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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import cv2
import json
import copy
import numpy as np
try:
from collections.abc import Sequence
except Exception:
from collections import Sequence
from ppdet.core.workspace import register, serializable
from ppdet.data.crop_utils.annotation_cropper import AnnoCropper
from .coco import COCODataSet
from .dataset import _make_dataset, _is_valid_file
from ppdet.utils.logger import setup_logger
logger = setup_logger('sniper_coco_dataset')
@register
@serializable
class SniperCOCODataSet(COCODataSet):
"""SniperCOCODataSet"""
def __init__(self,
dataset_dir=None,
image_dir=None,
anno_path=None,
proposals_file=None,
data_fields=['image'],
sample_num=-1,
load_crowd=False,
allow_empty=True,
empty_ratio=1.,
is_trainset=True,
image_target_sizes=[2000, 1000],
valid_box_ratio_ranges=[[-1, 0.1],[0.08, -1]],
chip_target_size=500,
chip_target_stride=200,
use_neg_chip=False,
max_neg_num_per_im=8,
max_per_img=-1,
nms_thresh=0.5):
super(SniperCOCODataSet, self).__init__(
dataset_dir=dataset_dir,
image_dir=image_dir,
anno_path=anno_path,
data_fields=data_fields,
sample_num=sample_num,
load_crowd=load_crowd,
allow_empty=allow_empty,
empty_ratio=empty_ratio
)
self.proposals_file = proposals_file
self.proposals = None
self.anno_cropper = None
self.is_trainset = is_trainset
self.image_target_sizes = image_target_sizes
self.valid_box_ratio_ranges = valid_box_ratio_ranges
self.chip_target_size = chip_target_size
self.chip_target_stride = chip_target_stride
self.use_neg_chip = use_neg_chip
self.max_neg_num_per_im = max_neg_num_per_im
self.max_per_img = max_per_img
self.nms_thresh = nms_thresh
def parse_dataset(self):
if not hasattr(self, "roidbs"):
super(SniperCOCODataSet, self).parse_dataset()
if self.is_trainset:
self._parse_proposals()
self._merge_anno_proposals()
self.ori_roidbs = copy.deepcopy(self.roidbs)
self.init_anno_cropper()
self.roidbs = self.generate_chips_roidbs(self.roidbs, self.is_trainset)
def set_proposals_file(self, file_path):
self.proposals_file = file_path
def init_anno_cropper(self):
logger.info("Init AnnoCropper...")
self.anno_cropper = AnnoCropper(
image_target_sizes=self.image_target_sizes,
valid_box_ratio_ranges=self.valid_box_ratio_ranges,
chip_target_size=self.chip_target_size,
chip_target_stride=self.chip_target_stride,
use_neg_chip=self.use_neg_chip,
max_neg_num_per_im=self.max_neg_num_per_im,
max_per_img=self.max_per_img,
nms_thresh=self.nms_thresh
)
def generate_chips_roidbs(self, roidbs, is_trainset):
if is_trainset:
roidbs = self.anno_cropper.crop_anno_records(roidbs)
else:
roidbs = self.anno_cropper.crop_infer_anno_records(roidbs)
return roidbs
def _parse_proposals(self):
if self.proposals_file:
self.proposals = {}
logger.info("Parse proposals file:{}".format(self.proposals_file))
with open(self.proposals_file, 'r') as f:
proposals = json.load(f)
for prop in proposals:
image_id = prop["image_id"]
if image_id not in self.proposals:
self.proposals[image_id] = []
x, y, w, h = prop["bbox"]
self.proposals[image_id].append([x, y, x + w, y + h])
def _merge_anno_proposals(self):
assert self.roidbs
if self.proposals and len(self.proposals.keys()) > 0:
logger.info("merge proposals to annos")
for id, record in enumerate(self.roidbs):
image_id = int(record["im_id"])
if image_id not in self.proposals.keys():
logger.info("image id :{} no proposals".format(image_id))
record["proposals"] = np.array(self.proposals.get(image_id, []), dtype=np.float32)
self.roidbs[id] = record
def get_ori_roidbs(self):
if not hasattr(self, "ori_roidbs"):
return None
return self.ori_roidbs
def get_roidbs(self):
if not hasattr(self, "roidbs"):
self.parse_dataset()
return self.roidbs
def set_roidbs(self, roidbs):
self.roidbs = roidbs
def check_or_download_dataset(self):
return
def _parse(self):
image_dir = self.image_dir
if not isinstance(image_dir, Sequence):
image_dir = [image_dir]
images = []
for im_dir in image_dir:
if os.path.isdir(im_dir):
im_dir = os.path.join(self.dataset_dir, im_dir)
images.extend(_make_dataset(im_dir))
elif os.path.isfile(im_dir) and _is_valid_file(im_dir):
images.append(im_dir)
return images
def _load_images(self):
images = self._parse()
ct = 0
records = []
for image in images:
assert image != '' and os.path.isfile(image), \
"Image {} not found".format(image)
if self.sample_num > 0 and ct >= self.sample_num:
break
im = cv2.imread(image)
h, w, c = im.shape
rec = {'im_id': np.array([ct]), 'im_file': image, "h": h, "w": w}
self._imid2path[ct] = image
ct += 1
records.append(rec)
assert len(records) > 0, "No image file found"
return records
def get_imid2path(self):
return self._imid2path
def set_images(self, images):
self._imid2path = {}
self.image_dir = images
self.roidbs = self._load_images()
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