official/legacy/detection/evaluation/coco_utils.py
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"""Util functions related to pycocotools and COCO eval."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import json
from absl import logging
import numpy as np
from PIL import Image
from pycocotools import coco
from pycocotools import mask as mask_api
import six
import tensorflow as tf, tf_keras
from official.legacy.detection.dataloader import tf_example_decoder
from official.legacy.detection.utils import box_utils
from official.legacy.detection.utils import mask_utils
class COCOWrapper(coco.COCO):
"""COCO wrapper class.
This class wraps COCO API object, which provides the following additional
functionalities:
1. Support string type image id.
2. Support loading the groundtruth dataset using the external annotation
dictionary.
3. Support loading the prediction results using the external annotation
dictionary.
"""
def __init__(self, eval_type='box', annotation_file=None, gt_dataset=None):
"""Instantiates a COCO-style API object.
Args:
eval_type: either 'box' or 'mask'.
annotation_file: a JSON file that stores annotations of the eval dataset.
This is required if `gt_dataset` is not provided.
gt_dataset: the groundtruth eval datatset in COCO API format.
"""
if ((annotation_file and gt_dataset) or
((not annotation_file) and (not gt_dataset))):
raise ValueError('One and only one of `annotation_file` and `gt_dataset` '
'needs to be specified.')
if eval_type not in ['box', 'mask']:
raise ValueError('The `eval_type` can only be either `box` or `mask`.')
coco.COCO.__init__(self, annotation_file=annotation_file)
self._eval_type = eval_type
if gt_dataset:
self.dataset = gt_dataset
self.createIndex()
def loadRes(self, predictions):
"""Loads result file and return a result api object.
Args:
predictions: a list of dictionary each representing an annotation in COCO
format. The required fields are `image_id`, `category_id`, `score`,
`bbox`, `segmentation`.
Returns:
res: result COCO api object.
Raises:
ValueError: if the set of image id from predctions is not the subset of
the set of image id of the groundtruth dataset.
"""
res = coco.COCO()
res.dataset['images'] = copy.deepcopy(self.dataset['images'])
res.dataset['categories'] = copy.deepcopy(self.dataset['categories'])
image_ids = [ann['image_id'] for ann in predictions]
if set(image_ids) != (set(image_ids) & set(self.getImgIds())):
raise ValueError('Results do not correspond to the current dataset!')
for ann in predictions:
x1, x2, y1, y2 = [ann['bbox'][0], ann['bbox'][0] + ann['bbox'][2],
ann['bbox'][1], ann['bbox'][1] + ann['bbox'][3]]
if self._eval_type == 'box':
ann['area'] = ann['bbox'][2] * ann['bbox'][3]
ann['segmentation'] = [
[x1, y1, x1, y2, x2, y2, x2, y1]]
elif self._eval_type == 'mask':
ann['area'] = mask_api.area(ann['segmentation'])
res.dataset['annotations'] = copy.deepcopy(predictions)
res.createIndex()
return res
def convert_predictions_to_coco_annotations(predictions):
"""Converts a batch of predictions to annotations in COCO format.
Args:
predictions: a dictionary of lists of numpy arrays including the following
fields. K below denotes the maximum number of instances per image.
Required fields:
- source_id: a list of numpy arrays of int or string of shape
[batch_size].
- num_detections: a list of numpy arrays of int of shape [batch_size].
- detection_boxes: a list of numpy arrays of float of shape
[batch_size, K, 4], where coordinates are in the original image
space (not the scaled image space).
- detection_classes: a list of numpy arrays of int of shape
[batch_size, K].
- detection_scores: a list of numpy arrays of float of shape
[batch_size, K].
Optional fields:
- detection_masks: a list of numpy arrays of float of shape
[batch_size, K, mask_height, mask_width].
Returns:
coco_predictions: prediction in COCO annotation format.
