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""" OpenImages dataset parser
Copyright 2020 Ross Wightman
"""
import numpy as np
import os
import logging
from .parser import Parser
from .parser_config import OpenImagesParserCfg
_logger = logging.getLogger(__name__)
class OpenImagesParser(Parser):
def __init__(self, cfg: OpenImagesParserCfg):
super().__init__(
bbox_yxyx=cfg.bbox_yxyx,
has_labels=cfg.has_labels,
include_masks=False, # FIXME to support someday
include_bboxes_ignore=False,
ignore_empty_gt=cfg.has_labels and cfg.ignore_empty_gt,
min_img_size=cfg.min_img_size
)
self.img_prefix_levels = cfg.prefix_levels
self.mask_prefix_levels = 1
self._anns = None # access via get_ann_info()
self._img_to_ann = None
self._load_annotations(
categories_filename=cfg.categories_filename,
img_info_filename=cfg.img_info_filename,
img_filename=cfg.img_filename,
masks_filename=cfg.masks_filename,
bbox_filename=cfg.bbox_filename
)
def _load_annotations(
self,
categories_filename: str,
img_info_filename: str,
img_filename: str,
masks_filename: str,
bbox_filename: str,
):
import pandas as pd # For now, blow up on pandas req only when trying to load open images anno
_logger.info('Loading categories...')
classes_df = pd.read_csv(categories_filename, header=None)
self.cat_ids = classes_df[0].tolist()
self.cat_names = classes_df[1].tolist()
self.cat_id_to_label = {c: i + self.label_offset for i, c in enumerate(self.cat_ids)}
def _img_filename(img_id):
# build image filenames that are relative to img_dir
filename = img_filename % img_id
if self.img_prefix_levels:
levels = [c for c in img_id[:self.img_prefix_levels]]
filename = os.path.join(*levels, filename)
return filename
def _mask_filename(mask_path):
# FIXME finish
if self.mask_prefix_levels:
levels = [c for c in mask_path[:self.mask_prefix_levels]]
mask_path = os.path.join(*levels, mask_path)
return mask_path
def _load_img_info(csv_file, select_img_ids=None):
_logger.info('Read img_info csv...')
img_info_df = pd.read_csv(csv_file, index_col='id')
_logger.info('Filter images...')
if select_img_ids is not None:
img_info_df = img_info_df.loc[select_img_ids]
img_info_df = img_info_df[
(img_info_df['width'] >= self.min_img_size) & (img_info_df['height'] >= self.min_img_size)]
_logger.info('Mapping ids...')
img_info_df['img_id'] = img_info_df.index
img_info_df['file_name'] = img_info_df.index.map(lambda x: _img_filename(x))
img_info_df = img_info_df[['img_id', 'file_name', 'width', 'height']]
img_sizes = img_info_df[['width', 'height']].values
self.img_infos = img_info_df.to_dict('records')
self.img_ids = img_info_df.index.values.tolist()
img_id_to_idx = {img_id: idx for idx, img_id in enumerate(self.img_ids)}
return img_sizes, img_id_to_idx
if self.include_masks and self.has_labels:
masks_df = pd.read_csv(masks_filename)
# NOTE currently using dataset masks anno ImageIDs to form valid img_ids from the dataset
anno_img_ids = sorted(masks_df['ImageID'].unique())
img_sizes, img_id_to_idx = _load_img_info(img_info_filename, select_img_ids=anno_img_ids)
masks_df['ImageIdx'] = masks_df['ImageID'].map(img_id_to_idx)
if np.issubdtype(masks_df.ImageIdx.dtype, np.floating):
masks_df = masks_df.dropna(axis='rows')
masks_df['ImageIdx'] = masks_df.ImageIdx.astype(np.int32)
masks_df.sort_values('ImageIdx', inplace=True)
ann_img_idx = masks_df['ImageIdx'].values
img_sizes = img_sizes[ann_img_idx]
masks_df['BoxXMin'] = masks_df['BoxXMin'] * img_sizes[:, 0]
masks_df['BoxXMax'] = masks_df['BoxXMax'] * img_sizes[:, 0]
masks_df['BoxYMin'] = masks_df['BoxYMin'] * img_sizes[:, 1]
masks_df['BoxYMax'] = masks_df['BoxYMax'] * img_sizes[:, 1]
masks_df['LabelIdx'] = masks_df['LabelName'].map(self.cat_id_to_label)
# FIXME remap mask filename with _mask_filename
self._anns = dict(
bbox=masks_df[['BoxXMin', 'BoxYMin', 'BoxXMax', 'BoxYMax']].values.astype(np.float32),
label=masks_df[['LabelIdx']].values.astype(np.int32),
mask_path=masks_df[['MaskPath']].values
)
_, ri, rc = np.unique(ann_img_idx, return_index=True, return_counts=True)
self._img_to_ann = list(zip(ri, rc)) # index, count tuples
elif self.has_labels:
_logger.info('Loading bbox...')
