def check_input_image_and_label(image_path, label_path):
"""
check the input image and label, they should have same width, height, and projection
:param image_path: the path of image
:param label_path: the path of label
:return: (width, height) of image if successful, Otherwise (None, None).
"""
img_obj = RSImageclass()
if img_obj.open(image_path) is False:
assert False
label_obj = RSImageclass()
if label_obj.open(label_path) is False:
assert False
# check width and height
width = img_obj.GetWidth()
height = img_obj.GetHeight()
if width != label_obj.GetWidth() or height != label_obj.GetHeight():
basic.outputlogMessage("Error, not the same width and height of image (%s) and label (%s)"%(image_path,label_path))
assert False
# check resolution
if img_obj.GetXresolution() != label_obj.GetXresolution() or img_obj.GetYresolution() != label_obj.GetYresolution():
basic.outputlogMessage(
"Error, not the same resolution of image (%s) and label (%s)" % (image_path, label_path))
assert False
# check projection
if img_obj.GetProjection() != label_obj.GetProjection():
basic.outputlogMessage(
"warning, not the same projection of image (%s) and label (%s)" % (image_path, label_path))
# assert False
return (width, height)
def main(options, args):
msi_files = args # all images in this file should have the same width and height
# output = options.output
name_index = options.name_index
if len(msi_files) < 1:
raise IOError('NO input images')
# test_TheilSen()
annual_based = options.annual_based
# sort the file to make
msi_files = sorted(msi_files)
# for file in msi_files:
# print(file)
# test
aoi = (300, 250, 600, 300) # (xoff, yoff ,xsize, ysize) in pixels
# aoi = (300, 250, 10, 20)
# band_index = [1,2,3] # for test
valid_month = [7, 8]
confidence_inter = 0.95
# split the image and label
img_obj = RSImageclass()
if img_obj.open(msi_files[0]) is False:
raise IOError('Open %s failed' % msi_files[0])
width = img_obj.GetWidth()
height = img_obj.GetHeight()
patch_w = 200
patch_h = 200
patch_boundary = split_image.sliding_window(
width, height, patch_w, patch_h, 0,
0) # boundary of patch (xoff,yoff ,xsize, ysize)
# use multiple thread
num_cores = multiprocessing.cpu_count()
print('number of thread %d' % num_cores)
# theadPool = mp.Pool(num_cores) # multi threads, can not utilize all the CPUs? not sure hlc 2018-4-19
theadPool = Pool(num_cores) # multi processes
# for idx, aoi in enumerate(patch_boundary):
# print(idx, aoi)
tmp_dir = '%s_trend_patches' % name_index
parameters_list = [(msi_files, aoi, name_index,
valid_month, confidence_inter,
os.path.join(tmp_dir, '%d.tif' % idx), annual_based)
for idx, aoi in enumerate(patch_boundary)]
results = theadPool.map(cal_trend_for_one_index_parallel, parameters_list)
def cal_the_mean_of_bands(input_path):
# if multi files in one line, then only consider the first file
image_path = get_first_path_in_line(input_path)
if image_path is False:
return False
img_obj = RSImageclass()
if img_obj.open(image_path):
width = img_obj.GetWidth()
height = img_obj.GetHeight()
mean_of_bands = RSImage.get_image_mean_value(image_path)
if mean_of_bands is False:
return (False, False, False)
return (width, height, mean_of_bands)
else:
basic.outputlogMessage('error, Open image %s failed' % image_path)
return (False, False, False)
def main(options, args):
mod_snow_file = args[0]
myd_snow_file = args[1]
# x = 10
# y = 100
# at least four decimal
# x = 92.9123
# y = 34.8485
# x = 92.76071
# y = 35.08546
# x = 92.80871
# y = 34.79564
# xy_srs = 'lon_lat_wgs84' # pixel
# snow_series = one_point_snowcover_series(x,y,xy_srs,mod_snow_file,myd_snow_file)
# save_year_monthly_days([snow_series], 1, 1)
# calculate for the whole image
rs_obj = RSImageclass()
if rs_obj.open(mod_snow_file) is False:
return False
img_width = rs_obj.GetWidth() #3 #
img_height = rs_obj.GetHeight() #3 #
xy_srs = 'pixel' # pixel lon_lat_wgs84
snow_series_wholeArea = []
# print(img_width,img_height)
for img_row in range(img_height):
