/
utils.py
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/
utils.py
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import gdal,osr
import tensorflow as tf
import collections
import os
import numpy as np
def array2raster(newRasterfn, rasterOrigin, pixelWidth, pixelHeight, array, bandSize):
rasterOrigin = (-123.25745,45.43013)
pixelWidth = 2.4
pixelHeight = 2.4
if (bandSize == 4):
cols = array.shape[2]
rows = array.shape[1]
originX = rasterOrigin[0]
originY = rasterOrigin[1]
driver = gdal.GetDriverByName('GTiff')
outRaster = driver.Create(newRasterfn, cols, rows, 4, gdal.GDT_UInt16)
outRaster.SetGeoTransform((originX, pixelWidth, 0, originY, 0, pixelHeight))
for i in range(1, 5):
outband = outRaster.GetRasterBand(i)
outband.WriteArray(array[i - 1, :, :])
outRasterSRS = osr.SpatialReference()
outRasterSRS.ImportFromEPSG(4326)
outRaster.SetProjection(outRasterSRS.ExportToWkt())
outband.FlushCache()
elif (bandSize == 1):
cols = array.shape[1]
rows = array.shape[0]
originX = rasterOrigin[0]
originY = rasterOrigin[1]
driver = gdal.GetDriverByName('GTiff')
outRaster = driver.Create(newRasterfn, cols, rows, 1, gdal.GDT_UInt16)
outRaster.SetGeoTransform((originX, pixelWidth, 0, originY, 0, pixelHeight))
outband = outRaster.GetRasterBand(1)
outband.WriteArray(array[:, :])
def load_examples(args):
if args.mode == 'train':
filename_queue = tf.train.string_input_producer([args.train_tfrecord])
elif args.mode =='test':
filename_queue = tf.train.string_input_producer([args.test_tfrecord])
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(serialized_example,
features={
'im_name': tf.FixedLenFeature([],tf.string),
'im_mul_raw': tf.FixedLenFeature([], tf.string),
'im_blur_raw': tf.FixedLenFeature([], tf.string),
'im_blur_u_raw': tf.FixedLenFeature([], tf.string),
'im_pan_raw': tf.FixedLenFeature([], tf.string),
'im_pan_d_raw': tf.FixedLenFeature([], tf.string)
})
im_mul_raw = tf.decode_raw(features['im_mul_raw'], tf.int16)
im_mul_raw = tf.reshape(im_mul_raw, [args.blk*4, args.blk*4, 4])
im_mul_raw = tf.cast(im_mul_raw,tf.float32)
im_blur_raw = tf.decode_raw(features['im_blur_raw'], tf.int16)
im_blur_raw = tf.reshape(im_blur_raw, [args.blk, args.blk, 4])
im_blur_raw=tf.cast(im_blur_raw, tf.float32)
im_blur_u_raw = tf.decode_raw(features['im_blur_u_raw'], tf.int16)
im_blur_u_raw = tf.reshape(im_blur_u_raw, [args.blk*4, args.blk*4, 4])
im_blur_u_raw = tf.cast(im_blur_u_raw, tf.float32)
im_pan_raw = tf.decode_raw(features['im_pan_raw'], tf.int16)
im_pan_raw = tf.reshape(im_pan_raw, [args.blk*4, args.blk*4, 1])
im_pan_raw = tf.cast(im_pan_raw, tf.float32)
im_pan_d_raw = tf.decode_raw(features['im_pan_d_raw'], tf.int16)
im_pan_d_raw = tf.reshape(im_pan_d_raw, [args.blk*4, args.blk*4, 1])
im_pan_d_raw = tf.cast(im_pan_d_raw, tf.float32)
if args.mode == 'train':
imnames_batch, blur_batch, pan_batch, blur_u_batch, pan_d_batch, mul_batch = \
tf.train.shuffle_batch(
[features['im_name'], im_blur_raw, im_pan_raw, im_blur_u_raw, im_pan_d_raw, im_mul_raw],
