释放双眼，带上耳机，听听看~！
参与掘金日新计划8月更文挑战的第20天，分享关于pycocotools中RLE格式的文章，介绍了对储存在RLE格式中的掩膜进行操作的接口。
携手创作，共同成长！这是我参与「掘金日新计划 · 8 月更文挑战」的第20天，github.com/cocodataset…
__author__ = 'tsungyi' | |
| ---------------------- | --------------------------------------------------------------------------------------------------- |
| | |
| | import pycocotools._mask as _mask |
| | |
| | # Interface for manipulating masks stored in RLE format. |
| | # |
| | # RLE is a simple yet efficient format for storing binary masks. RLE |
| | # first divides a vector (or vectorized image) into a series of piecewise |
| | # constant regions and then for each piece simply stores the length of |
| | # that piece. For example, given M=[0 0 1 1 1 0 1] the RLE counts would |
| | # be [2 3 1 1], or for M=[1 1 1 1 1 1 0] the counts would be [0 6 1] |
| | # (note that the odd counts are always the numbers of zeros). Instead of |
| | # storing the counts directly, additional compression is achieved with a |
| | # variable bitrate representation based on a common scheme called LEB128. |
| | # |
| | # Compression is greatest given large piecewise constant regions. |
| | # Specifically, the size of the RLE is proportional to the number of |
| | # *boundaries* in M (or for an image the number of boundaries in the y |
| | # direction). Assuming fairly simple shapes, the RLE representation is |
| | # O(sqrt(n)) where n is number of pixels in the object. Hence space usage |
| | # is substantially lower, especially for large simple objects (large n). |
| | # |
| | # Many common operations on masks can be computed directly using the RLE |
| | # (without need for decoding). This includes computations such as area, |
| | # union, intersection, etc. All of these operations are linear in the |
| | # size of the RLE, in other words they are O(sqrt(n)) where n is the area |
| | # of the object. Computing these operations on the original mask is O(n). |
| | # Thus, using the RLE can result in substantial computational savings. |
| | # |
| | # The following API functions are defined: |
| | # encode - Encode binary masks using RLE. |
| | # decode - Decode binary masks encoded via RLE. |
| | # merge - Compute union or intersection of encoded masks. |
| | # iou - Compute intersection over union between masks. |
| | # area - Compute area of encoded masks. |
| | # toBbox - Get bounding boxes surrounding encoded masks. |
| | # frPyObjects - Convert polygon, bbox, and uncompressed RLE to encoded RLE mask. |
| | # |
| | # Usage: |
| | # Rs = encode( masks ) |
| | # masks = decode( Rs ) |
| | # R = merge( Rs, intersect=false ) |
| | # o = iou( dt, gt, iscrowd ) |
| | # a = area( Rs ) |
| | # bbs = toBbox( Rs ) |
| | # Rs = frPyObjects( [pyObjects], h, w ) |
| | # |
| | # In the API the following formats are used: |
| | # Rs - [dict] Run-length encoding of binary masks |
| | # R - dict Run-length encoding of binary mask |
| | # masks - [hxwxn] Binary mask(s) (must have type np.ndarray(dtype=uint8) in column-major order) |
| | # iscrowd - [nx1] list of np.ndarray. 1 indicates corresponding gt image has crowd region to ignore |
| | # bbs - [nx4] Bounding box(es) stored as [x y w h] |
| | # poly - Polygon stored as [[x1 y1 x2 y2...],[x1 y1 ...],...] (2D list) |
| | # dt,gt - May be either bounding boxes or encoded masks |
| | # Both poly and bbs are 0-indexed (bbox=[0 0 1 1] encloses first pixel). |
| | # |
| | # Finally, a note about the intersection over union (iou) computation. |
| | # The standard iou of a ground truth (gt) and detected (dt) object is |
| | # iou(gt,dt) = area(intersect(gt,dt)) / area(union(gt,dt)) |
| | # For "crowd" regions, we use a modified criteria. If a gt object is |
| | # marked as "iscrowd", we allow a dt to match any subregion of the gt. |
| | # Choosing gt' in the crowd gt that best matches the dt can be done using |
| | # gt'=intersect(dt,gt). Since by definition union(gt',dt)=dt, computing |
| | # iou(gt,dt,iscrowd) = iou(gt',dt) = area(intersect(gt,dt)) / area(dt) |
| | # For crowd gt regions we use this modified criteria above for the iou. |
| | # |
| | # To compile run "python setup.py build_ext --inplace" |
| | # Please do not contact us for help with compiling. |
| | # |
| | # Microsoft COCO Toolbox. version 2.0 |
| | # Data, paper, and tutorials available at: http://mscoco.org/ |
| | # Code written by Piotr Dollar and Tsung-Yi Lin, 2015. |
| | # Licensed under the Simplified BSD License [see coco/license.txt]
RLE 是一种简单有效的二值 masks 存储格式.
