From fe6e694e177b7c85d9a54734d2ba602b03159536 Mon Sep 17 00:00:00 2001
From: AlexeyAB <alexeyab84@gmail.com>
Date: Wed, 13 Sep 2017 10:47:19 +0000
Subject: [PATCH] Fixed yolo_console_dll.cpp
---
scripts/voc_eval.py | 200 +++++++++++++++++++++++++++++++++
src/yolo_console_dll.cpp | 2
scripts/reval_voc.py | 101 ++++++++++++++++
3 files changed, 302 insertions(+), 1 deletions(-)
diff --git a/scripts/reval_voc.py b/scripts/reval_voc.py
new file mode 100644
index 0000000..1164f88
--- /dev/null
+++ b/scripts/reval_voc.py
@@ -0,0 +1,101 @@
+#!/usr/bin/env python
+
+# Adapt from ->
+# --------------------------------------------------------
+# Fast R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick
+# --------------------------------------------------------
+# <- Written by Yaping Sun
+
+"""Reval = re-eval. Re-evaluate saved detections."""
+
+import os, sys, argparse
+import numpy as np
+import cPickle
+
+from voc_eval import voc_eval
+
+def parse_args():
+ """
+ Parse input arguments
+ """
+ parser = argparse.ArgumentParser(description='Re-evaluate results')
+ parser.add_argument('output_dir', nargs=1, help='results directory',
+ type=str)
+ parser.add_argument('--voc_dir', dest='voc_dir', default='data/VOCdevkit', type=str)
+ parser.add_argument('--year', dest='year', default='2017', type=str)
+ parser.add_argument('--image_set', dest='image_set', default='test', type=str)
+
+ parser.add_argument('--classes', dest='class_file', default='data/voc.names', type=str)
+
+ if len(sys.argv) == 1:
+ parser.print_help()
+ sys.exit(1)
+
+ args = parser.parse_args()
+ return args
+
+def get_voc_results_file_template(image_set, out_dir = 'results'):
+ filename = 'comp4_det_' + image_set + '_{:s}.txt'
+ path = os.path.join(out_dir, filename)
+ return path
+
+def do_python_eval(devkit_path, year, image_set, classes, output_dir = 'results'):
+ annopath = os.path.join(
+ devkit_path,
+ 'VOC' + year,
+ 'Annotations',
+ '{:s}.xml')
+ imagesetfile = os.path.join(
+ devkit_path,
+ 'VOC' + year,
+ 'ImageSets',
+ 'Main',
+ image_set + '.txt')
+ 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
+ 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
+ filename = get_voc_results_file_template(image_set).format(cls)
+ 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'), 'w') as f:
+ cPickle.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('~~~~~~~~')
+ print('')
+ print('--------------------------------------------------------------')
+ print('Results computed with the **unofficial** Python eval code.')
+ print('Results should be very close to the official MATLAB eval code.')
+ print('-- Thanks, The Management')
+ print('--------------------------------------------------------------')
+
+
+
+if __name__ == '__main__':
+ args = parse_args()
+
+ output_dir = os.path.abspath(args.output_dir[0])
+ with open(args.class_file, 'r') as f:
+ lines = f.readlines()
+
+ classes = [t.strip('\n') for t in lines]
+
+ print 'Evaluating detections'
+ do_python_eval(args.voc_dir, args.year, args.image_set, classes, output_dir)
diff --git a/scripts/voc_eval.py b/scripts/voc_eval.py
new file mode 100644
index 0000000..3b69331
--- /dev/null
+++ b/scripts/voc_eval.py
@@ -0,0 +1,200 @@
+# --------------------------------------------------------
+# Fast/er R-CNN
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Bharath Hariharan
+# --------------------------------------------------------
+
+import xml.etree.ElementTree as ET
+import os
+import cPickle
+import numpy as np
+
+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, 'w') as f:
+ cPickle.dump(recs, f)
+ else:
+ # load
+ with open(cachefile, 'r') as f:
+ recs = cPickle.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)
+ 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.)
+ 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)
+ jmax = np.argmax(overlaps)
+
+ if ovmax > ovthresh:
+ if not R['difficult'][jmax]:
+ if not R['det'][jmax]:
+ tp[d] = 1.
+ R['det'][jmax] = 1
+ else:
+ fp[d] = 1.
+ else:
+ fp[d] = 1.
+
+ # 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
diff --git a/src/yolo_console_dll.cpp b/src/yolo_console_dll.cpp
index 7618a09..ffa5c45 100644
--- a/src/yolo_console_dll.cpp
+++ b/src/yolo_console_dll.cpp
@@ -70,7 +70,7 @@
std::ifstream file(filename);
std::vector<std::string> file_lines;
if (!file.is_open()) return file_lines;
- for(std::string line; file >> line;) file_lines.push_back(line);
+ for(std::string line; getline(file, line);) file_lines.push_back(line);
std::cout << "object names loaded \n";
return file_lines;
}
--
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