def GenerateData(ftxt, data_path, output, net, argument=False):
if net == "PNet":
size = 12
elif net == "RNet":
size = 24
elif net == "ONet":
size = 48
else:
print('Net type error')
return
image_id = 0
f = open(join(OUTPUT, "landmark_%s.txt" % (size)), 'w')
data = getDataFromTxt(ftxt, data_path)
idx = 0
#image_path bbox landmark(5*2)
for (imgPath, bbox, landmarkGt) in data:
#print imgPath
F_imgs = []
F_landmarks = []
img = cv2.imread(imgPath)
assert (img is not None)
img_h, img_w, img_c = img.shape
gt_box = np.array([bbox.left, bbox.top, bbox.right, bbox.bottom])
f_face = img[bbox.top:bbox.bottom + 1, bbox.left:bbox.right + 1]
f_face = cv2.resize(f_face, (size, size))
landmark = np.zeros((5, 2))
#normalize
for index, one in enumerate(landmarkGt):
rv = ((one[0] - gt_box[0]) / (gt_box[2] - gt_box[0]),
(one[1] - gt_box[1]) / (gt_box[3] - gt_box[1]))
landmark[index] = rv
F_imgs.append(f_face)
F_landmarks.append(landmark.reshape(10))
landmark = np.zeros((5, 2))
if argument:
idx = idx + 1
if idx % 100 == 0:
print(idx, "images done")
x1, y1, x2, y2 = gt_box
#gt's width
gt_w = x2 - x1 + 1
#gt's height
gt_h = y2 - y1 + 1
if max(gt_w, gt_h) < 40 or x1 < 0 or y1 < 0:
continue
#random shift
for i in range(10):
bbox_size = npr.randint(int(min(gt_w, gt_h) * 0.8),
np.ceil(1.25 * max(gt_w, gt_h)))
delta_x = npr.randint(-gt_w * 0.2, gt_w * 0.2)
delta_y = npr.randint(-gt_h * 0.2, gt_h * 0.2)
nx1 = int(max(x1 + gt_w / 2 - bbox_size / 2 + delta_x, 0))
ny1 = int(max(y1 + gt_h / 2 - bbox_size / 2 + delta_y, 0))
nx2 = nx1 + bbox_size
ny2 = ny1 + bbox_size
if nx2 > img_w or ny2 > img_h:
continue
crop_box = np.array([nx1, ny1, nx2, ny2])
cropped_im = img[ny1:ny2 + 1, nx1:nx2 + 1, :]
resized_im = cv2.resize(cropped_im, (size, size))
#cal iou
iou = IoU(crop_box, np.expand_dims(gt_box, 0))
if iou > 0.65:
F_imgs.append(resized_im)
#normalize
for index, one in enumerate(landmarkGt):
rv = ((one[0] - nx1) / bbox_size,
(one[1] - ny1) / bbox_size)
landmark[index] = rv
F_landmarks.append(landmark.reshape(10))
landmark = np.zeros((5, 2))
landmark_ = F_landmarks[-1].reshape(-1, 2)
bbox = BBox([nx1, ny1, nx2, ny2])
#mirror
if random.choice([0, 1]) > 0:
face_flipped, landmark_flipped = flip(
resized_im, landmark_)
face_flipped = cv2.resize(face_flipped, (size, size))
#c*h*w
F_imgs.append(face_flipped)
F_landmarks.append(landmark_flipped.reshape(10))
#rotate
if random.choice([0, 1]) > 0:
face_rotated_by_alpha, landmark_rotated = rotate(img, bbox, \
bbox.reprojectLandmark(landmark_), 5)#逆时针旋转
#landmark_offset
landmark_rotated = bbox.projectLandmark(
landmark_rotated)
face_rotated_by_alpha = cv2.resize(
face_rotated_by_alpha, (size, size))
F_imgs.append(face_rotated_by_alpha)
F_landmarks.append(landmark_rotated.reshape(10))
#flip
face_flipped, landmark_flipped = flip(
face_rotated_by_alpha, landmark_rotated)
face_flipped = cv2.resize(face_flipped, (size, size))
F_imgs.append(face_flipped)
