def __convert_file_annotation_rect(location):
file = open(location, 'r')
if file.mode == 'r':
lines = file.readlines()
rects = []
for l in lines:
tokens = l.split(' ')
rects.append(AnnotationRect(int(tokens[0]), int(tokens[1]), int(tokens[2]), int(tokens[3])))
return rects
def anchor_max_gt_overlaps(anchor_grid, gts):
output = np.zeros(anchor_grid.shape[:-1])
for y in range(anchor_grid.shape[0]):
for x in range(anchor_grid.shape[1]):
for s in range(anchor_grid.shape[2]):
for a in range(anchor_grid.shape[3]):
anchor_box = AnnotationRect(anchor_grid[y, x, s, a, 0],
anchor_grid[y, x, s, a, 1],
anchor_grid[y, x, s, a, 2],
anchor_grid[y, x, s, a, 3])
output[y, x, s,
a] = max([iou(anchor_box, gt) for gt in gts])
return output
def create_boxes_dict(data, anchor_grid, fg_threshold=0.05):
boxes_dict = {}
scores = []
calc_softmax = softmax(data, axis=-1)
foreground = np.delete(calc_softmax, [0], axis=-1)
# Get the scores from the data
shape = foreground.shape
for i in range(shape[0]):
for j in range(shape[1]):
for k in range(shape[2]):
for l in range(shape[3]):
for m in range(shape[4]):
if foreground[i, j, k, l, m] > fg_threshold:
scores.append(foreground[i, j, k, l, m])
# Get the boxes from the data
indices = np.where(foreground > fg_threshold)[:4]
max_boxes = anchor_grid[indices]
boxes = [AnnotationRect(*max_boxes[i]) for i in range(max_boxes.shape[0])]
for i in range(len(boxes)):
boxes_dict[boxes[i]] = scores[i]
return boxes_dict
for file in tqdm(os.listdir(src_folder)):
if file.endswith(".txt"):
src_txt = src_folder + '/' + file
src_img = src_folder + '/' + file.split('.', 1)[0] + '.jpg'
dest_txt = dest_folder + '/' + file
dest_img = dest_folder + '/' + file.split('.', 1)[0] + '.jpg'
dest_txt_remove = remove_folder + '/' + file
dest_img_remove = remove_folder + '/' + file.split('.', 1)[0] + '.jpg'
file = open(src_txt, 'r')
good_ratio = True
if file.mode == 'r':
lines = file.readlines()
for l in lines:
tokens = l.split(' ')
rect = AnnotationRect(int(tokens[0]), int(tokens[1]), int(tokens[2]), int(tokens[3]))
if rect.width() > rect.height():
good_ratio = False
if good_ratio:
shutil.copyfile(src_txt, dest_txt)
shutil.copyfile(src_img, dest_img)
num_kept += 1
else:
shutil.copyfile(src_txt, dest_txt_remove)
shutil.copyfile(src_img, dest_img_remove)
num_filtered += 1
print('kept: ' + str(num_kept))
print('removed: ' + str(num_filtered))
def convert_to_annotation_rects_label(anchor_grid, labels):
indices = np.where(labels == 1)[:4]
max_boxes = anchor_grid[indices]
annotated_boxes = [AnnotationRect(*max_boxes[i]) for i in range(len(max_boxes))]
return annotated_boxes
def convert_to_annotation_rects_output(anchor_grid, output, confidence=0.7):
calc_softmax = softmax(output, axis=-1)
foreground = np.delete(calc_softmax, [0], axis=-1)
indices = np.where(foreground > confidence)[:4]
max_boxes = anchor_grid[indices]
return [AnnotationRect(*max_boxes[i]) for i in range(len(max_boxes))]
for file in tqdm(os.listdir(src_folder)):
if file.endswith(".txt"):
src_txt = src_folder + '/' + file
src_img = src_folder + '/' + file.split('.', 1)[0] + '.jpg'
dest_txt = dest_folder + '/' + file
dest_img = dest_folder + '/' + file.split('.', 1)[0] + '.jpg'
dest_txt_remove = remove_folder + '/' + file
dest_img_remove = remove_folder + '/' + file.split('.', 1)[0] + '.jpg'
file = open(src_txt, 'r')
good_sizes = True
if file.mode == 'r':
lines = file.readlines()
for l in lines:
tokens = l.split(' ')
rect = AnnotationRect(int(tokens[0]), int(tokens[1]),
int(tokens[2]), int(tokens[3]))
if not min_size < rect.area():
good_sizes = False
if good_sizes:
shutil.copyfile(src_txt, dest_txt)
shutil.copyfile(src_img, dest_img)
num_kept += 1
else:
shutil.copyfile(src_txt, dest_txt_remove)
shutil.copyfile(src_img, dest_img_remove)
num_filtered += 1
print('kept: ' + str(num_kept))
print('removed: ' + str(num_filtered))