def clip_with_centroids(image_path, save_dir, centroids_dict, shape):
""" 裁剪得到ROI
以给定点为中心裁剪固定大小(取决于shape)的区域得到ROI, 并保存为.tif文件
Args:
image_path: 原始影像路径
str
save_dir: ROI影像保存目录
str
centroids_dict: 中心点字典
dict{'polygon_name': array([lon, lat]), ...}
shape: 裁剪ROI大小
tuple(height, width)
"""
gdal.AllRegister()
data_set = gdal.Open(image_path)
if data_set is None:
raise FileNotFoundError('File %s not found' % image_path)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
img_tran = data_set.GetGeoTransform()
img_proj = data_set.GetProjection()
img_data = data_set.ReadAsArray()
raster_num, img_rows, img_cols = img_data.shape
for plg_name, centroid in centroids_dict.items():
geo_x, geo_y = transform.lonlat2geo(data_set, centroid[0], centroid[1])
centroid_grid = transform.geo2imagexy(data_set, geo_x, geo_y)
sys.stdout.write(
'\rProcessing centroid (%f, %f) --> (%d, %d) ...' %
(centroid[0], centroid[1], centroid_grid[0], centroid_grid[1]))
sys.stdout.flush()
row_min = int(centroid_grid[0] - int(shape[0] / 2))
if row_min < 0:
row_min = 0
col_min = int(centroid_grid[1] - int(shape[1] / 2))
if col_min < 0:
col_min = 0
row_max = int(centroid_grid[0] + shape[0] - int(shape[0] / 2))
col_max = int(centroid_grid[1] + shape[1] - int(shape[1] / 2))
roi_arr = img_data[:, row_min:row_max, col_min:col_max].copy()
roi_tran = list(img_tran)
roi_tran[0], roi_tran[3] = transform.imagexy2geo(
data_set, row_min, col_min)
save_name = os.path.join(save_dir, '%s.tif' % plg_name)
save_tiff(save_name, roi_arr, roi_arr.shape[1], roi_arr.shape[2],
raster_num, roi_tran, img_proj)
del data_set
print('\nMission completed!')
def clip(image_path, save_path, x_size, y_size, offset_x=0, offset_y=0):
"""
裁剪影像至指定大小
Args:
image_path: 原始影像路径
str
save_path: 结果保存路径
str
x_size: 行大小
int
y_size: 列大小
int
offset_x: 行偏移量，默认为0
int
offset_y: 列偏移量，默认为0
int
"""
gdal.AllRegister()
print('Reading raw image ...')
data_set = gdal.Open(image_path)
if data_set is None:
raise FileNotFoundError('File %s Not found' % image_path)
img_tran = data_set.GetGeoTransform()
img_proj = data_set.GetProjection()
img_data = data_set.ReadAsArray()
raster_num, img_rows, img_cols = img_data.shape
if offset_x + x_size > img_rows:
end_x = img_rows
else:
end_x = offset_x + x_size
if offset_y + y_size > img_cols:
end_y = img_cols
else:
end_y = offset_y + y_size
res_data = img_data[:, offset_x:end_x, offset_y:end_y]
save_tiff(save_path,
res_data,
end_x - offset_x,
end_y - offset_y,
raster_num,
geotran_info=img_tran,
proj_info=img_proj)
def clip_with_sliding_window(image_path, save_dir, shape, step, save_img=True):
""" 以滑动窗口截取ROI
以固定大小的窗口、以固定步长在影像中滑动，截取ROI
Args:
image_path: 原始影像路径
str
save_dir: ROI保存目录
str
shape: 窗口大小
tuple(height, width)
step: 步长
int
save_img: 保存图像
bool
"""
gdal.AllRegister()
data_set = gdal.Open(image_path)
if data_set is None:
raise FileNotFoundError('File %s not found' % image_path)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
img_tran = data_set.GetGeoTransform()
img_proj = data_set.GetProjection()
img_data = data_set.ReadAsArray()
rect_dict = {}
raster_num, img_rows, img_cols = img_data.shape
box_rows = int((img_rows - shape[0]) / step + 1)
box_cols = int((img_cols - shape[1]) / step + 1)
total_num = box_rows * box_cols
roi_idx = 1
for i in range(0, img_rows, step):
for j in range(0, img_cols, step):
roi_arr = img_data[:, i:i + shape[0], j:j + shape[1]].copy()
if roi_arr.shape[1:3] != shape:
continue
sys.stdout.write('\rProcessing %d/%d ...' % (roi_idx, total_num))
sys.stdout.flush()
geo_x_nw, geo_y_nw = transform.imagexy2geo(data_set, i, j)
lon_nw, lat_nw = transform.geo2lonlat(data_set, geo_x_nw, geo_y_nw)
geo_x_se, geo_y_se = transform.imagexy2geo(data_set, i + shape[0],
j + shape[1])
lon_se, lat_se = transform.geo2lonlat(data_set, geo_x_se, geo_y_se)
rect_str = '%0.6f,%0.6f,%0.6f,%0.6f' % (lon_nw, lat_nw, lon_se,
lat_se)
rect_dict['%05d' % roi_idx] = rect_str
roi_tran = list(img_tran)
roi_tran[0], roi_tran[3] = geo_x_nw, geo_y_nw
if save_img:
save_name = os.path.join(
save_dir, '%03d%03d_%06d.tif' %
(roi_idx / box_cols, roi_idx % box_cols, roi_idx))
save_tiff(save_name, roi_arr, shape[0], shape[1], raster_num,
roi_tran, img_proj)
roi_idx += 1
del data_set
rect_save_name = os.path.join(save_dir,
'rect_dict_%d_%d.json' % (shape[0], step))
with open(rect_save_name, 'w') as d_f:
json.dump(rect_dict, d_f)
def mask_roi(image_path, mask_path, save_dir):
""" 从tif格式的掩膜文件中提取roi
可实现从包含多个ROI的.tif文件中自动提取所有ROI的所有区域，
并将每块影像保存至.tif文件中, ROI多边形dict保存至.json文件中
保存每个块的面积保存至area.json中
Args:
image_path: 带有地理信息的原始影像文件路径
str
mask_path: 与影像地理信息相同的tif格式的掩膜文件路径
str
save_dir: 保存文件目录
str
"""
gdal.AllRegister()
print('Reading raw image ...')
