Source code for detectron2.evaluation.pascal_voc_evaluation

# -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates.

import logging
import numpy as np
import os
import tempfile
import xml.etree.ElementTree as ET
from collections import OrderedDict, defaultdict
from functools import lru_cache
import torch

from detectron2.data import MetadataCatalog
from detectron2.utils import comm
from detectron2.utils.file_io import PathManager

from .evaluator import DatasetEvaluator


[docs]class PascalVOCDetectionEvaluator(DatasetEvaluator): """ Evaluate Pascal VOC style AP for Pascal VOC dataset. It contains a synchronization, therefore has to be called from all ranks. Note that the concept of AP can be implemented in different ways and may not produce identical results. This class mimics the implementation of the official Pascal VOC Matlab API, and should produce similar but not identical results to the official API. """
[docs] def __init__(self, dataset_name): """ Args: dataset_name (str): name of the dataset, e.g., "voc_2007_test" """ self._dataset_name = dataset_name meta = MetadataCatalog.get(dataset_name) # Too many tiny files, download all to local for speed. annotation_dir_local = PathManager.get_local_path( os.path.join(meta.dirname, "Annotations/") ) self._anno_file_template = os.path.join(annotation_dir_local, "{}.xml") self._image_set_path = os.path.join(meta.dirname, "ImageSets", "Main", meta.split + ".txt") self._class_names = meta.thing_classes assert meta.year in [2007, 2012], meta.year self._is_2007 = meta.year == 2007 self._cpu_device = torch.device("cpu") self._logger = logging.getLogger(__name__)
[docs] def reset(self): self._predictions = defaultdict(list) # class name -> list of prediction strings
[docs] def process(self, inputs, outputs): for input, output in zip(inputs, outputs): image_id = input["image_id"] instances = output["instances"].to(self._cpu_device) boxes = instances.pred_boxes.tensor.numpy() scores = instances.scores.tolist() classes = instances.pred_classes.tolist() for box, score, cls in zip(boxes, scores, classes): xmin, ymin, xmax, ymax = box # The inverse of data loading logic in `datasets/pascal_voc.py` xmin += 1 ymin += 1 self._predictions[cls].append( f"{image_id} {score:.3f} {xmin:.1f} {ymin:.1f} {xmax:.1f} {ymax:.1f}" )
[docs] def evaluate(self): """ Returns: dict: has a key "segm", whose value is a dict of "AP", "AP50", and "AP75". """ all_predictions = comm.gather(self._predictions, dst=0) if not comm.is_main_process(): return predictions = defaultdict(list) for predictions_per_rank in all_predictions: for clsid, lines in predictions_per_rank.items(): predictions[clsid].extend(lines) del all_predictions self._logger.info( "Evaluating {} using {} metric. " "Note that results do not use the official Matlab API.".format( self._dataset_name, 2007 if self._is_2007 else 2012 ) ) with tempfile.TemporaryDirectory(prefix="pascal_voc_eval_") as dirname: res_file_template = os.path.join(dirname, "{}.txt") aps = defaultdict(list) # iou -> ap per class for cls_id, cls_name in enumerate(self._class_names): lines = predictions.get(cls_id, [""]) with open(res_file_template.format(cls_name), "w") as f: f.write("\n".join(lines)) for thresh in range(50, 100, 5): rec, prec, ap = voc_eval( res_file_template, self._anno_file_template, self._image_set_path, cls_name, ovthresh=thresh / 100.0, use_07_metric=self._is_2007, ) aps[thresh].append(ap * 100) ret = OrderedDict() mAP = {iou: np.mean(x) for iou, x in aps.items()} ret["bbox"] = {"AP": np.mean(list(mAP.values())), "AP50": mAP[50], "AP75": mAP[75]} return ret
############################################################################## # # Below code is modified from # https://github.com/rbgirshick/py-faster-rcnn/blob/master/lib/datasets/voc_eval.py # -------------------------------------------------------- # Fast/er R-CNN # Licensed under The MIT License [see LICENSE for details] # Written by Bharath Hariharan # -------------------------------------------------------- """Python implementation of the PASCAL VOC devkit's AP evaluation code.""" @lru_cache(maxsize=None) def parse_rec(filename): """Parse a PASCAL VOC xml file.""" with PathManager.open(filename) as f: tree = ET.parse(f) objects = [] for obj in tree.findall("object"): obj_struct = {} obj_struct["name"] = obj.find("name").text obj_struct["pose"] = obj.find("pose").text