNorm(embedding_dim) 143 | 144 | self.norm4 = nn.LayerNorm(embedding_dim) 145 | self.cross_attn_image_to_token = Attention( 146 | embedding_dim, num_heads, downsample_rate=attention_downsample_rate 147 | ) 148 | 149 | self.skip_first_layer_pe = skip_first_layer_pe 150 | 151 | def forward( 152 | self, queries: Tensor, keys: Tensor, query_pe: Tensor, key_pe: Tensor 153 | ) -> Tuple[Tensor, Tensor]: 154 | # Self attention block 155 | if self.skip_first_layer_pe: 156 | queries = self.self_attn(q=queries, k=queries, v=queries) 157 | else: 158 | q = queries + query_pe 159 | attn_out = self.self_attn(q=q, k=q, v=queries) 160 | queries = queries + attn_out 161 | queries = self.norm1(queries) 162 | 163 | # Cross attention block, tokens attending to image embedding 164 | q = queries + query_pe 165 | k = keys + key_pe 166 | attn_out = self.cross_attn_token_to_image(q=q, k=k, v=keys) 167 | queries = queries + attn_out 168 | queries = self.norm2(queries) 169 | 170 | # MLP block 171 | mlp_out = self.mlp(queries) 172 | queries = queries + mlp_out 173 | queries = self.norm3(queries) 174 | 175 | # Cross attention block, image embedding attending to tokens 176 | q = queries + query_pe 177 | k = keys + key_pe 178 | attn_out = self.cross_attn_image_to_token(q=k, k=q, v=queries) 179 | keys = keys + attn_out 180 | keys = self.norm4(keys) 181 | 182 | return queries, keys 183 | 184 | 185 | class Attention(nn.Module): 186 | """ 187 | An attention layer that allows for downscaling the size of the embedding 188 | after projection to queries, keys, and values. 189 | """ 190 | 191 | def __init__( 192 | self, 193 | embedding_dim: int, 194 | num_heads: int, 195 | downsample_rate: int = 1, 196 | ) -> None: 197 | super().__init__() 198 | self.embedding_dim = embedding_dim 199 | self.internal_dim = embedding_dim // downsample_rate 200 | self.num_heads = num_heads 201 | assert self.internal_dim % num_heads == 0, "num_heads must divide embedding_dim." 202 | 203 | self.q_proj = nn.Linear(embedding_dim, self.internal_dim) 204 | self.k_proj = nn.Linear(embedding_dim, self.internal_dim) 205 | self.v_proj = nn.Linear(embedding_dim, self.internal_dim) 206 | self.out_proj = nn.Linear(self.internal_dim, embedding_dim) 207 | 208 | def _separate_heads(self, x: Tensor, num_heads: int) -> Tensor: 209 | b, n, c = x.shape 210 | x = x.reshape(b, n, num_heads, c // num_heads) 211 | return x.transpose(1, 2) # B x N_heads x N_tokens x C_per_head 212 | 213 | def _recombine_heads(self, x: Tensor) -> Tensor: 214 | b, n_heads, n_tokens, c_per_head = x.shape 215 | x = x.transpose(1, 2) 216 | return x.reshape(b, n_tokens, n_heads * c_per_head) # B x N_tokens x C 217 | 218 | def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tensor: 219 | # Input projections 220 | q = self.q_proj(q) 221 | k = self.k_proj(k) 222 | v = self.v_proj(v) 223 | 224 | # Separate into heads 225 | q = self._separate_heads(q, self.num_heads) 226 | k = self._separate_heads(k, self.num_heads) 227 | v = self._separate_heads(v, self.num_heads) 228 | 229 | # Attention 230 | _, _, _, c_per_head = q.shape 231 | attn = q @ k.permute(0, 1, 3, 2) # B x N_heads x N_tokens x N_tokens 232 | attn = attn / math.sqrt(c_per_head) 233 | attn = torch.softmax(attn, dim=-1) 234 | 235 | # Get output 236 | out = attn @ v 237 | out = self._recombine_heads(out) 238 | out = self.out_proj(out) 239 | 240 | return out 241 | -------------------------------------------------------------------------------- /segment_anything/predictor.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # All rights reserved. 3 | 4 | # This source code is licensed under the license found in the 5 | # LICENSE file in the root directory of this source tree. 6 | 7 | import numpy as np 8 | import torch 9 | 10 | from segment_anything.modeling import Sam 11 | 12 | from typing import Optional, Tuple 13 | 14 | from .utils.transforms import ResizeLongestSide 15 | 16 | 17 | class SamPredictor: 18 | def __init__( 19 | self, 20 | sam_model: Sam, 21 | ) -> None: 22 | """ 23 | Uses SAM to calculate the image embedding for an image, and then 24 | allow repeated, efficient mask prediction given prompts. 25 | 26 | Arguments: 27 | sam_model (Sam): The model to use for mask prediction. 28 | """ 29 | super().__init__() 30 | self.model = sam_model 31 | self.transform = ResizeLongestSide(sam_model.image_encoder.img_size) 32 | self.reset_image() 33 | 34 | def set_image( 35 | self, 36 | image: np.ndarray, 37 | image_format: str = "RGB", 38 | ) -> None: 39 | """ 40 | Calculates the image embeddings for the provided image, allowing 41 | masks to be predicted with the 'predict' method. 42 | 43 | Arguments: 44 | image (np.ndarray): The image for calculating masks. Expects an 45 | image in HWC uint8 format, with pixel values in [0, 255]. 46 | image_format (str): The color format of the image, in ['RGB', 'BGR']. 47 | """ 48 | assert image_format in [ 49 | "RGB", 50 | "BGR", 51 | ], f"image_format must be in ['RGB', 'BGR'], is {image_format}." 52 | if image_format != self.model.image_format: 53 | image = image[..., ::-1] 54 | 55 | # Transform the image to the form expected by the model 56 | input_image = self.transform.apply_image(image) 57 | input_image_torch = torch.as_tensor(input_image, device=self.device) 58 | input_image_torch = input_image_torch.permute(2, 0, 1).contiguous()[None, :, :, :] 59 | 60 | self.set_torch_image(input_image_torch, image.shape[:2]) 61 | 62 | @torch.no_grad() 63 | def set_torch_image( 64 | self, 65 | transformed_image: torch.Tensor, 66 | original_image_size: Tuple[int, ...], 67 | ) -> None: 68 | """ 69 | Calculates the image embeddings for the provided image, allowing 70 | masks to be predicted with the 'predict' method. Expects the input 71 | image to be already transformed to the format expected by the model. 72 | 73 | Arguments: 74 | transformed_image (torch.Tensor): The input image, with shape 75 | 1x3xHxW, which has been transformed with ResizeLongestSide. 76 | original_image_size (tuple(int, int)): The size of the image 77 | before transformation, in (H, W) format. 78 | """ 79 | assert ( 80 | len(transformed_image.shape) == 4 81 | and transformed_image.shape[1] == 3 82 | and max(*transformed_image.shape[2:]) == self.model.image_encoder.img_size 83 | ), f"set_torch_image input must be BCHW with long side {self.model.image_encoder.img_size}." 84 | self.reset_image() 85 | 86 | self.original_size = original_image_size 87 | self.input_size = tuple(transformed_image.shape[-2:]) 88 | input_image = self.model.preprocess(transformed_image) 89 | self.features = self.model.image_encoder(input_image) 90 | self.is_image_set = True 91 | 92 | def predict( 93 | self, 94 | point_coords: Optional[np.ndarray] = None, 95 | point_labels: Optional[np.ndarray] = None, 96 | box: Optional[np.ndarray] = None, 97 | mask_input: Optional[np.ndarray] = None, 98 | multimask_output: bool = True, 99 | return_logits: bool = False, 100 | ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: 101 | """ 102 | Predict masks for the given input prompts, using the currently set image. 103 | 104 | Arguments: 105 | point_coords (np.ndarray or None): A Nx2 array of point prompts to the 106 | model. Each point is in (X,Y) in pixels. 107 | point_labels (np.ndarray or None): A length N array of labels for the 108 | point prompts. 1 indicates a foreground point and 0 indicates a 109 | background point. 110 | box (np.ndarray or None): A length 4 array given a box prompt to the 111 | model, in XYXY format. 112 | mask_input (np.ndarray): A low resolution mask input to the model, typically 113 | coming from a previous prediction iteration. Has form 1xHxW, where 114 | for SAM, H=W=256. 115 | multimask_output (bool): If true, the model will return three masks. 116 | For ambiguous input prompts (such as a single click), this will often 117 | produce better masks than a single prediction. If only a single 118 | mask is needed, the model's predicted quality score can be used 119 | to select the best mask. For non-ambiguous prompts, such as multiple 120 | input prompts, multimask_output=False can give better results. 121 | return_logits (bool): If true, returns un-thresholded masks logits 122 | instead of a binary mask. 123 | 124 | Returns: 125 | (np.ndarray): The output masks in CxHxW format, where C is the 126 | number of masks, and (H, W) is the original image size. 127 | (np.ndarray): An array of length C containing the model's 128 | predictions for the quality of each mask. 129 | (np.ndarray): An array of shape CxHxW, where C is the number 130 | of masks and H=W=256. These low resolution logits can be passed to 131 | a subsequent iteration as mask input. 132 | """ 133 | if not self.is_image_set: 134 | raise RuntimeError("An image must be set with .set_image(...) before mask prediction.") 135 | 136 | # Transform input prompts 137 | coords_torch, labels_torch, box_torch, mask_input_torch = None, None, None, None 138 | if point_coords is not None: 139 | assert ( 140 | point_labels is not None 141 | ), "point_labels must be supplied if point_coords is supplied." 142 | point_coords = self.transform.apply_coords(point_coords, self.original_size) 143 | coords_torch = torch.as_tensor(point_coords, dtype=torch.float, device=self.device) 144 | labels_torch = torch.as_tensor(point_labels, dtype=torch.int, device=self.device) 145 | coords_torch, labels_torch = coords_torch[None, :, :], labels_torch[None, :] 146 | if box is not None: 147 | box = self.transform.apply_boxes(box, self.original_size) 148 | box_torch = torch.as_tensor(box, dtype=torch.float, device=self.device) 149 | box_torch = box_torch[None, :] 150 | if mask_input is not None: 151 | mask_input_torch = torch.as_tensor(mask_input, dtype=torch.float, device=self.device) 152 | mask_input_torch = mask_input_torch[None, :, :, :] 153 | 154 | masks, iou_predictions, low_res_masks = self.predict_torch( 155 | coords_torch, 156 | labels_torch, 157 | box_torch, 158 | mask_input_torch, 159 | multimask_output, 160 | return_logits=return_logits, 161 | ) 162 | 163 | masks = masks[0].detach().cpu().numpy() 164 | iou_predictions = iou_predictions[0].detach().cpu().numpy() 165 | low_res_masks = low_res_masks[0].detach().cpu().numpy() 166 | return masks, iou_predictions, low_res_masks 167 | 168 | @torch.no_grad() 169 | def predict_torch( 170 | self, 171 | point_coords: Optional[torch.Tensor], 172 | point_labels: Optional[torch.Tensor], 173 | boxes: Optional[torch.Tensor] = None, 174 | mask_input: Optional[torch.Tensor] = None, 175 | multimask_output: bool = True, 176 | return_logits: bool = False, 177 | ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: 178 | """ 179 | Predict masks for the given input prompts, using the currently set image. 180 | Input prompts are batched torch tensors and are expected to already be 181 | transformed to the input frame using ResizeLongestSide. 182 | 183 | Arguments: 184 | point_coords (torch.Tensor or None): A BxNx2 array of point prompts to the 185 | model. Each point is in (X,Y) in pixels. 186 | point_labels (torch.Tensor or None): A BxN array of labels for the 187 | point prompts. 1 indicates a foreground point and 0 indicates a 188 | background point. 189 | box (np.ndarray or None): A Bx4 array given a box prompt to the 190 | model, in XYXY format. 