def get_coco_stream(tags, ann_file, data_dir, threshold=0.1):
coco = COCO(ann_file)
catIds = coco.getCatIds(catNms=tags)
imgIds = sum([coco.getImgIds(catIds=catId) for catId in catIds], [])
stream = (coco.loadImgs(imgIds)
| mp.as_field('meta')
| mp.apply('meta', 'width', lambda x: x['width'])
| mp.apply('meta', 'height', lambda x: x['height'])
| mp.apply('meta', 'url', lambda x: x['coco_url'])
| mp.apply('meta', 'filename', lambda x: x['file_name'])
| mp.apply(
'meta', 'anns_ids', lambda x: coco.getAnnIds(
imgIds=x['id'], catIds=catIds, iscrowd=None))
| mp.apply('anns_ids', 'anns', lambda x: coco.loadAnns(x))
| mp.apply(
'anns', 'ground_truth', lambda x: x
| mp.select(lambda m: bbox_to_dict(
m['bbox'], coco.cats[m['category_id']]['name']))
| mp.as_list)
| mp.apply(
'ground_truth', 'class_id',
lambda x: most_common(x
| mp.select(lambda m: m['tag'])
| mp.as_list))
| mp.iter('meta', lambda x: coco.download(data_dir, [x['id']]))
| mp.delfield(['meta', 'anns_ids', 'anns']))
return stream
# mPyPl - Monadic Pipeline Library for Python
# http://github.com/shwars/mPyPl
# Simple samples
sys.path.append('z:\\GitWork\mPyPl')
import mPyPl as mp
from pipe import *
from mPyPl.utils.pipeutils import *
range(100) | mp.as_field('n') | mp.apply(
'n', 'n5', lambda x: x % 5) | mp.dict_group_by('n5')
data = range(100) | mp.as_field('n') | mp.apply(
'n', 'class_id', lambda x: x % 5) | mp.datasplit(split_value=0.2)
Tr, Te = data | mp.make_train_test_split()
len(Tr | as_list)
len(Te | as_list)
data = range(100) | mp.as_field('n') | mp.apply('n', 'class_id',
lambda x: x % 5) | pshuffle
data | mp.sample_classes('class_id', 1, classes=range(15)) | as_list
x = mp.get_xmlstream_fromdir('e:\\data\\babylon\\')
print("Face Dataset Generator")
print(" + Loading descriptions from {}".format(dir))
def loadjs(fn):
with open(fn) as f:
return json.load(f)
min_size = size / 3 if args.ignore_small else 0
max_faces_no = 2 if args.ignore_multiface else 99999
data = (mp.get_files(dir, ext='.json')
| mp.as_field('filename')
| mp.apply('filename', 'descr', loadjs)
| mp.filter('descr', lambda x: len(x) > 0 and len(x) < max_faces_no)
| mp.unroll('descr')
| mp.filter(
'descr', lambda x: abs(x['faceAttributes']['headPose']['yaw']) < 15
and abs(x['faceAttributes']['headPose']['pitch']) < 15)
| mp.filter(
'descr', lambda x: x['faceLandmarks']['pupilRight']['x'] - x[
'faceLandmarks']['pupilLeft']['x'] > min_size)
| mp.as_list)
print(" + Found {} faces".format(len(data)))
print(" + Storing dataset...")