img_data_set = gdal.Open(image_path)
print('Reading mask image ...')
mask_data_set = gdal.Open(mask_path)
if img_data_set is None:
raise FileNotFoundError('File %s not found' % image_path)
if mask_data_set is None:
raise FileNotFoundError('File %s not found!' % mask_path)
img_tran = img_data_set.GetGeoTransform()
img_proj = img_data_set.GetProjection()
mask_proj = mask_data_set.GetProjection()
if img_proj != mask_proj:
raise ValueError('Wrong mask file!')
if not os.path.exists(save_dir):
os.makedirs(save_dir)
img_data = img_data_set.ReadAsArray()
mask_data = mask_data_set.ReadAsArray()
dict_save_name = os.path.join(save_dir, 'all_polygons.json')
plg_str_list = []
region_dict = {}
area_dict = {}
raster_num, img_rows, img_cols = img_data.shape
rois_num = mask_data.max()
for i in range(1, rois_num + 1):
sys.stdout.write('\rProcessing %dth polygon ...' % i)
sys.stdout.flush()
mask_idxs = np.argwhere(mask_data == i)
if mask_idxs.shape[0] == 0:
continue
rect = get_extent(mask_idxs)
plg_row_min, plg_row_max, plg_col_min, plg_col_max = limit_boundary(
rect, (0, img_rows, 0, img_cols))
mask_rows = plg_row_max - plg_row_min + 1
mask_cols = plg_col_max - plg_col_min + 1
mask = mask_data[plg_row_min:plg_row_max + 1,
plg_col_min:plg_col_max + 1].copy()
mask_flag = mask.copy()
mask[mask_flag == i] = 1
mask[mask_flag != i] = 0
del mask_flag
area = mask.sum()
area_dict['%04d' % i] = int(area)
# 提取并保存凸包
plg = array2vector(mask)
plg_str = ''
for point in plg:
p_geo_x, p_geo_y = transform.imagexy2geo(mask_data_set,
plg_row_min + point[0],
plg_col_min + point[1])
p_lon, p_lat = transform.geo2lonlat(mask_data_set, p_geo_x,
p_geo_y)
plg_str += '%f,%f|' % (p_lon, p_lat)
print(plg_str)
print()
plg_str_list.append(plg_str[:-1])
region_dict['all_polygons'] = plg_str_list
with open(dict_save_name, 'w') as d_f:
js = json.dumps(region_dict)
d_f.write(js)
# 提取并保存影像
masked = img_data[:, plg_row_min:(plg_row_min + mask_rows),
plg_col_min:(plg_col_min + mask_cols)].copy()
if masked.shape[1:3] != mask.shape:
continue
for j in range(0, raster_num):
masked[j][mask != 1] = 0
save_name = os.path.join(save_dir, '%04d.tif' % i)
geo_x, geo_y = transform.imagexy2geo(img_data_set, plg_row_min,
plg_col_min)
masked_tran = list(img_tran)
masked_tran[0] = geo_x
masked_tran[3] = geo_y
save_tiff(save_name, masked, mask_rows, mask_cols, raster_num,
masked_tran, img_proj)
area_path = os.path.join(save_dir, 'area.json')
with open(area_path, 'w') as a_f:
json.dump(area_dict, a_f)
del mask_data_set
del img_data_set
print('\nMission completed!')
def extract_roi(image_path, xml_file_path, save_dir):
""" ROI提取
可实现从包含多个ROI的.xml文件中自动提取所有ROI的所有区域，并保存至.tif文件中
Args:
image_path: 带有地理信息的原始影像文件路径
str
xml_file_path: 带含多个ROI且具有与img_path文件相同地理参考信息的.xml文件路径
str
save_dir: 保存文件目录
str
"""
gdal.AllRegister()
print('Reading raw image ...')