191 | mask_input (np.ndarray): A low resolution mask input to the model, typically 192 | coming from a previous prediction iteration. Has form Bx1xHxW, where 193 | for SAM, H=W=256. Masks returned by a previous iteration of the 194 | predict method do not need further transformation. 195 | multimask_output (bool): If true, the model will return three masks. 196 | For ambiguous input prompts (such as a single click), this will often 197 | produce better masks than a single prediction. If only a single 198 | mask is needed, the model's predicted quality score can be used 199 | to select the best mask. For non-ambiguous prompts, such as multiple 200 | input prompts, multimask_output=False can give better results. 201 | return_logits (bool): If true, returns un-thresholded masks logits 202 | instead of a binary mask. 203 | 204 | Returns: 205 | (torch.Tensor): The output masks in BxCxHxW format, where C is the 206 | number of masks, and (H, W) is the original image size. 207 | (torch.Tensor): An array of shape BxC containing the model's 208 | predictions for the quality of each mask. 209 | (torch.Tensor): An array of shape BxCxHxW, where C is the number 210 | of masks and H=W=256. These low res logits can be passed to 211 | a subsequent iteration as mask input. 212 | """ 213 | if not self.is_image_set: 214 | raise RuntimeError("An image must be set with .set_image(...) before mask prediction.") 215 | 216 | if point_coords is not None: 217 | points = (point_coords, point_labels) 218 | else: 219 | points = None 220 | 221 | # Embed prompts 222 | sparse_embeddings, dense_embeddings = self.model.prompt_encoder( 223 | points=points, 224 | boxes=boxes, 225 | masks=mask_input, 226 | ) 227 | 228 | # Predict masks 229 | low_res_masks, iou_predictions = self.model.mask_decoder( 230 | image_embeddings=self.features, 231 | image_pe=self.model.prompt_encoder.get_dense_pe(), 232 | sparse_prompt_embeddings=sparse_embeddings, 233 | dense_prompt_embeddings=dense_embeddings, 234 | multimask_output=multimask_output, 235 | ) 236 | 237 | # Upscale the masks to the original image resolution 238 | masks = self.model.postprocess_masks(low_res_masks, self.input_size, self.original_size) 239 | 240 | if not return_logits: 241 | masks = masks > self.model.mask_threshold 242 | 243 | return masks, iou_predictions, low_res_masks 244 | 245 | def get_image_embedding(self) -> torch.Tensor: 246 | """ 247 | Returns the image embeddings for the currently set image, with 248 | shape 1xCxHxW, where C is the embedding dimension and (H,W) are 249 | the embedding spatial dimension of SAM (typically C=256, H=W=64). 250 | """ 251 | if not self.is_image_set: 252 | raise RuntimeError( 253 | "An image must be set with .set_image(...) to generate an embedding." 254 | ) 255 | assert self.features is not None, "Features must exist if an image has been set." 256 | return self.features 257 | 258 | @property 259 | def device(self) -> torch.device: 260 | return self.model.device 261 | 262 | def reset_image(self) -> None: 263 | """Resets the currently set image.""" 264 | self.is_image_set = False 265 | self.features = None 266 | self.orig_h = None 267 | self.orig_w = None 268 | self.input_h = None 269 | self.input_w = None 270 | -------------------------------------------------------------------------------- /segment_anything/utils/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # All rights reserved. 3 | 4 | # This source code is licensed under the license found in the 5 | # LICENSE file in the root directory of this source tree. 6 | -------------------------------------------------------------------------------- /segment_anything/utils/amg.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # All rights reserved. 3 | 4 | # This source code is licensed under the license found in the 5 | # LICENSE file in the root directory of this source tree. 6 | 7 | import numpy as np 8 | import torch 9 | 10 | import math 11 | from copy import deepcopy 12 | from itertools import product 13 | from typing import Any, Dict, Generator, ItemsView, List, Tuple 14 | 15 | 16 | class MaskData: 17 | """ 18 | A structure for storing masks and their related data in batched format. 19 | Implements basic filtering and concatenation. 20 | """ 21 | 22 | def __init__(self, **kwargs) -> None: 23 | for v in kwargs.values(): 24 | assert isinstance( 25 | v, (list, np.ndarray, torch.Tensor) 26 | ), "MaskData only supports list, numpy arrays, and torch tensors." 27 | self._stats = dict(**kwargs) 28 | 29 | def __setitem__(self, key: str, item: Any) -> None: 30 | assert isinstance( 31 | item, (list, np.ndarray, torch.Tensor) 32 | ), "MaskData only supports list, numpy arrays, and torch tensors." 33 | self._stats[key] = item 34 | 35 | def __delitem__(self, key: str) -> None: 36 | del self._stats[key] 37 | 38 | def __getitem__(self, key: str) -> Any: 39 | return self._stats[key] 40 | 41 | def items(self) -> ItemsView[str, Any]: 42 | return self._stats.items() 43 | 44 | def filter(self, keep: torch.Tensor) -> None: 45 | for k, v in self._stats.items(): 46 | if v is None: 47 | self._stats[k] = None 48 | elif isinstance(v, torch.Tensor): 49 | self._stats[k] = v[torch.as_tensor(keep, device=v.device)] 50 | elif isinstance(v, np.ndarray): 51 | self._stats[k] = v[keep.detach().cpu().numpy()] 52 | elif isinstance(v, list) and keep.dtype == torch.bool: 53 | self._stats[k] = [a for i, a in enumerate(v) if keep[i]] 54 | elif isinstance(v, list): 55 | self._stats[k] = [v[i] for i in keep] 56 | else: 57 | raise TypeError(f"MaskData key {k} has an unsupported type {type(v)}.") 58 | 59 | def cat(self, new_stats: "MaskData") -> None: 60 | for k, v in new_stats.items(): 61 | if k not in self._stats or self._stats[k] is None: 62 | self._stats[k] = deepcopy(v) 63 | elif isinstance(v, torch.Tensor): 64 | self._stats[k] = torch.cat([self._stats[k], v], dim=0) 65 | elif isinstance(v, np.ndarray): 66 | self._stats[k] = np.concatenate([self._stats[k], v], axis=0) 67 | elif isinstance(v, list): 68 | self._stats[k] = self._stats[k] + deepcopy(v) 69 | else: 70 | raise TypeError(f"MaskData key {k} has an unsupported type {type(v)}.") 71 | 72 | def to_numpy(self) -> None: 73 | for k, v in self._stats.items(): 74 | if isinstance(v, torch.Tensor): 75 | self._stats[k] = v.detach().cpu().numpy() 76 | 77 | 78 | def is_box_near_crop_edge( 79 | boxes: torch.Tensor, crop_box: List[int], orig_box: List[int], atol: float = 20.0 80 | ) -> torch.Tensor: 81 | """Filter masks at the edge of a crop, but not at the edge of the original image.""" 82 | crop_box_torch = torch.as_tensor(crop_box, dtype=torch.float, device=boxes.device) 83 | orig_box_torch = torch.as_tensor(orig_box, dtype=torch.float, device=boxes.device) 84 | boxes = uncrop_boxes_xyxy(boxes, crop_box).float() 85 | near_crop_edge = torch.isclose(boxes, crop_box_torch[None, :], atol=atol, rtol=0) 86 | near_image_edge = torch.isclose(boxes, orig_box_torch[None, :], atol=atol, rtol=0) 87 | near_crop_edge = torch.logical_and(near_crop_edge, ~near_image_edge) 88 | return torch.any(near_crop_edge, dim=1) 89 | 90 | 91 | def box_xyxy_to_xywh(box_xyxy: torch.Tensor) -> torch.Tensor: 92 | box_xywh = deepcopy(box_xyxy) 93 | box_xywh[2] = box_xywh[2] - box_xywh[0] 94 | box_xywh[3] = box_xywh[3] - box_xywh[1] 95 | return box_xywh 96 | 97 | 98 | def batch_iterator(batch_size: int, *args) -> Generator[List[Any], None, None]: 99 | assert len(args) > 0 and all( 100 | len(a) == len(args[0]) for a in args 101 | ), "Batched iteration must have inputs of all the same size." 102 | n_batches = len(args[0]) // batch_size + int(len(args[0]) % batch_size != 0) 103 | for b in range(n_batches): 104 | yield [arg[b * batch_size : (b + 1) * batch_size] for arg in args] 105 | 106 | 107 | def mask_to_rle_pytorch(tensor: torch.Tensor) -> List[Dict[str, Any]]: 108 | """ 109 | Encodes masks to an uncompressed RLE, in the format expected by 110 | pycoco tools. 111 | """ 112 | # Put in fortran order and flatten h,w 113 | b, h, w = tensor.shape 114 | tensor = tensor.permute(0, 2, 1).flatten(1) 115 | 116 | # Compute change indices 117 | diff = tensor[:, 1:] ^ tensor[:, :-1] 118 | change_indices = diff.nonzero() 119 | 120 | # Encode run length 121 | out = [] 122 | for i in range(b): 123 | cur_idxs = change_indices[change_indices[:, 0] == i, 1] 124 | cur_idxs = torch.cat( 125 | [ 126 | torch.tensor([0], dtype=cur_idxs.dtype, device=cur_idxs.device), 127 | cur_idxs + 1, 128 | torch.tensor([h * w], dtype=cur_idxs.dtype, device=cur_idxs.device), 129 | ] 130 | ) 131 | btw_idxs = cur_idxs[1:] - cur_idxs[:-1] 132 | counts = [] if tensor[i, 0] == 0 else [0] 133 | counts.extend(btw_idxs.detach().cpu().tolist()) 134 | out.append({"size": [h, w], "counts": counts}) 135 | return out 136 | 137 | 138 | def rle_to_mask(rle: Dict[str, Any]) -> np.ndarray: 139 | """Compute a binary mask from an uncompressed RLE.""" 140 | h, w = rle["size"] 141 | mask = np.empty(h * w, dtype=bool) 142 | idx = 0 143 | parity = False 144 | for count in rle["counts"]: 145 | mask[idx : idx + count] = parity 146 | idx += count 147 | parity ^= True 148 | mask = mask.reshape(w, h) 149 | return mask.transpose() # Put in C order 150 | 151 | 152 | def area_from_rle(rle: Dict[str, Any]) -> int: 153 | return sum(rle["counts"][1::2]) 154 | 155 | 156 | def calculate_stability_score( 157 | masks: torch.Tensor, mask_threshold: float, threshold_offset: float 158 | ) -> torch.Tensor: 159 | """ 160 | Computes the stability score for a batch of masks. The stability 161 | score is the IoU between the binary masks obtained by thresholding 162 | the predicted mask logits at high and low values. 163 | """ 164 | # One mask is always contained inside the other. 165 | # Save memory by preventing unnecesary cast to torch.int64 166 | intersections = ( 167 | (masks > (mask_threshold + threshold_offset)) 168 | .sum(-1, dtype=torch.int16) 169 | .sum(-1, dtype=torch.int32) 170 | ) 171 | unions = ( 172 | (masks > (mask_threshold - threshold_offset)) 173 | .sum(-1, dtype=torch.int16) 174 | .sum(-1, dtype=torch.int32) 175 | ) 176 | return intersections / unions 177 | 178 | 179 | def build_point_grid(n_per_side: int) -> np.ndarray: 180 | """Generates a 2D grid of points evenly spaced in [0,1]x[0,1].""" 181 | offset = 1 / (2 * n_per_side) 182 | points_one_side = np.linspace(offset, 1 - offset, n_per_side) 183 | points_x = np.tile(points_one_side[None, :], (n_per_side, 1)) 184 | points_y = np.tile(points_one_side[:, None], (1, n_per_side)) 185 | points = np.stack([points_x, points_y], axis=-1).reshape(-1, 2) 186 | return points 187 | 188 | 189 | def build_all_layer_point_grids( 190 | n_per_side: int, n_layers: int, scale_per_layer: int 191 | ) -> List[np.ndarray]: 192 | """Generates point grids for all crop layers.""" 193 | points_by_layer = [] 194 | for i in range(n_layers + 1): 195 | n_points = int(n_per_side / (scale_per_layer**i)) 196 | points_by_layer.append(build_point_grid(n_points)) 197 | return points_by_layer 198 | 199 | 200 | def generate_crop_boxes( 201 | im_size: Tuple[int, ...], n_layers: int, overlap_ratio: float 202 | ) -> Tuple[List[List[int]], List[int]]: 203 | """ 204 | Generates a list of crop boxes of different sizes. Each layer 205 | has (2**i)**2 boxes for the ith layer. 