RLE 首先将向量(或向量化的图片)分片，得到一系列分片的常量区域；然后，每一个分片只简单的保存其长度.
例如，对于向量 M=[0 0 1 1 1 0 1]，RLE 计算结果为 [2 3 1 1]；对于向量 M=[1 1 1 1 1 1 0]，RLE 计算结果为 [0 6 1].(注：索引从零开始).
相比于直接保存计数结果，采用了额外的基于 LEB128 的通用方案的可变比特率表示来实现压缩的.
给定大片常量区域，压缩效果是最佳的. 具体的，RLE 的大小正比于在 M 中边界(boundary)的数量(对于图片，则正比于在 y 方向的边界数量.)
假设对于相对简单的形状，RLE 表示的复杂度是 O(sqrt(n))，其中，n 表示目标的像素数. 因此，空间占用是相对较低的，尤其是对大面积的简单目标主体( n 比较大).
采用 RLE 可以直接实现很多计算(不需要反解码)，比如，面积、并集、交集等等. 这些计算都是关于 RLE 尺寸呈线性关系的. 换句话说，计算复杂度是 O(sqrt(n)) 的，其中 n 是目标主体的面积. 相对于原始 mask，这些计算的复杂度是 O(n) 的.
因此，RLE 是大量节省计算的。
3.COCO操作
__author__ = 'tylin' | |
| -------------------- | -------------------------------------------------------------------------------------------------------------------- |
| | __version__ = '2.0' |
| | # Interface for accessing the Microsoft COCO dataset. |
| | |
| | # Microsoft COCO is a large image dataset designed for object detection, |
| | # segmentation, and caption generation. pycocotools is a Python API that |
| | # assists in loading, parsing and visualizing the annotations in COCO. |
| | # Please visit http://mscoco.org/ for more information on COCO, including |
| | # for the data, paper, and tutorials. The exact format of the annotations |
| | # is also described on the COCO website. For example usage of the pycocotools |
| | # please see pycocotools_demo.ipynb. In addition to this API, please download both |
| | # the COCO images and annotations in order to run the demo. |
| | |
| | # An alternative to using the API is to load the annotations directly |
| | # into Python dictionary |
| | # Using the API provides additional utility functions. Note that this API |
| | # supports both *instance* and *caption* annotations. In the case of |
| | # captions not all functions are defined (e.g. categories are undefined). |
| | |
| | # The following API functions are defined: |
| | # COCO - COCO api class that loads COCO annotation file and prepare data structures. |
| | # decodeMask - Decode binary mask M encoded via run-length encoding. |
| | # encodeMask - Encode binary mask M using run-length encoding. |
| | # getAnnIds - Get ann ids that satisfy given filter conditions. |
| | # getCatIds - Get cat ids that satisfy given filter conditions. |
| | # getImgIds - Get img ids that satisfy given filter conditions. |
| | # loadAnns - Load anns with the specified ids. |
| | # loadCats - Load cats with the specified ids. |
| | # loadImgs - Load imgs with the specified ids. |
| | # annToMask - Convert segmentation in an annotation to binary mask. |
| | # showAnns - Display the specified annotations. |
| | # loadRes - Load algorithm results and create API for accessing them. |
| | # download - Download COCO images from mscoco.org server. |
| | # Throughout the API "ann"=annotation, "cat"=category, and "img"=image. |
| | # Help on each functions can be accessed by: "help COCO>function". |
| | |
| | # See also COCO>decodeMask, |
| | # COCO>encodeMask, COCO>getAnnIds, COCO>getCatIds, |
| | # COCO>getImgIds, COCO>loadAnns, COCO>loadCats, |
| | # COCO>loadImgs, COCO>annToMask, COCO>showAnns |
| | |
| | # Microsoft COCO Toolbox. version 2.0 |
| | # Data, paper, and tutorials available at: http://mscoco.org/ |
| | # Code written by Piotr Dollar and Tsung-Yi Lin, 2014. |
| | # Licensed under the Simplified BSD License [see bsd.txt] |
| | |
| | import json |
| | import time |
| | import matplotlib.pyplot as plt |
| | from matplotlib.collections import PatchCollection |
| | from matplotlib.patches import Polygon |
| | import numpy as np |
| | import copy |
| | import itertools |
| | from . import mask as maskUtils |
| | import os |
