From 327ee426c46a34c0e22599d4a17d7df66fdbea9f Mon Sep 17 00:00:00 2001 From: AI-Hao <2865467769@qq.com> Date: Fri, 10 Jan 2025 10:08:24 +0800 Subject: [PATCH 1/2] fix(sahi): Fix Polygon Repair and Empty Polygon Issues - Added `repair_polygon` and `repair_multipolygon` functions to repair invalid polygons and multipolygons. - Implemented `coco_segmentation_to_shapely` function to convert COCO format segmentation data into Shapely objects. - Enhanced the `get_union_polygon` function to handle empty polygons using the newly implemented conversion and repair methods. --- sahi/postprocess/utils.py | 82 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 82 insertions(+) diff --git a/sahi/postprocess/utils.py b/sahi/postprocess/utils.py index 193cb684f..2dd4180ae 100644 --- a/sahi/postprocess/utils.py +++ b/sahi/postprocess/utils.py @@ -4,6 +4,7 @@ import numpy as np import torch from shapely.geometry import MultiPolygon, Polygon +from shapely.geometry.collection import GeometryCollection from sahi.annotation import BoundingBox, Category, Mask from sahi.prediction import ObjectPrediction @@ -65,6 +66,81 @@ def tolist(self): else: return self.list +###################################################sunhao################################################### +def repair_polygon( + shapely_polygon: Polygon +) -> Polygon: + """ + Fix polygons + + :param shapely_polygon: Shapely polygon object + :return: + """ + if not shapely_polygon.is_valid: + fixed_polygon = shapely_polygon.buffer(0) + if fixed_polygon.is_valid: + if isinstance(fixed_polygon, Polygon): + return fixed_polygon + elif isinstance(fixed_polygon, MultiPolygon): + return max(fixed_polygon.geoms, key=lambda p: p.area) + elif isinstance(fixed_polygon, GeometryCollection): + polygons = [geom for geom in fixed_polygon.geoms if isinstance(geom, Polygon)] + return max(polygons, key=lambda p: p.area) if polygons else shapely_polygon + + return shapely_polygon + + +def repair_multipolygon( + shapely_multipolygon: MultiPolygon +) -> MultiPolygon: + """ + Fix invalid MultiPolygon objects + + :param shapely_multipolygon: Imported shapely MultiPolygon object + :return: + """ + if not shapely_multipolygon.is_valid: + fixed_geometry = shapely_multipolygon.buffer(0) + + if fixed_geometry.is_valid: + if isinstance(fixed_geometry, MultiPolygon): + return fixed_geometry + elif isinstance(fixed_geometry, Polygon): + return MultiPolygon([fixed_geometry]) + elif isinstance(fixed_geometry, GeometryCollection): + polygons = [geom for geom in fixed_geometry.geoms if isinstance(geom, Polygon)] + return MultiPolygon(polygons) if polygons else shapely_multipolygon + + return shapely_multipolygon + + +def coco_segmentation_to_shapely( + segmentation: Union[list, list[list]] +): + """ + Fix segment data in COCO format + + :param segmentation: segment data in COCO format + :return: + """ + if isinstance(segmentation, list) and all([not isinstance(seg, list) for seg in segmentation]): + segmentation = [segmentation] + elif isinstance(segmentation, list) and all([isinstance(seg, list) for seg in segmentation]): + pass + else: + raise ValueError("segmentation must be list or list[list]") + + polygon_list = [] + + for coco_polygon in segmentation: + point_list = list(zip(coco_polygon[::2], coco_polygon[1::2])) + shapely_polygon = Polygon(point_list) + polygon_list.append(repair_polygon(shapely_polygon)) + + shapely_multipolygon = repair_multipolygon(MultiPolygon(polygon_list)) + return shapely_multipolygon + +############################################################################################################ def object_prediction_list_to_torch(object_prediction_list: ObjectPredictionList) -> torch.tensor: """ @@ -167,6 +243,12 @@ def get_merged_mask(pred1: ObjectPrediction, pred2: ObjectPrediction) -> Mask: # buffer(0) is a quickhack