206 | """ 207 | crop_boxes, layer_idxs = [], [] 208 | im_h, im_w = im_size 209 | short_side = min(im_h, im_w) 210 | 211 | # Original image 212 | crop_boxes.append([0, 0, im_w, im_h]) 213 | layer_idxs.append(0) 214 | 215 | def crop_len(orig_len, n_crops, overlap): 216 | return int(math.ceil((overlap * (n_crops - 1) + orig_len) / n_crops)) 217 | 218 | for i_layer in range(n_layers): 219 | n_crops_per_side = 2 ** (i_layer + 1) 220 | overlap = int(overlap_ratio * short_side * (2 / n_crops_per_side)) 221 | 222 | crop_w = crop_len(im_w, n_crops_per_side, overlap) 223 | crop_h = crop_len(im_h, n_crops_per_side, overlap) 224 | 225 | crop_box_x0 = [int((crop_w - overlap) * i) for i in range(n_crops_per_side)] 226 | crop_box_y0 = [int((crop_h - overlap) * i) for i in range(n_crops_per_side)] 227 | 228 | # Crops in XYWH format 229 | for x0, y0 in product(crop_box_x0, crop_box_y0): 230 | box = [x0, y0, min(x0 + crop_w, im_w), min(y0 + crop_h, im_h)] 231 | crop_boxes.append(box) 232 | layer_idxs.append(i_layer + 1) 233 | 234 | return crop_boxes, layer_idxs 235 | 236 | 237 | def uncrop_boxes_xyxy(boxes: torch.Tensor, crop_box: List[int]) -> torch.Tensor: 238 | x0, y0, _, _ = crop_box 239 | offset = torch.tensor([[x0, y0, x0, y0]], device=boxes.device) 240 | # Check if boxes has a channel dimension 241 | if len(boxes.shape) == 3: 242 | offset = offset.unsqueeze(1) 243 | return boxes + offset 244 | 245 | 246 | def uncrop_points(points: torch.Tensor, crop_box: List[int]) -> torch.Tensor: 247 | x0, y0, _, _ = crop_box 248 | offset = torch.tensor([[x0, y0]], device=points.device) 249 | # Check if points has a channel dimension 250 | if len(points.shape) == 3: 251 | offset = offset.unsqueeze(1) 252 | return points + offset 253 | 254 | 255 | def uncrop_masks( 256 | masks: torch.Tensor, crop_box: List[int], orig_h: int, orig_w: int 257 | ) -> torch.Tensor: 258 | x0, y0, x1, y1 = crop_box 259 | if x0 == 0 and y0 == 0 and x1 == orig_w and y1 == orig_h: 260 | return masks 261 | # Coordinate transform masks 262 | pad_x, pad_y = orig_w - (x1 - x0), orig_h - (y1 - y0) 263 | pad = (x0, pad_x - x0, y0, pad_y - y0) 264 | return torch.nn.functional.pad(masks, pad, value=0) 265 | 266 | 267 | def remove_small_regions( 268 | mask: np.ndarray, area_thresh: float, mode: str 269 | ) -> Tuple[np.ndarray, bool]: 270 | """ 271 | Removes small disconnected regions and holes in a mask. Returns the 272 | mask and an indicator of if the mask has been modified. 273 | """ 274 | import cv2 # type: ignore 275 | 276 | assert mode in ["holes", "islands"] 277 | correct_holes = mode == "holes" 278 | working_mask = (correct_holes ^ mask).astype(np.uint8) 279 | n_labels, regions, stats, _ = cv2.connectedComponentsWithStats(working_mask, 8) 280 | sizes = stats[:, -1][1:] # Row 0 is background label 281 | small_regions = [i + 1 for i, s in enumerate(sizes) if s < area_thresh] 282 | if len(small_regions) == 0: 283 | return mask, False 284 | fill_labels = [0] + small_regions 285 | if not correct_holes: 286 | fill_labels = [i for i in range(n_labels) if i not in fill_labels] 287 | # If every region is below threshold, keep largest 288 | if len(fill_labels) == 0: 289 | fill_labels = [int(np.argmax(sizes)) + 1] 290 | mask = np.isin(regions, fill_labels) 291 | return mask, True 292 | 293 | 294 | def coco_encode_rle(uncompressed_rle: Dict[str, Any]) -> Dict[str, Any]: 295 | from pycocotools import mask as mask_utils # type: ignore 296 | 297 | h, w = uncompressed_rle["size"] 298 | rle = mask_utils.frPyObjects(uncompressed_rle, h, w) 299 | rle["counts"] = rle["counts"].decode("utf-8") # Necessary to serialize with json 300 | return rle 301 | 302 | 303 | def batched_mask_to_box(masks: torch.Tensor) -> torch.Tensor: 304 | """ 305 | Calculates boxes in XYXY format around masks. Return [0,0,0,0] for 306 | an empty mask. For input shape C1xC2x...xHxW, the output shape is C1xC2x...x4. 307 | """ 308 | # torch.max below raises an error on empty inputs, just skip in this case 309 | if torch.numel(masks) == 0: 310 | return torch.zeros(*masks.shape[:-2], 4, device=masks.device) 311 | 312 | # Normalize shape to CxHxW 313 | shape = masks.shape 314 | h, w = shape[-2:] 315 | if len(shape) > 2: 316 | masks = masks.flatten(0, -3) 317 | else: 318 | masks = masks.unsqueeze(0) 319 | 320 | # Get top and bottom edges 321 | in_height, _ = torch.max(masks, dim=-1) 322 | in_height_coords = in_height * torch.arange(h, device=in_height.device)[None, :] 323 | bottom_edges, _ = torch.max(in_height_coords, dim=-1) 324 | in_height_coords = in_height_coords + h * (~in_height) 325 | top_edges, _ = torch.min(in_height_coords, dim=-1) 326 | 327 | # Get left and right edges 328 | in_width, _ = torch.max(masks, dim=-2) 329 | in_width_coords = in_width * torch.arange(w, device=in_width.device)[None, :] 330 | right_edges, _ = torch.max(in_width_coords, dim=-1) 331 | in_width_coords = in_width_coords + w * (~in_width) 332 | left_edges, _ = torch.min(in_width_coords, dim=-1) 333 | 334 | # If the mask is empty the right edge will be to the left of the left edge. 335 | # Replace these boxes with [0, 0, 0, 0] 336 | empty_filter = (right_edges < left_edges) | (bottom_edges < top_edges) 337 | out = torch.stack([left_edges, top_edges, right_edges, bottom_edges], dim=-1) 338 | out = out * (~empty_filter).unsqueeze(-1) 339 | 340 | # Return to original shape 341 | if len(shape) > 2: 342 | out = out.reshape(*shape[:-2], 4) 343 | else: 344 | out = out[0] 345 | 346 | return out 347 | -------------------------------------------------------------------------------- /segment_anything/utils/onnx.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # All rights reserved. 3 | 4 | # This source code is licensed under the license found in the 5 | # LICENSE file in the root directory of this source tree. 6 | 7 | import torch 8 | import torch.nn as nn 9 | from torch.nn import functional as F 10 | 11 | from typing import Tuple 12 | 13 | from ..modeling import Sam 14 | from .amg import calculate_stability_score 15 | 16 | 17 | class SamOnnxModel(nn.Module): 18 | """ 19 | This model should not be called directly, but is used in ONNX export. 20 | It combines the prompt encoder, mask decoder, and mask postprocessing of Sam, 21 | with some functions modified to enable model tracing. Also supports extra 22 | options controlling what information. See the ONNX export script for details. 23 | """ 24 | 25 | def __init__( 26 | self, 27 | model: Sam, 28 | return_single_mask: bool, 29 | use_stability_score: bool = False, 30 | return_extra_metrics: bool = False, 31 | ) -> None: 32 | super().__init__() 33 | self.mask_decoder = model.mask_decoder 34 | self.model = model 35 | self.img_size = model.image_encoder.img_size 36 | self.return_single_mask = return_single_mask 37 | self.use_stability_score = use_stability_score 38 | self.stability_score_offset = 1.0 39 | self.return_extra_metrics = return_extra_metrics 40 | 41 | @staticmethod 42 | def resize_longest_image_size( 43 | input_image_size: torch.Tensor, longest_side: int 44 | ) -> torch.Tensor: 45 | input_image_size = input_image_size.to(torch.float32) 46 | scale = longest_side / torch.max(input_image_size) 47 | transformed_size = scale * input_image_size 48 | transformed_size = torch.floor(transformed_size + 0.5).to(torch.int64) 49 | return transformed_size 50 | 51 | def _embed_points(self, point_coords: torch.Tensor, point_labels: torch.Tensor) -> torch.Tensor: 52 | point_coords = point_coords + 0.5 53 | point_coords = point_coords / self.img_size 54 | point_embedding = self.model.prompt_encoder.pe_layer._pe_encoding(point_coords) 55 | point_labels = point_labels.unsqueeze(-1).expand_as(point_embedding) 56 | 57 | point_embedding = point_embedding * (point_labels != -1) 58 | point_embedding = point_embedding + self.model.prompt_encoder.not_a_point_embed.weight * ( 59 | point_labels == -1 60 | ) 61 | 62 | for i in range(self.model.prompt_encoder.num_point_embeddings): 63 | point_embedding = point_embedding + self.model.prompt_encoder.point_embeddings[ 64 | i 65 | ].weight * (point_labels == i) 66 | 67 | return point_embedding 68 | 69 | def _embed_masks(self, input_mask: torch.Tensor, has_mask_input: torch.Tensor) -> torch.Tensor: 70 | mask_embedding = has_mask_input * self.model.prompt_encoder.mask_downscaling(input_mask) 71 | mask_embedding = mask_embedding + ( 72 | 1 - has_mask_input 73 | ) * self.model.prompt_encoder.no_mask_embed.weight.reshape(1, -1, 1, 1) 74 | return mask_embedding 75 | 76 | def mask_postprocessing(self, masks: torch.Tensor, orig_im_size: torch.Tensor) -> torch.Tensor: 77 | masks = F.interpolate( 78 | masks, 79 | size=(self.img_size, self.img_size), 80 | mode="bilinear", 81 | align_corners=False, 82 | ) 83 | 84 | prepadded_size = self.resize_longest_image_size(orig_im_size, self.img_size) 85 | masks = masks[..., : int(prepadded_size[0]), : int(prepadded_size[1])] 86 | 87 | orig_im_size = orig_im_size.to(torch.int64) 88 | h, w = orig_im_size[0], orig_im_size[1] 89 | masks = F.interpolate(masks, size=(h, w), mode="bilinear", align_corners=False) 90 | return masks 91 | 92 | def select_masks( 93 | self, masks: torch.Tensor, iou_preds: torch.Tensor, num_points: int 94 | ) -> Tuple[torch.Tensor, torch.Tensor]: 95 | # Determine if we should return the multiclick mask or not from the number of points. 96 | # The reweighting is used to avoid control flow. 97 | score_reweight = torch.tensor( 98 | [[1000] + [0] * (self.model.mask_decoder.num_mask_tokens - 1)] 99 | ).to(iou_preds.device) 100 | score = iou_preds + (num_points - 2.5) * score_reweight 101 | best_idx = torch.argmax(score, dim=1) 102 | masks = masks[torch.arange(masks.shape[0]), best_idx, :, :].unsqueeze(1) 103 | iou_preds = iou_preds[torch.arange(masks.shape[0]), best_idx].unsqueeze(1) 104 | 105 | return masks, iou_preds 106 | 107 | @torch.no_grad() 108 | def forward( 109 | self, 110 | image_embeddings: torch.Tensor, 111 | point_coords: torch.Tensor, 112 | point_labels: torch.Tensor, 113 | mask_input: torch.Tensor, 114 | has_mask_input: torch.Tensor, 115 | orig_im_size: torch.Tensor, 116 | ): 117 | sparse_embedding = self._embed_points(point_coords, point_labels) 118 | dense_embedding = self._embed_masks(mask_input, has_mask_input) 119 | 120 | masks, scores = self.model.mask_decoder.predict_masks( 121 | image_embeddings=image_embeddings, 122 | image_pe=self.model.prompt_encoder.get_dense_pe(), 123 | sparse_prompt_embeddings=sparse_embedding, 124 | dense_prompt_embeddings=dense_embedding, 125 | ) 126 | 127 | if self.use_stability_score: 128 | scores = calculate_stability_score( 129 | masks, self.model.mask_threshold, self.stability_score_offset 130 | ) 131 | 132 | if self.return_single_mask: 133 | masks, scores = self.select_masks(masks, scores, point_coords.shape[1]) 134 | 135 | upscaled_masks = self.mask_postprocessing(masks, orig_im_size) 136 | 137 | if self.return_extra_metrics: 138 | stability_scores = calculate_stability_score( 139 | upscaled_masks, self.model.mask_threshold, self.stability_score_offset 140 | ) 141 | areas = (upscaled_masks > self.model.mask_threshold).sum(-1).sum(-1) 142 | return upscaled_masks, scores, stability_scores, areas, masks 143 | 144 | return upscaled_masks, scores, masks 145 | -------------------------------------------------------------------------------- /segment_anything/utils/transforms.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # All rights reserved. 