def show_polygons_on_image(masks,
img,
alpha=0.4,
output_file=None):
"""show masks on image
Args:
masks (list): list of coordinate
img (np.array): original image
alpha (int): compress
output_file (str): save path
"""
color_list = list(wwtool.COLORS.keys())
img_h, img_w, _ = img.shape
foreground = wwtool.generate_image(img_h, img_w, (0, 0, 0))
for idx, mask in enumerate(masks):
mask = np.array(mask).reshape(1, -1, 2)
cv2.fillPoly(foreground, mask, (wwtool.COLORS[color_list[idx % 20]][2], wwtool.COLORS[color_list[idx % 20]][1], wwtool.COLORS[color_list[idx % 20]][0]))
heatmap = wwtool.show_grayscale_as_heatmap(foreground / 255.0, show=False, return_img=True)
beta = (1.0 - alpha)
fusion = cv2.addWeighted(heatmap, alpha, img, beta, 0.0)
if output_file is not None:
cv2.imwrite(output_file, fusion)
else:
wwtool.show_image(fusion, save_name=None)
return fusion
def __airbus_ship_parse__(self, label_file, image_file):
"""
(xmin, ymin, xmax, ymax)
"""
image_file_name = os.path.splitext(os.path.basename(image_file))[0]
save_image = wwtool.generate_image(800, 800, color=(0, 0, 0))
img = cv2.imread(image_file)
img_height, img_width, _ = img.shape
image_objects = airbus_ship_parse.airbus_ship_parse(os.path.basename(image_file))
objects = []
objects_small_save = []
total_object_num = len(image_objects)
small_object_num = 0
large_object_num = 0
total_object_num = 0
for image_object in image_objects:
object_struct = {}
object_struct_small = {}
xmin, ymin, xmax, ymax = image_object['bbox']
bbox_w = xmax - xmin
bbox_h = ymax - ymin
if bbox_w * bbox_h <= self.small_object_area:
continue
total_object_num += 1
if bbox_h * bbox_w <= small_size:
small_object_num += 1
if bbox_h * bbox_w >= large_object_size:
large_object_num += 1
object_struct['bbox'] = [xmin, ymin, bbox_w, bbox_h]
object_struct['segmentation'] = wwtool.bbox2pointobb([xmin, ymin, xmax, ymax])
object_struct['label'] = 1
object_struct_small = object_struct.copy()
object_struct_small['bbox'] = [xmin, ymin, xmax, ymax]
object_struct_small['label'] = 'ship'
objects_small_save.append(object_struct_small)
objects.append(object_struct)
if total_object_num > self.max_object_num_per_image:
self.max_object_num_per_image = total_object_num
if just_keep_small or generate_small_dataset:
if small_object_num >= total_object_num * small_object_rate and large_object_num < 1 and len(objects_small_save) > 0:
# print(os.path.join(dst_image_path, image_file_name + '.png'))
save_image[0:img_height, 0:img_width, :] = img
cv2.imwrite(os.path.join(dst_image_path, image_file_name + '.png'), save_image)
anno_file = os.path.join(dst_label_path, image_file_name + '.txt')
wwtool.simpletxt_dump(objects_small_save, anno_file)
return objects
else:
return []
else:
return objects
def generate_sub_mask_anno(self, mask_image, category=(1, 3)):
height, width, _ = mask_image.shape
sub_mask_anno = wwtool.generate_image(height, width, color=0)
gray_mask_image = mask_image[:, :, 0]
if isinstance(category, (list, tuple)):
keep_bool = np.logical_or(gray_mask_image == category[0], gray_mask_image == category[1])
else:
keep_bool = (gray_mask_image == category)
sub_mask_anno[keep_bool] = 1
return sub_mask_anno
def split_image(img,
subsize=1024,
gap=200,
mode='keep_all',
expand_boundary=True):
img_height, img_width = img.shape[0], img.shape[1]
start_xs = np.arange(0, img_width, subsize - gap)
if mode == 'keep_all':
start_xs[-1] = img_width - subsize if img_width - start_xs[
-1] <= subsize else start_xs[-1]
elif mode == 'drop_boundary':
if img_width - start_xs[-1] < subsize - gap:
start_xs = np.delete(start_xs, -1)
start_xs[-1] = np.maximum(start_xs[-1], 0)
start_ys = np.arange(0, img_height, subsize - gap)
if mode == 'keep_all':
start_ys[-1] = img_height - subsize if img_height - start_ys[
-1] <= subsize else start_ys[-1]
elif mode == 'drop_boundary':
if img_height - start_ys[-1] < subsize - gap:
start_ys = np.delete(start_ys, -1)
start_ys[-1] = np.maximum(start_ys[-1], 0)
subimages = dict()
for start_x in start_xs:
for start_y in start_ys:
end_x = np.minimum(start_x + subsize, img_width)
end_y = np.minimum(start_y + subsize, img_height)
if expand_boundary:
subimage = wwtool.generate_image(subsize,
subsize,
color=(0, 0, 0))
subimage[0:end_y - start_y, 0:end_x - start_x,
...] = img[start_y:end_y, start_x:end_x, ...]