F_landmarks.append(landmark_flipped.reshape(10))
#inverse clockwise rotation
if random.choice([0, 1]) > 0:
face_rotated_by_alpha, landmark_rotated = rotate(img, bbox, \
bbox.reprojectLandmark(landmark_), -5)#顺时针旋转
landmark_rotated = bbox.projectLandmark(
landmark_rotated)
face_rotated_by_alpha = cv2.resize(
face_rotated_by_alpha, (size, size))
F_imgs.append(face_rotated_by_alpha)
F_landmarks.append(landmark_rotated.reshape(10))
face_flipped, landmark_flipped = flip(
face_rotated_by_alpha, landmark_rotated)
face_flipped = cv2.resize(face_flipped, (size, size))
F_imgs.append(face_flipped)
F_landmarks.append(landmark_flipped.reshape(10))
F_imgs, F_landmarks = np.asarray(F_imgs), np.asarray(F_landmarks)
#print F_imgs.shape
#print F_landmarks.shape
for i in range(len(F_imgs)):
#print(image_id)
if np.sum(np.where(F_landmarks[i] <= 0, 1, 0)) > 0:
continue
if np.sum(np.where(F_landmarks[i] >= 1, 1, 0)) > 0:
continue
cv2.imwrite(join(dstdir, "%d.jpg" % (image_id)), F_imgs[i])
landmarks = map(str, list(F_landmarks[i]))
f.write(
join(dstdir, "%d.jpg" % (image_id)) + " -2 " +
" ".join(landmarks) + "\n")
image_id = image_id + 1
#print F_imgs.shape
#print F_landmarks.shape
#F_imgs = processImage(F_imgs)
#shuffle_in_unison_scary(F_imgs, F_landmarks)
f.close()
return F_imgs, F_landmarks
def GenerateData(ftxt,
data_path,
output_path,
img_output_path,
net,
argument=False):
'''
参数
------------
ftxt: path of anno file
data_path: 数据集所在目录
output_path: 文本文件输出目录地址
img_output_path: 图片输出地址
net: String 三个网络之一的名字
argument: 是否使用数据增强
返回值
-------------
images and related landmarks
'''
if net == "PNet":
size = 12
elif net == "RNet":
size = 24
elif net == "ONet":
size = 48
else:
print('Net type error')
return
image_id = 0
#
f = open(join(output_path, "landmark_%s_aug.txt" % (size)), 'w')
#img_output_path = "train_landmark_few"
# get image path , bounding box, and landmarks from file 'ftxt'
data = getDataFromTxt(ftxt, data_path=data_path)
idx = 0
#image_path bbox landmark(5*2)
for (imgPath, bbox, landmarkGt) in data:
#print imgPath
F_imgs = []
F_landmarks = []
#print(imgPath)
img = cv2.imread(imgPath)
assert (img is not None)
img_h, img_w, img_c = img.shape
gt_box = np.array([bbox.left, bbox.top, bbox.right, bbox.bottom])
#get sub-image from bbox
f_face = img[bbox.top:bbox.bottom + 1, bbox.left:bbox.right + 1]
# resize the gt image to specified size
f_face = cv2.resize(f_face, (size, size))
#initialize the landmark
landmark = np.zeros((5, 2))
#normalize land mark by dividing the width and height of the ground truth bounding box
# landmakrGt is a list of tuples
for index, one in enumerate(landmarkGt):
# 重新计算因裁剪过后而改变的landmark的坐标，并且进行归一化
# (x - bbox.left) / width of bbox, (y - bbox.top) / height of bbox
rv = ((one[0] - gt_box[0]) / (gt_box[2] - gt_box[0]),
(one[1] - gt_box[1]) / (gt_box[3] - gt_box[1]))
landmark[index] = rv
F_imgs.append(f_face)
F_landmarks.append(landmark.reshape(10)) #[x1, y1, x2, y2, ...]