3 | 4 | # This source code is licensed under the license found in the 5 | # LICENSE file in the root directory of this source tree. 6 | 7 | import numpy as np 8 | import torch 9 | from torch.nn import functional as F 10 | from torchvision.transforms.functional import resize, to_pil_image # type: ignore 11 | 12 | from copy import deepcopy 13 | from typing import Tuple 14 | 15 | 16 | class ResizeLongestSide: 17 | """ 18 | Resizes images to longest side 'target_length', as well as provides 19 | methods for resizing coordinates and boxes. Provides methods for 20 | transforming both numpy array and batched torch tensors. 21 | """ 22 | 23 | def __init__(self, target_length: int) -> None: 24 | self.target_length = target_length 25 | 26 | def apply_image(self, image: np.ndarray) -> np.ndarray: 27 | """ 28 | Expects a numpy array with shape HxWxC in uint8 format. 29 | """ 30 | target_size = self.get_preprocess_shape(image.shape[0], image.shape[1], self.target_length) 31 | return np.array(resize(to_pil_image(image), target_size)) 32 | 33 | def apply_coords(self, coords: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray: 34 | """ 35 | Expects a numpy array of length 2 in the final dimension. Requires the 36 | original image size in (H, W) format. 37 | """ 38 | old_h, old_w = original_size 39 | new_h, new_w = self.get_preprocess_shape( 40 | original_size[0], original_size[1], self.target_length 41 | ) 42 | coords = deepcopy(coords).astype(float) 43 | coords[..., 0] = coords[..., 0] * (new_w / old_w) 44 | coords[..., 1] = coords[..., 1] * (new_h / old_h) 45 | return coords 46 | 47 | def apply_boxes(self, boxes: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray: 48 | """ 49 | Expects a numpy array shape Bx4. Requires the original image size 50 | in (H, W) format. 51 | """ 52 | boxes = self.apply_coords(boxes.reshape(-1, 2, 2), original_size) 53 | return boxes.reshape(-1, 4) 54 | 55 | def apply_image_torch(self, image: torch.Tensor) -> torch.Tensor: 56 | """ 57 | Expects batched images with shape BxCxHxW and float format. This 58 | transformation may not exactly match apply_image. apply_image is 59 | the transformation expected by the model. 60 | """ 61 | # Expects an image in BCHW format. May not exactly match apply_image. 62 | target_size = self.get_preprocess_shape(image.shape[0], image.shape[1], self.target_length) 63 | return F.interpolate( 64 | image, target_size, mode="bilinear", align_corners=False, antialias=True 65 | ) 66 | 67 | def apply_coords_torch( 68 | self, coords: torch.Tensor, original_size: Tuple[int, ...] 69 | ) -> torch.Tensor: 70 | """ 71 | Expects a torch tensor with length 2 in the last dimension. Requires the 72 | original image size in (H, W) format. 73 | """ 74 | old_h, old_w = original_size 75 | new_h, new_w = self.get_preprocess_shape( 76 | original_size[0], original_size[1], self.target_length 77 | ) 78 | coords = deepcopy(coords).to(torch.float) 79 | coords[..., 0] = coords[..., 0] * (new_w / old_w) 80 | coords[..., 1] = coords[..., 1] * (new_h / old_h) 81 | return coords 82 | 83 | def apply_boxes_torch( 84 | self, boxes: torch.Tensor, original_size: Tuple[int, ...] 85 | ) -> torch.Tensor: 86 | """ 87 | Expects a torch tensor with shape Bx4. Requires the original image 88 | size in (H, W) format. 89 | """ 90 | boxes = self.apply_coords_torch(boxes.reshape(-1, 2, 2), original_size) 91 | return boxes.reshape(-1, 4) 92 | 93 | @staticmethod 94 | def get_preprocess_shape(oldh: int, oldw: int, long_side_length: int) -> Tuple[int, int]: 95 | """ 96 | Compute the output size given input size and target long side length. 97 | """ 98 | scale = long_side_length * 1.0 / max(oldh, oldw) 99 | newh, neww = oldh * scale, oldw * scale 100 | neww = int(neww + 0.5) 101 | newh = int(newh + 0.5) 102 | return (newh, neww) 103 | -------------------------------------------------------------------------------- /sliding_window.py: -------------------------------------------------------------------------------- 1 | import tempfile 2 | import cv2 3 | import numpy as np 4 | import rasterio 5 | from tqdm import tqdm 6 | 7 | 8 | def chw_to_hwc(block): 9 | # Grab first 3 channels 10 | block = block[:3, ...] 11 | # CHW to HWC 12 | block = np.transpose(block, (1, 2, 0)) 13 | return block 14 | 15 | 16 | def hwc_to_hw(block, channel=0): 17 | 18 | # Grab first 3 channels 19 | block = block[..., channel].astype(np.uint8) 20 | return block 21 | 22 | def calculate_sample_grid(raster_h, raster_w, sample_h, sample_w, bound): 23 | 24 | h, w = sample_h, sample_w 25 | blocks = [] 26 | height = h + 2 * bound 27 | width = w + 2 * bound 28 | 29 | for y in range(- bound, raster_h, h): 30 | for x in range(- bound, raster_w, w): 31 | 32 | rigth_x_bound = max(bound, 33 | x + width - raster_w) 34 | bottom_y_bound = max(bound, 35 | y + height - raster_h) 36 | 37 | blocks.append({'x': x, 38 | 'y': y, 39 | 'height': height, 40 | 'width': width, 41 | 'bounds': 42 | [[bound, bottom_y_bound], [bound, rigth_x_bound]], 43 | }) 44 | return blocks 45 | 46 | 47 | def read_block(src, x, y, height, width, nodata=0, **kwargs): 48 | return src.read(window=((y, y + height), (x, x + width)), boundless=True, fill_value=nodata) 49 | 50 | 51 | def write_block(dst, raster, y, x, height, width, bounds=None): 52 | if bounds: 53 | raster = raster[bounds[0][0]:raster.shape[0]-bounds[0][1], bounds[1][0]:raster.shape[1]-bounds[1][1]] 54 | x += bounds[1][0] 55 | y += bounds[0][0] 56 | width = width - bounds[1][1] - bounds[1][0] 57 | height = height - bounds[0][1] - bounds[0][0] 58 | dst.write(raster, 1, window=((y, y+height), (x, x+width))) 59 | 60 | 61 | def tiff_to_tiff(src_fp, dst_fp, func, 62 | data_to_rgb=chw_to_hwc, 63 | sample_size=(512, 512), 64 | sample_resize=None, 65 | bound=128): 66 | 67 | with rasterio.open(src_fp) as src: 68 | profile = src.profile 69 | 70 | # Computer blocks 71 | rh, rw = profile['height'], profile['width'] 72 | sh, sw = sample_size 73 | bound = bound 74 | 75 | resize_hw = sample_resize 76 | 77 | sample_grid = calculate_sample_grid(raster_h=rh, raster_w=rw, sample_h=sh, sample_w=sw, bound=bound) 78 | # set 1 channel uint8 output 79 | profile['count'] = 1 80 | profile['dtype'] = 'uint8' 81 | 82 | with rasterio.open(dst_fp, 'w', **profile) as dst: 83 | for b in tqdm(sample_grid): 84 | r = read_block(src, **b) 85 | 86 | uint8_rgb_in = data_to_rgb(r) 87 | orig_size = uint8_rgb_in.shape[:2] 88 | if resize_hw is not None: 89 | uint8_rgb_in = cv2.resize(uint8_rgb_in, resize_hw, interpolation=cv2.INTER_LINEAR) 90 | 91 | # Do someting 92 | uin8_out = func(uint8_rgb_in) 93 | 94 | if resize_hw is not None: 95 | uin8_out = cv2.resize(uin8_out, orig_size, interpolation=cv2.INTER_NEAREST) 96 | # Zero chennel, becouse 97 | write_block(dst, uin8_out, **b) 98 | 99 | 100 | def image_to_image(image, func, 101 | sample_size=(384, 384), 102 | sample_resize=None, 103 | bound=128): 104 | 105 | with tempfile.NamedTemporaryFile() as src_tmpfile: 106 | s, b = cv2.imencode('.tif', image) 107 | src_tmpfile.write(b.tobytes()) 108 | src_fp = src_tmpfile.name 109 | with tempfile.NamedTemporaryFile() as dst_tmpfile: 110 | dst_fp = dst_tmpfile.name 111 | tiff_to_tiff(src_fp, dst_fp, func, 112 | data_to_rgb=chw_to_hwc, 113 | sample_size=sample_size, 114 | sample_resize=sample_resize, 115 | bound=bound) 116 | 117 | result = cv2.imread(dst_fp) 118 | return result[..., 0] 119 | 120 | 121 | def tiff_to_image(src_fp, func, 122 | data_to_rgb=chw_to_hwc, 123 | sample_size=(512, 512), 124 | sample_resize=None, 125 | bound=128): 126 | 127 | with tempfile.NamedTemporaryFile() as dst_tmpfile: 128 | dst_fp = dst_tmpfile.name 129 | tiff_to_tiff(src_fp, dst_fp, func, 130 | data_to_rgb=data_to_rgb, 131 | sample_size=sample_size, 132 | sample_resize=sample_resize, 133 | bound=bound) 134 | 135 | result = cv2.imread(dst_fp) 136 | return result[..., 0] -------------------------------------------------------------------------------- /title_sameo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aliaksandr960/segment-anything-eo/2f00b6fc4b8218e0f02c70114bbeeaa3fcc70712/title_sameo.png -------------------------------------------------------------------------------- /tms2geotiff/.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | MANIFEST 27 | 28 | # PyInstaller 29 | # Usually these files are written by a python script from a template 30 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 31 | *.manifest 32 | *.spec 33 | 34 | # Installer logs 35 | pip-log.txt 36 | pip-delete-this-directory.txt 37 | 38 | # Unit test / coverage reports 39 | htmlcov/ 40 | .tox/ 41 | .coverage 42 | .coverage.* 43 | .cache 44 | nosetests.xml 45 | coverage.xml 46 | *.cover 47 | .hypothesis/ 48 | .pytest_cache/ 49 | 50 | # Translations 51 | *.mo 52 | *.pot 53 | 54 | # Django stuff: 55 | *.log 56 | local_settings.py 57 | db.sqlite3 58 | 59 | # Flask stuff: 60 | instance/ 61 | .webassets-cache 62 | 63 | # Scrapy stuff: 64 | .scrapy 65 | 66 | # Sphinx documentation 67 | docs/_build/ 68 | 69 | # PyBuilder 70 | target/ 71 | 72 | # Jupyter Notebook 73 | .ipynb_checkpoints 74 | 75 | # pyenv 76 | .python-version 77 | 78 | # celery beat schedule file 79 | celerybeat-schedule 80 | 81 | # SageMath parsed files 82 | *.sage.py 83 | 84 | # Environments 85 | .env 86 | .venv 87 | env/ 88 | venv/ 89 | ENV/ 90 | env.bak/ 91 | venv.bak/ 92 | 93 | # Spyder project settings 94 | .spyderproject 95 | .spyproject 96 | 97 | # Rope project settings 98 | .ropeproject 99 | 100 | # mkdocs documentation 101 | /site 102 | 103 | # mypy 104 | .mypy_cache/ 105 | -------------------------------------------------------------------------------- /tms2geotiff/LICENSE: -------------------------------------------------------------------------------- 1 | BSD 2-Clause License 2 | 3 | Copyright (c) 2019, Dingyuan Wang 4 | All rights reserved. 5 | 6 | Redistribution and use in source and binary forms, with or without 7 | modification, are permitted provided that the following conditions are met: 8 | 9 | * Redistributions of source code must retain the above copyright notice, this 10 | list of conditions and the following disclaimer. 11 | 12 | * Redistributions in binary form must reproduce the above copyright notice, 13 | this list of conditions and the following disclaimer in the documentation 14 | and/or other materials provided with the distribution. 15 | 16 | THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" 17 | AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE 18 | IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE 19 | DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE 20 | FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL 21 | DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR 22 | SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER 23 | CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, 24 | OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE 25 | OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 26 | -------------------------------------------------------------------------------- /tms2geotiff/README.md: -------------------------------------------------------------------------------- 1 | # tms2geotiff 2 | Download tiles from [Tile Map Server](https://wiki.openstreetmap.org/wiki/Slippy_map_tilenames) (online maps) and make a large GeoTIFF image. 