__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 copy import itertools import json import os import time from collections import defaultdict from urllib.request import urlretrieve import matplotlib.pyplot as plt import numpy as np from matplotlib.collections import PatchCollection from matplotlib.patches import Polygon from . import mask as maskUtils 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 annotation_file is not None: print('loading annotations into memory...') tic = time.time() with open(annotation_file, 'r') as f: dataset = json.load(f) 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() self.img_ann_map = self.imgToAnns self.cat_img_map = self.catToImgs 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 iscrowd is not None: ids = [ann['id'] for ann in anns if ann['iscrowd'] == iscrowd] else: ids = [ann['id'] for ann in anns] return ids def get_ann_ids(self, img_ids=[], cat_ids=[], area_rng=[], iscrowd=None): return self.getAnnIds(img_ids, cat_ids, area_rng, iscrowd) 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 get_cat_ids(self, cat_names=[], sup_names=[], cat_ids=[]): return self.getCatIds(cat_names, sup_names, cat_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 get_img_ids(self, img_ids=[], cat_ids=[]): return self.getImgIds(img_ids, cat_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]] load_anns = loadAnns 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]] load_cats = loadCats 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]] load_imgs = loadImgs 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: with open(resFile) as f: anns = json.load(f) 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 'segmentation' not 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 'bbox' not 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 ann_to_rle = annToRLE 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 ann_to_mask = annToMask