data_set = gdal.Open(image_path)
if data_set is None:
raise FileNotFoundError('File %s Not found' % image_path)
img_tran = data_set.GetGeoTransform()
img_proj = data_set.GetProjection()
img_data = data_set.ReadAsArray()
raster_num, img_rows, img_cols = img_data.shape
print('Reading .xml file ...')
if xml_file_path[-4:] != '.xml':
raise TypeError('Unrecognizable type: \'%s\'' % xml_file_path[-4:])
xml_proj, regions_dict = extract_xml_info(xml_file_path)
print('Image Projection: %s' % img_proj)
print('ROI Projection: %s' % xml_proj)
if 'PROJCS' in xml_proj:
with_projcs = True
else:
with_projcs = False
roi_dict = transform_geo_to_ctype(data_set,
regions_dict,
ctype='grid',
with_projcs=with_projcs)
for region_name in roi_dict.keys():
plg_list = roi_dict[region_name]
i = 0
for plg in plg_list:
i += 1
sys.stdout.write('\rProcessing \"%s\" %04d ...' % (region_name, i))
sys.stdout.flush()
plg_points = np.array(plg[:-1], dtype='int32')
rect = get_extent(plg_points)
plg_row_min, plg_row_max, plg_col_min, plg_col_max = limit_boundary(
rect, (0, img_rows, 0, img_cols))
plg_points[:, 0] = plg_points[:, 0] - plg_row_min
plg_points[:, 1] = plg_points[:, 1] - plg_col_min
plg_points[:, [0, 1]] = plg_points[:, [1, 0]]
plg_points = plg_points[np.newaxis]
mask_rows = plg_row_max - plg_row_min
mask_cols = plg_col_max - plg_col_min
mask = np.zeros((mask_rows, mask_cols), dtype='uint8')
cv2.polylines(mask, plg_points, 1, 255)
cv2.fillPoly(mask, plg_points, 255)
cond = mask == 0
masked = img_data[:, plg_row_min:(plg_row_min + mask_rows),
plg_col_min:(plg_col_min + mask_cols)].copy()
for j in range(0, raster_num):
masked[j][cond] = 0
directory = os.path.join(save_dir, region_name)
folder = os.path.exists(directory)
if not folder:
os.makedirs(directory)
save_name = os.path.join(directory, '%04d.tif' % i)
geo_x, geo_y = transform.imagexy2geo(data_set, plg_row_min,
plg_col_min)
masked_tran = list(img_tran)
masked_tran[0] = geo_x
masked_tran[3] = geo_y
save_tiff(save_name, masked, mask_rows, mask_cols, raster_num,
masked_tran, img_proj)
del data_set
print('\nMission completed!')
def extract_roi_box(image_path, xml_file_path, save_dir):
""" ROI最小外接矩形提取
可实现从包含多个ROI的.xml文件中自动提取所有ROI的所有区域的最小外接矩形，并保存至.tif文件中
Args:
image_path: 带有地理信息的原始影像文件路径
str
xml_file_path: 带含多个ROI且具有与img_path文件相同地理参考信息的.xml文件路径
str
save_dir: 保存文件目录
str
Returns:
res: 外接矩形列表
"""
gdal.AllRegister()
print('Reading raw image ...')
data_set = gdal.Open(image_path)
if data_set is None:
raise FileNotFoundError('File %s Not found' % image_path)
img_tran = data_set.GetGeoTransform()
img_proj = data_set.GetProjection()
img_data = data_set.ReadAsArray()
raster_num, img_rows, img_cols = img_data.shape
print('Reading .xml file ...')
if xml_file_path[-4:] != '.xml':
raise TypeError('Unrecognizable type: \'%s\'' % xml_file_path[-4:])
xml_proj, regions_dict = extract_xml_info(xml_file_path)
print('Image Projection: %s' % img_proj)
print('ROI Projection: %s' % xml_proj)
if 'PROJCS' in xml_proj:
with_projcs = True
else:
with_projcs = False
roi_dict = transform_geo_to_ctype(data_set,
regions_dict,
ctype='grid',
with_projcs=with_projcs)
res = []
for region_name in roi_dict.keys():
plg_list = roi_dict[region_name]
i = 0
for plg in plg_list:
i += 1
print('\rProcessing \"%s\" %04d ...' % (region_name, i), end='')
plg_points = np.array(plg[:-1], dtype='int32')
rect = limit_boundary(get_extent(plg_points),
(0, img_rows, 0, img_cols))
res.append(rect)
box_img = img_data[:, rect[0]:rect[1], rect[2]:rect[3]].copy()
directory = os.path.join(save_dir, region_name)
folder = os.path.exists(directory)
if not folder:
os.makedirs(directory)
save_name = os.path.join(directory, '%04d.tif' % i)
geo_x, geo_y = transform.imagexy2geo(data_set, rect[0], rect[2])
masked_tran = list(img_tran)
masked_tran[0] = geo_x
masked_tran[3] = geo_y
save_tiff(save_name, box_img, rect[1] - rect[0], rect[3] - rect[2],
raster_num, masked_tran, img_proj)
del data_set
print('\nMission completed!')
return res