# Copyright (c) Facebook, Inc. and its affiliates.
import itertools
import json
import numpy as np
import os
import torch
from pycocotools.cocoeval import COCOeval, maskUtils
from detectron2.structures import BoxMode, RotatedBoxes, pairwise_iou_rotated
from detectron2.utils.file_io import PathManager
from .coco_evaluation import COCOEvaluator
class RotatedCOCOeval(COCOeval):
@staticmethod
def is_rotated(box_list):
if type(box_list) == np.ndarray:
return box_list.shape[1] == 5
elif type(box_list) == list:
if box_list == []: # cannot decide the box_dim
return False
return np.all(
np.array(
[
(len(obj) == 5) and ((type(obj) == list) or (type(obj) == np.ndarray))
for obj in box_list
]
)
)
return False
@staticmethod
def boxlist_to_tensor(boxlist, output_box_dim):
if type(boxlist) == np.ndarray:
box_tensor = torch.from_numpy(boxlist)
elif type(boxlist) == list:
if boxlist == []:
return torch.zeros((0, output_box_dim), dtype=torch.float32)
else:
box_tensor = torch.FloatTensor(boxlist)
else:
raise Exception("Unrecognized boxlist type")
input_box_dim = box_tensor.shape[1]
if input_box_dim != output_box_dim:
if input_box_dim == 4 and output_box_dim == 5:
box_tensor = BoxMode.convert(box_tensor, BoxMode.XYWH_ABS, BoxMode.XYWHA_ABS)
else:
raise Exception(
"Unable to convert from {}-dim box to {}-dim box".format(
input_box_dim, output_box_dim
)
)
return box_tensor
def compute_iou_dt_gt(self, dt, gt, is_crowd):
if self.is_rotated(dt) or self.is_rotated(gt):
# TODO: take is_crowd into consideration
assert all(c == 0 for c in is_crowd)
dt = RotatedBoxes(self.boxlist_to_tensor(dt, output_box_dim=5))
gt = RotatedBoxes(self.boxlist_to_tensor(gt, output_box_dim=5))
return pairwise_iou_rotated(dt, gt)
else:
# This is the same as the classical COCO evaluation
return maskUtils.iou(dt, gt, is_crowd)
def computeIoU(self, imgId, catId):
p = self.params
if p.useCats:
gt = self._gts[imgId, catId]
dt = self._dts[imgId, catId]
else:
gt = [_ for cId in p.catIds for _ in self._gts[imgId, cId]]
dt = [_ for cId in p.catIds for _ in self._dts[imgId, cId]]
if len(gt) == 0 and len(dt) == 0:
return []
inds = np.argsort([-d["score"] for d in dt], kind="mergesort")
dt = [dt[i] for i in inds]
if len(dt) > p.maxDets[-1]:
dt = dt[0 : p.maxDets[-1]]
assert p.iouType == "bbox", "unsupported iouType for iou computation"
g = [g["bbox"] for g in gt]
d = [d["bbox"] for d in dt]
# compute iou between each dt and gt region
iscrowd = [int(o["iscrowd"]) for o in gt]
# Note: this function is copied from cocoeval.py in cocoapi
# and the major difference is here.
ious = self.compute_iou_dt_gt(d, g, iscrowd)
return ious
[docs]class RotatedCOCOEvaluator(COCOEvaluator):
"""
Evaluate object proposal/instance detection outputs using COCO-like metrics and APIs,
with rotated boxes support.
Note: this uses IOU only and does not consider angle differences.
"""
[docs] def process(self, inputs, outputs):
"""
Args:
inputs: the inputs to a COCO model (e.g., GeneralizedRCNN).
It is a list of dict. Each dict corresponds to an image and
contains keys like "height", "width", "file_name", "image_id".
outputs: the outputs of a COCO model. It is a list of dicts with key
"instances" that contains :class:`Instances`.
"""
for input, output in zip(inputs, outputs):
prediction = {"image_id": input["image_id"]}
if "instances" in output:
instances = output["instances"].to(self._cpu_device)
prediction["instances"] = self.instances_to_json(instances, input["image_id"])
if "proposals" in output:
prediction["proposals"] = output["proposals"].to(self._cpu_device)
self._predictions.append(prediction)
[docs] def instances_to_json(self, instances, img_id):
num_instance = len(instances)
if num_instance == 0:
return []
boxes = instances.pred_boxes.tensor.numpy()
if boxes.shape[1] == 4:
boxes = BoxMode.convert(boxes, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS)
boxes = boxes.tolist()
scores = instances.scores.tolist()
classes = instances.pred_classes.tolist()
results = []
for k in range(num_instance):
result = {
"image_id": img_id,
"category_id": classes[k],
"bbox": boxes[k],
"score": scores[k],
}
results.append(result)
return results
def _eval_predictions(self, predictions, img_ids=None): # img_ids: unused
"""
Evaluate predictions on the given tasks.
Fill self._results with the metrics of the tasks.
"""
self._logger.info("Preparing results for COCO format ...")
coco_results = list(itertools.chain(*[x["instances"] for x in predictions]))
# unmap the category ids for COCO
if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"):
reverse_id_mapping = {
v: k for k, v in self._metadata.thing_dataset_id_to_contiguous_id.items()
}
for result in coco_results:
result["category_id"] = reverse_id_mapping[result["category_id"]]
if self._output_dir:
file_path = os.path.join(self._output_dir, "coco_instances_results.json")
self._logger.info("Saving results to {}".format(file_path))
with PathManager.open(file_path, "w") as f:
f.write(json.dumps(coco_results))
f.flush()
if not self._do_evaluation:
self._logger.info("Annotations are not available for evaluation.")
return
self._logger.info("Evaluating predictions ...")
assert self._tasks is None or set(self._tasks) == {
"bbox"
}, "[RotatedCOCOEvaluator] Only bbox evaluation is supported"
coco_eval = (
self._evaluate_predictions_on_coco(self._coco_api, coco_results)
if len(coco_results) > 0
else None # cocoapi does not handle empty results very well
)
task = "bbox"
res = self._derive_coco_results(
coco_eval, task, class_names=self._metadata.get("thing_classes")
)
self._results[task] = res
def _evaluate_predictions_on_coco(self, coco_gt, coco_results):
"""
Evaluate the coco results using COCOEval API.
"""
assert len(coco_results) > 0
coco_dt = coco_gt.loadRes(coco_results)
# Only bbox is supported for now
coco_eval = RotatedCOCOeval(coco_gt, coco_dt, iouType="bbox")
coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()
return coco_eval