def get_data(self):
self.data_path = os.path.join(self.data_dir, "F3Seis_IL190_490_Amplitude.segy") # [iline, xline, time]
self.label_path = os.path.join(self.data_dir, "F3Seis_IL190_490_Label.segy")
self.data_cube = np.transpose(segyio.cube(self.data_path), (0,2,1)) # 只是hw互换 [c,h,w]
self.label_cube = np.transpose(segyio.cube(self.label_path), (0,2,1))
self.labels = np.unique(self.label_cube)
self.num_class = self.labels.max()
self.label_inv = labelInverse(self.num_class) # 得到从PIL图片到标签[0,1,2,3,4]的映射
def marmousi_model():
"""Function returns the marmousi acoustic impedance model"""
den_file = segyio.open(pjoin('data', 'MODEL_DENSITY_1.25m.segy'))
rho = segyio.cube(den_file).squeeze().T
rho = rho[:, ::5]
v_file = segyio.open('./data/MODEL_P-WAVE_VELOCITY_1.25m.segy')
vp = segyio.cube(v_file).squeeze().T
vp = vp[:, ::5]
AI = vp * rho
return AI
def test_from_array3D(tmpdir, create):
fresh = str(tmpdir / 'fresh.sgy')
with segyio.open("test-data/small.sgy") as f:
cube = segyio.cube(f)
dt, delrt = 4000, 0
create(fresh, cube, dt=dt, delrt=delrt)
with segyio.open(fresh) as g:
assert int(g.format) == SegySampleFormat.IBM_FLOAT_4_BYTE
assert int(g.sorting) == TraceSortingFormat.INLINE_SORTING
assert len(g.samples) == len(f.samples)
assert g.tracecount == f.tracecount
assert np.array_equal(f.trace, g.trace)
assert list(g.ilines) == list(range(1, 6))
assert list(g.xlines) == list(range(1, 6))
assert list(f.offsets) == list(range(1, 2))
assert list(
f.samples) == list((np.arange(len(f.samples)) * dt / 1000) +
delrt)
xlines = np.tile(np.arange(1, 6), 5)
ilines = np.repeat(np.arange(1, 6), 5)
offsets = np.ones(25)
assert list(g.attributes(TraceField.INLINE_3D)) == list(ilines)
assert list(g.attributes(TraceField.CROSSLINE_3D)) == list(xlines)
assert list(g.attributes(TraceField.offset)) == list(offsets)
assert list(g.attributes(TraceField.TraceNumber)) == list(
range(25))
assert list(g.attributes(TraceField.CDP_TRACE)) == list(range(25))
assert list(g.attributes(
TraceField.TRACE_SAMPLE_INTERVAL)) == list(4000 * np.ones(25))
assert list(g.attributes(TraceField.TRACE_SAMPLE_COUNT)) == list(
50 * np.ones(25))
assert g.bin[BinField.SortingCode] == 2
def marmousi_seismic():
"""Function returns the seismic data that comes with the marmousi model"""
seismic = segyio.cube(pjoin('data', 'Kirchhoff_PoSDM.segy'))
seismic = np.transpose(seismic, axes=[1, 0, 2])
return seismic
import numpy as np
import segyio
from PIL import Image
import os
from matplotlib import pyplot as plt
from torchvision.transforms import ToPILImage
import torch
from torchvision import transforms
data_dir = "/home/cym/Datasets/StData-12/F3_block/"
data_path = os.path.join(data_dir, "F3Seis_IL190_490_Amplitude.segy")
label_path = os.path.join(data_dir, "F3Seis_IL190_490_Label.segy")
data_cube = np.transpose(segyio.cube(data_path), (0, 2, 1))
label_cube = np.transpose(segyio.cube(label_path), (0, 2, 1))
# min4 = label_cube.min(axis=(1,2))
# print(min4)
# label_cube = label_cube - np.expand_dims(min4, axis=(1,2))
# print(label_cube.min(axis=(1,2)))
# range4 = label_cube.max(axis=(1,2)) - label_cube.min(axis=(1,2))
# range4 = np.expand_dims(range4, axis=(1,2))
# label_cube = (label_cube / range4 * 255).astype(np.uint8)
# out_path_tmp = "outs/img_data_{num}_img.png"
# for i in range(data_cube.shape[0]):
# out_path = out_path_tmp.format(num=i)
# img = Image.fromarray(data_cube[i], mode="L")
# img.save(out_path)
# out_path_tmp = "outs/img_label_{num}_img.png"
# for i in range(label_cube.shape[0]):
"""
Created on Thu Jan 24 17:31:42 2019
@author: Henry.Campos
(Actually is a merge and modification of software created by other people!)