| | from collections import defaultdict |
| | import sys |
| | PYTHON_VERSION = sys.version_info[0] |
| | if PYTHON_VERSION == 2: |
| | from urllib import urlretrieve |
| | elif PYTHON_VERSION == 3: |
| | from urllib.request import urlretrieve |
| | |
| | |
| | def _isArrayLike(obj): |
| | return hasattr(obj, '__iter__') and hasattr(obj, '__len__') |
| | |
| | |
| | class COCO: |
| | def __init__(self, annotation_file=None): |
| | """ |
| | Constructor of Microsoft COCO helper class for reading and visualizing annotations. |
| | :param annotation_file (str): location of annotation file |
| | :param image_folder (str): location to the folder that hosts images. |
| | :return: |
| | """ |
| | # load dataset |
| | self.dataset,self.anns,self.cats,self.imgs = dict(),dict(),dict(),dict() |
| | self.imgToAnns, self.catToImgs = defaultdict(list), defaultdict(list) |
| | if not annotation_file == None: |
| | print('loading annotations into memory...') |
| | tic = time.time() |
| | dataset = json.load(open(annotation_file, 'r')) |
| | assert type(dataset)==dict, 'annotation file format {} not supported'.format(type(dataset)) |
| | print('Done (t={:0.2f}s)'.format(time.time()- tic)) |
| | self.dataset = dataset |
| | self.createIndex() |
| | |
| | def createIndex(self): |
| | # create index |
| | print('creating index...') |
| | anns, cats, imgs = {}, {}, {} |
| | imgToAnns,catToImgs = defaultdict(list),defaultdict(list) |
| | if 'annotations' in self.dataset: |
| | for ann in self.dataset['annotations']: |
| | imgToAnns[ann['image_id']].append(ann) |
| | anns[ann['id']] = ann |
| | |
| | if 'images' in self.dataset: |
| | for img in self.dataset['images']: |
| | imgs[img['id']] = img |
| | |
| | if 'categories' in self.dataset: |
| | for cat in self.dataset['categories']: |
| | cats[cat['id']] = cat |
| | |
| | if 'annotations' in self.dataset and 'categories' in self.dataset: |
| | for ann in self.dataset['annotations']: |
| | catToImgs[ann['category_id']].append(ann['image_id']) |
| | |
| | print('index created!') |
| | |
| | # create class members |
| | self.anns = anns |
| | self.imgToAnns = imgToAnns |
| | self.catToImgs = catToImgs |
| | self.imgs = imgs |
| | self.cats = cats |
| | |
| | def info(self): |
| | """ |
| | Print information about the annotation file. |
| | :return: |
| | """ |
| | for key, value in self.dataset['info'].items(): |
| | print('{}: {}'.format(key, value)) |
| | |
| | def getAnnIds(self, imgIds=[], catIds=[], areaRng=[], iscrowd=None): |
| | """ |
| | Get ann ids that satisfy given filter conditions. default skips that filter |
| | :param imgIds (int array) : get anns for given imgs |
| | catIds (int array) : get anns for given cats |
| | areaRng (float array) : get anns for given area range (e.g. [0 inf]) |
| | iscrowd (boolean) : get anns for given crowd label (False or True) |
| | :return: ids (int array) : integer array of ann ids |
| | """ |
| | imgIds = imgIds if _isArrayLike(imgIds) else [imgIds] |
| | catIds = catIds if _isArrayLike(catIds) else [catIds] |
| | |
| | if len(imgIds) == len(catIds) == len(areaRng) == 0: |
| | anns = self.dataset['annotations'] |
| | else: |
| | if not len(imgIds) == 0: |
| | lists = [self.imgToAnns[imgId] for imgId in imgIds if imgId in self.imgToAnns] |
| | anns = list(itertools.chain.from_iterable(lists)) |
| | else: |
| | anns = self.dataset['annotations'] |
| | anns = anns if len(catIds) == 0 else [ann for ann in anns if ann['category_id'] in catIds] |
| | anns = anns if len(areaRng) == 0 else [ann for ann in anns if ann['area'] > areaRng[0] and ann['area'] < areaRng[1]] |
