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@@ -0,0 +1,71 @@
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+import numpy as np
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+import simplejson as json
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+
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+from os.path import join
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+
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+from nabirds.utils import _MetaInfo
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+
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+from .base import BaseAnnotations
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+
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+class INAT19_Annotations(BaseAnnotations):
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+
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+ name="INAT19"
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+
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+ @property
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+ def meta(self):
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+ info = _MetaInfo(
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+ images_folder="",
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+ content="trainval2019.json",
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+ val_content="val2019.json",
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+ # train_content="train2019.json",
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+
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+ # fake bounding boxes: the whole image
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+ bounding_box_dtype=np.dtype([(v, np.int32) for v in "xywh"])
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+ )
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+
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+ info.structure = [
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+ [info.content, "_content"],
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+ [info.val_content, "_val_content"],
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+ ]
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+ return info
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+
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+ def read_content(self, json_file, attr):
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+ with self._open(json_file) as f:
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+ content = json.load(f)
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+ setattr(self, attr, content)
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+
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+ def parts(self, uuids):
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+ return None
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+
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+ def bounding_box(self, uuid):
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+ return self.bounding_boxes[self.uuid_to_idx[uuid]].copy()
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+
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+ def _load_bounding_boxes(self):
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+ self.bounding_boxes = np.zeros(len(self.uuids), dtype=self.meta.bounding_box_dtype)
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+
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+ for i, im in enumerate(self._content["images"]):
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+ self.bounding_boxes[i]["w"] = im["width"]
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+ self.bounding_boxes[i]["h"] = im["height"]
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+
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+ def _load_parts(self):
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+ self.part_names = {}
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+ self._load_bounding_boxes()
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+
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+ def _load_split(self):
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+ self.train_split = np.ones(len(self.uuids), dtype=bool)
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+ val_uuids = [im["id"] for im in self._val_content["images"]]
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+ for v_uuid in val_uuids:
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+ self.train_split[self.uuid_to_idx[v_uuid]] = False
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+
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+ self.test_split = np.logical_not(self.train_split)
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+
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+ def _load_labels(self):
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+ self.labels = np.zeros(len(self.uuids), dtype=np.int32)
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+ labs = {annot["image_id"]: annot["category_id"] for annot in self._content["annotations"]}
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+ for uuid in self.uuids:
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+ self.labels[self.uuid_to_idx[uuid]] = labs[uuid]
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+
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+ def _load_uuids(self):
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+ uuid_fnames = [(im["id"], im["file_name"]) for im in self._content["images"]]
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+ self.uuids, self.images = map(np.array, zip(*uuid_fnames))
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+ self.uuid_to_idx = {uuid: i for i, uuid in enumerate(self.uuids)}
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