3 | 4 | Dependencies: GDAL, Pillow, numpy, requests/httpx 5 | 6 | usage: tms2geotiff.py [-h] [-s URL] [-f LAT,LON] [-t LAT,LON] [-z ZOOM] output 7 | 8 | Merge TMS tiles to a big GeoTIFF image. 9 | 10 | positional arguments: 11 | output output file 12 | 13 | optional arguments: 14 | -h, --help show this help message and exit 15 | -s URL, --source URL TMS server url (default is OpenStreetMap: 16 | https://tile.openstreetmap.org/{z}/{x}/{y}.png) 17 | -f LAT,LON, --from LAT,LON 18 | one corner 19 | -t LAT,LON, --to LAT,LON 20 | the other corner 21 | -z ZOOM, --zoom ZOOM zoom level 22 | 23 | The -f, -t, -z arguments are required 24 | 25 | For example, 26 | 27 | python3 tms2geotiff.py -s https://tile.openstreetmap.org/{z}/{x}/{y}.png -f 45.699,127 -t 30,148.492 -z 6 output.tiff 28 | 29 | downloads a map of Japan. 30 | 31 | If the coordinates are negative, use `--from=-12.34,56.78 --to=-13.45,57.89` 32 | 33 | 34 | # tmssplit 35 | Split a large GeoTIFF image into tiles for a Tile Map Server. 36 | 37 | Dependencies: GDAL, Pillow, numpy, scipy, pyproj 38 | 39 | usage: tmssplit.py [-h] [-z ZOOM] [-n NAME] [-s SIZE] [-p PROJ] [-t THREADS] 40 | inputfile outputdir 41 | 42 | Split a big GeoTIFF image to TMS tiles. 43 | 44 | positional arguments: 45 | inputfile input GeoTIFF file 46 | outputdir output directory 47 | 48 | optional arguments: 49 | -h, --help show this help message and exit 50 | -z ZOOM, --zoom ZOOM zoom level(s), eg. 15 or 14-17 51 | -n NAME, --name NAME image file name format, default {z}_{x}_{y}.png 52 | -s SIZE, --size SIZE image size in px, default 256px 53 | -p PROJ, --proj PROJ set projection id 54 | -t THREADS, --threads THREADS 55 | set thread number 56 | 57 | -z is required 58 | 59 | -------------------------------------------------------------------------------- /tms2geotiff/_.git/HEAD: -------------------------------------------------------------------------------- 1 | ref: refs/heads/master 2 | -------------------------------------------------------------------------------- /tms2geotiff/_.git/config: -------------------------------------------------------------------------------- 1 | [core] 2 | repositoryformatversion = 0 3 | filemode = true 4 | bare = false 5 | logallrefupdates = true 6 | [remote "origin"] 7 | url = https://github.com/gumblex/tms2geotiff.git 8 | fetch = +refs/heads/*:refs/remotes/origin/* 9 | [branch "master"] 10 | remote = origin 11 | merge = refs/heads/master 12 | -------------------------------------------------------------------------------- /tms2geotiff/_.git/description: -------------------------------------------------------------------------------- 1 | Unnamed repository; edit this file 'description' to name the repository. 2 | -------------------------------------------------------------------------------- /tms2geotiff/_.git/hooks/applypatch-msg.sample: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | # 3 | # An example hook script to check the commit log message taken by 4 | # applypatch from an e-mail message. 5 | # 6 | # The hook should exit with non-zero status after issuing an 7 | # appropriate message if it wants to stop the commit. The hook is 8 | # allowed to edit the commit message file. 9 | # 10 | # To enable this hook, rename this file to "applypatch-msg". 11 | 12 | . git-sh-setup 13 | commitmsg="$(git rev-parse --git-path hooks/commit-msg)" 14 | test -x "$commitmsg" && exec "$commitmsg" ${1+"$@"} 15 | : 16 | -------------------------------------------------------------------------------- /tms2geotiff/_.git/hooks/commit-msg.sample: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | # 3 | # An example hook script to check the commit log message. 4 | # Called by "git commit" with one argument, the name of the file 5 | # that has the commit message. The hook should exit with non-zero 6 | # status after issuing an appropriate message if it wants to stop the 7 | # commit. The hook is allowed to edit the commit message file. 8 | # 9 | # To enable this hook, rename this file to "commit-msg". 10 | 11 | # Uncomment the below to add a Signed-off-by line to the message. 12 | # Doing this in a hook is a bad idea in general, but the prepare-commit-msg 13 | # hook is more suited to it. 14 | # 15 | # SOB=$(git var GIT_AUTHOR_IDENT | sed -n 's/^\(.*>\).*$/Signed-off-by: \1/p') 16 | # grep -qs "^$SOB" "$1" || echo "$SOB" >> "$1" 17 | 18 | # This example catches duplicate Signed-off-by lines. 19 | 20 | test "" = "$(grep '^Signed-off-by: ' "$1" | 21 | sort | uniq -c | sed -e '/^[ ]*1[ ]/d')" || { 22 | echo >&2 Duplicate Signed-off-by lines. 23 | exit 1 24 | } 25 | -------------------------------------------------------------------------------- /tms2geotiff/_.git/hooks/fsmonitor-watchman.sample: -------------------------------------------------------------------------------- 1 | #!/usr/bin/perl 2 | 3 | use strict; 4 | use warnings; 5 | use IPC::Open2; 6 | 7 | # An example hook script to integrate Watchman 8 | # (https://facebook.github.io/watchman/) with git to speed up detecting 9 | # new and modified files. 10 | # 11 | # The hook is passed a version (currently 2) and last update token 12 | # formatted as a string and outputs to stdout a new update token and 13 | # all files that have been modified since the update token. Paths must 14 | # be relative to the root of the working tree and separated by a single NUL. 15 | # 16 | # To enable this hook, rename this file to "query-watchman" and set 17 | # 'git config core.fsmonitor .git/hooks/query-watchman' 18 | # 19 | my ($version, $last_update_token) = @ARGV; 20 | 21 | # Uncomment for debugging 22 | # print STDERR "$0 $version $last_update_token\n"; 23 | 24 | # Check the hook interface version 25 | if ($version ne 2) { 26 | die "Unsupported query-fsmonitor hook version '$version'.\n" . 27 | "Falling back to scanning...\n"; 28 | } 29 | 30 | my $git_work_tree = get_working_dir(); 31 | 32 | my $retry = 1; 33 | 34 | my $json_pkg; 35 | eval { 36 | require JSON::XS; 37 | $json_pkg = "JSON::XS"; 38 | 1; 39 | } or do { 40 | require JSON::PP; 41 | $json_pkg = "JSON::PP"; 42 | }; 43 | 44 | launch_watchman(); 45 | 46 | sub launch_watchman { 47 | my $o = watchman_query(); 48 | if (is_work_tree_watched($o)) { 49 | output_result($o->{clock}, @{$o->{files}}); 50 | } 51 | } 52 | 53 | sub output_result { 54 | my ($clockid, @files) = @_; 55 | 56 | # Uncomment for debugging watchman output 57 | # open (my $fh, ">", ".git/watchman-output.out"); 58 | # binmode $fh, ":utf8"; 59 | # print $fh "$clockid\n@files\n"; 60 | # close $fh; 61 | 62 | binmode STDOUT, ":utf8"; 63 | print $clockid; 64 | print "\0"; 65 | local $, = "\0"; 66 | print @files; 67 | } 68 | 69 | sub watchman_clock { 70 | my $response = qx/watchman clock "$git_work_tree"/; 71 | die "Failed to get clock id on '$git_work_tree'.\n" . 72 | "Falling back to scanning...\n" if $? != 0; 73 | 74 | return $json_pkg->new->utf8->decode($response); 75 | } 76 | 77 | sub watchman_query { 78 | my $pid = open2(\*CHLD_OUT, \*CHLD_IN, 'watchman -j --no-pretty') 79 | or die "open2() failed: $!\n" . 80 | "Falling back to scanning...\n"; 81 | 82 | # In the query expression below we're asking for names of files that 83 | # changed since $last_update_token but not from the .git folder. 84 | # 85 | # To accomplish this, we're using the "since" generator to use the 86 | # recency index to select candidate nodes and "fields" to limit the 87 | # output to file names only. Then we're using the "expression" term to 88 | # further constrain the results. 89 | if (substr($last_update_token, 0, 1) eq "c") { 90 | $last_update_token = "\"$last_update_token\""; 91 | } 92 | my $query = <<" END"; 93 | ["query", "$git_work_tree", { 94 | "since": $last_update_token, 95 | "fields": ["name"], 96 | "expression": ["not", ["dirname", ".git"]] 97 | }] 98 | END 99 | 100 | # Uncomment for debugging the watchman query 101 | # open (my $fh, ">", ".git/watchman-query.json"); 102 | # print $fh $query; 103 | # close $fh; 104 | 105 | print CHLD_IN $query; 106 | close CHLD_IN; 107 | my $response = do {local $/; }; 108 | 109 | # Uncomment for debugging the watch response 110 | # open ($fh, ">", ".git/watchman-response.json"); 111 | # print $fh $response; 112 | # close $fh; 113 | 114 | die "Watchman: command returned no output.\n" . 115 | "Falling back to scanning...\n" if $response eq ""; 116 | die "Watchman: command returned invalid output: $response\n" . 117 | "Falling back to scanning...\n" unless $response =~ /^\{/; 118 | 119 | return $json_pkg->new->utf8->decode($response); 120 | } 121 | 122 | sub is_work_tree_watched { 123 | my ($output) = @_; 124 | my $error = $output->{error}; 125 | if ($retry > 0 and $error and $error =~ m/unable to resolve root .* directory (.*) is not watched/) { 126 | $retry--; 127 | my $response = qx/watchman watch "$git_work_tree"/; 128 | die "Failed to make watchman watch '$git_work_tree'.\n" . 129 | "Falling back to scanning...\n" if $? != 0; 130 | $output = $json_pkg->new->utf8->decode($response); 131 | $error = $output->{error}; 132 | die "Watchman: $error.\n" . 133 | "Falling back to scanning...\n" if $error; 134 | 135 | # Uncomment for debugging watchman output 136 | # open (my $fh, ">", ".git/watchman-output.out"); 137 | # close $fh; 138 | 139 | # Watchman will always return all files on the first query so 140 | # return the fast "everything is dirty" flag to git and do the 141 | # Watchman query just to get it over with now so we won't pay 142 | # the cost in git to look up each individual file. 143 | my $o = watchman_clock(); 144 | $error = $output->{error}; 145 | 146 | die "Watchman: $error.\n" . 147 | "Falling back to scanning...\n" if $error; 148 | 149 | output_result($o->{clock}, ("/")); 150 | $last_update_token = $o->{clock}; 151 | 152 | eval { launch_watchman() }; 153 | return 0; 154 | } 155 | 156 | die "Watchman: $error.\n" . 157 | "Falling back to scanning...\n" if $error; 158 | 159 | return 1; 160 | } 161 | 162 | sub get_working_dir { 163 | my $working_dir; 164 | if ($^O =~ 'msys' || $^O =~ 'cygwin') { 165 | $working_dir = Win32::GetCwd(); 166 | $working_dir =~ tr/\\/\//; 167 | } else { 168 | require Cwd; 169 | $working_dir = Cwd::cwd(); 170 | } 171 | 172 | return $working_dir; 173 | } 174 | -------------------------------------------------------------------------------- /tms2geotiff/_.git/hooks/post-update.sample: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | # 3 | # An example hook script to prepare a packed repository for use over 4 | # dumb transports. 5 | # 6 | # To enable this hook, rename this file to "post-update". 7 | 8 | exec git update-server-info 9 | -------------------------------------------------------------------------------- /tms2geotiff/_.git/hooks/pre-applypatch.sample: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | # 3 | # An example hook script to verify what is about to be committed 4 | # by applypatch from an e-mail message. 5 | # 6 | # The hook should exit with non-zero status after issuing an 7 | # appropriate message if it wants to stop the commit. 8 | # 9 | # To enable this hook, rename this file to "pre-applypatch". 10 | 11 | . git-sh-setup 12 | precommit="$(git rev-parse --git-path hooks/pre-commit)" 13 | test -x "$precommit" && exec "$precommit" ${1+"$@"} 14 | : 15 | -------------------------------------------------------------------------------- /tms2geotiff/_.git/hooks/pre-commit.sample: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | # 3 | # An example hook script to verify what is about to be committed. 