def main():
#calib = calibration.calibration(visualtion=True)
base_dataset_path = os.path.join(os.getcwd(), "datasets", "test_lab6")
file_name = os.path.join(base_dataset_path, "imageData.txt")
image_directory = base_dataset_path
drone_location = os.path.join(base_dataset_path, "drone_postion.txt")
write_img_dir_path = os.path.join(base_dataset_path, "results")
all_images, data_matrix = util.importData(file_name, drone_location,
image_directory)
all_images = all_images[:12]
data_matrix = data_matrix[:12]
# for i in range(0,3):
# all_images[i] = all_images[i][::10, ::10, :]
#all_imgs_undistorted = calib.calibrate(all_images)
# stitcher = cv2.createStitcher() if imutils.is_cv3() else cv2.Stitcher_create()
# (status, stitched) = stitcher.stitch(all_images)
my_combiner = Combiner.Combiner(all_images, data_matrix)
result = my_combiner.createMosaic()
util.display("RESULT", result)
if not os.path.exists(write_img_dir_path):
os.makedirs(write_img_dir_path)
cv2.imwrite(os.path.join(write_img_dir_path, "finalResult3.png"), result)
if os.path.isdir('results') == True:
os.rename('results', 'results - ' + str(now))
os.mkdir('results')
fileName = "datasets/imageData.txt"
imageDirectory = "datasets/images/"
print("Creating Temp Directory")
if os.path.isdir('temp') == True:
shutil.rmtree('temp', ignore_errors=False, onerror=None)
os.mkdir('temp')
print("Copying Images to Temp Directory")
allImages, dataMatrix = util.importData(fileName, imageDirectory)
# Perspective.changePerspective(allImages, dataMatrix)
print("Sitiching Images")
start = time.time()
result = Combiner.combine()
end = time.time()
util.display("RESULT", result, 4000000)
cv2.imwrite("results/final_result.jpg", result)
print("Time --->>>>>", end - start)
print("Done. Find your final image in results folder as final_result.jpg")
imgcols = 23
num_classes = 2
epochs = 500
batch_size = 64
ismodelsaved = True
undersampling = False
flpath = '/data/'
#
print('\n\n')
print('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')
print('!!! PREDICTIONS ON CRISPOR !!!')
print('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')
#
print('\n!!! DATA PIPELINE !!!\n')
loaddata = utilities.importData(flpath=flpath,
encoding=str(imgrows) + 'x' + str(imgcols),
sim='crispor',
tl=False)
x_train, x_test, y_train, y_test = train_test_split(
loaddata.images,
pd.Series(loaddata.target), #loaddata.target,
test_size=0.3,
shuffle=True,
random_state=42)
xtraincnn, xtestcnn, ytraincnn, ytestcnn, inputshapecnn = cnns.transformImages(
x_train, x_test, y_train, y_test, imgrows, imgcols, num_classes)
xtrainffn, xtestffn, ytrainfnn, ytestffn, inputshapeffn = ffns.transformImages(
x_train, x_test, y_train, y_test, imgrows, imgcols, num_classes)
xtrainrf, xtestrf, ytrainrf, ytestrf = mltrees.transformImages(
x_train, x_test, y_train, y_test, imgrows, imgcols)
#
print('\n!!! TRAINING PIPELINE !!!\n')
'''
Driver script. Execute this to perform the mosaic procedure.
'''
import utilities as util
import Combiner
import cv2
fileName = "datasets/imageData.txt"
imageDirectory = "datasets/images/"
allImages, dataMatrix = util.importData(fileName, imageDirectory)
myCombiner = Combiner.Combiner(allImages, dataMatrix)
result = myCombiner.createMosaic()
util.display("RESULT", result)
cv2.imwrite("results/finalResult.png", result)