This program starts by opening a .segy seismic file and converting it into a numpy array.
Then it calculates single-frequency freqs volumes from that numpy array and saves them as 'Piedras{freqs}Hz.npy
"""
import segyio
with segyio.open(
'../Desktop/PythonClass/data/d_recon_cwt_Piedras_pcfilt2-48.segy'
) as s:
c = segyio.cube(s)
import matplotlib.pyplot as plt
plt.imshow(c[100].T, cmap='Greys')
import numpy as np
#fL = 0.1 # Cutoff frequency as a fraction of the sampling rate (in (0, 0.5)).
#fH = 0.2 # Cutoff frequency as a fraction of the sampling rate (in (0, 0.5)).
#b = 0.08 # Transition band, as a fraction of the sampling rate (in (0, 0.5)).
#N = int(np.ceil((4 / b)))
#if not N % 2: N += 1 # Make sure that N is odd.
#n = np.arange(N)
# Compute a low-pass filter with cutoff frequency fH.
#hlpf = np.sinc(2 * fH * (n - (N - 1) / 2.))
def seisresult():
form = InputForm(request.form)
script = None
div = None
if request.method == 'POST' and form.validate() == False:
NumSamp = np.load('static/QC.npz')['arr_2']
InlineKeyNumber = np.load('static/QC.npz')['arr_0']
XlineKeyNumber = np.load('static/QC.npz')['arr_1']
upload_file = request.files['file']
filename = secure_filename(upload_file.filename)
upload_file.save(os.path.join(UPLOAD_FOLDER, filename))
image_height = 156
image_width = 156
image_depth = 100
num_channels = 1
images = []
# Reading the seismic using Pickle
#with open(os.path.join(UPLOAD_FOLDER, filename), 'rb') as f:
#test = pickle.load(f, encoding='bytes')
#test=np.array(test)
try:
seis = segyio.open(os.path.join(UPLOAD_FOLDER, filename),
iline=int(InlineKeyNumber),
xline=int(XlineKeyNumber))
seismic = segyio.cube(seis)
except:
seismic = np.load(os.path.join(UPLOAD_FOLDER, filename))
#np.savez_compressed('static/seishape.npz',seismic.shape[0],seismic.shape[1],seismic.shape[2])
seismic.tofile('static/seismic.asc', sep=" ")
os.system(
'echo in=static/seismic.asc n1=%d n2=%d n3=%d data_format=ascii_float | sfdd form=native | sfpatch w=100,156,156>static/seismicaf.rsf'
% (seismic.shape[2], seismic.shape[1], seismic.shape[0]))
e = m8r.File('static/seismicaf.rsf')
c = e[:]
test = c.reshape(-1, 156, 156, 100)
#print('Size of seismic volume is: %s' % str(test.shape))
m = -6.40475426972431e-05
s = 0.006666915856214509
test = (test - m) / s
graph = app.graph
## NOW the complete graph with values has been restored
y_pred = tf.nn.softmax(graph.get_tensor_by_name("Classifier/logits:0"))
## Let's feed the images to the input placeholders
#using the model for prediction
x_tensor = graph.get_tensor_by_name("Input/Placeholder:0")
#keep_prob is not always necessary it depends on your model
keep_prob = graph.get_tensor_by_name("Input/Placeholder_2:0")
is_training = graph.get_tensor_by_name("Input/Placeholder_3:0")
config = tf.compat.v1.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.9
sess = tf.compat.v1.Session(graph=graph, config=config)
### Creating the feed_dict that is required to be fed to calculate y_pred
feed_dict_testing = {x_tensor: test, keep_prob: 0.7, is_training: True}
prob_iter_tot = []
pred_iter_tot = []
prob_variance_tot = []
for i in range(NumSamp):
result = sess.run(y_pred, feed_dict=feed_dict_testing)
prob_iter_tot.append(result)
#pred_iter_tot.append(np.reshape(np.argmax(result,axis = -1),[result.shape[0],-1]))
#pred_iter_tot = np.array(pred_iter_tot)
#pred_iter_tot = np.reshape(pred_iter_tot,(NumSamp,result.shape[0],-1))
#pred_iter_tot = np.transpose(pred_iter_tot,(1,0,2))
prob_iter_tot = np.array(prob_iter_tot)
prob_iter_tot = np.reshape(prob_iter_tot,
(NumSamp, test.shape[0], 156, 156, 100, 2))
#prob_iter_tot_transp = np.transpose(prob_iter_tot,(1,0,2,3,4,5))
