x_max = T_mult.shape[1]
y_max = T_mult.shape[2]
save = 1
#outall = "D:\\Juanma\\Seville 2014\\"+beam+"\\Change_detection\\"
outall = root + beam + "\\Change_detection\\Consecutive_changes\\"
application = "Difference change detection" # This is to initialise a class. Other options are: "Single image","ratio change detection".
for img_2 in range(N - 1):
date = dates[img_2] + ' - ' + dates[img_2 + 1]
T11 = T_mult[
img_2, :, :, :, :] # img_2 can be = 0 in T11 to do change detection of stack wrt to first image.
T22 = T_mult[img_2 + 1, :, :, :, :]
#sar.visRGB_from_T(T_mult[img_2+1,:,:,1,1], T_mult[img_2+1,:,:,2,2], T_mult[img_2+1,:,:,0,0],"",factor=3.5,save=0,outall="")
Tc = (T22 - T11)
eigen = sar.eigendecompositions(application)
List_RGB = eigen.gral_eigendecomposition(
Tc) # to store eigendecomposition results in the class
R_i, G_i, B_i = eigen.vis(eigen.L1_inc,
eigen.L2_inc,
eigen.L3_inc,
add_or_remove='added') # added SMs
R_d, G_d, B_d = eigen.vis(eigen.L1_dec,
eigen.L2_dec,
eigen.L3_dec,
add_or_remove='removed') # removed SMs
size = R_i.shape
a = np.zeros([size[0], size[1], 3])
# Enable or disable denominator to enable or disable normalizing the colours. Makes green stronger.
a[:, :, 0] = R_i #/np.nanmean(R_i) # To plot Added SMs
#folder = folders[i]
"""
###########################################################################################################################################################
# Read the covariance matrix for each resolution cell ############################
###########################################################################################################################################################
"""
T = T_mult[i, :, :, :, :]
#T=sar.array2D_of_coherency_matrices(folder,basis,in_format,ROI_size,header,datatype)
# show RGB
#sar.visRGB_from_T(T[:,:,1,1], T[:,:,2,2], T[:,:,0,0],"",factor=2.5)
"""
###########################################################################################################################################################
# return the eigenvalue and eigenvector decomposition. 2D array of image size, in which each position contains 3 eigenvelues, or 3x3 eigenvectors #########
###########################################################################################################################################################
"""
L1, L2, L3, U_11, U_21, U_31, U_12, U_22, U_32, U_13, U_23, U_33 = sar.eigendecomposition_vectorized(
T)
"""
###########################################################################################################################################################
# return the alpha, entropy, anisotropy and main scattering mechanishms images (Cloude-Pottier) #########
###########################################################################################################################################################
"""
title1 = ""
save = ""
outall = ""
alpha_avg, entropy, anisotropy, R_avg, G_avg, B_avg = sar.Alpha_Entropy_Anisotropy_decomp(
L1,
L2,
L3,
U_11,
U_21,
U_31,
for poly in range(len(Polygons_in_shp)):
pol_names.append(Polygons_in_shp[poly]['properties']['Name'])
print(pol_names)
################################################# repeat for every beam
#beams=["FQ8W","FQ13W","FQ19W"] #"AllAll"
#beams=["FQ8W"] #"AllAll"
#dates2 = ['22May2014','15June2014','09July2014','02August2014','26August2014','19Sept2014']
#beams=["FQ13W"]
#dates2 = ['22June2014','16July2014','09August2014','02Sept2014','26Sept2014']
beams = ["FQ19W"]
dates2 = [
'05June2014', '29June2014', '23July2014', '16August2014', '09Sept2014'
]
save = 1
application = "Difference change detection"
eigen = sar.eigendecompositions(application)
for t in range(len(beams)):
beam = beams[t]
################################################# repeat for every polygon in the shp
#for k in range(len(pol_names)):
for k in range(1):
#k=0
print("Parcel " + str(k + 1) + " of " + str(len(pol_names)))
