Module sagemaker_defect_detection.utils.coco_utils
BSD 3-Clause License
Copyright (c) Soumith Chintala 2016, All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
-
Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
-
Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
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Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
This module is a modified version of https://github.com/pytorch/vision/tree/03b1d38ba3c67703e648fb067570eeb1a1e61265/references/detection
View Source
"""
BSD 3-Clause License
Copyright (c) Soumith Chintala 2016,
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
* Neither the name of the copyright holder nor the names of its
contributors may be used to endorse or promote products derived from
this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
This module is a modified version of https://github.com/pytorch/vision/tree/03b1d38ba3c67703e648fb067570eeb1a1e61265/references/detection
"""
from pycocotools.coco import COCO
def convert_to_coco_api(ds):
coco_ds = COCO()
# annotation IDs need to start at 1, not 0, see torchvision issue #1530
ann_id = 1
dataset = {"images": [], "categories": [], "annotations": []}
categories = set()
for img_idx in range(len(ds)):
# find better way to get target
# targets = ds.get_annotations(img_idx)
img, targets, _ = ds[img_idx]
image_id = targets["image_id"].item()
img_dict = {}
img_dict["id"] = image_id
img_dict["height"] = img.shape[-2]
img_dict["width"] = img.shape[-1]
dataset["images"].append(img_dict)
bboxes = targets["boxes"]
bboxes[:, 2:] -= bboxes[:, :2]
bboxes = bboxes.tolist()
labels = targets["labels"].tolist()
areas = targets["area"].tolist()
iscrowd = targets["iscrowd"].tolist()
num_objs = len(bboxes)
for i in range(num_objs):
ann = {}
ann["image_id"] = image_id
ann["bbox"] = bboxes[i]
ann["category_id"] = labels[i]
categories.add(labels[i])
ann["area"] = areas[i]
ann["iscrowd"] = iscrowd[i]
ann["id"] = ann_id
dataset["annotations"].append(ann)
ann_id += 1
dataset["categories"] = [{"id": i} for i in sorted(categories)]
coco_ds.dataset = dataset
coco_ds.createIndex()
return coco_ds
Functions
convert_to_coco_api
def convert_to_coco_api(
ds
)
View Source
def convert_to_coco_api(ds):
coco_ds = COCO()
# annotation IDs need to start at 1, not 0, see torchvision issue #1530
ann_id = 1
dataset = {"images": [], "categories": [], "annotations": []}
categories = set()
for img_idx in range(len(ds)):
# find better way to get target
# targets = ds.get_annotations(img_idx)
img, targets, _ = ds[img_idx]
image_id = targets["image_id"].item()
img_dict = {}
img_dict["id"] = image_id
img_dict["height"] = img.shape[-2]
img_dict["width"] = img.shape[-1]
dataset["images"].append(img_dict)
bboxes = targets["boxes"]
bboxes[:, 2:] -= bboxes[:, :2]
bboxes = bboxes.tolist()
labels = targets["labels"].tolist()
areas = targets["area"].tolist()
iscrowd = targets["iscrowd"].tolist()
num_objs = len(bboxes)
for i in range(num_objs):
ann = {}
ann["image_id"] = image_id
ann["bbox"] = bboxes[i]
ann["category_id"] = labels[i]
categories.add(labels[i])
ann["area"] = areas[i]
ann["iscrowd"] = iscrowd[i]
ann["id"] = ann_id
dataset["annotations"].append(ann)
ann_id += 1
dataset["categories"] = [{"id": i} for i in sorted(categories)]
coco_ds.dataset = dataset
coco_ds.createIndex()
return coco_ds