to fix invalid polygons most of the time poly1 = get_shapely_multipolygon(mask1.segmentation).buffer(0) poly2 = get_shapely_multipolygon(mask2.segmentation).buffer(0) + ###################################################sunhao################################################### + if poly1.is_empty: + poly1 = coco_segmentation_to_shapely(mask1.segmentation) + if poly2.is_empty: + poly2 = coco_segmentation_to_shapely(mask2.segmentation) + ############################################################################################################ union_poly = poly1.union(poly2) if not hasattr(union_poly, "geoms"): union_poly = MultiPolygon([union_poly]) From 7a9bcef09128940c92546a31c54cc72aed3debf6 Mon Sep 17 00:00:00 2001 From: AI-Hao <2865467769@qq.com> Date: Fri, 28 Feb 2025 17:18:42 +0800 Subject: [PATCH 2/2] =?UTF-8?q?feat(slicing):=20=E6=B7=BB=E5=8A=A0?= =?UTF-8?q?=E5=85=A8=E5=9B=BE=E6=8E=A8=E7=90=86=E5=8A=9F=E8=83=BD?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - 在 slice_image 和 SliceDataset 中添加 inference_org_image 参数- 如果 inference_org_image 为 True，将原图添加到裁剪列表中 - 更新函数文档和注释以支持新功能 --- sahi/slicing.py | 18 +++++++++++++----- 1 file changed, 13 insertions(+), 5 deletions(-) diff --git a/sahi/slicing.py b/sahi/slicing.py index 1b756985a..180cb7732 100644 --- a/sahi/slicing.py +++ b/sahi/slicing.py @@ -4,7 +4,6 @@ import concurrent.futures import logging import os -import time from pathlib import Path from typing import Dict, List, Optional, Sequence, Tuple, Union @@ -36,6 +35,7 @@ def get_slice_bboxes( auto_slice_resolution: bool = True, overlap_height_ratio: float = 0.2, overlap_width_ratio: float = 0.2, + inference_org_image: bool = False ) -> List[List[int]]: """Slices `image_pil` in crops. Corner values of each slice will be generated using the `slice_height`, @@ -54,6 +54,7 @@ def get_slice_bboxes( overlap of 20 pixels). Default 0.2. auto_slice_resolution (bool): if not set slice parameters such as slice_height and slice_width, it enables automatically calculate these params from image resolution and orientation. + inference_org_image (bool): 如果为True, 则在裁剪列表的最后将原图加入 Returns: List[List[int]]: List of 4 corner coordinates for each N slices. @@ -89,7 +90,11 @@ def get_slice_bboxes( slice_bboxes.append([x_min, y_min, x_max, y_max]) x_min = x_max - x_overlap y_min = y_max - y_overlap - return slice_bboxes + + if inference_org_image: + return slice_bboxes + [[0, 0, image_width, image_height]] + else: + return slice_bboxes def annotation_inside_slice(annotation: Dict, slice_bbox: List[int]) -> bool: @@ -237,10 +242,10 @@ def filenames(self) -> List[int]: def __getitem__(self, i): def _prepare_ith_dict(i): return { - "image": self.images[i], - "coco_image": self.coco_images[i], + "image" : self.images[i], + "coco_image" : self.coco_images[i], "starting_pixel": self.starting_pixels[i], - "filename": self.filenames[i], + "filename" : self.filenames[i], } if isinstance(i, np.ndarray): @@ -274,6 +279,7 @@ def slice_image( min_area_ratio: float = 0.1, out_ext: Optional[str] = None, verbose: bool = False, + inference_org_image: bool = False ) -> SliceImageResult: """Slice a large image into smaller windows. If output_file_name is given export sliced images. @@ -300,6 +306,7 @@ def slice_image( original suffix for lossless image formats and png for lossy formats ('.jpg','.jpeg'). verbose (bool, optional): Switch to print relevant values to screen. Default 'False'. + inference_org_image (bool): If True, the original image is used for inference. Default False. Returns: sliced_image_result: SliceImageResult: @@ -342,6 +349,7 @@ def _export_single_slice(image: np.ndarray, output_dir: str, slice_file_name: st slice_width=slice_width, overlap_height_ratio=overlap_height_ratio, overlap_width_ratio=overlap_width_ratio, + inference_org_image=inference_org_image ) n_ims = 0