4 | # Called by "git commit" with no arguments. The hook should 5 | # exit with non-zero status after issuing an appropriate message if 6 | # it wants to stop the commit. 7 | # 8 | # To enable this hook, rename this file to "pre-commit". 9 | 10 | if git rev-parse --verify HEAD >/dev/null 2>&1 11 | then 12 | against=HEAD 13 | else 14 | # Initial commit: diff against an empty tree object 15 | against=$(git hash-object -t tree /dev/null) 16 | fi 17 | 18 | # If you want to allow non-ASCII filenames set this variable to true. 19 | allownonascii=$(git config --type=bool hooks.allownonascii) 20 | 21 | # Redirect output to stderr. 22 | exec 1>&2 23 | 24 | # Cross platform projects tend to avoid non-ASCII filenames; prevent 25 | # them from being added to the repository. We exploit the fact that the 26 | # printable range starts at the space character and ends with tilde. 27 | if [ "$allownonascii" != "true" ] && 28 | # Note that the use of brackets around a tr range is ok here, (it's 29 | # even required, for portability to Solaris 10's /usr/bin/tr), since 30 | # the square bracket bytes happen to fall in the designated range. 31 | test $(git diff --cached --name-only --diff-filter=A -z $against | 32 | LC_ALL=C tr -d '[ -~]\0' | wc -c) != 0 33 | then 34 | cat <<\EOF 35 | Error: Attempt to add a non-ASCII file name. 36 | 37 | This can cause problems if you want to work with people on other platforms. 38 | 39 | To be portable it is advisable to rename the file. 40 | 41 | If you know what you are doing you can disable this check using: 42 | 43 | git config hooks.allownonascii true 44 | EOF 45 | exit 1 46 | fi 47 | 48 | # If there are whitespace errors, print the offending file names and fail. 49 | exec git diff-index --check --cached $against -- 50 | -------------------------------------------------------------------------------- /tms2geotiff/_.git/hooks/pre-merge-commit.sample: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | # 3 | # An example hook script to verify what is about to be committed. 4 | # Called by "git merge" with no arguments. The hook should 5 | # exit with non-zero status after issuing an appropriate message to 6 | # stderr if it wants to stop the merge commit. 7 | # 8 | # To enable this hook, rename this file to "pre-merge-commit". 9 | 10 | . git-sh-setup 11 | test -x "$GIT_DIR/hooks/pre-commit" && 12 | exec "$GIT_DIR/hooks/pre-commit" 13 | : 14 | -------------------------------------------------------------------------------- /tms2geotiff/_.git/hooks/pre-push.sample: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | 3 | # An example hook script to verify what is about to be pushed. Called by "git 4 | # push" after it has checked the remote status, but before anything has been 5 | # pushed. If this script exits with a non-zero status nothing will be pushed. 6 | # 7 | # This hook is called with the following parameters: 8 | # 9 | # $1 -- Name of the remote to which the push is being done 10 | # $2 -- URL to which the push is being done 11 | # 12 | # If pushing without using a named remote those arguments will be equal. 13 | # 14 | # Information about the commits which are being pushed is supplied as lines to 15 | # the standard input in the form: 16 | # 17 | # 18 | # 19 | # This sample shows how to prevent push of commits where the log message starts 20 | # with "WIP" (work in progress). 21 | 22 | remote="$1" 23 | url="$2" 24 | 25 | zero=$(git hash-object --stdin &2 "Found WIP commit in $local_ref, not pushing" 48 | exit 1 49 | fi 50 | fi 51 | done 52 | 53 | exit 0 54 | -------------------------------------------------------------------------------- /tms2geotiff/_.git/hooks/pre-rebase.sample: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | # 3 | # Copyright (c) 2006, 2008 Junio C Hamano 4 | # 5 | # The "pre-rebase" hook is run just before "git rebase" starts doing 6 | # its job, and can prevent the command from running by exiting with 7 | # non-zero status. 8 | # 9 | # The hook is called with the following parameters: 10 | # 11 | # $1 -- the upstream the series was forked from. 12 | # $2 -- the branch being rebased (or empty when rebasing the current branch). 13 | # 14 | # This sample shows how to prevent topic branches that are already 15 | # merged to 'next' branch from getting rebased, because allowing it 16 | # would result in rebasing already published history. 17 | 18 | publish=next 19 | basebranch="$1" 20 | if test "$#" = 2 21 | then 22 | topic="refs/heads/$2" 23 | else 24 | topic=`git symbolic-ref HEAD` || 25 | exit 0 ;# we do not interrupt rebasing detached HEAD 26 | fi 27 | 28 | case "$topic" in 29 | refs/heads/??/*) 30 | ;; 31 | *) 32 | exit 0 ;# we do not interrupt others. 33 | ;; 34 | esac 35 | 36 | # Now we are dealing with a topic branch being rebased 37 | # on top of master. Is it OK to rebase it? 38 | 39 | # Does the topic really exist? 40 | git show-ref -q "$topic" || { 41 | echo >&2 "No such branch $topic" 42 | exit 1 43 | } 44 | 45 | # Is topic fully merged to master? 46 | not_in_master=`git rev-list --pretty=oneline ^master "$topic"` 47 | if test -z "$not_in_master" 48 | then 49 | echo >&2 "$topic is fully merged to master; better remove it." 50 | exit 1 ;# we could allow it, but there is no point. 51 | fi 52 | 53 | # Is topic ever merged to next? If so you should not be rebasing it. 54 | only_next_1=`git rev-list ^master "^$topic" ${publish} | sort` 55 | only_next_2=`git rev-list ^master ${publish} | sort` 56 | if test "$only_next_1" = "$only_next_2" 57 | then 58 | not_in_topic=`git rev-list "^$topic" master` 59 | if test -z "$not_in_topic" 60 | then 61 | echo >&2 "$topic is already up to date with master" 62 | exit 1 ;# we could allow it, but there is no point. 63 | else 64 | exit 0 65 | fi 66 | else 67 | not_in_next=`git rev-list --pretty=oneline ^${publish} "$topic"` 68 | /usr/bin/perl -e ' 69 | my $topic = $ARGV[0]; 70 | my $msg = "* $topic has commits already merged to public branch:\n"; 71 | my (%not_in_next) = map { 72 | /^([0-9a-f]+) /; 73 | ($1 => 1); 74 | } split(/\n/, $ARGV[1]); 75 | for my $elem (map { 76 | /^([0-9a-f]+) (.*)$/; 77 | [$1 => $2]; 78 | } split(/\n/, $ARGV[2])) { 79 | if (!exists $not_in_next{$elem->[0]}) { 80 | if ($msg) { 81 | print STDERR $msg; 82 | undef $msg; 83 | } 84 | print STDERR " $elem->[1]\n"; 85 | } 86 | } 87 | ' "$topic" "$not_in_next" "$not_in_master" 88 | exit 1 89 | fi 90 | 91 | <<\DOC_END 92 | 93 | This sample hook safeguards topic branches that have been 94 | published from being rewound. 95 | 96 | The workflow assumed here is: 97 | 98 | * Once a topic branch forks from "master", "master" is never 99 | merged into it again (either directly or indirectly). 100 | 101 | * Once a topic branch is fully cooked and merged into "master", 102 | it is deleted. If you need to build on top of it to correct 103 | earlier mistakes, a new topic branch is created by forking at 104 | the tip of the "master". This is not strictly necessary, but 105 | it makes it easier to keep your history simple. 106 | 107 | * Whenever you need to test or publish your changes to topic 108 | branches, merge them into "next" branch. 109 | 110 | The script, being an example, hardcodes the publish branch name 111 | to be "next", but it is trivial to make it configurable via 112 | $GIT_DIR/config mechanism. 113 | 114 | With this workflow, you would want to know: 115 | 116 | (1) ... if a topic branch has ever been merged to "next". Young 117 | topic branches can have stupid mistakes you would rather 118 | clean up before publishing, and things that have not been 119 | merged into other branches can be easily rebased without 120 | affecting other people. But once it is published, you would 121 | not want to rewind it. 122 | 123 | (2) ... if a topic branch has been fully merged to "master". 124 | Then you can delete it. More importantly, you should not 125 | build on top of it -- other people may already want to 126 | change things related to the topic as patches against your 127 | "master", so if you need further changes, it is better to 128 | fork the topic (perhaps with the same name) afresh from the 129 | tip of "master". 130 | 131 | Let's look at this example: 132 | 133 | o---o---o---o---o---o---o---o---o---o "next" 134 | / / / / 135 | / a---a---b A / / 136 | / / / / 137 | / / c---c---c---c B / 138 | / / / \ / 139 | / / / b---b C \ / 140 | / / / / \ / 141 | ---o---o---o---o---o---o---o---o---o---o---o "master" 142 | 143 | 144 | A, B and C are topic branches. 145 | 146 | * A has one fix since it was merged up to "next". 147 | 148 | * B has finished. It has been fully merged up to "master" and "next", 149 | and is ready to be deleted. 150 | 151 | * C has not merged to "next" at all. 152 | 153 | We would want to allow C to be rebased, refuse A, and encourage 154 | B to be deleted. 155 | 156 | To compute (1): 157 | 158 | git rev-list ^master ^topic next 159 | git rev-list ^master next 160 | 161 | if these match, topic has not merged in next at all. 162 | 163 | To compute (2): 164 | 165 | git rev-list master..topic 166 | 167 | if this is empty, it is fully merged to "master". 168 | 169 | DOC_END 170 | -------------------------------------------------------------------------------- /tms2geotiff/_.git/hooks/pre-receive.sample: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | # 3 | # An example hook script to make use of push options. 4 | # The example simply echoes all push options that start with 'echoback=' 5 | # and rejects all pushes when the "reject" push option is used. 6 | # 7 | # To enable this hook, rename this file to "pre-receive". 8 | 9 | if test -n "$GIT_PUSH_OPTION_COUNT" 10 | then 11 | i=0 12 | while test "$i" -lt "$GIT_PUSH_OPTION_COUNT" 13 | do 14 | eval "value=\$GIT_PUSH_OPTION_$i" 15 | case "$value" in 16 | echoback=*) 17 | echo "echo from the pre-receive-hook: ${value#*=}" >&2 18 | ;; 19 | reject) 20 | exit 1 21 | esac 22 | i=$((i + 1)) 23 | done 24 | fi 25 | -------------------------------------------------------------------------------- /tms2geotiff/_.git/hooks/prepare-commit-msg.sample: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | # 3 | # An example hook script to prepare the commit log message. 4 | # Called by "git commit" with the name of the file that has the 5 | # commit message, followed by the description of the commit 6 | # message's source. The hook's purpose is to edit the commit 7 | # message file. If the hook fails with a non-zero status, 8 | # the commit is aborted. 9 | # 10 | # To enable this hook, rename this file to "prepare-commit-msg". 11 | 12 | # This hook includes three examples. The first one removes the 13 | # "# Please enter the commit message..." help message. 14 | # 15 | # The second includes the output of "git diff --name-status -r" 16 | # into the message, just before the "git status" output. It is 17 | # commented because it doesn't cope with --amend or with squashed 18 | # commits. 