def test_read_classes_duplicate_name():
with pytest.raises(ValueError):
try:
csv_generator._read_classes(csv_str('a,1\nb,2\na,3'))
except ValueError as e:
assert str(e).startswith('line 3: duplicate class name')
raise
def test_read_classes_duplicate_name():
with pytest.raises(ValueError):
try:
csv_generator._read_classes(csv_str('a,1\nb,2\na,3'))
except ValueError as e:
assert str(e).startswith('line 3: duplicate class name')
raise
def test_read_classes_wrong_format():
with pytest.raises(ValueError):
try:
csv_generator._read_classes(csv_str('a,b,c'))
except ValueError as e:
assert str(e).startswith('line 1: format should be')
raise
with pytest.raises(ValueError):
try:
csv_generator._read_classes(csv_str('a,1\nb,c,d'))
except ValueError as e:
assert str(e).startswith('line 2: format should be')
raise
def test_read_classes_wrong_format():
with pytest.raises(ValueError):
try:
csv_generator._read_classes(csv_str('a,b,c'))
except ValueError as e:
assert str(e).startswith('line 1: format should be')
raise
with pytest.raises(ValueError):
try:
csv_generator._read_classes(csv_str('a,1\nb,c,d'))
except ValueError as e:
assert str(e).startswith('line 2: format should be')
raise
def test_read_classes_malformed_class_id():
with pytest.raises(ValueError):
try:
csv_generator._read_classes(csv_str('a,b'))
except ValueError as e:
assert str(e).startswith("line 1: malformed class ID:")
raise
with pytest.raises(ValueError):
try:
csv_generator._read_classes(csv_str('a,1\nb,c'))
except ValueError as e:
assert str(e).startswith('line 2: malformed class ID:')
raise
def test_read_classes_malformed_class_id():
with pytest.raises(ValueError):
try:
csv_generator._read_classes(csv_str('a,b'))
except ValueError as e:
assert str(e).startswith("line 1: malformed class ID:")
raise
with pytest.raises(ValueError):
try:
csv_generator._read_classes(csv_str('a,1\nb,c'))
except ValueError as e:
assert str(e).startswith('line 2: malformed class ID:')
raise
def read_classes(self):
"""Read class file in case of multi-class training. If no file has been created, DeepForest assume there is 1 class, Tree"""
# parse the provided class file
self.labels = {}
try:
with open(self.classes_file, 'r') as file:
self.classes = _read_classes(csv.reader(file, delimiter=','))
for key, value in self.classes.items():
self.labels[value] = key
except:
self.labels[0] = "Tree"
def read_class_csv(csv_class_file):
"""
function that reads in a class csv file that is used as an input to the retinanet training routine, and outputs
a list of class names which is the format required by Keras ImageGenerator
:param csv_class_file: the filename of the csv containing class names and indices
:return: list of classnames
"""
try:
with csv_generator._open_for_csv(csv_class_file) as file:
classes = csv_generator._read_classes(
csv.reader(file, delimiter=','))
class_list = list(classes.keys())
for c in classes:
class_idx = classes[c]
class_list[class_idx] = c
return class_list
except ValueError as e:
raise_from(
ValueError('invalid CSV class file: {}: {}'.format(
csv_class_file, e)), None)
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_classes():
assert csv_generator._read_classes(csv_str('')) == {}
assert csv_generator._read_classes(csv_str('a,1')) == {'a': 1}
assert csv_generator._read_classes(csv_str('a,1\nb,2')) == {'a': 1, 'b': 2}
def test_read_classes():
assert csv_generator._read_classes(csv_str('')) == {}
assert csv_generator._read_classes(csv_str('a,1')) == {'a': 1}
assert csv_generator._read_classes(csv_str('a,1\nb,2')) == {'a': 1, 'b': 2}