landmark = np.zeros((5, 2))
# data augment
if argument:
idx = idx + 1
if idx % 100 == 0:
print(idx, "images done")
x1, y1, x2, y2 = gt_box
#gt's width
gt_w = x2 - x1 + 1
#gt's height
gt_h = y2 - y1 + 1
if max(gt_w, gt_h) < 40 or x1 < 0 or y1 < 0:
continue
#random shift
for i in range(10):
bbox_size = npr.randint(int(min(gt_w, gt_h) * 0.8),
np.ceil(1.25 * max(gt_w, gt_h)))
delta_x = npr.randint(-gt_w * 0.2, gt_w * 0.2)
delta_y = npr.randint(-gt_h * 0.2, gt_h * 0.2)
nx1 = int(max(x1 + gt_w / 2 - bbox_size / 2 + delta_x, 0))
ny1 = int(max(y1 + gt_h / 2 - bbox_size / 2 + delta_y, 0))
nx2 = nx1 + bbox_size
ny2 = ny1 + bbox_size
if nx2 > img_w or ny2 > img_h:
continue
crop_box = np.array([nx1, ny1, nx2, ny2])
cropped_im = img[ny1:ny2 + 1, nx1:nx2 + 1, :]
resized_im = cv2.resize(cropped_im, (size, size))
#calculate iou
iou = IoU(crop_box, np.expand_dims(gt_box, 0))
if iou > 0.65:
F_imgs.append(resized_im)
#normalize
for index, one in enumerate(landmarkGt):
rv = ((one[0] - nx1) / bbox_size,
(one[1] - ny1) / bbox_size)
landmark[index] = rv
F_landmarks.append(landmark.reshape(10))
landmark = np.zeros((5, 2))
landmark_ = F_landmarks[-1].reshape(-1, 2)
bbox = BBox([nx1, ny1, nx2, ny2])
#mirror
if random.choice([0, 1]) > 0:
face_flipped, landmark_flipped = flip(
resized_im, landmark_)
face_flipped = cv2.resize(face_flipped, (size, size))
#c*h*w
F_imgs.append(face_flipped)
F_landmarks.append(landmark_flipped.reshape(10))
#rotate
if random.choice([0, 1]) > 0:
face_rotated_by_alpha, landmark_rotated = rotate(img, bbox, \
bbox.reprojectLandmark(landmark_), 5)#逆时针旋转
#landmark_offset
landmark_rotated = bbox.projectLandmark(
landmark_rotated)
face_rotated_by_alpha = cv2.resize(
face_rotated_by_alpha, (size, size))
F_imgs.append(face_rotated_by_alpha)
F_landmarks.append(landmark_rotated.reshape(10))
#flip
face_flipped, landmark_flipped = flip(
face_rotated_by_alpha, landmark_rotated)
face_flipped = cv2.resize(face_flipped, (size, size))
F_imgs.append(face_flipped)
F_landmarks.append(landmark_flipped.reshape(10))
#anti-clockwise rotation
if random.choice([0, 1]) > 0:
face_rotated_by_alpha, landmark_rotated = rotate(img, bbox, \
bbox.reprojectLandmark(landmark_), -5)#顺时针旋转
landmark_rotated = bbox.projectLandmark(
landmark_rotated)
face_rotated_by_alpha = cv2.resize(
face_rotated_by_alpha, (size, size))
F_imgs.append(face_rotated_by_alpha)
F_landmarks.append(landmark_rotated.reshape(10))
face_flipped, landmark_flipped = flip(
face_rotated_by_alpha, landmark_rotated)
face_flipped = cv2.resize(face_flipped, (size, size))
F_imgs.append(face_flipped)
F_landmarks.append(landmark_flipped.reshape(10))
F_imgs, F_landmarks = np.asarray(F_imgs), np.asarray(F_landmarks)
#print F_imgs.shape
#print F_landmarks.shape
for i in range(len(F_imgs)):
# 只要有一个坐标小于0或大于1就舍弃
if np.sum(np.where(F_landmarks[i] <= 0, 1, 0)) > 0:
continue
if np.sum(np.where(F_landmarks[i] >= 1, 1, 0)) > 0:
continue
cv2.imwrite(join(img_output_path, "%d.jpg" % (image_id)),
F_imgs[i])
landmarks = map(str, list(F_landmarks[i]))
f.write(
join(dstdir, "%d.jpg" % (image_id)) + " -2 " +
" ".join(landmarks) + "\n")
image_id = image_id + 1
#print F_imgs.shape
#print F_landmarks.shape
#F_imgs = processImage(F_imgs)
