+++ /dev/null
-__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):
- """
- 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)
- 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 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
\ No newline at end of file
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