obj_struct["truncated"] = int(obj.find("truncated").text) obj_struct["difficult"] = int(obj.find("difficult").text) bbox = obj.find("bndbox") obj_struct["bbox"] = [ int(bbox.find("xmin").text), int(bbox.find("ymin").text), int(bbox.find("xmax").text), int(bbox.find("ymax").text), ] objects.append(obj_struct) return objects def voc_ap(rec, prec, use_07_metric=False): """Compute VOC AP given precision and recall. If use_07_metric is true, uses the VOC 07 11-point method (default:False). """ if use_07_metric: # 11 point metric ap = 0.0 for t in np.arange(0.0, 1.1, 0.1): if np.sum(rec >= t) == 0: p = 0 else: p = np.max(prec[rec >= t]) ap = ap + p / 11.0 else: # correct AP calculation # first append sentinel values at the end mrec = np.concatenate(([0.0], rec, [1.0])) mpre = np.concatenate(([0.0], prec, [0.0])) # compute the precision envelope for i in range(mpre.size - 1, 0, -1): mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i]) # to calculate area under PR curve, look for points # where X axis (recall) changes value i = np.where(mrec[1:] != mrec[:-1])[0] # and sum (\Delta recall) * prec ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) return ap def voc_eval(detpath, annopath, imagesetfile, classname, ovthresh=0.5, use_07_metric=False): """rec, prec, ap = voc_eval(detpath, annopath, imagesetfile, classname, [ovthresh], [use_07_metric]) Top level function that does the PASCAL VOC evaluation. detpath: Path to detections detpath.format(classname) should produce the detection results file. annopath: Path to annotations annopath.format(imagename) should be the xml annotations file. imagesetfile: Text file containing the list of images, one image per line. classname: Category name (duh) [ovthresh]: Overlap threshold (default = 0.5) [use_07_metric]: Whether to use VOC07's 11 point AP computation (default False) """ # assumes detections are in detpath.format(classname) # assumes annotations are in annopath.format(imagename) # assumes imagesetfile is a text file with each line an image name # first load gt # read list of images with PathManager.open(imagesetfile, "r") as f: lines = f.readlines() imagenames = [x.strip() for x in lines] # load annots recs = {} for imagename in imagenames: recs[imagename] = parse_rec(annopath.format(imagename)) # extract gt objects for this class class_recs = {} npos = 0 for imagename in imagenames: R = [obj for obj in recs[imagename] if obj["name"] == classname] bbox = np.array([x["bbox"] for x in R]) difficult = np.array([x["difficult"] for x in R]).astype(bool) # difficult = np.array([False for x in R]).astype(bool) # treat all "difficult" as GT det = [False] * len(R) npos = npos + sum(~difficult) class_recs[imagename] = {"bbox": bbox, "difficult": difficult, "det": det} # read dets detfile = detpath.format(classname) with open(detfile, "r") as f: lines = f.readlines() splitlines = [x.strip().split(" ") for x in lines] image_ids = [x[0] for x in splitlines] confidence = np.array([float(x[1]) for x in splitlines]) BB = np.array([[float(z) for z in x[2:]] for x in splitlines]).reshape(-1, 4) # sort by confidence sorted_ind = np.argsort(-confidence) BB = BB[sorted_ind, :] image_ids = [image_ids[x] for x in sorted_ind] # go down dets and mark TPs and FPs nd = len(image_ids) tp = np.zeros(nd) fp = np.zeros(nd) for d in range(nd): R = class_recs[image_ids[d]] bb = BB[d, :].astype(float) ovmax = -np.inf BBGT = R["bbox"].astype(float) if BBGT.size > 0: # compute overlaps # intersection ixmin = np.maximum(BBGT[:, 0], bb[0]) iymin = np.maximum(BBGT[:, 1], bb[1]) ixmax = np.minimum(BBGT[:, 2], bb[2]) iymax = np.minimum(BBGT[:, 3], bb[3]) iw = np.maximum(ixmax - ixmin + 1.0, 0.0) ih = np.maximum(iymax - iymin + 1.0, 0.0) inters = iw * ih # union uni = ( (bb[2] - bb[0] + 1.0) * (bb[3] - bb[1] + 1.0) + (BBGT[:, 2] - BBGT[:, 0] + 1.0) * (BBGT[:, 3] - BBGT[:, 1] + 1.0) - inters ) overlaps = inters / uni ovmax = np.max(overlaps) jmax = np.argmax(overlaps) if ovmax > ovthresh: if not R["difficult"][jmax]: if not R["det"][jmax]: tp[d] = 1.0 R["det"][jmax] = 1 else: fp[d] = 1.0 else: fp[d] = 1.0 # compute precision recall fp = np.cumsum(fp) tp = np.cumsum(tp) rec = tp / float(npos) # avoid divide by zero in case the first detection matches a difficult # ground truth prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps) ap = voc_ap(rec, prec, use_07_metric) return rec, prec, ap