batch_size=args.batch_size, capacity=200, min_after_dequeue=100)
steps_per_epoch = args.train_count // args.batch_size + (args.train_count % args.batch_size != 0)
elif args.mode =='test':
imnames_batch, blur_batch, pan_batch, blur_u_batch, pan_d_batch, mul_batch = \
tf.train.batch(
[features['im_name'],im_blur_raw, im_pan_raw, im_blur_u_raw, im_pan_d_raw, im_mul_raw],
batch_size=args.batch_size, capacity=200)
steps_per_epoch = args.test_count // args.batch_size + (args.test_count % args.batch_size != 0)
Examples = collections.namedtuple("Examples",
"imnames, blur, pan, blur_u, pan_d, mul, steps_per_epoch")
return Examples(
imnames = imnames_batch,
blur = blur_batch, # blur 32*32*4
pan = pan_batch, # pan 128*128*1
blur_u = blur_u_batch, # blur_u 128*128*4
pan_d = pan_d_batch, # pan_d 128*128*1
mul = mul_batch, # mul 128*128*4
steps_per_epoch = steps_per_epoch,
)
def save_images(fetches, args, step=None):
image_dir = os.path.join(args.output_dir, "images")
if not os.path.exists(image_dir):
os.makedirs(image_dir)
for i, in_path in enumerate(fetches["imnames"]):
name, _ = os.path.splitext(os.path.basename(in_path.decode("utf8")))
print (fetches['mul'][i][33][33], ':', fetches['mul_hat'][i][33][33])
for kind in fetches:
filename = name + "-" + kind + ".tif"
if step is not None:
filename = "%08d-%s" % (step, filename)
out_path = os.path.join(image_dir, filename)
contents = fetches[kind][i]
if kind in ["blur"]:
array2raster(out_path, [0, 0], 4, 4, contents.transpose(2,0,1), 4)
elif kind in ["pan", "pan_d", "pan_d_hat"]:
array2raster(out_path, [0, 0], 1, 1, contents.reshape((args.blk*4,args.blk*4)), 1)
elif kind in ["blur_u", "mul", "mul_hat"]:
array2raster(out_path, [0, 0], 1, 1, contents.transpose(2,0,1), 4)
def trim_image(image, L = 0, R = 2**11):
return tf.minimum(tf.maximum(image, L), R)
def arrayToHist(grayArray, nums):
if(len(grayArray.shape) != 2):
print("length error")
return None
w, h = grayArray.shape
hist = {}
for k in range(nums):
hist[k] = 0
for i in range(w):
for j in range(h):
if(hist.get(grayArray[i][j]) is None):
hist[grayArray[i][j]] = 0
hist[grayArray[i][j]] += 1
# normalize
n = w * h
for key in hist.keys():
hist[key] = float(hist[key]) / n
return hist
def histMatch(grayArray, grayArray_d, nums):
h_d = arrayToHist(grayArray_d, nums)
tmp = 0.0
h_acc = h_d.copy()
for i in range(nums):
tmp += h_d[i]
h_acc[i] = tmp
h1 = arrayToHist(grayArray, nums)
tmp = 0.0
h1_acc = h1.copy()
for i in range(nums):
tmp += h1[i]
h1_acc[i] = tmp
M = np.zeros(nums)
j = 0
for i in range(nums):
while j < nums and h1_acc[i] > h_acc[j]:
j = j + 1
if j == 0:
M[i] = 0
else:
M[i] = j if np.fabs(h_acc[j] - h1_acc[i]) < np.fabs(h_acc[j - 1] - h1_acc[i]) else j - 1
des = M[grayArray]
return des
def imagesHistMatch(x, y, nums):
print("run imagesHistMatch")
x = np.array(x, dtype=np.int32)
y = np.array(y, dtype=np.int32)
B, H, W, C = x.shape
for b in range(B):
for c in range(C):
print(b, c)
x[b, :, :, c] = histMatch(x[b, :, :, c], y[b, :, :, c], nums)
return x