bbox_df = pd.read_csv(bbox_filename)
# NOTE currently using dataset box anno ImageIDs to form valid img_ids from the larger dataset.
# FIXME use *imagelabels.csv or imagelabels-boxable.csv for negative examples (without box?)
anno_img_ids = sorted(bbox_df['ImageID'].unique())
img_sizes, img_id_to_idx = _load_img_info(img_info_filename, select_img_ids=anno_img_ids)
_logger.info('Process bbox...')
bbox_df['ImageIdx'] = bbox_df['ImageID'].map(img_id_to_idx)
if np.issubdtype(bbox_df.ImageIdx.dtype, np.floating):
bbox_df = bbox_df.dropna(axis='rows')
bbox_df['ImageIdx'] = bbox_df.ImageIdx.astype(np.int32)
bbox_df.sort_values('ImageIdx', inplace=True)
ann_img_idx = bbox_df['ImageIdx'].values
img_sizes = img_sizes[ann_img_idx]
bbox_df['XMin'] = bbox_df['XMin'] * img_sizes[:, 0]
bbox_df['XMax'] = bbox_df['XMax'] * img_sizes[:, 0]
bbox_df['YMin'] = bbox_df['YMin'] * img_sizes[:, 1]
bbox_df['YMax'] = bbox_df['YMax'] * img_sizes[:, 1]
bbox_df['LabelIdx'] = bbox_df['LabelName'].map(self.cat_id_to_label).astype(np.int32)
self._anns = dict(
bbox=bbox_df[['XMin', 'YMin', 'XMax', 'YMax']].values.astype(np.float32),
label=bbox_df[['LabelIdx', 'IsGroupOf']].values.astype(np.int32),
)
_, ri, rc = np.unique(ann_img_idx, return_index=True, return_counts=True)
self._img_to_ann = list(zip(ri, rc)) # index, count tuples
else:
_load_img_info(img_info_filename)
_logger.info('Annotations loaded!')
def get_ann_info(self, idx):
if not self.has_labels:
return dict()
start_idx, num_ann = self._img_to_ann[idx]
ann_keys = tuple(self._anns.keys())
ann_values = tuple(self._anns[k][start_idx:start_idx + num_ann] for k in ann_keys)
return self._parse_ann_info(idx, ann_keys, ann_values)
def _parse_ann_info(self, img_idx, ann_keys, ann_values):
"""
"""
gt_bboxes = []
gt_labels = []
gt_bboxes_ignore = []
if self.include_masks:
assert 'mask_path' in ann_keys
gt_masks = []
for ann in zip(*ann_values):
ann = dict(zip(ann_keys, ann))
x1, y1, x2, y2 = ann['bbox']
if x2 - x1 < 1 or y2 - y1 < 1:
continue
label = ann['label'][0]
iscrowd = False
if len(ann['label']) > 1:
iscrowd = ann['label'][1]
if self.yxyx:
bbox = np.array([y1, x1, y2, x2], dtype=np.float32)
else:
bbox = ann['bbox']
if iscrowd:
gt_bboxes_ignore.append(bbox)
else:
gt_bboxes.append(bbox)
gt_labels.append(label)
# if self.include_masks:
# img_info = self.img_infos[img_idx]
# mask_img = SegmentationMask(ann['mask_filename'], img_info['width'], img_info['height'])
# gt_masks.append(mask_img)
if gt_bboxes:
gt_bboxes = np.array(gt_bboxes, ndmin=2, dtype=np.float32)
gt_labels = np.array(gt_labels, dtype=np.int64)
else:
gt_bboxes = np.zeros((0, 4), dtype=np.float32)
gt_labels = np.array([], dtype=np.int64)
if self.include_bboxes_ignore:
if gt_bboxes_ignore:
gt_bboxes_ignore = np.array(gt_bboxes_ignore, ndmin=2, dtype=np.float32)
else:
gt_bboxes_ignore = np.zeros((0, 4), dtype=np.float32)
ann = dict(bbox=gt_bboxes, cls=gt_labels)
if self.include_bboxes_ignore:
ann.update(dict(bbox_ignore=gt_bboxes_ignore, cls_ignore=np.array([], dtype=np.int64)))
if self.include_masks:
ann['masks'] = gt_masks
return ann
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