| | if not iscrowd == None: |
| | ids = [ann['id'] for ann in anns if ann['iscrowd'] == iscrowd] |
| | else: |
| | ids = [ann['id'] for ann in anns] |
| | return ids |
| | |
| | def getCatIds(self, catNms=[], supNms=[], catIds=[]): |
| | """ |
| | filtering parameters. default skips that filter. |
| | :param catNms (str array) : get cats for given cat names |
| | :param supNms (str array) : get cats for given supercategory names |
| | :param catIds (int array) : get cats for given cat ids |
| | :return: ids (int array) : integer array of cat ids |
| | """ |
| | catNms = catNms if _isArrayLike(catNms) else [catNms] |
| | supNms = supNms if _isArrayLike(supNms) else [supNms] |
| | catIds = catIds if _isArrayLike(catIds) else [catIds] |
| | |
| | if len(catNms) == len(supNms) == len(catIds) == 0: |
| | cats = self.dataset['categories'] |
| | else: |
| | cats = self.dataset['categories'] |
| | cats = cats if len(catNms) == 0 else [cat for cat in cats if cat['name'] in catNms] |
| | cats = cats if len(supNms) == 0 else [cat for cat in cats if cat['supercategory'] in supNms] |
| | cats = cats if len(catIds) == 0 else [cat for cat in cats if cat['id'] in catIds] |
| | ids = [cat['id'] for cat in cats] |
| | return ids |
| | |
| | def getImgIds(self, imgIds=[], catIds=[]): |
| | ''' |
| | Get img ids that satisfy given filter conditions. |
| | :param imgIds (int array) : get imgs for given ids |
| | :param catIds (int array) : get imgs with all given cats |
| | :return: ids (int array) : integer array of img ids |
| | ''' |
| | imgIds = imgIds if _isArrayLike(imgIds) else [imgIds] |
| | catIds = catIds if _isArrayLike(catIds) else [catIds] |
| | |
| | if len(imgIds) == len(catIds) == 0: |
| | ids = self.imgs.keys() |
| | else: |
| | ids = set(imgIds) |
| | for i, catId in enumerate(catIds): |
| | if i == 0 and len(ids) == 0: |
| | ids = set(self.catToImgs[catId]) |
| | else: |
| | ids &= set(self.catToImgs[catId]) |
| | return list(ids) |
| | |
| | def loadAnns(self, ids=[]): |
| | """ |
| | Load anns with the specified ids. |
| | :param ids (int array) : integer ids specifying anns |
| | :return: anns (object array) : loaded ann objects |
| | """ |
| | if _isArrayLike(ids): |
| | return [self.anns[id] for id in ids] |
| | elif type(ids) == int: |
| | return [self.anns[ids]] |
| | |
| | def loadCats(self, ids=[]): |
| | """ |
| | Load cats with the specified ids. |
| | :param ids (int array) : integer ids specifying cats |
| | :return: cats (object array) : loaded cat objects |
| | """ |
| | if _isArrayLike(ids): |
| | return [self.cats[id] for id in ids] |
| | elif type(ids) == int: |
| | return [self.cats[ids]] |
| | |
| | def loadImgs(self, ids=[]): |
| | """ |
| | Load anns with the specified ids. |
| | :param ids (int array) : integer ids specifying img |
| | :return: imgs (object array) : loaded img objects |
| | """ |
| | if _isArrayLike(ids): |
| | return [self.imgs[id] for id in ids] |
| | elif type(ids) == int: |
| | return [self.imgs[ids]] |
| | |
| | def showAnns(self, anns, draw_bbox=False): |
| | """ |
| | Display the specified annotations. |
| | :param anns (array of object): annotations to display |
| | :return: None |
| | """ |
| | if len(anns) == 0: |
| | return 0 |
| | if 'segmentation' in anns[0] or 'keypoints' in anns[0]: |
| | datasetType = 'instances' |
| | elif 'caption' in anns[0]: |
| | datasetType = 'captions' |
| | else: |
| | raise Exception('datasetType not supported') |
| | if datasetType == 'instances': |
| | ax = plt.gca() |
| | ax.set_autoscale_on(False) |
| | polygons = [] |
| | color = [] |
| | for ann in anns: |