#shuffle_in_unison_scary(F_imgs, F_landmarks)
f.close()
return F_imgs, F_landmarks
def GenerateData(ftxt, data_path, net, argument=False):
'''
:param ftxt: name/path of the text file that contains image path,
bounding box, and landmarks
:param output: path of the output dir
:param net: one of the net in the cascaded networks
:param argument: apply augmentation or not
:return: images and related landmarks
'''
if net == "PNet":
size = 12
elif net == "RNet":
size = 24
elif net == "ONet":
size = 48
else:
print('Net type error')
return
image_id = 0
#
f = open(join(OUTPUT, "landmark_%s_aug.txt" % (size)), 'w')
#dstdir = "train_landmark_few"
# get image path , bounding box, and landmarks from file 'ftxt'
data = getDataFromTxt(ftxt, data_path=data_path) # 图片路径，框-4，标注-（5，2）
idx = 0
#image_path bbox landmark(5*2)
for (imgPath, bbox, landmarkGt) in data:
#print imgPath
F_imgs = []
F_landmarks = []
#print(imgPath)
img = cv2.imread(imgPath)
assert (img is not None)
img_h, img_w, img_c = img.shape
gt_box = np.array([bbox.left, bbox.top, bbox.right, bbox.bottom])
#get sub-image from bbox 得到框出来的图
f_face = img[bbox.top:bbox.bottom + 1, bbox.left:bbox.right + 1]
# resize the gt image to specified size 将大小调整指定的尺寸
f_face = cv2.resize(f_face, (size, size))
#initialize the landmark
landmark = np.zeros((5, 2))
#normalize land mark by dividing the width and height of the ground truth bounding box
# landmakrGt is a list of tuples 对标注进行归一化（除以框）
for index, one in enumerate(landmarkGt):
# (( x - bbox.left)/ width of bounding box, (y - bbox.top)/ height of bounding box
rv = ((one[0] - gt_box[0]) / (gt_box[2] - gt_box[0]),
(one[1] - gt_box[1]) / (gt_box[3] - gt_box[1]))
# put the normalized value into the new list landmark
landmark[index] = rv
F_imgs.append(f_face)
F_landmarks.append(landmark.reshape(10))
landmark = np.zeros((5, 2))
if argument: # 数据集扩展
idx = idx + 1
if idx % 100 == 0:
print(idx, "images done")
x1, y1, x2, y2 = gt_box
#gt's width
gt_w = x2 - x1 + 1
#gt's height
gt_h = y2 - y1 + 1
if max(gt_w, gt_h) < 40 or x1 < 0 or y1 < 0: # 框的大小限制
continue
#random shift
for i in range(10):
bbox_size = npr.randint(int(min(gt_w, gt_h) * 0.8),
np.ceil(1.25 *
max(gt_w, gt_h))) # 框的大小
delta_x = npr.randint(-gt_w * 0.2, gt_w * 0.2)
delta_y = npr.randint(-gt_h * 0.2, gt_h * 0.2)
nx1 = int(max(x1 + gt_w / 2 - bbox_size / 2 + delta_x, 0))
ny1 = int(max(y1 + gt_h / 2 - bbox_size / 2 + delta_y, 0))
nx2 = nx1 + bbox_size
ny2 = ny1 + bbox_size
if nx2 > img_w or ny2 > img_h:
continue
crop_box = np.array([nx1, ny1, nx2, ny2])
cropped_im = img[ny1:ny2 + 1, nx1:nx2 + 1, :]
resized_im = cv2.resize(cropped_im, (size, size))
#cal iou
iou = IoU(crop_box, np.expand_dims(gt_box, 0))
if iou > 0.65:
F_imgs.append(resized_im)
#normalize
for index, one in enumerate(landmarkGt):
rv = ((one[0] - nx1) / bbox_size,
(one[1] - ny1) / bbox_size)
landmark[index] = rv
F_landmarks.append(landmark.reshape(10))
landmark = np.zeros((5, 2))
landmark_ = F_landmarks[-1].reshape(-1, 2)
bbox = BBox([nx1, ny1, nx2, ny2])
#mirror
if random.choice([0, 1]) > 0:
face_flipped, landmark_flipped = flip(
resized_im, landmark_)
face_flipped = cv2.resize(face_flipped, (size, size))
#c*h*w
F_imgs.append(face_flipped)
F_landmarks.append(landmark_flipped.reshape(10))
