+++ /dev/null
-# --------------------------------------------------------
-# Fast/er R-CNN
-# Licensed under The MIT License [see LICENSE for details]
-# Written by Bharath Hariharan
-# --------------------------------------------------------
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import xml.etree.ElementTree as ET
-import os
-import pickle
-import numpy as np
-
-import matplotlib.pyplot as plt
-import pylab as pl
-from sklearn.metrics import precision_recall_curve
-from itertools import cycle
-
-from libs.label_name_dict.label_dict import NAME_LABEL_MAP
-from libs.configs import cfgs
-from help_utils.tools import *
-
-def write_voc_results_file(all_boxes, test_imgid_list, det_save_dir):
- '''
-
- :param all_boxes: is a list. each item reprensent the detections of a img.
- the detections is a array. shape is [-1, 6]. [category, score, xmin, ymin, xmax, ymax]
- Note that: if none detections in this img. that the detetions is : []
-
- :param test_imgid_list:
- :param det_save_path:
- :return:
- '''
- for cls, cls_id in NAME_LABEL_MAP.items():
- if cls == 'back_ground':
- continue
- print("Writing {} VOC resutls file".format(cls))
-
- mkdir(det_save_dir)
- det_save_path = os.path.join(det_save_dir, "det_"+cls+".txt")
- with open(det_save_path, 'wt') as f:
- for index, img_name in enumerate(test_imgid_list):
- this_img_detections = all_boxes[index]
-
- this_cls_detections = this_img_detections[this_img_detections[:, 0]==cls_id]
- if this_cls_detections.shape[0] == 0:
- continue # this cls has none detections in this img
- for a_det in this_cls_detections:
- f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.
- format(img_name, a_det[1],
- a_det[2], a_det[3],
- a_det[4], a_det[5])) # that is [img_name, score, xmin, ymin, xmax, ymax]
-
-
-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, test_imgid_list, cls_name, ovthresh=0.5,
- use_07_metric=False, use_diff=False):
- '''
-
- :param detpath:
- :param annopath:
- :param test_imgid_list: it 's a list that contains the img_name of test_imgs
- :param cls_name:
- :param ovthresh:
- :param use_07_metric:
- :param use_diff:
- :return:
- '''
- # 1. parse xml to get gtboxes
-
- # read list of images
- imagenames = test_imgid_list
-
- recs = {}
- for i, imagename in enumerate(imagenames):
- recs[imagename] = parse_rec(os.path.join(annopath, imagename+'.xml'))
- # if i % 100 == 0:
- # print('Reading annotation for {:d}/{:d}'.format(
- # i + 1, len(imagenames)))
-
- # 2. get gtboxes for this class.
- class_recs = {}
- num_pos = 0
- # if cls_name == 'person':
- # print ("aaa")
- for imagename in imagenames:
- R = [obj for obj in recs[imagename] if obj['name'] == cls_name]
- bbox = np.array([x['bbox'] for x in R])
- if use_diff:
- difficult = np.array([False for x in R]).astype(np.bool)
- else:
- difficult = np.array([x['difficult'] for x in R]).astype(np.bool)
- det = [False] * len(R)
- num_pos = num_pos + sum(~difficult) # ignored the diffcult boxes
- class_recs[imagename] = {'bbox': bbox,
- 'difficult': difficult,
- 'det': det} # det means that gtboxes has already been detected
-
- # 3. read the detection file
- detfile = os.path.join(detpath, "det_"+cls_name+".txt")
- with open(detfile, 'r') as f:
- lines = f.readlines()
-
- # for a line. that is [img_name, confidence, xmin, ymin, xmax, ymax]
- splitlines = [x.strip().split(' ') for x in lines] # a list that include a list
- image_ids = [x[0] for x in splitlines] # img_id is img_name
- confidence = np.array([float(x[1]) for x in splitlines])
- BB = np.array([[float(z) for z in x[2:]] for x in splitlines])
-
- nd = len(image_ids) # num of detections. That, a line is a det_box.
- tp = np.zeros(nd)
- fp = np.zeros(nd)
-
- if BB.shape[0] > 0:
- # 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] #reorder the img_name
-
- # go down dets and mark TPs and FPs
- for d in range(nd):
- R = class_recs[image_ids[d]] # img_id is img_name
- 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.
-
- # 4. get recall, precison and AP
- fp = np.cumsum(fp)
- tp = np.cumsum(tp)
- rec = tp / float(num_pos)
- # 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=cfgs.USE_07_METRIC)
-
- return rec, prec, ap
-
-
-def do_python_eval(test_imgid_list, test_annotation_path):
- AP_list = []
- #import matplotlib.pyplot as plt
- #import matplotlib.colors as colors
- #color_list = colors.cnames.keys()[::6]
-
- for cls, index in NAME_LABEL_MAP.items():
- if cls == 'back_ground':
- continue
- recall, precision, AP = voc_eval(detpath=os.path.join(cfgs.EVALUATE_DIR, cfgs.VERSION),
- test_imgid_list=test_imgid_list,
- cls_name=cls,
- annopath=test_annotation_path)
- AP_list += [AP]
- pl.plot(recall, precision, lw=2, label='{} (AP = {:.4f})'''.format(cls, AP))
- print(10*"__")
- pl.xlabel('Recall')
- pl.ylabel('Precision')
- pl.grid(True)
- pl.ylim([0.0, 1.05])
- pl.xlim([0.0, 1.0])
- pl.title('Precision-Recall')
- pl.legend(loc="lower left")
- pl.show()
- pl.savefig(cfgs.VERSION+'_eval.jpg')
- print("hello")
- print("mAP is : {}".format(np.mean(AP_list)))
-
-
-def voc_evaluate_detections(all_boxes, test_annotation_path, test_imgid_list):
- '''
-
- :param all_boxes: is a list. each item reprensent the detections of a img.
-
- The detections is a array. shape is [-1, 6]. [category, score, xmin, ymin, xmax, ymax]
- Note that: if none detections in this img. that the detetions is : []
- :return:
- '''
- test_imgid_list = [item.split('.')[0] for item in test_imgid_list]
-
- write_voc_results_file(all_boxes, test_imgid_list=test_imgid_list,
- det_save_dir=os.path.join(cfgs.EVALUATE_DIR, cfgs.VERSION))
- do_python_eval(test_imgid_list, test_annotation_path=test_annotation_path)
-
-
-
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Code Review / ealt-edge.git / blobdiff