pycocotools.coco源码分析
coco.py的主要目的是加载标注文件，通过各类定义的API函数能够方便的获取categories、annotation、image的id和具体的内容。
定义了以下的API functions：
# 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.
- COCO: COCO类加载COCO annotation文件并且准备数据结构,例如可以通过COCO.dataset或者COCO.imgs来调取原annotation .json文件下的数据内容(一般是字典格式)
- getAnnIds(Cat&Img): 返回满足给定过滤条件的ann的id
- LoadAnns(Cat&Img): 加载给定id的ann文件
- annToMask: 将 segmentation 标注信息转换为二值 mask
- showAnns: 显示指定 ids 的标注信息到对应的图片上
- loadRes: 加载算法的结果并创建可用于访问数据的 API
- download: 从 mscoco.org 服务器下载 COCO 图片数据集
1. _isArrayLike()函数定义
def _isArrayLike(obj):
return hasattr(obj, '__iter__') and hasattr(obj, '__len__') 如果对象具备'__iter__'和'__len__'属性,即可迭代且有长度,例如list,那么返回true,否则false
2. COCO类初始化(ann文件读取和idx构建)
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()
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()
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
- self.dataset = json.load(annfile_path),类型是字典,把ann文件中的整个大字典读取了进来;
- anns={ann_id:ann}, imgToAnns={img_id:[ann...该照片包含的所有标注文件]}
- 同理imgs={img_id:img},cats={cat_id:cat}, 相当于对于整个dataset(标注文件大字典)的一个分块,把ann\cat\img都按照对应的id做一个分块
- catToImgs={cat_id:[img_id...该类别出现过的所有照片id]},为后续给定cat_id返回所有包含该cat的照片id做准备
3. getAnnIds(self, imgIds=[], catIds=[], areaRng=[], iscrowd=None)
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
给定imgIds/catIds/areaRng/iscrowd这几个condition，来筛选出满足条件的ann文件的id，返回ids=[id...]，由于annotation包含了img_id、cat_id、area大小、iscrowd这几个信息，所以能够去过滤去满足条件的ann_id。如果什么过滤条件都没给出，那就直接返回dataset中所有的ann文件的id。
4. getCatIds(self, catNms=[], supNms=[], catIds=[])
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 类似于getAnnIds()函数，getCatIds()过滤条件为catNms、supNms、catIds，返回对应cat/supcat名称的cat_id，如果给定了catIds那就更为直接，就相当于复制了一下。如果没有给定任何参数，那么就返回dataset的”categories“中包含的所有cat_id。
5. getImgIds(self, imgIds=[], catIds=[])
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) 给定的过滤条件为catid与imgid，imgid就很直接，相当于又直接复制一遍，catid则利用在初始化阶段初始化的self.catToImgs，返回出现了该种类物体的所有img_id。
6. loadAnns/Cats/Imgs()
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]] 给定ids=[id...]，返回对应在dataset中”categories“or”annotations“or”images“下对应id的字典，返回值也是一个list=[{annotation_1},...]
7. showAnns(self, anns, draw_bbox=False)
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() # 获取当前figure的轴,将后续的标注信息显示在之前打印过的原图上
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)) # 转换成（x，y）格式
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'])后续有需求再进行补充