19 | # 20 | # The third example adds a Signed-off-by line to the message, that can 21 | # still be edited. This is rarely a good idea. 22 | 23 | COMMIT_MSG_FILE=$1 24 | COMMIT_SOURCE=$2 25 | SHA1=$3 26 | 27 | /usr/bin/perl -i.bak -ne 'print unless(m/^. Please enter the commit message/..m/^#$/)' "$COMMIT_MSG_FILE" 28 | 29 | # case "$COMMIT_SOURCE,$SHA1" in 30 | # ,|template,) 31 | # /usr/bin/perl -i.bak -pe ' 32 | # print "\n" . `git diff --cached --name-status -r` 33 | # if /^#/ && $first++ == 0' "$COMMIT_MSG_FILE" ;; 34 | # *) ;; 35 | # esac 36 | 37 | # SOB=$(git var GIT_COMMITTER_IDENT | sed -n 's/^\(.*>\).*$/Signed-off-by: \1/p') 38 | # git interpret-trailers --in-place --trailer "$SOB" "$COMMIT_MSG_FILE" 39 | # if test -z "$COMMIT_SOURCE" 40 | # then 41 | # /usr/bin/perl -i.bak -pe 'print "\n" if !$first_line++' "$COMMIT_MSG_FILE" 42 | # fi 43 | -------------------------------------------------------------------------------- /tms2geotiff/_.git/hooks/push-to-checkout.sample: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | 3 | # An example hook script to update a checked-out tree on a git push. 4 | # 5 | # This hook is invoked by git-receive-pack(1) when it reacts to git 6 | # push and updates reference(s) in its repository, and when the push 7 | # tries to update the branch that is currently checked out and the 8 | # receive.denyCurrentBranch configuration variable is set to 9 | # updateInstead. 10 | # 11 | # By default, such a push is refused if the working tree and the index 12 | # of the remote repository has any difference from the currently 13 | # checked out commit; when both the working tree and the index match 14 | # the current commit, they are updated to match the newly pushed tip 15 | # of the branch. This hook is to be used to override the default 16 | # behaviour; however the code below reimplements the default behaviour 17 | # as a starting point for convenient modification. 18 | # 19 | # The hook receives the commit with which the tip of the current 20 | # branch is going to be updated: 21 | commit=$1 22 | 23 | # It can exit with a non-zero status to refuse the push (when it does 24 | # so, it must not modify the index or the working tree). 25 | die () { 26 | echo >&2 "$*" 27 | exit 1 28 | } 29 | 30 | # Or it can make any necessary changes to the working tree and to the 31 | # index to bring them to the desired state when the tip of the current 32 | # branch is updated to the new commit, and exit with a zero status. 33 | # 34 | # For example, the hook can simply run git read-tree -u -m HEAD "$1" 35 | # in order to emulate git fetch that is run in the reverse direction 36 | # with git push, as the two-tree form of git read-tree -u -m is 37 | # essentially the same as git switch or git checkout that switches 38 | # branches while keeping the local changes in the working tree that do 39 | # not interfere with the difference between the branches. 40 | 41 | # The below is a more-or-less exact translation to shell of the C code 42 | # for the default behaviour for git's push-to-checkout hook defined in 43 | # the push_to_deploy() function in builtin/receive-pack.c. 44 | # 45 | # Note that the hook will be executed from the repository directory, 46 | # not from the working tree, so if you want to perform operations on 47 | # the working tree, you will have to adapt your code accordingly, e.g. 48 | # by adding "cd .." or using relative paths. 49 | 50 | if ! git update-index -q --ignore-submodules --refresh 51 | then 52 | die "Up-to-date check failed" 53 | fi 54 | 55 | if ! git diff-files --quiet --ignore-submodules -- 56 | then 57 | die "Working directory has unstaged changes" 58 | fi 59 | 60 | # This is a rough translation of: 61 | # 62 | # head_has_history() ? "HEAD" : EMPTY_TREE_SHA1_HEX 63 | if git cat-file -e HEAD 2>/dev/null 64 | then 65 | head=HEAD 66 | else 67 | head=$(git hash-object -t tree --stdin &2 35 | echo " (if you want, you could supply GIT_DIR then run" >&2 36 | echo " $0 )" >&2 37 | exit 1 38 | fi 39 | 40 | if [ -z "$refname" -o -z "$oldrev" -o -z "$newrev" ]; then 41 | echo "usage: $0 " >&2 42 | exit 1 43 | fi 44 | 45 | # --- Config 46 | allowunannotated=$(git config --type=bool hooks.allowunannotated) 47 | allowdeletebranch=$(git config --type=bool hooks.allowdeletebranch) 48 | denycreatebranch=$(git config --type=bool hooks.denycreatebranch) 49 | allowdeletetag=$(git config --type=bool hooks.allowdeletetag) 50 | allowmodifytag=$(git config --type=bool hooks.allowmodifytag) 51 | 52 | # check for no description 53 | projectdesc=$(sed -e '1q' "$GIT_DIR/description") 54 | case "$projectdesc" in 55 | "Unnamed repository"* | "") 56 | echo "*** Project description file hasn't been set" >&2 57 | exit 1 58 | ;; 59 | esac 60 | 61 | # --- Check types 62 | # if $newrev is 0000...0000, it's a commit to delete a ref. 63 | zero=$(git hash-object --stdin &2 76 | echo "*** Use 'git tag [ -a | -s ]' for tags you want to propagate." >&2 77 | exit 1 78 | fi 79 | ;; 80 | refs/tags/*,delete) 81 | # delete tag 82 | if [ "$allowdeletetag" != "true" ]; then 83 | echo "*** Deleting a tag is not allowed in this repository" >&2 84 | exit 1 85 | fi 86 | ;; 87 | refs/tags/*,tag) 88 | # annotated tag 89 | if [ "$allowmodifytag" != "true" ] && git rev-parse $refname > /dev/null 2>&1 90 | then 91 | echo "*** Tag '$refname' already exists." >&2 92 | echo "*** Modifying a tag is not allowed in this repository." >&2 93 | exit 1 94 | fi 95 | ;; 96 | refs/heads/*,commit) 97 | # branch 98 | if [ "$oldrev" = "$zero" -a "$denycreatebranch" = "true" ]; then 99 | echo "*** Creating a branch is not allowed in this repository" >&2 100 | exit 1 101 | fi 102 | ;; 103 | refs/heads/*,delete) 104 | # delete branch 105 | if [ "$allowdeletebranch" != "true" ]; then 106 | echo "*** Deleting a branch is not allowed in this repository" >&2 107 | exit 1 108 | fi 109 | ;; 110 | refs/remotes/*,commit) 111 | # tracking branch 112 | ;; 113 | refs/remotes/*,delete) 114 | # delete tracking branch 115 | if [ "$allowdeletebranch" != "true" ]; then 116 | echo "*** Deleting a tracking branch is not allowed in this repository" >&2 117 | exit 1 118 | fi 119 | ;; 120 | *) 121 | # Anything else (is there anything else?) 122 | echo "*** Update hook: unknown type of update to ref $refname of type $newrev_type" >&2 123 | exit 1 124 | ;; 125 | esac 126 | 127 | # --- Finished 128 | exit 0 129 | -------------------------------------------------------------------------------- /tms2geotiff/_.git/index: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aliaksandr960/segment-anything-eo/2f00b6fc4b8218e0f02c70114bbeeaa3fcc70712/tms2geotiff/_.git/index -------------------------------------------------------------------------------- /tms2geotiff/_.git/info/exclude: -------------------------------------------------------------------------------- 1 | # git ls-files --others --exclude-from=.git/info/exclude 2 | # Lines that start with '#' are comments. 3 | # For a project mostly in C, the following would be a good set of 4 | # exclude patterns (uncomment them if you want to use them): 5 | # *.[oa] 6 | # *~ 7 | -------------------------------------------------------------------------------- /tms2geotiff/_.git/logs/HEAD: -------------------------------------------------------------------------------- 1 | 0000000000000000000000000000000000000000 d7f4ab4f5e510c02b65f67bcb928135c570583c8 Aliakasndr Hancharenka 1676873804 +0400 clone: from https://github.com/gumblex/tms2geotiff.git 2 | -------------------------------------------------------------------------------- /tms2geotiff/_.git/logs/refs/heads/master: -------------------------------------------------------------------------------- 1 | 0000000000000000000000000000000000000000 d7f4ab4f5e510c02b65f67bcb928135c570583c8 Aliakasndr Hancharenka 1676873804 +0400 clone: from https://github.com/gumblex/tms2geotiff.git 2 | -------------------------------------------------------------------------------- /tms2geotiff/_.git/logs/refs/remotes/origin/HEAD: -------------------------------------------------------------------------------- 1 | 0000000000000000000000000000000000000000 d7f4ab4f5e510c02b65f67bcb928135c570583c8 Aliakasndr Hancharenka 1676873804 +0400 clone: from https://github.com/gumblex/tms2geotiff.git 2 | -------------------------------------------------------------------------------- /tms2geotiff/_.git/objects/pack/pack-66bf8968b6af3c5470882dc8a09fd50bee45e557.idx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aliaksandr960/segment-anything-eo/2f00b6fc4b8218e0f02c70114bbeeaa3fcc70712/tms2geotiff/_.git/objects/pack/pack-66bf8968b6af3c5470882dc8a09fd50bee45e557.idx -------------------------------------------------------------------------------- /tms2geotiff/_.git/objects/pack/pack-66bf8968b6af3c5470882dc8a09fd50bee45e557.pack: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aliaksandr960/segment-anything-eo/2f00b6fc4b8218e0f02c70114bbeeaa3fcc70712/tms2geotiff/_.git/objects/pack/pack-66bf8968b6af3c5470882dc8a09fd50bee45e557.pack -------------------------------------------------------------------------------- /tms2geotiff/_.git/packed-refs: -------------------------------------------------------------------------------- 1 | # pack-refs with: peeled fully-peeled sorted 2 | d7f4ab4f5e510c02b65f67bcb928135c570583c8 refs/remotes/origin/master 3 | -------------------------------------------------------------------------------- /tms2geotiff/_.git/refs/heads/master: -------------------------------------------------------------------------------- 1 | d7f4ab4f5e510c02b65f67bcb928135c570583c8 2 | -------------------------------------------------------------------------------- /tms2geotiff/_.git/refs/remotes/origin/HEAD: -------------------------------------------------------------------------------- 1 | ref: refs/remotes/origin/master 2 | -------------------------------------------------------------------------------- /tms2geotiff/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aliaksandr960/segment-anything-eo/2f00b6fc4b8218e0f02c70114bbeeaa3fcc70712/tms2geotiff/__init__.py -------------------------------------------------------------------------------- /tms2geotiff/requirements.txt: -------------------------------------------------------------------------------- 1 | gdal 2 | pillow 3 | numpy 4 | httpx 5 | -------------------------------------------------------------------------------- /tms2geotiff/tms2geotiff.