#rotate
if random.choice([0, 1]) > 0:
face_rotated_by_alpha, landmark_rotated = rotate(img, bbox, \
bbox.reprojectLandmark(landmark_), 5)#逆时针旋转
#landmark_offset
landmark_rotated = bbox.projectLandmark(
landmark_rotated)
face_rotated_by_alpha = cv2.resize(
face_rotated_by_alpha, (size, size))
F_imgs.append(face_rotated_by_alpha)
F_landmarks.append(landmark_rotated.reshape(10))
#flip
face_flipped, landmark_flipped = flip(
face_rotated_by_alpha, landmark_rotated)
face_flipped = cv2.resize(face_flipped, (size, size))
F_imgs.append(face_flipped)
F_landmarks.append(landmark_flipped.reshape(10))
#anti-clockwise rotation
if random.choice([0, 1]) > 0:
face_rotated_by_alpha, landmark_rotated = rotate(img, bbox, \
bbox.reprojectLandmark(landmark_), -5)#顺时针旋转
landmark_rotated = bbox.projectLandmark(
landmark_rotated)
face_rotated_by_alpha = cv2.resize(
face_rotated_by_alpha, (size, size))
F_imgs.append(face_rotated_by_alpha)
F_landmarks.append(landmark_rotated.reshape(10))
face_flipped, landmark_flipped = flip(
face_rotated_by_alpha, landmark_rotated)
face_flipped = cv2.resize(face_flipped, (size, size))
F_imgs.append(face_flipped)
F_landmarks.append(landmark_flipped.reshape(10))
F_imgs, F_landmarks = np.asarray(F_imgs), np.asarray(F_landmarks)
#print F_imgs.shape
#print F_landmarks.shape
for i in range(len(F_imgs)):
#if image_id % 100 == 0:
#print('image id : ', image_id)
if np.sum(np.where(F_landmarks[i] <= 0, 1, 0)) > 0:
continue
if np.sum(np.where(F_landmarks[i] >= 1, 1, 0)) > 0:
continue
cv2.imwrite(join(dstdir, "%d.jpg" % (image_id)), F_imgs[i])
landmarks = map(str, list(F_landmarks[i]))
f.write(
join(dstdir, "%d.jpg" % (image_id)) + " -2 " +
" ".join(landmarks) + "\n")
image_id = image_id + 1
#print F_imgs.shape
#print F_landmarks.shape
#F_imgs = processImage(F_imgs)
#shuffle_in_unison_scary(F_imgs, F_landmarks)
f.close()
return F_imgs, F_landmarks
def generateData_aug(data_dir, net, argument=False):
if net == "PNet":
size = 12
elif net == "RNet":
size = 24
elif net == "ONet":
size = 48
else:
print("Net type error! ")
return
OUTPUT = data_dir + "/%d" % size
if not exists(OUTPUT): os.mkdir(OUTPUT)
dstdir = data_dir + "/%d/train_%s_landmark_aug" % (size, net)
if not exists(dstdir): os.mkdir(dstdir)
assert (exists(dstdir) and exists(OUTPUT))
# get image path , bounding box, and landmarks from file 'ftxt'
data = getDataFromTxt("./prepare_data/trainImageList.txt",
data_path=data_dir + '/Align')
f = open(join(OUTPUT, "landmark_%s_aug.txt" % (size)), 'w')
image_id = 0
idx = 0
for (imgPath, bbox, landmarkGt) in data:
F_imgs = []
F_landmarks = []
img = cv2.imread(imgPath)
assert (img is not None)
img_h, img_w, img_c = img.shape
gt_box = np.array([bbox.left, bbox.top, bbox.right, bbox.bottom])
# get sub-image from bbox
f_face = img[bbox.top:bbox.bottom + 1, bbox.left:bbox.right + 1]
# resize the gt image to specified size
f_face = cv2.resize(f_face, (size, size))
# initialize the landmark
landmark = np.zeros((5, 2))
# normalize land mark by dividing the width and height of the ground truth bounding box
# landmakrGt is a list of tuples
for index, one in enumerate(landmarkGt):
# (( x - bbox.left)/ width of bounding box, (y - bbox.top)/ height of bounding box
rv = ((one[0] - gt_box[0]) / (gt_box[2] - gt_box[0]),