| | c = (np.random.random((1, 3))*0.6+0.4).tolist()[0] |
| | if 'segmentation' in ann: |
| | if type(ann['segmentation']) == list: |
| | # polygon |
| | for seg in ann['segmentation']: |
| | poly = np.array(seg).reshape((int(len(seg)/2), 2)) |
| | polygons.append(Polygon(poly)) |
| | color.append(c) |
| | else: |
| | # mask |
| | t = self.imgs[ann['image_id']] |
| | if type(ann['segmentation']['counts']) == list: |
| | rle = maskUtils.frPyObjects([ann['segmentation']], t['height'], t['width']) |
| | else: |
| | rle = [ann['segmentation']] |
| | m = maskUtils.decode(rle) |
| | img = np.ones( (m.shape[0], m.shape[1], 3) ) |
| | if ann['iscrowd'] == 1: |
| | color_mask = np.array([2.0,166.0,101.0])/255 |
| | if ann['iscrowd'] == 0: |
| | color_mask = np.random.random((1, 3)).tolist()[0] |
| | for i in range(3): |
| | img[:,:,i] = color_mask[i] |
| | ax.imshow(np.dstack( (img, m*0.5) )) |
| | if 'keypoints' in ann and type(ann['keypoints']) == list: |
| | # turn skeleton into zero-based index |
| | sks = np.array(self.loadCats(ann['category_id'])[0]['skeleton'])-1 |
| | kp = np.array(ann['keypoints']) |
| | x = kp[0::3] |
| | y = kp[1::3] |
| | v = kp[2::3] |
| | for sk in sks: |
| | if np.all(v[sk]>0): |
| | plt.plot(x[sk],y[sk], linewidth=3, color=c) |
| | plt.plot(x[v>0], y[v>0],'o',markersize=8, markerfacecolor=c, markeredgecolor='k',markeredgewidth=2) |
| | plt.plot(x[v>1], y[v>1],'o',markersize=8, markerfacecolor=c, markeredgecolor=c, markeredgewidth=2) |
| | |
| | if draw_bbox: |
| | [bbox_x, bbox_y, bbox_w, bbox_h] = ann['bbox'] |
| | poly = [[bbox_x, bbox_y], [bbox_x, bbox_y+bbox_h], [bbox_x+bbox_w, bbox_y+bbox_h], [bbox_x+bbox_w, bbox_y]] |
| | np_poly = np.array(poly).reshape((4,2)) |
| | polygons.append(Polygon(np_poly)) |
| | color.append(c) |
| | |
| | p = PatchCollection(polygons, facecolor=color, linewidths=0, alpha=0.4) |
| | ax.add_collection(p) |
| | p = PatchCollection(polygons, facecolor='none', edgecolors=color, linewidths=2) |
| | ax.add_collection(p) |
| | elif datasetType == 'captions': |
| | for ann in anns: |
| | print(ann['caption']) |
| | |
| | def loadRes(self, resFile): |
| | """ |
| | Load result file and return a result api object. |
| | :param resFile (str) : file name of result file |
| | :return: res (obj) : result api object |
| | """ |
| | res = COCO() |
| | res.dataset['images'] = [img for img in self.dataset['images']] |
| | |
| | print('Loading and preparing results...') |
| | tic = time.time() |
| | if type(resFile) == str or (PYTHON_VERSION == 2 and type(resFile) == unicode): |
| | anns = json.load(open(resFile)) |
| | elif type(resFile) == np.ndarray: |
| | anns = self.loadNumpyAnnotations(resFile) |
| | else: |
| | anns = resFile |
| | assert type(anns) == list, 'results in not an array of objects' |
| | annsImgIds = [ann['image_id'] for ann in anns] |
| | assert set(annsImgIds) == (set(annsImgIds) & set(self.getImgIds())), |
| | 'Results do not correspond to current coco set' |
| | if 'caption' in anns[0]: |
| | imgIds = set([img['id'] for img in res.dataset['images']]) & set([ann['image_id'] for ann in anns]) |
| | res.dataset['images'] = [img for img in res.dataset['images'] if img['id'] in imgIds] |
| | for id, ann in enumerate(anns): |
| | ann['id'] = id+1 |
| | elif 'bbox' in anns[0] and not anns[0]['bbox'] == []: |
| | res.dataset['categories'] = copy.deepcopy(self.dataset['categories']) |
| | for id, ann in enumerate(anns): |
| | bb = ann['bbox'] |
| | x1, x2, y1, y2 = [bb[0], bb[0]+bb[2], bb[1], bb[1]+bb[3]] |
| | if not 'segmentation' in ann: |
| | ann['segmentation'] = [[x1, y1, x1, y2, x2, y2, x2, y1]] |