"""
coco_predictions = []
num_batches = len(predictions['source_id'])
batch_size = predictions['source_id'][0].shape[0]
max_num_detections = predictions['detection_classes'][0].shape[1]
use_outer_box = 'detection_outer_boxes' in predictions
for i in range(num_batches):
predictions['detection_boxes'][i] = box_utils.yxyx_to_xywh(
predictions['detection_boxes'][i])
if use_outer_box:
predictions['detection_outer_boxes'][i] = box_utils.yxyx_to_xywh(
predictions['detection_outer_boxes'][i])
mask_boxes = predictions['detection_outer_boxes']
else:
mask_boxes = predictions['detection_boxes']
for j in range(batch_size):
if 'detection_masks' in predictions:
image_masks = mask_utils.paste_instance_masks(
predictions['detection_masks'][i][j],
mask_boxes[i][j],
int(predictions['image_info'][i][j, 0, 0]),
int(predictions['image_info'][i][j, 0, 1]))
binary_masks = (image_masks > 0.0).astype(np.uint8)
encoded_masks = [
mask_api.encode(np.asfortranarray(binary_mask))
for binary_mask in list(binary_masks)]
for k in range(max_num_detections):
ann = {}
ann['image_id'] = predictions['source_id'][i][j]
ann['category_id'] = predictions['detection_classes'][i][j, k]
ann['bbox'] = predictions['detection_boxes'][i][j, k]
ann['score'] = predictions['detection_scores'][i][j, k]
if 'detection_masks' in predictions:
ann['segmentation'] = encoded_masks[k]
coco_predictions.append(ann)
for i, ann in enumerate(coco_predictions):
ann['id'] = i + 1
return coco_predictions
def convert_groundtruths_to_coco_dataset(groundtruths, label_map=None):
"""Converts groundtruths to the dataset in COCO format.
Args:
groundtruths: a dictionary of numpy arrays including the fields below.
Note that each element in the list represent the number for a single
example without batch dimension. K below denotes the actual number of
instances for each image.
Required fields:
- source_id: a list of numpy arrays of int or string of shape
[batch_size].
- height: a list of numpy arrays of int of shape [batch_size].
- width: a list of numpy arrays of int of shape [batch_size].
- num_detections: a list of numpy arrays of int of shape [batch_size].
- boxes: a list of numpy arrays of float of shape [batch_size, K, 4],
where coordinates are in the original image space (not the
normalized coordinates).
- classes: a list of numpy arrays of int of shape [batch_size, K].
Optional fields:
- is_crowds: a list of numpy arrays of int of shape [batch_size, K]. If
th field is absent, it is assumed that this instance is not crowd.
- areas: a list of numy arrays of float of shape [batch_size, K]. If the
field is absent, the area is calculated using either boxes or
masks depending on which one is available.
- masks: a list of numpy arrays of string of shape [batch_size, K],
label_map: (optional) a dictionary that defines items from the category id
to the category name. If `None`, collect the category mappping from the
`groundtruths`.
Returns:
coco_groundtruths: the groundtruth dataset in COCO format.
"""
source_ids = np.concatenate(groundtruths['source_id'], axis=0)
heights = np.concatenate(groundtruths['height'], axis=0)
widths = np.concatenate(groundtruths['width'], axis=0)
gt_images = [{'id': int(i), 'height': int(h), 'width': int(w)} for i, h, w
in zip(source_ids, heights, widths)]
gt_annotations = []
num_batches = len(groundtruths['source_id'])
batch_size = groundtruths['source_id'][0].shape[0]
for i in range(num_batches):
for j in range(batch_size):
num_instances = groundtruths['num_detections'][i][j]
for k in range(num_instances):
ann = {}
ann['image_id'] = int(groundtruths['source_id'][i][j])
if 'is_crowds' in groundtruths:
ann['iscrowd'] = int(groundtruths['is_crowds'][i][j, k])
else:
ann['iscrowd'] = 0
ann['category_id'] = int(groundtruths['classes'][i][j, k])
boxes = groundtruths['boxes'][i]
ann['bbox'] = [
float(boxes[j, k, 1]),
float(boxes[j, k, 0]),
float(boxes[j, k, 3] - boxes[j, k, 1]),
float(boxes[j, k, 2] - boxes[j, k, 0])]
if 'areas' in groundtruths:
ann['area'] = float(groundtruths['areas'][i][j, k])
else:
ann['area'] = float(
(boxes[j, k, 3] - boxes[j, k, 1]) *
(boxes[j, k, 2] - boxes[j, k, 0]))
if 'masks' in groundtruths:
mask = Image.open(six.BytesIO(groundtruths['masks'][i][j, k]))
np_mask = np.array(mask, dtype=np.uint8)
np_mask[np_mask > 0] = 255
encoded_mask = mask_api.encode(np.asfortranarray(np_mask))
ann['segmentation'] = encoded_mask
if 'areas' not in groundtruths:
ann['area'] = mask_api.area(encoded_mask)
gt_annotations.append(ann)
for i, ann in enumerate(gt_annotations):
ann['id'] = i + 1
if label_map:
gt_categories = [{'id': i, 'name': label_map[i]} for i in label_map]
else:
category_ids = [gt['category_id'] for gt in gt_annotations]
gt_categories = [{'id': i} for i in set(category_ids)]
gt_dataset = {
'images': gt_images,
'categories': gt_categories,
'annotations': copy.deepcopy(gt_annotations),
}
return gt_dataset
class COCOGroundtruthGenerator(object):
"""Generates the groundtruth annotations from a single example."""