(one[1] - gt_box[1]) / (gt_box[3] - gt_box[1]))
landmark[index] = rv
F_imgs.append(f_face)
F_landmarks.append(landmark.reshape(10))
landmark = np.zeros((5, 2))
if argument:
idx = idx + 1
if idx % 100 == 0:
print(idx, "images done")
x1, y1, x2, y2 = gt_box
gt_w = x2 - x1 + 1
gt_h = y2 - y1 + 1
if max(gt_w, gt_h) < 40 or x1 < 0 or y1 < 0:
continue
#random shift
for i in range(10):
bbox_size = npr.randint(int(min(gt_w, gt_h) * 0.8),
np.ceil(1.25 * max(gt_w, gt_h)))
delta_x = npr.randint(-gt_w * 0.2, gt_w * 0.2)
delta_y = npr.randint(-gt_h * 0.2, gt_h * 0.2)
nx1 = int(max(x1 + gt_w / 2 - bbox_size / 2 + delta_x, 0))
ny1 = int(max(y1 + gt_h / 2 - bbox_size / 2 + delta_y, 0))
nx2 = nx1 + bbox_size
ny2 = ny1 + bbox_size
if nx2 > img_w or ny2 > img_h:
continue
crop_box = np.array([nx1, ny1, nx2, ny2])
cropped_im = img[ny1:ny2 + 1, nx1:nx2 + 1, :]
resized_im = cv2.resize(cropped_im, (size, size))
#cal iou
iou = IoU(crop_box, np.expand_dims(gt_box, 0))
if iou > 0.65:
F_imgs.append(resized_im)
#normalize
for index, one in enumerate(landmarkGt):
rv = ((one[0] - nx1) / bbox_size,
(one[1] - ny1) / bbox_size)
landmark[index] = rv
F_landmarks.append(landmark.reshape(10))
landmark = np.zeros((5, 2))
landmark_ = F_landmarks[-1].reshape(-1, 2)
bbox = BBox([nx1, ny1, nx2, ny2])
#mirror
if random.choice([0, 1]) > 0:
face_flipped, landmark_flipped = flip(
resized_im, landmark_)
face_flipped = cv2.resize(face_flipped, (size, size))
#c*h*w
F_imgs.append(face_flipped)
F_landmarks.append(landmark_flipped.reshape(10))
#rotate
if random.choice([0, 1]) > 0:
face_rotated_by_alpha, landmark_rotated = rotate(img, bbox, \
bbox.reprojectLandmark(landmark_), 5)#逆时针旋转
#landmark_offset
landmark_rotated = bbox.projectLandmark(
landmark_rotated)
face_rotated_by_alpha = cv2.resize(
face_rotated_by_alpha, (size, size))
F_imgs.append(face_rotated_by_alpha)
F_landmarks.append(landmark_rotated.reshape(10))
#flip
face_flipped, landmark_flipped = flip(
face_rotated_by_alpha, landmark_rotated)
face_flipped = cv2.resize(face_flipped, (size, size))
F_imgs.append(face_flipped)
F_landmarks.append(landmark_flipped.reshape(10))
#anti-clockwise rotation
if random.choice([0, 1]) > 0:
face_rotated_by_alpha, landmark_rotated = rotate(img, bbox, \
bbox.reprojectLandmark(landmark_), -5)#顺时针旋转
landmark_rotated = bbox.projectLandmark(
landmark_rotated)
face_rotated_by_alpha = cv2.resize(
face_rotated_by_alpha, (size, size))
F_imgs.append(face_rotated_by_alpha)
F_landmarks.append(landmark_rotated.reshape(10))
face_flipped, landmark_flipped = flip(
face_rotated_by_alpha, landmark_rotated)
face_flipped = cv2.resize(face_flipped, (size, size))
F_imgs.append(face_flipped)
F_landmarks.append(landmark_flipped.reshape(10))
F_imgs, F_landmarks = np.asarray(F_imgs), np.asarray(F_landmarks)
for i in range(len(F_imgs)):
if np.sum(np.where(F_landmarks[i] <= 0, 1, 0)) > 0:
continue
if np.sum(np.where(F_landmarks[i] >= 1, 1, 0)) > 0:
continue
cv2.imwrite(join(dstdir, "%d.jpg" % (image_id)), F_imgs[i])
landmarks = map(str, list(F_landmarks[i]))
f.write(dstdir + "/%d.jpg" % (image_id) + " -2 " +
" ".join(landmarks) + "\n")
image_id = image_id + 1
f.close()
return F_imgs, F_landmarks