I am new in GDAL. I use landsat image in classification but I apply these code line band data just give me 0, what I don't understand:
raster_dataset = gdal.Open(‘LC81850552017065LGN00_B3.tif’, gdal.GA_ReadOnly)
geo_transform = raster_dataset.GetGeoTransform()
proj = raster_dataset.GetProjectionRef()
bands_data = []
for b in range(1, raster_dataset.RasterCount+1):
band = raster_dataset.GetRasterBand(b)
bands_data.append(band.ReadAsArray())
bands_data = np.dstack(bands_data)
You are reassigning bands_data from a list to a numpy array, which is causing your trouble. Perform the np.dstack operation after all bands have been read in (after the loop has been completed).
See the numpy docs to better understand how dstack works.
Related
# masking
masked = mask.copy()
for py in range(0,h):
for px in range(0,w):
if masked[py][px][0] != 0:
masked[py][px] = im[py][px]
masked = cv2.cvtColor(masked, cv2.COLOR_RGB2BGR)
Is there any fancy way to convert my dirty code to a simple code with cv2 or numpy function?
I think this could help to replace the loop (quite self-explanatory similar to your loop):
indices = np.where(masked[:,:,0] != 0)
masked[indices]=im[indices]
I was trying to read tflite model and pull all the parameters of the layers out.
My steps:
I generated flatbuffers model representation by running (please build flatc before):
flatc -python tensorflow/tensorflow/lite/schema/schema.fbs
Result is tflite/ folder that contains layer description files (*.py) and some utilitarian files.
I successfully loaded model:
in case of import Error: set PYTHONPATH to point to the folder where tflite/ is
from tflite.Model import Model
def read_tflite_model(file):
buf = open(file, "rb").read()
buf = bytearray(buf)
model = Model.GetRootAsModel(buf, 0)
return model
I partly pulled model and node parameters out and stacked in iterating over nodes:
Model part:
def print_model_info(model):
version = model.Version()
print("Model version:", version)
description = model.Description().decode('utf-8')
print("Description:", description)
subgraph_len = model.SubgraphsLength()
print("Subgraph length:", subgraph_len)
Nodes part:
def print_nodes_info(model):
# what does this 0 mean? should it always be zero?
subgraph = model.Subgraphs(0)
operators_len = subgraph.OperatorsLength()
print('Operators length:', operators_len)
from collections import deque
nodes = deque(subgraph.InputsAsNumpy())
STEP_N = 0
MAX_STEPS = operators_len
print("Nodes info:")
while len(nodes) != 0 and STEP_N <= MAX_STEPS:
print("MAX_STEPS={} STEP_N={}".format(MAX_STEPS, STEP_N))
print("-" * 60)
node_id = nodes.pop()
print("Node id:", node_id)
tensor = subgraph.Tensors(node_id)
print("Node name:", tensor.Name().decode('utf-8'))
print("Node shape:", tensor.ShapeAsNumpy())
# which type is it? what does it mean?
type_of_tensor = tensor.Type()
print("Tensor type:", type_of_tensor)
quantization = tensor.Quantization()
min = quantization.MinAsNumpy()
max = quantization.MaxAsNumpy()
scale = quantization.ScaleAsNumpy()
zero_point = quantization.ZeroPointAsNumpy()
print("Quantization: ({}, {}), s={}, z={}".format(min, max, scale, zero_point))
# I do not understand it again. what is j, that I set to 0 here?
operator = subgraph.Operators(0)
for i in operator.OutputsAsNumpy():
nodes.appendleft(i)
STEP_N += 1
print("-"*60)
Please point me to documentation or some example of using this API.
My problems are:
I can not get documentation on this API
Iterating over Tensor objects seems not possible for me, as it doesn't have Inputs and Outputs methods. + subgraph.Operators(j=0) I do not understand what j means in here. Because of that my cycle goes through two nodes: input (once) and the next one over and over again.
Iterating over Operator objects is surely possible:
Here we iterate over them all but I can not get how to map Operator and Tensor.
def print_in_out_info_of_all_operators(model):
# what does this 0 mean? should it always be zero?
subgraph = model.Subgraphs(0)
for i in range(subgraph.OperatorsLength()):
operator = subgraph.Operators(i)
print('Outputs', operator.OutputsAsNumpy())
print('Inputs', operator.InputsAsNumpy())
I do not understand how to pull parameters out Operator object. BuiltinOptions method gives me Table object, that I do not know what to map at.
subgraph = model.Subgraphs(0)
What does this 0 mean? should it always be zero? obviously no, but what is it? Id of the subgraph? If so - I'm happy. If no, please try to explain it.
How exactly does gather_tree in contrib.seq2seq work? I can see that it takes the predicted ids and beam parent ids and somehow returns the final beams, but what's actually going underneath the hood? There doesn't seem to be any Python code base I could examine to figure it out. The API isn't very explanatory;
Is there any code source for tf.contrib.seq2seq.gather_tree? I am using TensorFlow 1.3 and looking inside gen_beam_search_ops.py doesn't seem helpful.