for img_col in range(img_width):
snow_series = one_point_snowcover_series(img_col, img_row, xy_srs,
mod_snow_file,
myd_snow_file)
snow_series_wholeArea.append(snow_series)
save_yearly_days(mod_snow_file, snow_series_wholeArea, img_width,
img_height)
save_year_monthly_days(mod_snow_file, snow_series_wholeArea, img_width,
img_height)
def save_oneband_list(save_path, ndvi_list, band_name_list, org_img):
'''
save ndvi of multiple images to a file
:param save_path:
:param ndvi_list:
:param band_name_list:
:param org_img:
:return:
'''
if len(ndvi_list) < 1 or len(band_name_list) < 1:
raise ValueError('the input of ndvi or name list is empty')
if len(ndvi_list) != len(band_name_list):
raise ValueError(
'the length of ndvi list and band name list is different')
rsImg_org = RSImageclass()
rsImg_org.open(org_img)
org_width = rsImg_org.GetWidth()
org_height = rsImg_org.GetHeight()
datetype = rsImg_org.GetGDALDataType()
prj = rsImg_org.GetProjection()
geo_tran = rsImg_org.GetGeoTransform()
save_bandcount = len(ndvi_list)
rsImg_obj = RSImageclass()
if rsImg_obj.New(save_path, org_width, org_height, save_bandcount,
6): # 6 for float 32
for idx in range(save_bandcount):
height, width = ndvi_list[idx].shape
ndvi_str = ndvi_list[idx].tobytes(
) # struct.pack('%f' % width * height, *templist)
if rsImg_obj.WritebandData(idx + 1, 0, 0, width, height, ndvi_str,
6):
rsImg_obj.set_band_name(idx + 1, band_name_list[idx])
# set projection and transform
rsImg_obj.SetProjection(prj)
rsImg_obj.SetGeoTransform(geo_tran)
def make_dataset(root,list_txt,patch_w,patch_h,adj_overlay_x,adj_overlay_y,train=True):
"""
get the patches information of the remote sensing images.
:param root: data root
:param list_txt: a list file contain images (one row contain one train image and one label
image with space in the center if the input is for training; one row contain one image if it is for inference)
:param patch_w: the width of the expected patch
:param patch_h: the height of the expected patch
:param adj_overlay: the extended distance (in pixel) to adjacent patch, make each patch has overlay with adjacent patch
:param train: indicate training or inference
:return: dataset (list)
"""
dataset = []
crop_height=patch_h+2*adj_overlay_y
crop_width=patch_w+2*adj_overlay_x
if os.path.isfile(list_txt) is False:
basic.outputlogMessage("error, file %s not exist"%list_txt)
assert False
with open(list_txt) as file_obj:
files_list = file_obj.readlines()
if len(files_list) < 1:
basic.outputlogMessage("error, no file name in the %s" % list_txt)
assert False
if train:
abandon_number = 0
count_for_pure_image = 0
for line in files_list:
names_list = line.split()
if len(names_list) < 1: # empty line
continue
image_name = names_list[0]
label_name = names_list[1].strip()
# img_path = os.path.join(root,image_name)
# label_path = os.path.join(root,label_name)
img_path=image_name
label_path=label_name
#
(width,height) = check_input_image_and_label(img_path,label_path)
# split the image and label
patch_boundary = split_image.sliding_window(width, height, patch_w, patch_h, adj_overlay_x,adj_overlay_y)
for patch in patch_boundary:
# remove the patch small than model input size
# if patch[2] < crop_width or patch[3] < crop_height:# xSize < 480 or ySize < 480
#
# # print ('not in edge mode')
# continue
img_patch = patchclass(img_path,patch)
label_patch = patchclass(label_path,patch)
gt_test = read_patch2(label_patch)
max=np.amax(gt_test)
min=np.amin(gt_test)
if max==min:
count_for_pure_image = count_for_pure_image+1
#print ("this is %d image"%min)
a=random.randint(0,4)
if a == 1:
dataset.append([img_patch, label_patch])
else:
abandon_number=abandon_number+1
continue
else:
dataset.append([img_patch, label_patch])
print("%d images are abandoned"%abandon_number)
print("%d images are 1 or 0 images"%count_for_pure_image)
print("%d images in total"%len(dataset))
else:
for line in files_list:
names_list = line.split()
image_name = names_list[0]
label_name = names_list[1].strip()
