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| # 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): | |
| 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 | |
| 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 | |
| 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. | |
| """ | |
| 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) | |
| 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 | |