| | ann['area'] = bb[2]*bb[3] |
| | ann['id'] = id+1 |
| | ann['iscrowd'] = 0 |
| | elif 'segmentation' in anns[0]: |
| | res.dataset['categories'] = copy.deepcopy(self.dataset['categories']) |
| | for id, ann in enumerate(anns): |
| | # now only support compressed RLE format as segmentation results |
| | ann['area'] = maskUtils.area(ann['segmentation']) |
| | if not 'bbox' in ann: |
| | ann['bbox'] = maskUtils.toBbox(ann['segmentation']) |
| | ann['id'] = id+1 |
| | ann['iscrowd'] = 0 |
| | elif 'keypoints' in anns[0]: |
| | res.dataset['categories'] = copy.deepcopy(self.dataset['categories']) |
| | for id, ann in enumerate(anns): |
| | s = ann['keypoints'] |
| | x = s[0::3] |
| | y = s[1::3] |
| | x0,x1,y0,y1 = np.min(x), np.max(x), np.min(y), np.max(y) |
| | ann['area'] = (x1-x0)*(y1-y0) |
| | ann['id'] = id + 1 |
| | ann['bbox'] = [x0,y0,x1-x0,y1-y0] |
| | print('DONE (t={:0.2f}s)'.format(time.time()- tic)) |
| | |
| | res.dataset['annotations'] = anns |
| | res.createIndex() |
| | return res |
| | |
| | def download(self, tarDir = None, imgIds = [] ): |
| | ''' |
| | Download COCO images from mscoco.org server. |
| | :param tarDir (str): COCO results directory name |
| | imgIds (list): images to be downloaded |
| | :return: |
| | ''' |
| | if tarDir is None: |
| | print('Please specify target directory') |
| | return -1 |
| | if len(imgIds) == 0: |
| | imgs = self.imgs.values() |
| | else: |
| | imgs = self.loadImgs(imgIds) |
| | N = len(imgs) |
| | if not os.path.exists(tarDir): |
| | os.makedirs(tarDir) |
| | for i, img in enumerate(imgs): |
| | tic = time.time() |
| | fname = os.path.join(tarDir, img['file_name']) |
| | if not os.path.exists(fname): |
| | urlretrieve(img['coco_url'], fname) |
| | print('downloaded {}/{} images (t={:0.1f}s)'.format(i, N, time.time()- tic)) |
| | |
| | def loadNumpyAnnotations(self, data): |
| | """ |
| | Convert result data from a numpy array [Nx7] where each row contains {imageID,x1,y1,w,h,score,class} |
| | :param data (numpy.ndarray) |
| | :return: annotations (python nested list) |
| | """ |
| | print('Converting ndarray to lists...') |
| | assert(type(data) == np.ndarray) |
| | print(data.shape) |
| | assert(data.shape[1] == 7) |
| | N = data.shape[0] |
| | ann = [] |
| | for i in range(N): |
| | if i % 1000000 == 0: |
| | print('{}/{}'.format(i,N)) |
| | ann += [{ |
| | 'image_id' : int(data[i, 0]), |
| | 'bbox' : [ data[i, 1], data[i, 2], data[i, 3], data[i, 4] ], |
| | 'score' : data[i, 5], |
| | 'category_id': int(data[i, 6]), |
| | }] |
| | return ann |
| | |
| | def annToRLE(self, ann): |
| | """ |
| | Convert annotation which can be polygons, uncompressed RLE to RLE. |
| | :return: binary mask (numpy 2D array) |
| | """ |
| | t = self.imgs[ann['image_id']] |
| | h, w = t['height'], t['width'] |
| | segm = ann['segmentation'] |
| | if type(segm) == list: |
| | # polygon -- a single object might consist of multiple parts |
| | # we merge all parts into one mask rle code |
| | rles = maskUtils.frPyObjects(segm, h, w) |
| | rle = maskUtils.merge(rles) |
| | elif type(segm['counts']) == list: |
| | # uncompressed RLE |
| | rle = maskUtils.frPyObjects(segm, h, w) |
| | else: |
| | # rle |
| | rle = ann['segmentation'] |
| | return rle |
| | |
| | def annToMask(self, ann): |
| | """ |
| | Convert annotation which can be polygons, uncompressed RLE, or RLE to binary mask. |
| | :return: binary mask (numpy 2D array) |
| | """ |
| | rle = self.annToRLE(ann) |
| | m = maskUtils.decode(rle) |
| | return m
常用方法都在里头了，可以大干一场了。
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