# Copyright (c) Alibaba, Inc. and its affiliates.
import json
import logging
from multiprocessing import cpu_count
import numpy as np
from easycv.datasets.detection.data_sources.base import DetSourceBase
from easycv.datasets.registry import DATASOURCES
from easycv.file import io
from easycv.framework.errors import NotImplementedError, ValueError
[docs]def get_prior_task_id(keys):
""""The task id ends with `check` is the highest priority.
"""
k_list = []
check_k_list = []
verify_k_list = []
for k in keys:
if k.startswith('label-'):
if k.endswith('check'):
check_k_list.append(k)
elif k.endswith('verify'):
verify_k_list.append(k)
else:
k_list.append(k)
if len(check_k_list):
return check_k_list
if len(k_list):
return k_list
if len(verify_k_list):
return verify_k_list
return []
[docs]def is_itag_v2(row):
"""
The keyword of the data source is `picUrl` in v1, but is `source` in v2
"""
if 'source' in row['data']:
return True
return False
[docs]def parser_manifest_row_str(row_str, classes):
row = json.loads(row_str.strip())
_is_itag_v2 = is_itag_v2(row)
parse_results = {}
# check img_url
img_url = row['data']['source']
if img_url.startswith(('http://', 'https://')):
logging.warning(
'Not support http url, only support `oss://`, skip the sample: %s!'
% img_url)
return parse_results
# check task ids
if _is_itag_v2:
task_ids = get_prior_task_id(row.keys())
else:
task_ids = [
row_k for row_k in row.keys() if row_k.startswith('label-')
]
if len(task_ids) > 1:
raise NotImplementedError('Not support multi label task ids: %s!' %
task_ids)
if not len(task_ids):
logging.warning('Not find label task id in sample: %s, skip it!' %
img_url)
return parse_results
ann_json = row[task_ids[0]]
if not ann_json:
return parse_results
bboxes, gt_labels = [], []
for result in ann_json['results']:
if result['type'] != 'image':
continue
objs_list = result['data']
for obj in objs_list:
if _is_itag_v2:
if obj['type'] != 'image/polygon':
logging.warning(
'Result type should be `image/polygon`, but get %s, skip object %s in %s'
% (obj['type'], obj, img_url))
continue
sort_points = sorted(obj['value'], key=sum)
(x0, y0, x1, y1) = np.concatenate(
(sort_points[0], sort_points[-1]), axis=0)
bboxes.append([x0, y0, x1, y1])
class_name = list(obj['labels'].values())
if len(class_name) > 1:
raise ValueError(
'Not support multi label, get class name %s!' %
class_name)
gt_labels.append(classes.index(class_name[0]))
else:
if obj['type'] != 'image/rectangleLabel':
logging.warning(
'result type [%s] in %s is not image/rectangleLabel, skip it!'
% (obj['type'], img_url))
continue
value = obj['value']
x, y, w, h = value['x'], value['y'], value['width'], value[
'height']
bnd = [x, y, x + w, y + h]
class_name = obj['labels'][0]
bboxes.append(bnd)
gt_labels.append(classes.index(class_name))
break
if len(bboxes) == 0:
bboxes = np.zeros((0, 4), dtype=np.float32)
parse_results['filename'] = img_url
parse_results['gt_bboxes'] = np.array(bboxes, dtype=np.float32)
parse_results['gt_labels'] = np.array(gt_labels, dtype=np.int64)
return parse_results
[docs]@DATASOURCES.register_module
class DetSourcePAI(DetSourceBase):
"""
data format please refer to: https://help.aliyun.com/document_detail/311173.html
"""
[docs] def __init__(self,
path,
classes=[],
cache_at_init=False,
cache_on_the_fly=False,
parse_fn=parser_manifest_row_str,
num_processes=int(cpu_count() / 2),
**kwargs):
"""
Args:
path: Path of manifest path with pai label format
classes: classes list
cache_at_init: if set True, will cache in memory in __init__ for faster training
cache_on_the_fly: if set True, will cache in memroy during training
parse_fn: parse function to parse item of source iterator
num_processes: number of processes to parse samples
"""
self.manifest_path = path
super(DetSourcePAI, self).__init__(
classes=classes,
cache_at_init=cache_at_init,
cache_on_the_fly=cache_on_the_fly,
parse_fn=parse_fn,
num_processes=num_processes)
[docs] def get_source_iterator(self):
with io.open(self.manifest_path, 'r') as f:
rows = f.read().splitlines()
return rows