from __future__ import absolute_import from __future__ import division from __future__ import print_function __author__ = 'tylin' __version__ = '1.0.1' # 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. # segToMask - Convert polygon segmentation to binary mask. # showAnns - Display the specified annotations. # loadRes - Load result file and create result api object. # 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>segToMask, COCO>showAnns # Microsoft COCO Toolbox. Version 1.0 # 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 datetime import matplotlib.pyplot as plt from matplotlib.collections import PatchCollection from matplotlib.patches import Polygon import numpy as np from skimage.draw import polygon import copy 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.imgToAnns = {} self.catToImgs = {} self.imgs = [] self.cats = [] if not annotation_file == None: print('loading annotations into memory...') time_t = datetime.datetime.utcnow() dataset = json.load(open(annotation_file, 'r')) print(datetime.datetime.utcnow() - time_t) self.dataset = dataset self.createIndex() def createIndex(self): # create index print('creating index...') imgToAnns = {ann['image_id']: [] for ann in self.dataset['annotations']} anns = {ann['id']: [] for ann in self.dataset['annotations']} if 'annotations' in self.dataset: for ann in self.dataset['annotations']: imgToAnns[ann['image_id']] += [ann] anns[ann['id']] = ann if 'images' in self.dataset: imgs = {im['id']: {} for im in self.dataset['images']} for img in self.dataset['images']: imgs[img['id']] = img catToImgs = [] cats = [] if 'categories' in self.dataset: cats = {cat['id']: [] for cat in self.dataset['categories']} for cat in self.dataset['categories']: cats[cat['id']] = cat catToImgs = {cat['id']: [] for cat in self.dataset['categories']} for ann in self.dataset['annotations']: catToImgs[ann['category_id']] += [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 list(self.datset['info'].items()): print('%s: %s'%(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 type(imgIds) == list else [imgIds] catIds = catIds if type(catIds) == list else [catIds] if len(imgIds) == len(catIds) == len(areaRng) == 0: anns = self.dataset['annotations'] else: if not len(imgIds) == 0: anns = sum([self.imgToAnns[imgId] for imgId in imgIds if imgId in self.imgToAnns],[]) 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 self.dataset['type'] == 'instances': if not iscrowd == None: ids = [ann['id'] for ann in anns if ann['iscrowd'] == iscrowd] else: ids = [ann['id'] for ann in anns] 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 type(catNms) == list else [catNms] supNms = supNms if type(supNms) == list else [supNms] catIds = catIds if type(catIds) == list 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 type(imgIds) == list else [imgIds] catIds = catIds if type(catIds) == list else [catIds] if len(imgIds) == len(catIds) == 0: ids = list(self.imgs.keys()) else: ids = set(imgIds) for catId in catIds: if 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 type(ids) == list: 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 type(ids) == list: 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 type(ids) == list: 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 self.dataset['type'] == 'instances': ax = plt.gca() polygons = [] color = [] for ann in anns: c = np.random.random((1, 3)).tolist()[0] if type(ann['segmentation']) == list: # polygon for seg in ann['segmentation']: poly = np.array(seg).reshape((len(seg)/2, 2)) polygons.append(Polygon(poly, True,alpha=0.4)) color.append(c) else: # mask mask = COCO.decodeMask(ann['segmentation']) img = np.ones( (mask.shape[0], mask.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, mask*0.5) )) p = PatchCollection(polygons, facecolors=color, edgecolors=(0,0,0,1), linewidths=3, alpha=0.4) ax.add_collection(p) if self.dataset['type'] == '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... ') time_t = datetime.datetime.utcnow() anns = json.load(open(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 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]] ann['segmentation'] = [[x1, y1, x1, y2, x2, y2, x2, y1]] ann['area'] = bb[2]*bb[3] ann['id'] = id ann['iscrowd'] = 0 elif 'segmentation' in anns[0]: res.dataset['categories'] = copy.deepcopy(self.dataset['categories']) for id, ann in enumerate(anns): ann['area']=sum(ann['segmentation']['counts'][2:-1:2]) ann['bbox'] = [] ann['id'] = id ann['iscrowd'] = 0 print('DONE (t=%0.2fs)'%((datetime.datetime.utcnow() - time_t).total_seconds())) res.dataset['annotations'] = anns res.createIndex() return res @staticmethod def decodeMask(R): """ Decode binary mask M encoded via run-length encoding. :param R (object RLE) : run-length encoding of binary mask :return: M (bool 2D array) : decoded binary mask """ N = len(R['counts']) M = np.zeros( (R['size'][0]*R['size'][1], )) n = 0 val = 1 for pos in range(N): val = not val for c in range(R['counts'][pos]): R['counts'][pos] M[n] = val n += 1 return M.reshape((R['size']), order='F') @staticmethod def encodeMask(M): """ Encode binary mask M using run-length encoding. :param M (bool 2D array) : binary mask to encode :return: R (object RLE) : run-length encoding of binary mask """ [h, w] = M.shape M = M.flatten(order='F') N = len(M) counts_list = [] pos = 0 # counts counts_list.append(1) diffs = np.logical_xor(M[0:N-1], M[1:N]) for diff in diffs: if diff: pos +=1 counts_list.append(1) else: counts_list[pos] += 1 # if array starts from 1. start with 0 counts for 0 if M[0] == 1: counts_list = [0] + counts_list return {'size': [h, w], 'counts': counts_list , } @staticmethod def segToMask( S, h, w ): """ Convert polygon segmentation to binary mask. :param S (float array) : polygon segmentation mask :param h (int) : target mask height :param w (int) : target mask width :return: M (bool 2D array) : binary mask """ M = np.zeros((h,w), dtype=np.bool) for s in S: N = len(s) rr, cc = polygon(np.array(s[1:N:2]), np.array(s[0:N:2])) # (y, x) M[rr, cc] = 1 return M