poly = k # Type polygon to process
polygon_A = Polygons_in_shp[poly][
"geometry"] # filter the polygon from shp
################################################# Mask a polygon
# use rasterio to mask and obtain the pixels inside the polygon.
# Because the mask method of rasterio requires an interable object to work, we do:
polygon_A_list = [polygon_A]
# out_image in line below is a np array of the polygon desired
dates1 = [w.replace('Aug', 'August') for w in dates1]
dates1 = [w.replace('Jul', 'July') for w in dates1]
dates1 = [w.replace('Sep', 'September') for w in dates1]
#################################### sort the dates
import datetime
sorted_dates = sorted(dates1,
key=lambda x: datetime.datetime.strptime(x, '%d%B%Y'))
dates2 = sorted_dates.copy()
dates2 = [w.replace('June', 'Jun') for w in dates2]
dates2 = [w.replace('August', 'Aug') for w in dates2]
dates2 = [w.replace('July', 'Jul') for w in dates2]
dates2 = [w.replace('September', 'Sep') for w in dates2]
all_files = os.listdir(folder1)
cols, rows, header = sar.read_config_file_snap(folder1 + "\\" + T11_master)
datatype = 'float32'
basis = "P" # Lexicographic (L) or Pauli (P)
in_format = "img" # .Bin
x_min = 0
y_min = 0
x_max = cols
y_max = rows
ROI_size = [y_min, y_max, x_min, x_max]
stack_name = "T_stack" #"T_stack_asc"
#path_to_save_stack = "D:\\Juanma\\Seville 2014\\"+beam+"\\"+stack_name
path_to_RGB = "D:\\Juanma - Agrisar 2009\\My_results\\RGBs\\No_scale\\"
path_to_SM = "D:\\Juanma - Agrisar 2009\\My_results\\SMs\\No_scale\\รพ"
#date = "2014-08-09"
T_mult = np.zeros((len(dates), (x_max - x_min), (y_max - y_min), 3, 3),
dates1 = [w.replace('Jun', 'June') for w in dates1] # replace months name to names python understands
dates1 = [w.replace('Aug', 'August') for w in dates1]
dates1 = [w.replace('Jul', 'July') for w in dates1]
dates1 = [w.replace('Sep', 'September') for w in dates1]
#################################### sort the dates
import datetime
sorted_dates=sorted(dates1, key=lambda x: datetime.datetime.strptime(x, '%d%B%Y'))
dates2=sorted_dates.copy() # replace back months name to be able to call the files in the folder
dates2 = [w.replace('June', 'Jun') for w in dates2]
dates2 = [w.replace('August', 'Aug') for w in dates2]
dates2 = [w.replace('July', 'Jul') for w in dates2]
dates2 = [w.replace('September', 'Sep') for w in dates2]
all_files=os.listdir(folder1)
x_max,y_max,header=sar.read_config_file_snap(folder1 +"\\"+T11_master)
#T_mult=np.zeros((len(dates2),x_max,y_max,3,3))
N=len(dates2)
datatype = 'float32'
basis="P" # Lexicographic (L) or Pauli (P)
in_format="img" # Bin or img
# instantiate class to perform eigendecompositions indicating that we will use a covariance matrix correspondint to difference of two images
application = "Difference change detection"
#eigen = sar.eigendecompositions(application)
#create dataframe with same info
df=pd.DataFrame(index=np.arange(1,len(pol_names)+1),columns=['IDENT','CROP_TYPE','AREA_HA'])
df['IDENT']=pol_names
df['CROP_TYPE']=pol_crops
dates2 = [w.replace('June', 'Jun') for w in dates2]
dates2 = [w.replace('August', 'Aug') for w in dates2]
dates2 = [w.replace('July', 'Jul') for w in dates2]
dates2 = [w.replace('September', 'Sep') for w in dates2]
#for k in range(len(dates2)):
# a=[]
# for file in os.listdir(folder1):
# if file.endswith(".img"):
# if dates[k] in file:
# a.append(file)
# T11,T22,T33,T12,T13,T23=read_Img_components_from_stack_SNAP(basis,folder1,a)
# sar.visRGB_from_T(T22, T33, T11,"",factor=5)
all_files = os.listdir(folder1)
x_max, y_max, header = sar.read_config_file_snap(folder1 + "\\" + T11_master)
#T_mult=np.zeros((len(dates2),x_max,y_max,3,3))
N = len(dates2)
# Empty array to save outputs of multitemporal change detection
R_avg = np.zeros([N, N, x_max, y_max])
G_avg = np.zeros([N, N, x_max, y_max])
B_avg = np.zeros([N, N, x_max, y_max])
datatype = 'float32'
basis = "P" # Lexicographic (L) or Pauli (P)
in_format = "img" # Bin or img
# Multitemporal change detection of the cropped region
# First row of change matrix, Open the image 1 (i) and do change detection with respecto to all other images in the stack
# Second row: Open next image (i+1) in the stack and do change detection
# Continue for the n images in the stack
dates2 = [w.replace('June', 'Jun') for w in dates2]
dates2 = [w.replace('August', 'Aug') for w in dates2]
dates2 = [w.replace('July', 'Jul') for w in dates2]
dates2 = [w.replace('September', 'Sep') for w in dates2]
#for k in range(len(dates2)):
# a=[]
# for file in os.listdir(folder1):
# if file.endswith(".img"):
# if dates[k] in file:
# a.append(file)
# T11,T22,T33,T12,T13,T23=read_Img_components_from_stack_SNAP(basis,folder1,a)
# sar.visRGB_from_T(T22, T33, T11,"",factor=5)
all_files=os.listdir(folder1)
