def main(directory: str, delay: int, max_cnt: int, thickness: int,
labels_path: str, show_box_centres: bool):
xml_paths = glob.glob(os.path.join(directory, "*.xml")) # Search for xml files
max_cnt = max_cnt if max_cnt >= 0 else len(xml_paths)
cnt = 0
# Read labels from file
show_labels = labels_path is not None
if show_labels:
with open(labels_path, 'r') as token:
labels = token.read().split('\n')
pass
else:
labels = None
for xml_path in xml_paths:
if cnt >= max_cnt:
break
xml_obj = XMLImage(xml_path=xml_path)
img_path = xml_obj.image_path()
image = cv2.imread(img_path)
draw_bounding_boxes(image, xml_obj.boxes_classes, labels=labels, thickness=thickness, delay=delay,
dot_center=show_box_centres)
cnt += 1
pass # for xml_path
pass # def main
def main(directory: str, shape2d: Tuple[int, int]):
# Counters
print(type(shape2d), shape2d)
shape2d = (int(shape2d[0]), int(shape2d[1]))
print(type(shape2d), shape2d)
cnt = 0
cnt_no_resize_needed = 0
paths = glob.glob(f"{directory}/*.xml")
print(f"Found {len(paths)} xml files in {directory}")
for xml_path in paths:
cnt += 1
xml_obj = XMLImage(xml_path=xml_path)
image_path = xml_obj.image_path()
image = cv2.imread(image_path)
if xml_obj.height == shape2d[1] and xml_obj.width == shape2d[0]:
cnt_no_resize_needed += 1
continue
image, xml_obj = resize(image, shape2d, xml_obj)
cv2.imwrite(image_path, image)
xml_obj.save()
pass
# Show summarry
print(
f"Processed {cnt} images. {cnt_no_resize_needed} image were already in good size. Output size was {shape2d}."
)
pass
def main(read_directory: str, save_directory: str, sub_size, prepare_size):
xml_paths = glob.glob(f"{read_directory}/*.xml")
Trace(f"read_directory is '{read_directory}' got {len(xml_paths)} to '{save_directory}'\nsub_size is {sub_size}"
f"\tprepare_size is {prepare_size}", Trace.TRACE_INFO, __file__, main.__name__)
for xml_path in xml_paths:
# Create xml object
xml_obj = XMLImage(xml_path=xml_path)
# Read image path
img_path = xml_obj.image_path()
# Read image
image = cv2.imread(img_path)
# Run workflow
list_image, list_xml_obj = workflow(image, sub_size, prepare_size, xml_obj)
for i in range(len(list_image)):
# Save sub data
sub_image = list_image[i]
sub_xml_obj = list_xml_obj[i]
img_file_name = create_sub_name(img_path, i, save_directory)
sub_xml_obj.image_filename = img_file_name.split(settings.SLASH)[-1]
cv2.imwrite(img_file_name, sub_image)
sub_xml_obj.save(create_sub_name(xml_path, i, save_directory))
pass # for i
pass # for xml_path
pass # main
def correct_labels(directory: str, shape: Tuple[int, int], old_label: str,
new_label: str, del_label: str, change_type: int):
if change_type == LOWER_CASE:
change_function = str.lower
elif change_type == UPPER_CASE:
change_function = str.upper
elif change_type == TITLE:
change_function = str.title
else:
change_function = str
switch_labels = bool(old_label) and bool(new_label)
delete_label_flag = bool(del_label)
any_change = switch_labels or (change_function
is not str) or delete_label_flag
if not any_change:
Trace(f"\nNo change needed.\n", Trace.TRACE_INFO, __file__,
correct_labels.__name__)
return
old_label = old_label.lower()
del_label = del_label.lower()
xml_paths = glob.glob(os.path.join(directory, '*.xml'))
for xml_path in xml_paths:
# Read
xml_obj = XMLImage(xml_path=xml_path)
# Edit
index = -1
to_delete = []
for box in xml_obj.boxes_classes:
index += 1
# Delete label
if delete_label_flag and box.label.lower() == del_label:
to_delete.append(index)
# xml_obj.boxes_classes.pop(index)
continue
# Switch label
if switch_labels and box.label.lower() == old_label:
box.label = new_label
# Correct label
box.label = change_function(box.label).strip()
pass # for box
for i in range(len(to_delete) - 1, -1, -1):
xml_obj.boxes_classes.pop(to_delete[i])
# Save
xml_obj.save()
pass # for xml_path
pass
def black_bars(image: settings.CV2_IMAGE, shape2d,
xml_obj: XMLImage) -> Tuple[settings.CV2_IMAGE, XMLImage]:
h_img, w_img = image.shape
h_out, w_out = shape2d
if h_img > h_out or w_img > w_out:
Trace(
f"Given image ({image.shape}) is bigger than give output ({shape2d})",
Trace.TRACE_WARNING, __file__, black_bars.__name__)
image = image[:h_img, :w_img]
pass
new_image = np.zeros(shape2d).astype('uint8')
new_image[:h_img, :w_img] = image
return new_image, xml_obj.black_bars(shape2d)
def check_img_sizes_from_xml(directory):
# Read all xml files in directory
paths = glob.glob(f"{directory}/*.xml")
# Show info
print(f"Found {len(paths)} xml files in {directory}")
# Prepare dict
img_shapes = dict()
box_areas = dict()
labels = dict()
# Read all sizes from xml files:
for xml_path in paths:
xml_obj = XMLImage(xml_path=xml_path)
img_size = (xml_obj.width, xml_obj.height, xml_obj.depth)
# Store size
add_key_counter(img_size, img_shapes)
for box in xml_obj.boxes_classes:
# Box label
label = box.label
add_key_counter(label, labels)
# Box area
b_area = box.area
add_key_counter(b_area, box_areas)
pass
# Show counted sizes
print(f'Image size:')
for key, value in img_shapes.items():
print(
f"\t{'x'.join([str(elem) for elem in key]).ljust(10)} -> {value} time(s)"
)
pass
print(f'\nLabels:')
# Show counted labels
for key, value in labels.items():
_key = ("'" + key + "'").ljust(10)
print(f"\t{_key} -> {value} time(s)")
pass
# print(f'\nBounding boxes:')
# for key, value in box_areas.items():
# print(f"\t{str(key).ljust(6)} -> {value} time(s)")
# pass
pass
def xml_to_csv2(path: str):
xml_list = []
xml_paths = glob.glob(path + '/*.xml')
print(f"Got {len(xml_paths)} in directory {path}")
for xml_path in xml_paths:
xml_obj = XMLImage(xml_path=xml_path)
filename = xml_obj.image_filename
width = xml_obj.width
height = xml_obj.height
for box in xml_obj.boxes_classes:
value = [
filename, width, height, box.label, box.x_min, box.y_min,
box.x_max, box.y_max
]
xml_list.append(value)
pass # for box
pass # for xml_path
column_name = [
'filename', 'width', 'height', 'class', 'xmin', 'ymin', 'xmax', 'ymax'
]
xml_df = pd.DataFrame(xml_list, columns=column_name)
return xml_df
def resize(image: settings.CV2_IMAGE, shape2d,
xml_obj: XMLImage) -> Tuple[settings.CV2_IMAGE, XMLImage]:
return cv2.resize(image, shape2d[:2]), xml_obj.resize(shape2d)