def evaluate_detections(self, all_boxes, output_dir, det_salt = '', eval_salt = '', overlap_thresh = 0.5):
num_classes = self.num_classes
num_images = self.num_images
gt_roidb = self.gt_roidb()
ap = [[]]; prec = [[]]; rec = [[]]
ap_file = os.path.join(output_dir, 'eval' + det_salt + eval_salt + '.txt')
with open(ap_file, 'wt') as f:
for i in xrange(1, self.num_classes):
dt = []; gt = [];
# Prepare the output
for j in xrange(0,num_images):
bs = all_boxes[i][j]
if len(bs) == 0:
bb = np.zeros((0,4)).astype(np.float32)
sc = np.zeros((0,1)).astype(np.float32)
else:
bb = bs[:,:4].reshape(bs.shape[0],4)
sc = bs[:,4].reshape(bs.shape[0],1)
dtI = dict({'sc': sc, 'boxInfo': bb})
dt.append(dtI)
# Prepare the annotations
for j in xrange(0,num_images):
cls_ind = np.where(gt_roidb[j]['gt_classes'] == i)[0]
bb = gt_roidb[j]['boxes'][cls_ind,:]
diff = np.zeros((len(cls_ind),1)).astype(np.bool)
gt.append(dict({'diff': diff, 'boxInfo': bb}))
bOpts = dict({'minoverlap': overlap_thresh})
ap_i, rec_i, prec_i = eval.inst_bench(dt, gt, bOpts)[:3]
ap.append(ap_i[0]); prec.append(prec_i); rec.append(rec_i)
ap_str = '{:20s}:{:10f}'.format(self.classes[i], ap_i[0]*100)
f.write(ap_str + '\n')
print ap_str
ap_str = '{:20s}:{:10f}'.format('mean', np.mean(ap[1:])*100)
f.write(ap_str + '\n')
print ap_str
eval_file = os.path.join(output_dir, 'eval' + det_salt + eval_salt + '.pkl')
g_utils.save_variables(eval_file, [ap, prec, rec, self._classes, self._class_to_ind], \
['ap', 'prec', 'rec', 'classes', 'class_to_ind'], overwrite = True)
eval_file = os.path.join(output_dir, 'eval' + det_salt + eval_salt + '.mat')
g_utils.scio.savemat(eval_file, {'ap': ap, 'prec': prec, 'rec': rec, 'classes': self._classes}, do_compression = True);
return ap, prec, rec, self._classes, self._class_to_ind
def evaluate_detections(self, all_boxes, output_dir, det_salt = '', eval_salt = '', overlap_thresh = 0.5):
num_classes = self.num_classes
num_images = self.num_images
gt_roidb = self.gt_roidb()
ap = [[]]; prec = [[]]; rec = [[]]
ap_file = os.path.join(output_dir, 'eval' + det_salt + eval_salt + '.txt')
with open(ap_file, 'wt') as f:
for i in xrange(1, self.num_classes):
dt = []; gt = [];
# Prepare the output
for j in xrange(0,num_images):
bs = all_boxes[i][j]
if len(bs) == 0:
bb = np.zeros((0,4)).astype(np.float32)
sc = np.zeros((0,1)).astype(np.float32)
else:
bb = bs[:,:4].reshape(bs.shape[0],4)
sc = bs[:,4].reshape(bs.shape[0],1)
dtI = dict({'sc': sc, 'boxInfo': bb})
dt.append(dtI)
# Prepare the annotations
for j in xrange(0,num_images):
cls_ind = np.where(gt_roidb[j]['gt_classes'] == i)[0]
bb = gt_roidb[j]['boxes'][cls_ind,:]
diff = np.zeros((len(cls_ind),1)).astype(np.bool)
gt.append(dict({'diff': diff, 'boxInfo': bb}))
bOpts = dict({'minoverlap': overlap_thresh})
ap_i, rec_i, prec_i = eval.inst_bench(dt, gt, bOpts)[:3]
ap.append(ap_i[0]); prec.append(prec_i); rec.append(rec_i)
ap_str = '{:20s}:{:10f}'.format(self.classes[i], ap_i[0]*100)
f.write(ap_str + '\n')
print ap_str
ap_str = '{:20s}:{:10f}'.format('mean', np.mean(ap[1:])*100)
f.write(ap_str + '\n')
print ap_str
eval_file = os.path.join(output_dir, 'eval' + det_salt + eval_salt + '.pkl')
g_utils.save_variables(eval_file, [ap, prec, rec, self._classes, self._class_to_ind], \
['ap', 'prec', 'rec', 'classes', 'class_to_ind'], overwrite = True)
eval_file = os.path.join(output_dir, 'eval' + det_salt + eval_salt + '.mat')
g_utils.scio.savemat(eval_file, {'ap': ap, 'prec': prec, 'rec': rec, 'classes': self._classes}, do_compression = True);
return ap, prec, rec, self._classes, self._class_to_ind