def cropImages_mp(self,
xRange,
yRange,
padding,
out_bands=[],
Process_cnt=10):
"""
:param prefix_output:
:param xRange:
:param yRange:
:param padding: overlap
:param out_bands:
:return:
"""
czhUtils.gdal.UseExceptions()
#get images in inputPath
imgFiles = []
czhUtils.getfilepath(self.inputPath, imgFiles)
imgFiles_mp = mp.Manager().list()
for file in natsorted(imgFiles):
imgFiles_mp.append(file)
# print(file)
self.imgFiles = imgFiles_mp
pool = mp.Pool(processes=Process_cnt)
for i in range(Process_cnt):
print(i)
pool.apply_async(self.cropImages,
args=(xRange, yRange, padding, out_bands))
pool.close()
pool.join()
def cropImages(self, xRange, yRange, padding, out_bands=[]):
"""
:param prefix_output:
:param xRange:
:param yRange:
:param padding: overlap
:param out_bands:
:return:
"""
czhUtils.gdal.UseExceptions()
#get images in inputPath
imgFiles = []
czhUtils.getfilepath(self.inputPath, imgFiles)
cnt = 0
if len(imgFiles) > 0:
for imgFilePath in imgFiles:
cnt += 1
print("Processing: {} / {}, {}".format(cnt, len(imgFiles),
imgFilePath))
rasters = czhUtils.gdal.Open(imgFilePath)
srcArray = czhUtils.gdalnumeric.LoadFile(imgFilePath)
img_Width = rasters.RasterXSize
img_Height = rasters.RasterYSize
geoTrans = rasters.GetGeoTransform()
#get full path of file without ext
filename, fileext = os.path.splitext(
os.path.basename(imgFilePath))
#origin point world coordinate
# originX = geoTrans[0]
# originY = geoTrans[3]
h0, w0 = 0, 0
col, row = 1, 1
while (h0 < img_Height):
while (w0 < img_Width):
#recalculate originX and originY
# originX,originY = czhUtils.pixeloffset2coord(geoTrans,w0,h0)
#crop image
# clip, pixel_ul_x, pixel_ul_y, geoTrans2 = self.cropImage(rasters,srcArray,originX,originY,xRange,yRange,out_bands)
clip, pixel_ul_x, pixel_ul_y, geoTrans2 = self.cropImage(
rasters, srcArray, w0, h0, xRange, yRange,
out_bands)
#save image
# rasterOutputPath = czhUtils.os.path.join(self.outputPath,filename +"_{}_{}".format(row,col)+fileext)
rasterOutputPath = czhUtils.os.path.join(
self.outputPath,
filename + "_{}_{}".format(row, col))
self.saveCropImage(rasters, clip, rasterOutputPath,
pixel_ul_x, pixel_ul_y, geoTrans2)
#moving window along x-axil
w0 = w0 + xRange - padding
col = col + 1
#moving window alogn y_axil
h0 = h0 + yRange - padding
w0 = 0
col = 1
row = row + 1
def mergeImages(self, image_dic, out_dic, t_w, t_h, n_w, n_h, overlap=0):
pngs = []
#（filename ,row ,col)
fileSplits = []
pathSplits = []
czhUtils.getfilepath(image_dic, pngs, ('png', 'PNG'))
for png in pngs:
png_base, _ = czhUtils.os.path.splitext(
czhUtils.os.path.basename(png))
png_path = czhUtils.os.path.dirname(png)
fileSplits.append(png_base.split('_'))
pathSplits.append(png_path)
#loop fileSplits
fileNames = czhUtils.getUniqueValue(fileSplits, 1)
#bug if n_w/t_w or n_h/t_h is exactly divisible
# if (n_w+2*padding) % t_w ==0:
if (n_w - overlap) % (t_w - overlap) == 0:
# maxRows = math.floor((n_w+padding)/t_w)
maxRows = math.floor((n_w - overlap) / (t_w - overlap))
else:
# maxRows = math.floor((n_w+padding)/t_w)+1
maxRows = math.floor((n_w - overlap) / (t_w - overlap)) + 1
# if (n_h+2*padding)%t_h ==0:
if (n_h - overlap) / (t_h - overlap) == 0:
# maxCols = math.floor((n_h+padding)/t_h)
maxCols = math.floor((n_h - overlap) / (t_h - overlap))
else:
# maxCols = math.floor((n_h+padding)/t_h)+1
maxCols = math.floor((n_h - overlap) / (t_h - overlap)) + 1
for filename in fileNames:
#reconstruct image
#calculate rows and colsl
rows = max([
int(filesplit[1]) for filesplit in fileSplits
if filesplit[0] == filename
])
cols = max([
int(filesplit[2]) for filesplit in fileSplits
if filesplit[0] == filename
])
bStart = True
#topleft point
# cur_l = 0
# cur_t = 0
for row in range(rows):
# cur_l=padding
cur_l = 0
if row == 0:
# img_t = padding #tile image
img_t = 0
cur_t = 0
else:
# get image data in roi
cur_t = cur_t + t_h - img_t
if cur_t + t_h - img_t > n_h:
img_t = imgH - n_h + cur_t
else:
img_t = overlap
for col in range(cols):
#check file exist
#get image width and height
try:
idx = fileSplits.index(
[filename, str(row + 1),
str(col + 1)])
except:
idx = -1
if idx != -1:
png_path = pathSplits[
idx] + "\\" + "{}_{}_{}.png".format(
filename, row + 1, col + 1)
img = cv2.imread(png_path)
imgH = img.shape[0]
imgW = img.shape[1]
assert imgH == t_h
assert imgW == t_w
channels = img.shape[2]
dType = img.dtype
if bStart:
dst = czhUtils.np.zeros((n_h, n_w, channels),
dtype=dType)
bStart = False
#calculate actual valid image left top point
if col == 0:
# img_l = padding
img_l = 0
else:
#get image data in roi
cur_l = cur_l + t_w - img_l
if cur_l + t_w - img_l > n_w:
img_l = imgW - n_w + cur_l
else:
img_l = overlap
roi = img[img_t:imgH, img_l:imgW, :]
dst[cur_t:cur_t + imgH - img_t,
cur_l:cur_l + imgW - img_l] = roi
#write image file if possible export tif with tfw file.
cv2.imwrite(out_dic + "\\" + filename + ".tif", dst)