def test_read_annotations_wrong_y2():
with pytest.raises(ValueError):
try:
csv_generator._read_annotations(csv_str('a.png,0,1,2,a,a'), {'a': 1})
except ValueError as e:
assert str(e).startswith("line 1: malformed y2:")
raise
def test_read_annotations_wrong_class():
with pytest.raises(ValueError):
try:
csv_generator._read_annotations(csv_str('a.png,0,1,2,3,g'), {'a': 1})
except ValueError as e:
assert str(e).startswith("line 1: unknown class name:")
raise
def test_read_annotations_wrong_y2():
with pytest.raises(ValueError):
try:
csv_generator._read_annotations(csv_str('a.png,0,1,2,a,a'),
{'a': 1})
except ValueError as e:
assert str(e).startswith("line 1: malformed y2:")
raise
def test_read_annotations_wrong_class():
with pytest.raises(ValueError):
try:
csv_generator._read_annotations(csv_str('a.png,0,1,2,3,g'),
{'a': 1})
except ValueError as e:
assert str(e).startswith("line 1: unknown class name:")
raise
def test_read_annotations_invalid_bb_x():
with pytest.raises(ValueError):
try:
csv_generator._read_annotations(csv_str('a.png,1,2,1,3,g'), {'a': 1})
except ValueError as e:
assert str(e).startswith("line 1: x2 (1) must be higher than x1 (1)")
raise
with pytest.raises(ValueError):
try:
csv_generator._read_annotations(csv_str('a.png,9,2,5,3,g'), {'a': 1})
except ValueError as e:
assert str(e).startswith("line 1: x2 (5) must be higher than x1 (9)")
raise
def test_read_annotations_empty_image():
# Check that images without annotations are parsed.
assert csv_generator._read_annotations(csv_str('a.png,,,,,\nb.png,,,,,'),
{'a': 1}) == {
'a.png': [],
'b.png': []
}
# Check that lines without annotations don't clear earlier annotations.
assert csv_generator._read_annotations(
csv_str('a.png,0,1,2,3,a\na.png,,,,,'), {'a': 1}) == {
'a.png': [annotation(0, 1, 2, 3, 'a')]
}
def test_read_annotations_invalid_bb_y():
with pytest.raises(ValueError):
try:
csv_generator._read_annotations(csv_str('a.png,1,2,3,2,a'), {'a': 1})
except ValueError as e:
assert str(e).startswith("line 1: y2 (2) must be higher than y1 (2)")
raise
with pytest.raises(ValueError):
try:
csv_generator._read_annotations(csv_str('a.png,1,8,3,5,a'), {'a': 1})
except ValueError as e:
assert str(e).startswith("line 1: y2 (5) must be higher than y1 (8)")
raise
def test_read_annotations_invalid_bb_y():
with pytest.raises(ValueError):
try:
annotations = csv_generator._read_annotations(
csv_str('a.png,1,2,3,2,a'), {'a': 1})
except ValueError as e:
assert str(e).startswith(
"line 1: y2 (2) must be higher than y1 (2)")
raise
with pytest.raises(ValueError):
try:
annotations = csv_generator._read_annotations(
csv_str('a.png,1,8,3,5,a'), {'a': 1})
except ValueError as e:
assert str(e).startswith(
"line 1: y2 (5) must be higher than y1 (8)")
raise
def test_read_annotations_invalid_bb_x():
with pytest.raises(ValueError):
try:
annotations = csv_generator._read_annotations(
csv_str('a.png,1,2,1,3,g'), {'a': 1})
except ValueError as e:
assert str(e).startswith(
"line 1: x2 (1) must be higher than x1 (1)")
raise
with pytest.raises(ValueError):
try:
annotations = csv_generator._read_annotations(
csv_str('a.png,9,2,5,3,g'), {'a': 1})
except ValueError as e:
assert str(e).startswith(
"line 1: x2 (5) must be higher than x1 (9)")
raise
def test_read_annotations_wrong_format():
classes = {'a': 1, 'b': 2, 'c': 4, 'd': 10}