#for k in range(test.shape[0]):
#prob_variance,pred = MAX_VOTE(pred_iter_tot[k,:,:], prob_iter_tot_transp[k,:,:,:,:,:],2)
#prob_variance_tot.append(prob_variance)
prob = np.nanmean(prob_iter_tot, axis=0)
var = np.nanvar(prob_iter_tot, axis=0)
#prob_variance_tot = np.array(prob_variance_tot)
#prob_variance_tot = np.reshape(prob_variance_tot,(test.shape[0],156,156,100,2))
test = np.reshape(test,
(c.shape[0], c.shape[1], c.shape[2], 156, 156, 100))
result1 = np.reshape(
prob[:, :, :, :,
1], (c.shape[0], c.shape[1], c.shape[2], 156, 156, 100))
var1 = np.reshape(var[:, :, :, :, 1],
(c.shape[0], c.shape[1], c.shape[2], 156, 156, 100))
#var2 = np.reshape(prob_variance_tot[:,:,:,:,1],(c.shape[0],c.shape[2],156,156,100))
var1.tofile('static/var1.asc', sep=" ")
#var2.tofile('static/var2.asc', sep=" ")
result1.tofile('static/result1.asc', sep=" ")
test.tofile('static/test.asc', sep=" ")
os.system(
'echo in=static/test.asc n1=100 n2=156 n3=156 n4=%d n5=%d n6=%d data_format=ascii_float | sfdd form=native | sfpatch inv=y weight=y n0=%d,%d,%d>static/test.rsf'
% (c.shape[2], c.shape[1], c.shape[0], seismic.shape[2],
seismic.shape[1], seismic.shape[0]))
f = m8r.File('static/test.rsf')
a = f[:]
a = np.reshape(a,
(seismic.shape[0], seismic.shape[1], seismic.shape[2]))
os.system(
'echo in=static/result1.asc n1=100 n2=156 n3=156 n4=%d n5=%d n6=%d data_format=ascii_float | sfdd form=native | sfpatch inv=y weight=y n0=%d,%d,%d>static/result1.rsf'
% (c.shape[2], c.shape[1], c.shape[0], seismic.shape[2],
seismic.shape[1], seismic.shape[0]))
g = m8r.File('static/result1.rsf')
b = g[:]
b = np.reshape(b,
(seismic.shape[0], seismic.shape[1], seismic.shape[2]))
os.system(
'echo in=static/var1.asc n1=100 n2=156 n3=156 n4=%d n5=%d n6=%d data_format=ascii_float | sfdd form=native | sfpatch inv=y weight=y n0=%d,%d,%d>static/var1.rsf'
% (c.shape[2], c.shape[1], c.shape[0], seismic.shape[2],
seismic.shape[1], seismic.shape[0]))
h = m8r.File('static/var1.rsf')
k = h[:]
k = np.reshape(k,
(seismic.shape[0], seismic.shape[1], seismic.shape[2]))
#os.system('echo in=static/var2.asc n1=100 n2=156 n3=156 n4=%d n5=%d n6=%d data_format=ascii_float | sfdd form=native | sfpatch inv=y weight=y n0=%d,%d,%d>static/var2.rsf' % (c.shape[2],c.shape[1],c.shape[0],seismic.shape[2],seismic.shape[1],seismic.shape[0]))
#l = m8r.File('static/var2.rsf')
#l1 = l[:]
#l1 = np.reshape(l1,(seismic.shape[0],seismic.shape[1],seismic.shape[2]))
np.savez_compressed('static/result.npz', b)
np.savez_compressed('static/test.npz', seismic)
np.savez_compressed('static/var.npz', k)
#np.savez_compressed('static/var2.npz',l1)
#form = InputForm(request.form)
return render_template("channel_index6.html",
form=form,
script=script,
div=div)
if request.method == "POST" and form.validate():
# Determine the selected slice
current_slice = form.Position.data
# Determine the selected slice number
current_slice_number = form.Number.data
result = np.load('static/result.npz')['arr_0']
test = np.load('static/test.npz')['arr_0']
var = np.load('static/var.npz')['arr_0']
#var2 = np.load('static/var2.npz')['arr_0']
#rsfarray_1 = m8r.File(test)
#rsfarray_2 = m8r.put(d1=0.004, o1=0).patch(inv=True, weight=True, n0=[100,312,312])[rsfarray_1]
#test = rsfarray_2[:]
#rsfarray_1 = m8r.File(result1)
#rsfarray_2 = m8r.put(d1=0.004, o1=0).patch(inv=True, weight=True, n0=[100,312,312])[rsfarray_1]
#result = rsfarray_2[:]
#print(result.shape)
#print(test.shape)
# Create the plot
plot = create_figure(result, var, test, current_slice,
current_slice_number)
# Embed plot into HTML via Flask Render
script, div = components(plot)
return render_template("channel_index6.html",
form=form,
script=script,
div=div)