def test_writeImage(self):
img_dims = (25600,25600)
img_1 = np.ones(img_dims,'uint8') * 123
img_2 = img_1.copy()
le.writeImage(img_1, self.filename)
read_img = np.zeros(img_dims,'uint8')
le.getImage(read_img,self.filename)
assert((read_img==img_2).all(), True )
def makePredictions(inputFile):
X, image, blocklist = getPredictionData(inputFile)
click.echo('Loading trained network data....')
#Load stored data from network
try:
with open('net.pickle', 'rb') as f:
net_pretrain = pickle.load(f)
net_pretrain.max_epochs = 25 # Train the previous model over more epochs
except IOError as e:
print "No trained network is available. Use train command to train first. "
click.echo('')
click.echo('Making predictions....')
#Make predictions
y_pred = net_pretrain.predict(X)
#Checking to see if predictions detect any sand dunes. Outputs the indice where a 1 is found.
ones = 0
zeroes = 0
array_dunes = []
for i in range(y_pred.shape[0]):
if y_pred[i] == 1:
ones+=1
array_dunes.append(i)
elif y_pred[i] == 0:
zeroes += 1
for x, y in enumerate(y_pred):
if y == 1:
blocklist[x][:] = 255
click.echo('')
click.echo('Dune blocks detected followed by negative blocks.')
print ones, zeroes
click.echo('')
click.echo('Adding predictions to input image....')
#Adding predictions to image data
load_extension.writeImage(image, 'prediction.tif')
click.echo('')
click.echo('Writing image to directory....')
import Tkinter root = Tkinter.Tk() image_file = "PSP_009650_1755_RED" train_file = image_file+"_dunes.tif" image_file += ".tif" rows, cols = load_extension.getDims(image_file) ratio = max((cols)/root.winfo_screenwidth(),(rows)/root.winfo_screenheight()) size = (cols /ratio , rows / ratio) sub_rows = rows/8 sub_cols = cols/4 print sub_rows, sub_cols image = np.zeros((rows,cols),"uint8") load_extension.getImage(image,image_file) # im = Image.fromarray(image[:sub_rows, sub_cols:]) #im.thumbnail(size,Image.ANTIALIAS) #im.show() # ones = np.ones((sub_rows,sub_cols),'uint8') # image = np.multiply(ones, image[:sub_rows, :sub_cols]) load_extension.writeImage( image[:sub_rows][sub_cols:], "test.tif") load_extension.getImage(image,train_file) load_extension.writeImage(image[:sub_rows][sub_cols:], "train.tif")
import load_extension
import numpy as np
import matplotlib.pyplot as plt
import Tkinter
import datetime
from PIL import Image
root = Tkinter.Tk()
start = datetime.datetime.now()
#assumes you have Ryans images in the same folder as this script
filename ="PSP_009650_1755_RED.tif"
#filename = "example.tif"
#filename = "training_image.tif"
training_file = "PSP_009650_1755_RED_dunes.tif"
rows,cols = load_extension.getDims(filename)
train_image = np.zeros((rows,cols),'uint8')
image = np.zeros((rows,cols),'uint8')
load_extension.getImage(image,filename)
newfile = filename.split(".")[0] +"_flipped."+filename.split(".")[1]
start = datetime.datetime.now()
image = image[::-1]
ones = np.ones((rows,cols),'uint8')
image = np.multiply(ones, image)
load_extension.writeImage(image,newfile)
print datetime.datetime.now() -start
import load_extension
import numpy as np
import matplotlib.pyplot as plt
import Tkinter
import datetime
from PIL import Image
root = Tkinter.Tk()
start = datetime.datetime.now()
#assumes you have Ryans images in the same folder as this script
filename = "PSP_009650_1755_RED.tif"
#filename = "example.tif"
#filename = "training_image.tif"
training_file = "PSP_009650_1755_RED_dunes.tif"
rows, cols = load_extension.getDims(filename)
train_image = np.zeros((rows, cols), 'uint8')
image = np.zeros((rows, cols), 'uint8')
load_extension.getImage(image, filename)
newfile = filename.split(".")[0] + "_flipped." + filename.split(".")[1]
start = datetime.datetime.now()
image = image[::-1]
ones = np.ones((rows, cols), 'uint8')
image = np.multiply(ones, image)
load_extension.writeImage(image, newfile)
print datetime.datetime.now() - start
import numpy as np
from PIL import Image
import Tkinter
root = Tkinter.Tk()
image_file = "PSP_009650_1755_RED"
train_file = image_file + "_dunes.tif"
image_file += ".tif"
rows, cols = load_extension.getDims(image_file)
ratio = max((cols) / root.winfo_screenwidth(),
(rows) / root.winfo_screenheight())
size = (cols / ratio, rows / ratio)
sub_rows = rows / 8
sub_cols = cols / 4
print sub_rows, sub_cols
image = np.zeros((rows, cols), "uint8")
load_extension.getImage(image, image_file)
# im = Image.fromarray(image[:sub_rows, sub_cols:])
#im.thumbnail(size,Image.ANTIALIAS)
#im.show()
# ones = np.ones((sub_rows,sub_cols),'uint8')
# image = np.multiply(ones, image[:sub_rows, :sub_cols])
load_extension.writeImage(image[:sub_rows][sub_cols:], "test.tif")
load_extension.getImage(image, train_file)
load_extension.writeImage(image[:sub_rows][sub_cols:], "train.tif")