else:
subimage = img[start_y:end_y, start_x:end_x, ...]
coordinate = (start_x, start_y)
subimages[coordinate] = subimage
return subimages
import numpy as np
import wwtool
import mmcv
if __name__ == '__main__':
thetaobbs = [[400, 400, 300, 150, 45 * np.pi / 180],
[600, 600, 300, 200, 135 * np.pi / 180]]
pointobbs = [wwtool.thetaobb2pointobb(thetaobb) for thetaobb in thetaobbs]
img = wwtool.generate_image(1024, 1024)
img_origin = img.copy()
wwtool.imshow_rbboxes(img, thetaobbs, win_name='origin')
rotation_angle = 45
rotation_anchor = [img.shape[0] // 2, img.shape[1] // 2]
rotated_img = mmcv.imrotate(img_origin, rotation_angle)
rotated_pointobbs = [
wwtool.rotate_pointobb(pointobb, rotation_angle * np.pi / 180,
rotation_anchor) for pointobb in pointobbs
]
rotated_thetaobbs = [
wwtool.pointobb2thetaobb(rotated_pointobb)
for rotated_pointobb in rotated_pointobbs
]
wwtool.imshow_rbboxes(rotated_img, rotated_thetaobbs, win_name='rotated')
def __sn6_parse__(self, label_file, image_file):
"""
(xmin, ymin, xmax, ymax)
"""
objects = []
if self.groundtruth:
image_file_name = os.path.splitext(os.path.basename(image_file))[0]
image_name_postfix = image_file_name + '.tif'
if imageset == 'val':
if image_name_postfix in valset_image_names:
pass
else:
return []
if imageset == 'train':
if image_name_postfix in valset_image_names:
return []
else:
pass
save_image = wwtool.generate_image(800, 800, color=(0, 0, 0))
img = cv2.imread(image_file)
img_height, img_width, _ = img.shape
image_name = os.path.basename(image_file).split(
'SN6_{}_AOI_11_Rotterdam_{}_'.format(
imageset_file_name, data_source))[1].split('.tif')[0]
masks = sn6_parse.sn6_parse(image_name)
objects_small_save = []
total_object_num = len(masks)
small_object_num = 0
large_object_num = 0
total_object_num = 0
for mask in masks:
object_struct = {}
object_struct_small = {}
xmin, ymin, xmax, ymax = wwtool.pointobb2bbox(
mask['segmentation'])
bbox_w = xmax - xmin
bbox_h = ymax - ymin
total_object_num += 1
if bbox_h * bbox_w <= small_size:
small_object_num += 1
if bbox_h * bbox_w >= large_object_size:
large_object_num += 1
object_struct['bbox'] = [xmin, ymin, bbox_w, bbox_h]
object_struct['segmentation'] = mask['segmentation']
object_struct['label'] = 1
object_struct_small = object_struct.copy()
object_struct_small['bbox'] = [xmin, ymin, xmax, ymax]
object_struct_small['label'] = 'building'
objects_small_save.append(object_struct_small)
objects.append(object_struct)
if total_object_num > self.max_object_num_per_image:
self.max_object_num_per_image = total_object_num
if just_keep_small or generate_small_dataset:
if small_object_num >= total_object_num * small_object_rate and large_object_num < 1 and len(
objects_small_save) > 0:
# print(os.path.join(dst_image_path, image_file_name + '.png'))
save_image[0:img_height, 0:img_width, :] = img
cv2.imwrite(
os.path.join(dst_image_path, image_file_name + '.png'),
save_image)
anno_file = os.path.join(dst_label_path,
image_file_name + '.txt')
wwtool.simpletxt_dump(objects_small_save, anno_file)
return objects
else:
return []
else:
return objects
else:
obj_struct = {}
obj_struct['segmentation'] = [0, 0, 0, 0, 0, 0, 0, 0]
obj_struct['bbox'] = [0, 0, 0, 0]
obj_struct['label'] = 0
objects.append(obj_struct)
return objects
image_file = '/data/buildchange/v0/shanghai/images/L18_106968_219320.jpg'
mask_file = '/data/buildchange/v0/shanghai/anno_v2/L18_106968_219320.png'
image_file_name = os.path.splitext(os.path.basename(image_file))[0]
rgb_img = cv2.imread(image_file)
mask_parser = wwtool.MaskParse()
objects = mask_parser(mask_file, category=(1, 3))
gt_masks = []
for obj in objects:
mask = obj['segmentation']
gt_masks.append([mask])
img = wwtool.generate_image(2048, 2048, (0, 0, 0))
COLORS = {'Blue': (0, 130, 200), 'Red': (230, 25, 75), 'Yellow': (255, 225, 25), 'Green': (60, 180, 75), 'Orange': (245, 130, 48), 'Purple': (145, 30, 180), 'Cyan': (70, 240, 240), 'Magenta': (240, 50, 230), 'Lavender': (230, 190, 255), 'Lime': (210, 245, 60), 'Teal': (0, 128, 128), 'Pink': (250, 190, 190), 'Brown': (170, 110, 40), 'Beige': (255, 250, 200), 'Maroon': (128, 0, 0), 'Mint': (170, 255, 195), 'Olive': (128, 128, 0), 'Apricot': (255, 215, 180), 'Navy': (0, 0, 128), 'Grey': (128, 128, 128), 'White': (255, 255, 255), 'Black': (0, 0, 0)}
color_list = list(COLORS.keys())
masks = wwtool.generate_image(2048, 2048)
for idx, gt_mask in enumerate(gt_masks):
mask = poly2mask(gt_mask, 2048, 2048) * 1
masks[:, :, 0] = mask * COLORS[color_list[idx % 20]][2]
masks[:, :, 1] = mask * COLORS[color_list[idx % 20]][1]
masks[:, :, 2] = mask * COLORS[color_list[idx % 20]][0]
img += masks
heatmap = wwtool.show_grayscale_as_heatmap(img / 255.0, show=False, return_img=True)
alpha = 0.4
image_file = os.path.join(image_dir, image_fn)
label_file = os.path.join(label_dir, image_fn.replace('png', 'txt'))
save_file = os.path.join(save_dir, image_fn)
img = cv2.imread(image_file)
with open(label_file, 'r') as f:
lines = f.readlines()
gt_masks = []
for line in lines:
object_struct = {}
line = line.rstrip().split(' ')
mask = [float(_) for _ in line[0:-1]]
gt_masks.append([mask])
img_mask = wwtool.generate_image(img_scale, img_scale, (0, 0, 0))
COLORS = {
'Blue': (0, 130, 200),
'Red': (230, 25, 75),
'Yellow': (255, 225, 25),
'Green': (60, 180, 75),
'Orange': (245, 130, 48),
'Purple': (145, 30, 180),
'Cyan': (70, 240, 240),
'Magenta': (240, 50, 230),
'Lavender': (230, 190, 255),
'Lime': (210, 245, 60),
'Teal': (0, 128, 128),
'Pink': (250, 190, 190),
'Brown': (170, 110, 40),