def model_test(net, test_list, input_dir, iter_num=1):
total_score = 0.0
score = 0.0
class_score = {}
for im_name in test_list:
if DEBUG:
print "############## scoring {0} ##############".format(im_name)
im_file = os.path.join(input_dir, 'img', im_name)
label_file = os.path.join(input_dir, 'annotation', im_name)
index = label_file.rindex('.')
label_file = label_file[:index] + ".xml"
if not os.path.isfile(label_file):
sys.stderr.write("can't find label file for {0}".format(im_file))
continue
label_reader = PascalVocReader(label_file)
size = label_reader.getSize()
shapes = label_reader.getShapes()
true_label = convert_shapes(shapes)
pre_label = predict_label(net, im_file)
t_s, s = compare_label(true_label, pre_label, class_score)
print "scoring {0}".format(im_name), t_s, s, s / t_s
total_score += t_s
score += s
print "iter {0} total_score".format(
iter_num), score, total_score, score / total_score
for i in class_score.iterkeys():
print "iter {0} class_score:".format(
iter_num), i, class_score[i], class_score[i][0] / class_score[i][1]
return total_score, score
for i in xrange(2):
_, _= im_detect(net, im)
im_names = os.listdir(input_dir+"/img")
for im_name in im_names:
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print "auto label {0}".format(im_name)
im_file = os.path.join(input_dir, 'img', im_name)
label_file = os.path.join(input_dir, 'annotation', im_name)
index = label_file.rindex('.')
label_file = label_file[:index] + ".xml"
if os.path.isfile(label_file):
tmp_reader = PascalVocReader(label_file)
size = tmp_reader.getSize()
tmp_writer = PascalVocWriter(input_dir, im_name, size)
shapes = tmp_reader.getShapes()
for i in shapes:
tmp_writer.addBndBox(i[1][0][0], i[1][0][1], i[1][2][0], i[1][2][1], i[0])
print "load {0} labels from {1}".format(len(shapes), label_file)
else:
print "can not find label file {0}".format(label_file)
size = cv2.imread(im_file).shape
tmp_writer = PascalVocWriter(input_dir, im_name, size)
n = auto_label(net, im_file, tmp_writer)
tmp_writer.save(output_dir + '/' + os.path.basename(label_file))
print "auto label {0} labels".format(n)
plt.show()