The codes are detailed as follows:
def gather_tree_py(values, parents):
"""Gathers path through a tree backwards from the leave nodes. Used
to reconstruct beams given their parents."""
beam_length = values.shape[0]
num_beams = values.shape[1]
res = np.zeros_like(values)
res[-1, :] = values[-1, :]
for beam_id in range(num_beams):
parent = parents[-1][beam_id]
for level in reversed(range(beam_length - 1)):
res[level, beam_id] = values[level][parent]
parent = parents[level][parent]
return np.array(res).astype(values.dtype)
def gather_tree(values, parents):
"""Tensor version of gather_tree_py"""
res = tf.py_func(
func=gather_tree_py, inp=[values, parents], Tout=values.dtype)
res.set_shape(values.get_shape().as_list())
return res
github: seq2seq beam_search
I'm trying to set up TensorFlow to accept one image at a time but I believe I'm getting incorrect results because I pass a regular array without first performing tf.image.per_image_whitening() beforehand. Is there an easy way to do this in Python to an individual image without using the image queue?
Here's my code so far:
im = Image.open(request.FILES.values()[0])
im = im.convert('RGB')
im = im.crop((0, 0, cifar10.IMAGE_SIZE, cifar10.IMAGE_SIZE))
(width, height) = im.size
image_array = list(im.getdata())
image_array = np.array(image_array)
image_array = image_array.reshape((1, height, width, 3))
# tf.image.per_image_whitening() should be done here
#mean = numpy.mean(image_array)
#stddev = numpy.std(image_array)
#adjusted_stddev = max(stddev, 1.0/len(image_array.flatten())))
feed_dict = {"shuffle_batch:0": image_array}
# predictions always returns something close to [1, 0]
predictions = sess.run(tf.nn.softmax(logits), feed_dict=feed_dict)
If you want to avoid the image queue and do the predictions one by one, I think
image_array = (image_array - mean) / adjusted_stddev
should be able to do the trick.
If you want to do the prediction by batches, it's a little bit complicated as per_image_whitening (now per_image_standardization) only works with single images. So you need to do it before you form the batch like the way above or setup a preprocess procedure.
Hello im new to Gdal and im struggling a with my codes. Everything seems to go well in my code mut the output bands at the end is empty. The no data value is set to 256 when i specify 255, so I don't really know whats wrong. Thanks any help will be appreciated!!!
Here is my code
from osgeo import gdal
from osgeo import gdalconst
from osgeo import osr
from osgeo import ogr
import numpy
#graticule
src_ds = gdal.Open("E:\\NFI_photo_plot\\photoplotdownloadAllCanada\\provincial_merge\\Aggregate\\graticule1.tif")
band = src_ds.GetRasterBand(1)
band.SetNoDataValue(0)
graticule = band.ReadAsArray()
print('graticule done')
band="none"
#Biomass
dataset1 = gdal.Open("E:\\NFI_photo_plot\\photoplotdownloadAllCanada\provincial_merge\\Aggregate\\Biomass_NFI.tif")
band1 = dataset1.GetRasterBand(1)
band1.SetNoDataValue(-1)
Biomass = band1.ReadAsArray()
maskbiomass = numpy.greater(Biomass, -1).astype(int)
print("biomass done")
Biomass="none"
band1="none"
dataset1="none"
#Baseline
dataset2 = gdal.Open("E:\\NFI_photo_plot\\Baseline\\TOTBM_250.tif")
band2 = dataset2.GetRasterBand(1)
band2.SetNoDataValue(0)
baseline = band2.ReadAsArray()
maskbaseline = numpy.greater(baseline, 0).astype(int)
print('baseline done')
baseline="none"
band2="none"
dataset2="none"
#sommation
biosource=(graticule+maskbiomass+maskbaseline)
biosource1=numpy.uint8(biosource)
biosource="none"
#Écriture
dst_file="E:\\NFI_photo_plot\\photoplotdownloadAllCanada\\provincial_merge\\Aggregate\\Biosource.tif"
dst_driver = gdal.GetDriverByName('GTiff')
dst_ds = dst_driver.Create(dst_file, src_ds.RasterXSize,
src_ds.RasterYSize, 1, gdal.GDT_Byte)
#projection
dst_ds.SetProjection( src_ds.GetProjection() )
dst_ds.SetGeoTransform( src_ds.GetGeoTransform() )
outband=dst_ds.GetRasterBand(1)
outband.WriteArray(biosource1,0,0)
outband.SetNoDataValue(255)
biosource="none"
graticule="none"
A few pointers:
Where you have ="none", these need to be = None to close/cleanup the objects, otherwise you are setting the objects to an array of characters: n o n e, which is not what you intend to do.
Why do you have band1.SetNoDataValue(-1), while other NoData values are 0? Is this data source signed or unsigned? If unsigned, then -1 doesn't exist.
When you open rasters with gdal.Open without the access option, it defaults to gdal.GA_ReadOnly, which means your subsequent SetNoDataValue calls do nothing. If you want to modify the dataset, you need to use gdal.GA_Update as your second parameter to gdal.Open.
Another strategy to create a new raster is to use driver.CreateCopy; see the tutorial for details.