with pytest.raises(ValueError):
try:
csv_generator._read_annotations(csv_str('a.png,1,2,3,a'), classes)
except ValueError as e:
assert str(e).startswith("line 1: format should be")
raise
with pytest.raises(ValueError):
try:
csv_generator._read_annotations(csv_str(
'a.png,0,1,2,3,a' '\n'
'a.png,1,2,3,a' '\n'
), classes)
except ValueError as e:
assert str(e).startswith("line 2: format should be")
raise
def test_read_annotations_wrong_format():
classes = {'a': 1, 'b': 2, 'c': 4, 'd': 10}
with pytest.raises(ValueError):
try:
csv_generator._read_annotations(csv_str('a.png,1,2,3,a'), classes)
except ValueError as e:
assert str(e).startswith("line 1: format should be")
raise
with pytest.raises(ValueError):
try:
csv_generator._read_annotations(
csv_str('a.png,0,1,2,3,a'
'\n'
'a.png,1,2,3,a'
'\n'), classes)
except ValueError as e:
assert str(e).startswith("line 2: format should be")
raise
def test_read_annotations_multiple():
classes = {'a': 1, 'b': 2, 'c': 4, 'd': 10}
annotations = csv_generator._read_annotations(csv_str(
'a.png,0,1,2,3,a' '\n'
'b.png,4,5,6,7,b' '\n'
'a.png,8,9,10,11,c' '\n'
), classes)
assert annotations == {
'a.png': [
annotation(0, 1, 2, 3, 'a'),
annotation(8, 9, 10, 11, 'c'),
],
'b.png': [annotation(4, 5, 6, 7, 'b')],
}
def test_read_annotations():
classes = {'a': 1, 'b': 2, 'c': 4, 'd': 10}
annotations = csv_generator._read_annotations(csv_str(
'a.png,0,1,2,3,a' '\n'
'b.png,4,5,6,7,b' '\n'
'c.png,8,9,10,11,c' '\n'
'd.png,12,13,14,15,d' '\n'
), classes)
assert annotations == {
'a.png': [annotation( 0, 1, 2, 3, 'a')],
'b.png': [annotation( 4, 5, 6, 7, 'b')],
'c.png': [annotation( 8, 9, 10, 11, 'c')],
'd.png': [annotation(12, 13, 14, 15, 'd')],
}
def test_read_annotations_multiple():
classes = {'a': 1, 'b': 2, 'c': 4, 'd': 10}
annotations = csv_generator._read_annotations(
csv_str('a.png,0,1,2,3,a'
'\n'
'b.png,4,5,6,7,b'
'\n'
'a.png,8,9,10,11,c'
'\n'), classes)
assert annotations == {
'a.png': [
annotation(0, 1, 2, 3, 'a'),
annotation(8, 9, 10, 11, 'c'),
],
'b.png': [annotation(4, 5, 6, 7, 'b')],
}
def test_read_annotations():
classes = {'a': 1, 'b': 2, 'c': 4, 'd': 10}
annotations = csv_generator._read_annotations(
csv_str('a.png,0,1,2,3,a'
'\n'
'b.png,4,5,6,7,b'
'\n'
'c.png,8,9,10,11,c'
'\n'
'd.png,12,13,14,15,d'
'\n'), classes)
assert annotations == {
'a.png': [annotation(0, 1, 2, 3, 'a')],
'b.png': [annotation(4, 5, 6, 7, 'b')],
'c.png': [annotation(8, 9, 10, 11, 'c')],
'd.png': [annotation(12, 13, 14, 15, 'd')],
}
def main(args=None):
# parse arguments
if args is None:
args = sys.argv[1:]
args = parse_args(args)
# make sure keras is the minimum required version
check_keras_version()
# optionally choose specific GPU
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
keras.backend.tensorflow_backend.set_session(get_session())
# make save path if it doesn't exist
if args.save_path is not None and not os.path.exists(args.save_path):
os.makedirs(args.save_path)
# optionally load config parameters
if args.config:
args.config = read_config_file(args.config)
# create the generator
#generator = create_generator(args)
# optionally load anchor parameters
anchor_params = None
if args.config and 'anchor_parameters' in args.config:
anchor_params = parse_anchor_parameters(args.config)
# load the model
print('Loading model, this may take a second...')