def __init__(self, file_pattern, num_examples, include_mask):
self._file_pattern = file_pattern
self._num_examples = num_examples
self._include_mask = include_mask
self._dataset_fn = tf.data.TFRecordDataset
def _parse_single_example(self, example):
"""Parses a single serialized tf.Example proto.
Args:
example: a serialized tf.Example proto string.
Returns:
A dictionary of groundtruth with the following fields:
source_id: a scalar tensor of int64 representing the image source_id.
height: a scalar tensor of int64 representing the image height.
width: a scalar tensor of int64 representing the image width.
boxes: a float tensor of shape [K, 4], representing the groundtruth
boxes in absolute coordinates with respect to the original image size.
classes: a int64 tensor of shape [K], representing the class labels of
each instances.
is_crowds: a bool tensor of shape [K], indicating whether the instance
is crowd.
areas: a float tensor of shape [K], indicating the area of each
instance.
masks: a string tensor of shape [K], containing the bytes of the png
mask of each instance.
"""
decoder = tf_example_decoder.TfExampleDecoder(
include_mask=self._include_mask)
decoded_tensors = decoder.decode(example)
image = decoded_tensors['image']
image_size = tf.shape(image)[0:2]
boxes = box_utils.denormalize_boxes(
decoded_tensors['groundtruth_boxes'], image_size)
groundtruths = {
'source_id': tf.string_to_number(
decoded_tensors['source_id'], out_type=tf.int64),
'height': decoded_tensors['height'],
'width': decoded_tensors['width'],
'num_detections': tf.shape(decoded_tensors['groundtruth_classes'])[0],
'boxes': boxes,
'classes': decoded_tensors['groundtruth_classes'],
'is_crowds': decoded_tensors['groundtruth_is_crowd'],
'areas': decoded_tensors['groundtruth_area'],
}
if self._include_mask:
groundtruths.update({
'masks': decoded_tensors['groundtruth_instance_masks_png'],
})
return groundtruths
def _build_pipeline(self):
"""Builds data pipeline to generate groundtruth annotations."""
dataset = tf.data.Dataset.list_files(self._file_pattern, shuffle=False)
dataset = dataset.apply(
tf.data.experimental.parallel_interleave(
lambda filename: self._dataset_fn(filename).prefetch(1),
cycle_length=32,
sloppy=False))
dataset = dataset.map(self._parse_single_example, num_parallel_calls=64)
dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE)
dataset = dataset.batch(1, drop_remainder=False)
return dataset
def __call__(self):
with tf.Graph().as_default():
dataset = self._build_pipeline()
groundtruth = dataset.make_one_shot_iterator().get_next()
with tf.Session() as sess:
for _ in range(self._num_examples):
groundtruth_result = sess.run(groundtruth)
yield groundtruth_result
def scan_and_generator_annotation_file(file_pattern,
num_samples,
include_mask,
annotation_file):
"""Scans and generate the COCO-style annotation JSON file given a dataset."""
groundtruth_generator = COCOGroundtruthGenerator(
file_pattern, num_samples, include_mask)
generate_annotation_file(groundtruth_generator, annotation_file)
def generate_annotation_file(groundtruth_generator,
annotation_file):
"""Generates COCO-style annotation JSON file given a groundtruth generator."""
groundtruths = {}
logging.info('Loading groundtruth annotations from dataset to memory...')
for groundtruth in groundtruth_generator():
for k, v in six.iteritems(groundtruth):
if k not in groundtruths:
groundtruths[k] = [v]
else:
groundtruths[k].append(v)
gt_dataset = convert_groundtruths_to_coco_dataset(groundtruths)
logging.info('Saving groundtruth annotations to the JSON file...')
with tf.io.gfile.GFile(annotation_file, 'w') as f:
f.write(json.dumps(gt_dataset))
logging.info('Done saving the JSON file...')