eval_voc.py

# --------------------------------------------------------
# Fast/er R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Bharath Hariharan
# --------------------------------------------------------
from __future__ import print_function
import argparse
import xml.etree.ElementTree as ET
import os,sys
import pickle
import numpy as np
def parse_args():
    """Parse input arguments."""
    parser = argparse.ArgumentParser(description='mAP Calculation')

    parser.add_argument('--path', dest='path', help='The data path', type=str)
    args = parser.parse_args()

    return args
    
def parse_rec(filename):
    """ Parse a PASCAL VOC xml file """
    tree = ET.parse(filename)
    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):
    """ ap = voc_ap(rec, prec, [use_07_metric])
    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.
        for t in np.arange(0., 1.1, 0.1):
            if np.sum(rec >= t) == 0:
                p = 0
            else:
                p = np.max(prec[rec >= t])
            ap = ap + p / 11.
    else:
        # correct AP calculation
        # first append sentinel values at the end
        mrec = np.concatenate(([0.], rec, [1.]))
        mpre = np.concatenate(([0.], prec, [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,
             cachedir,
             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)
    cachedir: Directory for caching the annotations
    [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
    # cachedir caches the annotations in a pickle file

    # first load gt
    if not os.path.isdir(cachedir):
        os.mkdir(cachedir)
    cachefile = os.path.join(cachedir, 'annots.pkl')
    # read list of images
    with open(imagesetfile, 'r') as f:
        lines = f.readlines()
    imagenames = [x.strip() for x in lines]

    if not os.path.isfile(cachefile):
        # load annots
        recs = {}
        for i, imagename in enumerate(imagenames):
            recs[imagename] = parse_rec(annopath.format(imagename))
            if i % 100 == 0:
                print('Reading annotation for {:d}/{:d}'.format(i + 1, len(imagenames)))
        # save
        print('Saving cached annotations to {:s}'.format(cachefile))
        with open(cachefile, 'wb') as f:
            pickle.dump(recs, f)
    else:
        # load
        with open(cachefile, 'rb') as f:
            recs = pickle.load(f)

    # 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(np.bool)
        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])

    # sort by confidence
    sorted_ind = np.argsort(-confidence)
    sorted_scores = np.sort(-confidence)#对confidence的index根据值大小进行降序排列。
    BB = BB[sorted_ind, :]#重排bbox，由大概率到小概率。
    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.)
            ih = np.maximum(iymax - iymin + 1., 0.)
            inters = iw * ih

            # union
            uni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) +
                   (BBGT[:, 2] - BBGT[:, 0] + 1.) *
                   (BBGT[:, 3] - BBGT[:, 1] + 1.) - inters)

            overlaps = inters / uni
            ovmax = np.max(overlaps)#最大重合度值
            jmax = np.argmax(overlaps)#最大重合度值得所在位置

        if ovmax > ovthresh:
            if not R['difficult'][jmax]:#R['difficult'][jmax]初始都为0
                if not R['det'][jmax]:#R['det][jmax]初始都为0
                    tp[d] = 1.#R['difficult'][jmax]初始都为0
                    R['det'][jmax] = 1#该gt被置为已检测到，下一次若还有另一个检测结果与之重合率满足阈值，则不能认为多检测到一个目标，fp加1
                else:
                    fp[d] = 1.
        else:
            fp[d] = 1.

    # compute precision recall
    fp = np.cumsum(fp)# 非目标预测为目标与 预测正确但重复数量
    tp = np.cumsum(tp) # 预测准确的框数
    rec = tp / float(npos)# npos实际标签数，查全率
    # 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) # np.finfo(np.float64).eps浮点型最小正数，查准率
    ap = voc_ap(rec, prec, use_07_metric)

    return rec, prec, ap
    


def _do_python_eval(res_prefix, output_dir = 'output'):
    _devkit_path = '/home/tju/Data/VOC/VOCdevkit'
    _year = '2007'
    _classes = ('__background__', # always index 0
        'aeroplane', 'bicycle', 'bird', 'boat',
        'bottle', 'bus', 'car', 'cat', 'chair',
        'cow', 'diningtable', 'dog', 'horse',
        'motorbike', 'person', 'pottedplant',
        'sheep', 'sofa', 'train', 'tvmonitor') 
    
    res_prefix = res_prefix + 'comp3_det_test_'
    filename = res_prefix + '{:s}.txt'
"""该文件格式：imagename1 confidence xmin ymin xmax ymax  (图像1的第一个结果)
                   imagename1 confidence xmin ymin xmax ymax  (图像1的第二个结果)
                   imagename1 confidence xmin ymin xmax ymax  (图像2的第一个结果)
                   ...."""
    annopath = os.path.join(
        _devkit_path,
        'VOC' + _year,
        'Annotations',
        '{:s}.xml')
    imagesetfile = os.path.join(
        _devkit_path,
        'VOC' + _year,
        'ImageSets',
        'Main',
        'test.txt')#文件列表
    cachedir = os.path.join(_devkit_path, 'annotations_cache')
    aps = []
    # The PASCAL VOC metric changed in 2010
    # use_07_metric = True if int(_year) < 2010 else False
    use_07_metric = False
    print('VOC07 metric? ' + ('Yes' if use_07_metric else 'No'))
    if not os.path.isdir(output_dir):
        os.mkdir(output_dir)
    for i, cls in enumerate(_classes):
        if cls == '__background__':
            continue
        
        rec, prec, ap = voc_eval(
            filename, annopath, imagesetfile, cls, cachedir, ovthresh=0.5,
            use_07_metric=use_07_metric)
        aps += [ap]
        print('AP for {} = {:.4f}'.format(cls, ap))
        with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:
            pickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
    print('Mean AP = {:.4f}'.format(np.mean(aps)))
    print('~~~~~~~~')
    print('Results:')
    for ap in aps:
        print('{:.3f}'.format(ap))
    print('{:.3f}'.format(np.mean(aps)))
    print('~~~~~~~~')

if __name__ == '__main__':
    args = parse_args()
    _do_python_eval(args.path, output_dir = 'output')