x_max,y_max,header=sar.read_config_file_snap(folder1 +"\\"+T11_master)
T_mult=np.zeros((len(dates2),x_max,y_max,3,3),dtype=complex)
basis = "P"
for i in range(len(dates2)):
a=[]
for file in os.listdir(folder1):
if file.endswith(".img"):
if dates[i] in file:
a.append(file)
print(a)
#folder=folders[i]
#x_max,y_max,header=sar.read_config_file_snap(folder+"T11")
basis="P" # Lexicographic (L) or Pauli (P)
in_format="img" # Bin or img
]
##############################################'FQ13W'
#beam = 'FQ13W'
#dates=["2014-06-22","2014-07-16","2014-08-09","2014-09-02","2014-09-26"]
##############################################'FQ13W'
#beam = 'FQ19W'
#dates=["2014-06-05","2014-06-29","2014-07-23","2014-08-16","2014-09-09"]
folder = "D:\\Juanma\\Seville 2014\\" + beam + "\\" + dates[
0] + ".rds2\\" #files[img] # path of the image
stack_name = "T_stack" #"T_stack_asc"
path_to_save_stack = "D:\\Juanma\\Seville 2014\\" + beam + "\\" + stack_name
path_to_RGB = "D:\\Juanma\\Seville 2014\\" + beam + "\\RGBs\\No_scale\\"
path_to_SM = "D:\\Juanma\\Seville 2014\\" + beam + "\\SMs\\No_scale\\"
cols, rows, header = sar.read_config_file(folder)
datatype = 'float32'
basis = "L" # Lexicographic (L) or Pauli (P)
in_format = "Bin" # .Bin
x_min = 2000
y_min = 2000
x_max = cols - 500
y_max = rows - 500
ROI_size = [y_min, y_max, x_min, x_max]
#date = "2014-08-09"
T_mult = np.zeros((len(dates), (x_max - x_min), (y_max - y_min), 3, 3),
dtype=complex)
save_RGBs = 1
N = len(dates)
for i in range(N):
dates1 = [w.replace('Jul', 'July') for w in dates1]
dates1 = [w.replace('Sep', 'September') for w in dates1]
#################################### sort the dates
import datetime
sorted_dates = sorted(dates1,
key=lambda x: datetime.datetime.strptime(x, '%d%B%Y'))
dates2 = sorted_dates.copy(
) # replace back months name to be able to call the files in the folder
dates2 = [w.replace('June', 'Jun') for w in dates2]
dates2 = [w.replace('August', 'Aug') for w in dates2]
dates2 = [w.replace('July', 'Jul') for w in dates2]
dates2 = [w.replace('September', 'Sep') for w in dates2]
all_files = os.listdir(folder1)
x_max, y_max, header = sar.read_config_file_snap(folder1 + "\\" + T11_master)
#T_mult=np.zeros((len(dates2),x_max,y_max,3,3))
N = len(dates2)
# Empty array to save outputs of multitemporal change detection
R_avg = np.zeros([N, N, x_max, y_max])
G_avg = np.zeros([N, N, x_max, y_max])
B_avg = np.zeros([N, N, x_max, y_max])
datatype = 'float32'
basis = "P" # Lexicographic (L) or Pauli (P)
in_format = "img" # Bin or img
# Multitemporal change detection of the cropped region
# First row of change matrix, Open the image 1 (i) and do change detection with respecto to all other images in the stack
# Second row: Open next image (i+1) in the stack and do change detection
# Continue for the n images in the stack
shp_name = "AgriSAR2009.shp"
T_mult_path = "X:\\crs2\\Paper2_Agrisar\\T_Stack\\"
T_mult_name = "T_stack.npy"
"""
###################################################################################################################################################
Loading datacube of array_2D_of_coherency matrices
###########################################################################################################################################################
"""
print("Loading datacube...")
T_mult = np.load(T_mult_path + T_mult_name)
"""
###################################################################################################################################################
read_raster_and_shp
###################################################################################################################################################
"""
raster, Polygons_in_shp = sar.read_raster_and_shp(tiff_path, tiff_name,
shp_path, shp_name)
"""
###################################################################################################################################################
Crop T_mult to polygon size
###################################################################################################################################################
"""
#crop_types_IDs=['L-17','P-06','MP-31','B-02','CF-01','CL-03','FL-26','CY-15','O-35','SF-24','FMIX-01','G-05','D-32','W-19']
# list of crops
pol_names = []
pol_crops = []
for poly in range(len(Polygons_in_shp)):
pol_names.append(Polygons_in_shp[poly]['properties']['IDENT'])
pol_crops.append(Polygons_in_shp[poly]['properties']['CROP_TYPE'])
#print(pol_names)
# initializing lists