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | 4 | import io 5 | import math 6 | import argparse 7 | import itertools 8 | import concurrent.futures 9 | import random 10 | 11 | import numpy 12 | from tqdm import tqdm 13 | from PIL import Image 14 | from osgeo import gdal 15 | 16 | import requests 17 | SESSION = requests.Session() 18 | 19 | EARTH_EQUATORIAL_RADIUS = 6378137.0 20 | 21 | Image.MAX_IMAGE_PIXELS = None 22 | 23 | DEFAULT_TMS = 'https://tile.openstreetmap.org/{z}/{x}/{y}.png' 24 | 25 | gdal.UseExceptions() 26 | 27 | WKT_3857 = 'PROJCS["WGS 84 / Pseudo-Mercator",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Mercator_1SP"],PARAMETER["central_meridian",0],PARAMETER["scale_factor",1],PARAMETER["false_easting",0],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["X",EAST],AXIS["Y",NORTH],EXTENSION["PROJ4","+proj=merc +a=6378137 +b=6378137 +lat_ts=0.0 +lon_0=0.0 +x_0=0.0 +y_0=0 +k=1.0 +units=m +nadgrids=@null +wktext +no_defs"],AUTHORITY["EPSG","3857"]]' 28 | 29 | user_agents = [ 30 | "Mozilla/5.0 (Windows NT 10.0; rv:91.0) Gecko/20100101 Firefox/91.0", 31 | "Mozilla/5.0 (Windows NT 10.0; rv:78.0) Gecko/20100101 Firefox/78.0", 32 | "Mozilla/5.0 (X11; Linux x86_64; rv:95.0) Gecko/20100101 Firefox/95.0" 33 | ] 34 | random_user_agent = random.choice(user_agents) 35 | headers = { 36 | 'User-Agent': random_user_agent 37 | } 38 | 39 | def from4326_to3857(lat, lon): 40 | xtile = math.radians(lon) * EARTH_EQUATORIAL_RADIUS 41 | ytile = math.log(math.tan(math.radians(45 + lat / 2.0))) * EARTH_EQUATORIAL_RADIUS 42 | return (xtile, ytile) 43 | 44 | def deg2num(lat, lon, zoom): 45 | lat_r = math.radians(lat) 46 | n = 2 ** zoom 47 | xtile = ((lon + 180) / 360 * n) 48 | ytile = ((1 - math.log(math.tan(lat_r) + 1/math.cos(lat_r)) / math.pi) / 2 * n) 49 | return (xtile, ytile) 50 | 51 | def is_empty(im): 52 | extrema = im.getextrema() 53 | if len(extrema) >= 3: 54 | if len(extrema) > 3 and extrema[-1] == (0, 0): 55 | return True 56 | for ext in extrema[:3]: 57 | if ext != (0, 0): 58 | return False 59 | return True 60 | else: 61 | return extrema[0] == (0, 0) 62 | 63 | def paste_tile(bigim, base_size, tile, corner_xy, bbox): 64 | if tile is None: 65 | return bigim 66 | im = Image.open(io.BytesIO(tile)) 67 | mode = 'RGB' if im.mode == 'RGB' else 'RGBA' 68 | size = im.size 69 | if bigim is None: 70 | base_size[0] = size[0] 71 | base_size[1] = size[1] 72 | newim = Image.new(mode, ( 73 | size[0]*(bbox[2]-bbox[0]), size[1]*(bbox[3]-bbox[1]))) 74 | else: 75 | newim = bigim 76 | 77 | dx = abs(corner_xy[0] - bbox[0]) 78 | dy = abs(corner_xy[1] - bbox[1]) 79 | xy0 = (size[0]*dx, size[1]*dy) 80 | if mode == 'RGB': 81 | newim.paste(im, xy0) 82 | else: 83 | if im.mode != mode: 84 | im = im.convert(mode) 85 | if not is_empty(im): 86 | newim.paste(im, xy0) 87 | im.close() 88 | return newim 89 | 90 | def finish_picture(bigim, base_size, bbox, x0, y0, x1, y1): 91 | xfrac = x0 - bbox[0] 92 | yfrac = y0 - bbox[1] 93 | x2 = round(base_size[0]*xfrac) 94 | y2 = round(base_size[1]*yfrac) 95 | imgw = round(base_size[0]*(x1-x0)) 96 | imgh = round(base_size[1]*(y1-y0)) 97 | retim = bigim.crop((x2, y2, x2+imgw, y2+imgh)) 98 | if retim.mode == 'RGBA' and retim.getextrema()[3] == (255, 255): 99 | retim = retim.convert('RGB') 100 | bigim.close() 101 | return retim 102 | 103 | def get_tile(url): 104 | retry = 3 105 | while 1: 106 | try: 107 | r = SESSION.get(url, timeout=60, headers=headers) 108 | break 109 | except Exception: 110 | retry -= 1 111 | if not retry: 112 | raise 113 | if r.status_code == 404: 114 | return None 115 | elif not r.content: 116 | return None 117 | r.raise_for_status() 118 | return r.content 119 | 120 | def draw_tile(source, lat0, lon0, lat1, lon1, zoom, filename): 121 | x0, y0 = deg2num(lat0, lon0, zoom) 122 | x1, y1 = deg2num(lat1, lon1, zoom) 123 | if x0 > x1: 124 | x0, x1 = x1, x0 125 | if y0 > y1: 126 | y0, y1 = y1, y0 127 | corners = tuple(itertools.product( 128 | range(math.floor(x0), math.ceil(x1)), 129 | range(math.floor(y0), math.ceil(y1)))) 130 | totalnum = len(corners) 131 | futures = [] 132 | with concurrent.futures.ThreadPoolExecutor(5) as executor: 133 | for x, y in corners: 134 | futures.append(executor.submit(get_tile, 135 | source.format(z=zoom, x=x, y=y))) 136 | bbox = (math.floor(x0), math.floor(y0), math.ceil(x1), math.ceil(y1)) 137 | bigim = None 138 | base_size = [256, 256] 139 | for k, (fut, corner_xy) in tqdm(enumerate(zip(futures, corners), 1)): 140 | bigim = paste_tile(bigim, base_size, fut.result(), corner_xy, bbox) 141 | img = finish_picture(bigim, base_size, bbox, x0, y0, x1, y1) 142 | imgbands = len(img.getbands()) 143 | driver = gdal.GetDriverByName('GTiff') 144 | gtiff = driver.Create(filename, img.size[0], img.size[1], 145 | imgbands, gdal.GDT_Byte, 146 | options=['COMPRESS=DEFLATE', 'PREDICTOR=2', 'ZLEVEL=9', 'TILED=YES']) 147 | xp0, yp0 = from4326_to3857(lat0, lon0) 148 | xp1, yp1 = from4326_to3857(lat1, lon1) 149 | pwidth = abs(xp1 - xp0) / img.size[0] 150 | pheight = abs(yp1 - yp0) / img.size[1] 151 | gtiff.SetGeoTransform(( 152 | min(xp0, xp1), pwidth, 0, max(yp0, yp1), 0, -pheight)) 153 | gtiff.SetProjection(WKT_3857) 154 | for band in range(imgbands): 155 | array = numpy.array(img.getdata(band), dtype='u8') 156 | array = array.reshape((img.size[1], img.size[0])) 157 | band = gtiff.GetRasterBand(band + 1) 158 | band.WriteArray(array) 159 | gtiff.FlushCache() 160 | return img 161 | 162 | def main(): 163 | parser = argparse.ArgumentParser( 164 | description="Merge TMS tiles to a big GeoTIFF image.", 165 | epilog="The -f, -t, -z arguments are required") 166 | parser.add_argument( 167 | "-s", "--source", metavar='URL', default=DEFAULT_TMS, 168 | help="TMS server url (default is OpenStreetMap: %s)" % DEFAULT_TMS) 169 | parser.add_argument("-f", "--from", metavar='LAT,LON', help="one corner") 170 | parser.add_argument("-t", "--to", metavar='LAT,LON', help="the other corner") 171 | parser.add_argument("-z", "--zoom", type=int, help="zoom level") 172 | parser.add_argument("output", help="output file") 173 | args = parser.parse_args() 174 | if not all(getattr(args, opt, None) for opt in 175 | ('from', 'to', 'zoom', 'output')): 176 | parser.print_help() 177 | return 1 178 | try: 179 | coords0 = tuple(map(float, getattr(args, 'from').split(','))) 180 | coords1 = tuple(map(float, getattr(args, 'to').split(','))) 181 | except Exception: 182 | parser.print_help() 183 | return 1 184 | draw_tile(args.source, coords0[0], coords0[1], coords1[0], coords1[1], 185 | args.zoom, args.output) 186 | return 0 187 | 188 | if __name__ == '__main__': 189 | import sys 190 | sys.exit(main()) 191 | -------------------------------------------------------------------------------- /tms2geotiff/tmssplit.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | 4 | import os 5 | import math 6 | import argparse 7 | import itertools 8 | import concurrent.futures 9 | 10 | import pyproj 11 | import numpy as np 12 | import scipy.ndimage 13 | from PIL import Image 14 | from osgeo import gdal 15 | 16 | gdal.UseExceptions() 17 | 18 | 19 | def num2deg(xtile, ytile, zoom): 20 | n = 2 ** zoom 21 | lat = math.degrees(math.atan(math.sinh(math.pi * (1 - 2 * ytile / n)))) 22 | lon = xtile / n * 360 - 180 23 | return (lat, lon) 24 | 25 | 26 | def deg2num(lat, lon, zoom): 27 | lat_r = math.radians(lat) 28 | n = 2 ** zoom 29 | xtile = ((lon + 180) / 360 * n) 30 | ytile = ((1 - math.log(math.tan(lat_r) + 1/math.cos(lat_r)) / math.pi) / 2 * n) 31 | return (xtile, ytile) 32 | 33 | 34 | def transform_tile(imgdata, invmatrix, crs, 35 | corner_x, corner_y, size, zoom, dirname, outname): 36 | projrev = pyproj.Transformer.from_crs("EPSG:4326", crs) 37 | newimgdata = np.zeros((imgdata.shape[0], size, size), dtype=float) 38 | def transform(out_coords): 39 | # px -> [corner_xy + 0, corner_xy + 1] -> 4326 -> projrev -> img 40 | tile_x = corner_x + (out_coords[1] / size) 41 | tile_y = corner_y + (out_coords[0] / size) 42 | lat, lon = num2deg(tile_x, tile_y, zoom) 43 | local_xy = projrev.transform(lat, lon) 44 | img_xy = invmatrix.dot(np.array(local_xy + (1,)))[:2] 45 | return tuple(reversed(img_xy)) 46 | 47 | for b, band in enumerate(imgdata): 48 | newband = scipy.ndimage.geometric_transform( 49 | band.astype(float), transform, (size, size), float, 50 | mode='constant', cval=(255 if b < 3 else 0)) 51 | newimgdata[b] = np.clip(newband, 0, 255) 52 | 53 | newim = Image.fromarray(np.rollaxis(newimgdata, 0, 3).astype('uint8')) 54 | outpath = os.path.join(dirname, outname.format(x=corner_x, y=corner_y, z=zoom)) 55 | os.makedirs(os.path.dirname(outpath), exist_ok=True) 56 | newim.save(outpath) 57 | 58 | 59 | def split_tile(imgfile, dirname, outname, zoom, size, proj=None, threads=None): 60 | # img --aff--> local --proj--> 4326 -> tile xyz (corners) 61 | # -> 4326 --proj--> 3857 --> tile 62 | img = gdal.Open(imgfile) 63 | crs = proj or img.GetProjection() or "EPSG:3857" 64 | projfwd = pyproj.Transformer.from_crs(crs, "EPSG:4326") 65 | # x' = a*x + b*y + c 66 | # y' = d*x + e*y + f 67 | try: 68 | tfwfile = os.path.splitext(imgfile)[0] + '.tfw' 69 | with open(tfwfile, 'r', encoding='utf-8') as f: 70 | content = tuple(map(float, f.read().strip().split())) 71 | imgmatrix = np.array(( 72 | (content[0], content[2], content[4]), 73 | (content[1], content[3], content[5]), 74 | (0, 0, 1) 75 | )) 76 | except Exception: 77 | geotrans = img.GetGeoTransform() 78 | # (c, a, b, f, d, e) 79 | imgmatrix = np.array(( 80 | (geotrans[1], geotrans[2], geotrans[0]), 81 | (geotrans[4], geotrans[5], geotrans[3]), 82 | (0, 0, 1) 83 | )) 84 | invmatrix = np.linalg.inv(imgmatrix) 85 | local_corners = imgmatrix.dot(np.array(( 86 | (0, 0, 1), 87 | (img.RasterXSize, 0, 1), 88 | (0, img.RasterYSize, 1), 89 | (img.RasterXSize, img.RasterYSize, 1), 90 | )).T)[:2].T 91 | latlon_corners = np.array(tuple( 92 | projfwd.transform(*row) for row in local_corners)) 93 | min_lat, min_lon = np.amin(latlon_corners, axis=0) 94 | max_lat, max_lon = np.amax(latlon_corners, axis=0) 95 | tile_x0, tile_y0 = deg2num(min_lat, min_lon, zoom) 96 | tile_x1, tile_y1 = deg2num(max_lat, max_lon, zoom) 97 | if tile_x0 > tile_x1: 98 | tile_x0, tile_x1 = tile_x1, tile_x0 99 | if tile_y0 > tile_y1: 100 | tile_y0, tile_y1 = tile_y1, tile_y0 101 | corners = tuple(itertools.product( 102 | range(math.floor(tile_x0), math.ceil(tile_x1)), 103 | range(math.floor(tile_y0), math.ceil(tile_y1)))) 104 | totalnum = len(corners) 105 | imgdata = img.ReadAsArray() 106 | 107 | worker_num = threads or os.cpu_count() 108 | with concurrent.futures.ProcessPoolExecutor(max_workers=worker_num) as exc: 109 | futures = [] 110 | for corner_x, corner_y in corners: 111 | futures.append(exc.submit( 112 | transform_tile, imgdata, invmatrix, crs, 113 | corner_x, corner_y, size, zoom, dirname, outname)) 114 | for k, future in enumerate(futures): 115 | future.result() 116 | print('Image %d/%d' % (k, totalnum)) 117 | 118 | 119 | def main(): 120 | parser = argparse.ArgumentParser( 121 | description="Split a big GeoTIFF image to TMS tiles.", 122 | epilog="-z is required") 123 | parser.add_argument("-z", "--zoom", help="zoom level(s), eg. 15 or 14-17") 124 | parser.add_argument("-n", "--name", default='{z}_{x}_{y}.png', help="image file name format, default {z}_{x}_{y}.png") 125 | parser.add_argument("-s", "--size", type=int, default=256, 126 | help="image size in px, default 256px") 127 | parser.add_argument("-p", "--proj", help="set projection id") 128 | parser.add_argument("-t", "--threads", type=int, help="set thread number") 129 | parser.add_argument("inputfile", help="input GeoTIFF file") 130 | parser.add_argument("outputdir", help="output directory") 131 | args = parser.parse_args() 132 | if not hasattr(args, 'zoom'): 133 | parser.print_help() 134 | return 1 135 | zooms = args.zoom.split('-') 136 | try: 137 | if len(zooms) == 1: 138 | zoomrange = (int(zooms[0]),) 139 | elif len(zooms) == 2: 140 | zoomrange = range(int(zooms[0]), int(zooms[1])+1) 141 | else: 142 | raise ValueError 143 | except (TypeError, ValueError): 144 | parser.print_help() 145 | return 1 146 | 147 | for zoom in zoomrange: 148 | split_tile( 149 | args.inputfile, args.outputdir, args.name, 150 | zoom, args.size, args.proj, args.threads 151 | ) 152 | return 0 153 | 154 | 155 | if __name__ == '__main__': 156 | import sys 157 | sys.exit(main()) 158 | --------------------------------------------------------------------------------