img_path = image_name
label_path = label_name
(width, height) = check_input_image_and_label(img_path, label_path)
#
img_obj = RSImageclass()
if img_obj.open(img_path) is False:
assert False
width = img_obj.GetWidth()
height = img_obj.GetHeight()
# split the image and label
patch_boundary = split_image.sliding_window_test(width, height, patch_w, patch_h, adj_overlay_x,adj_overlay_y)
for patch in patch_boundary:
# need to handle the patch with smaller size
# if patch[2] < crop_width or patch[3] < crop_height: # xSize < 480 or ySize < 480
# continue
img_patch = patchclass(img_path, patch)
label_patch = patchclass(label_path, patch)
dataset.append([img_patch, label_patch])
return dataset
# band_num = 1 # only consider the first band
# bucket_count, hist_min, hist_max, hist_buckets = RSImage.get_image_histogram_oneband(img_file, band_num)
img_name = os.path.basename(img_file)
if img_name in calculated_files:
basic.outputlogMessage('image: %s already be calcuated, skip' %
img_name)
continue
try:
valid_count = RSImage.get_valid_pixel_count(
img_file) # count the pixels without nodata pixel
except Exception as e: # can get all the exception, and the program will not exit
print(str(e))
basic.outputlogMessage("get grey histrogram of %s failed" % img_file)
continue
# get image width and height
img_obj = RSImageclass()
if img_obj.open(img_file):
width = img_obj.GetWidth()
height = img_obj.GetHeight()
nodata_per = 100.0 * (width * height - valid_count) / (width * height)
basic.outputlogMessage('Nodata percentage %.2lf' % nodata_per)
f_obj.writelines("%d: %s Nodata percentage: %.2lf \n" %
(idx + 1, img_name, nodata_per))
f_obj.flush()
f_obj.close()
def make_dataset(root, list_txt, patch_w, patch_h, adj_overlay, train=True):
"""
get the patches information of the remote sensing images.
:param root: data root
:param list_txt: a list file contain images (one row contain one train image and one label
image with space in the center if the input is for training; one row contain one image if it is for inference)
:param patch_w: the width of the expected patch
:param patch_h: the height of the expected patch
:param adj_overlay: the extended distance (in pixel) to adjacent patch, make each patch has overlay with adjacent patch
:param train: indicate training or inference
:return: dataset (list)
"""
dataset = []
if os.path.isfile(list_txt) is False:
basic.outputlogMessage("error, file %s not exist" % list_txt)
assert False
with open(list_txt) as file_obj:
files_list = file_obj.readlines()
if len(files_list) < 1:
basic.outputlogMessage("error, no file name in the %s" % list_txt)
assert False
if train:
for line in files_list:
names_list = line.split()
if len(names_list) < 1: # empty line
continue
image_name = names_list[0]
label_name = names_list[1].strip()
img_path = os.path.join(root, image_name)
label_path = os.path.join(root, label_name)
#
(width, height) = check_input_image_and_label(img_path, label_path)
# split the image and label
patch_boundary = split_image.sliding_window(
width, height, patch_w, patch_h, adj_overlay)
for patch in patch_boundary:
# remove the patch small than model input size
if patch[2] < crop_width or patch[
3] < crop_height: # xSize < 480 or ySize < 480
continue
img_patch = patchclass(img_path, patch)
label_patch = patchclass(label_path, patch)
dataset.append([img_patch, label_patch])
else:
for line in files_list:
names_list = line.split()
image_name = names_list[0].strip()
img_path = os.path.join(root, image_name)
#
img_obj = RSImageclass()
if img_obj.open(img_path) is False:
assert False
width = img_obj.GetWidth()
height = img_obj.GetHeight()
# split the image and label
patch_boundary = split_image.sliding_window(
width, height, patch_w, patch_h, adj_overlay)
for patch in patch_boundary:
# need to handle the patch with smaller size
# if patch[2] < crop_width or patch[3] < crop_height: # xSize < 480 or ySize < 480
# continue
img_patch = patchclass(img_path, patch)
dataset.append(img_patch)
return dataset