--- /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)
+
+
+
+
+
+
+
+
Code Review / ealt-edge.git / blobdiff