model = models.load_model(args.model, backbone_name=args.backbone)
# optionally convert the model
if args.convert_model:
model = models.convert_model(model, anchor_params=anchor_params)
# print model summary
print(model.summary())
print("annotations", args.annotations)
print("classes", args.classes)
print("min", args.image_min_side)
print("max", args.image_max_side)
print("configs", args.config)
iou_threshold = args.iou_threshold
score_threshold = args.score_threshold
max_detections = args.max_detections
image_names = []
image_data = {}
# Take base_dir from annotations file if not explicitly specified.
# parse the provided class file
try:
with _open_for_csv(args.classes) as file:
classes = _read_classes(csv.reader(file, delimiter=','))
except ValueError as e:
raise_from(
ValueError('invalid CSV class file: {}: {}'.format(
args.classes, e)), None)
labels = {}
for key, value in classes.items():
labels[value] = key
# csv with img_path, x1, y1, x2, y2, class_name
try:
with _open_for_csv(args.annotations) as file:
file_annotations = _read_annotations(
csv.reader(file, delimiter=','), classes)
except ValueError as e:
raise_from(
ValueError('invalid CSV annotations file: {}: {}'.format(
args.annotations, e)), None)
image_names = list(file_annotations.keys())
num_classes = len(labels)
all_detections = [[None for i in range(num_classes) if i in labels]
for j in range(len(image_names))]
for image_index in range(len(image_names)):
""" Load annotations for an image_index.
"""
path = file_annotations[image_names[image_index]]
annotations = {'labels': np.empty((0, )), 'bboxes': np.empty((0, 4))}
for idx, annot in enumerate(
file_annotations[image_names[image_index]]):
for key, value in classes.items():
if annot['class'] == key:
break
annotations['labels'] = np.concatenate(
(annotations['labels'], [value]))
annotations['bboxes'] = np.concatenate((annotations['bboxes'], [[
float(annot['x1']),
float(annot['y1']),
float(annot['x2']),
float(annot['y2']),
]]))
f = []
for label in range(num_classes):
for cls in classes:
if classes[cls] == label:
print('class', cls)
break
f.append(
open(
"results/comp4_det_" +
args.annotations.split("/")[-1].split(".")[0] + "_" + cls +
".txt", 'w'))
for i in range(0, len(image_names)):
print('image num', i)
file_name = image_names[i]
# load image
image = read_image_bgr(file_name)
image = preprocess_image(image)
image, scale = resize_image(image)
boxes, scores, labels = model.predict_on_batch(
np.expand_dims(image, axis=0))
boxes /= scale
# select indices which have a score above the threshold
indices = np.where(scores[0, :] > score_threshold)[0]
# select those scores
scores = scores[0][indices]
# find the order with which to sort the scores
scores_sort = np.argsort(-scores)[:max_detections]
# select detections
image_boxes = boxes[0, indices[scores_sort], :]
image_scores = scores[scores_sort]
image_labels = labels[0, indices[scores_sort]]
image_detections = np.concatenate([
image_boxes,
np.expand_dims(image_scores, axis=1),
np.expand_dims(image_labels, axis=1)
],
axis=1)
# copy detections to all_detections
for label in range(num_classes):
if not label in labels:
continue
dets = image_detections[image_detections[:, -1] == label, :-1]
for cls in classes:
if classes[cls] == label:
print('class', cls)
break
for scr in dets:
f[label].write(
file_name.split("/")[-1].split(".")[0] + " " +
str(scr[4]) + " " + str(scr[0]) + " " + str(scr[1]) + " " +
str(scr[2]) + " " + str(scr[3]) + "\n")
for label in range(num_classes):
f[label].close()
def test_read_annotations_empty_image():
# Check that images without annotations are parsed.
assert csv_generator._read_annotations(csv_str('a.png,,,,,\nb.png,,,,,'), {'a': 1}) == {'a.png': [], 'b.png': []}
# Check that lines without annotations don't clear earlier annotations.
assert csv_generator._read_annotations(csv_str('a.png,0,1,2,3,a\na.png,,,,,'), {'a': 1}) == {'a.png': [annotation(0, 1, 2, 3, 'a')]}
