COCO的分割物品标注在annotation属性的segmentation属性中，具体格式为一系列表示多边形各个端点的xy坐标。具体为[x1,y1,x2,y2,x3,y3…xn,yn]，即标注的形式是由(x1,y1),(x2,y2),(x3,y3)…(xn,yn)点依次连接起来形成的多边形。
核心代码位于
showAnns
函数中，主要体会标注是一系列xy坐标端点形成的多边形即可。核心部分代码如下，该部分代码并不可运行。
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(img['width']/100,img['height']/100))
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))
# coco图片的坐标原点位于左上，plt的坐标原点位于左下。
#故需要对图片的Y坐标进行翻转，即图片高度减原Y值得到翻转后的Y值。
poly[:, 1] = 334 - poly[:, 1]
polygons.append(Polygon(poly))
color.append(c)
colors = 100*np.random.rand(len(polygons))
p = PatchCollection(polygons, alpha=0.4)
p.set_array(np.array(colors))
ax.add_collection(p)
fig.colorbar(p, ax=ax)
print(img)
#设置x,y轴坐标
my_x_ticks = np.arange(0, img['width']+1, 50)
my_y_ticks = np.arange(0, img['height']+1,50)
plt.xticks(my_x_ticks)
plt.yticks(my_y_ticks)
plt.show()
详细测试代码如下。完整可运行。
import time as time
import json
import numpy as np
from collections import defaultdict
import itertools
import matplotlib.pyplot as plt
from matplotlib.patches import Polygon
from matplotlib.collections import PatchCollection
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 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:
print('进入if,不进行筛选时默认获取全部的cats')
cats = self.dataset['categories']
else:
print('进入else，根据筛选条件对cats进行筛选')
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]
print(cats)
ids = [cat['id'] for cat in cats]
return 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 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 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 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:
#根据imgIds找到所有的ann
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))
print('共有{}个anns.'.format(len(anns)))
for ann in anns:
print(ann)
else:
anns = self.dataset['annotations']
#通过各类条件如catIds对anns进行筛选
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]
print(' ')
print('共有{}个ids.'.format(len(ids)))
print('进入else因为is_crowd为None')
return 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 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'])
dataDir = '../..'
dataType = 'val2017'
annDir = '{}/annotations'.format(dataDir)
annFile = '{}/instances_{}.json'.format(annDir, dataType)
coco = COCO(annFile)
catIds = coco.getCatIds(catNms=['person','dog','skateboard'])
print('catIds')
print(catIds)
imgIds = coco.getImgIds(catIds=catIds )
print('imgIds')
print(imgIds)
imgIds = coco.getImgIds(imgIds = [324158])
print('imgIds')
print(imgIds)
img = coco.loadImgs(imgIds[np.random.randint(0,len(imgIds))])[0]
print('img')
print(img)
print(img['id'])
annIds = coco.getAnnIds(imgIds=img['id'], catIds=catIds, iscrowd=None)
print('annIds')
print(annIds)
anns = coco.loadAnns(annIds)
# for ann in anns:
# print(ann)
# coco.showAnns(anns)
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(img['width']/100,img['height']/100))
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))
# coco图片的坐标原点位于左上，plt的坐标原点位于左下。
#故需要对图片的Y坐标进行翻转，即图片高度减原Y值得到翻转后的Y值。
poly[:, 1] = 334 - poly[:, 1]
polygons.append(Polygon(poly))
color.append(c)
colors = 100*np.random.rand(len(polygons))
p = PatchCollection(polygons, alpha=0.4)
p.set_array(np.array(colors))
ax.add_collection(p)
fig.colorbar(p, ax=ax)
print(img)
#设置x,y轴坐标
my_x_ticks = np.arange(0, img['width']+1, 50)
my_y_ticks = np.arange(0, img['height']+1,50)
plt.xticks(my_x_ticks)
plt.yticks(my_y_ticks)
plt.show()
原图如下
对应的多边形显示的标注如下
