def test_2DSegmentation():
os.makedirs(os.getcwd() + "/tests/segmentation/testimages/train/image/", exist_ok=True)
os.makedirs(os.getcwd() + "/tests/segmentation/testimages/train/groundtruth/", exist_ok=True)
os.makedirs(os.getcwd() + "/tests/segmentation/testimages/test/image/", exist_ok=True)
os.makedirs(os.getcwd() + "/tests/segmentation/testimages/test/groundtruth/", exist_ok=True)
X_true = np.ones((32, 32, 3))
Y_true = 255. * np.ones((32, 32, 3))
for i in range(0,20):
imsave(os.getcwd() + "/tests/segmentation/testimages/train/image/image"+str(i)+".jpg", X_true)
imsave(os.getcwd() + "/tests/segmentation/testimages/train/groundtruth/image"+str(i)+".jpg", Y_true)
for i in range(0,5):
imsave(os.getcwd() + "/tests/segmentation/testimages/test/image/image"+str(i)+".jpg", X_true)
imsave(os.getcwd() + "/tests/segmentation/testimages/test/groundtruth/image"+str(i)+".jpg", Y_true)
configs = {"use_algorithm": "SemanticSegmentation",
"path": "./tests/segmentation/testimages/",
"pretrained_weights": False,
"batchsize": 1,
"iterations_over_dataset": 1,
"image_size": False,
"evaluation": False}
pipeline = GetPipeLine(configs)
pipeline.run()
K.clear_session()
assert (os.path.isfile(os.getcwd() + "/tests/segmentation/testimages/logs/pretrained_weights.hdf5"))
# Make sure the networks has changed something
for i in range(0, 5):
assert (X_true != np.array((imread(os.getcwd() + "/tests/segmentation/testimages/results/image"+str(i)+".jpg_predict.tif")).astype("uint8"))).all
#Delete created test data
if os.path.exists(os.getcwd()+"/tests/segmentation/testimages") and os.path.isdir(os.getcwd()+"/tests/segmentation/testimages"):
shutil.rmtree(os.getcwd()+"/tests/segmentation/testimages")
def start_learning(configs):
logging.info("Start learning")
logging.info(configs["use_algorithm"])
pipeline = GetPipeLine(configs)
pipeline.run()
K.clear_session()
def test_Classification():
os.makedirs(os.getcwd() + "/tests/classification/testimages/train/image/",
exist_ok=True)
os.makedirs(os.getcwd() +
"/tests/classification/testimages/train/groundtruth/",
exist_ok=True)
os.makedirs(os.getcwd() + "/tests/classification/testimages/test/image/",
exist_ok=True)
os.makedirs(os.getcwd() +
"/tests/classification/testimages/test/groundtruth/",
exist_ok=True)
X_true = np.ones((32, 32))
with open(
os.getcwd() +
"/tests/classification/testimages/train/groundtruth/" +
'groundtruth.csv', 'w') as writeFile:
writer = csv.writer(writeFile)
writer.writerow(['filename', 'groundtruth'])
for i in range(0, 20):
imsave(
os.getcwd() +
"/tests/classification/testimages/train/image/image" + str(i) +
".jpg", X_true)
writer.writerow(["image" + str(i) + ".jpg", str(1)])
with open(
os.getcwd() +
"/tests/classification/testimages/test/groundtruth/" +
'groundtruth.csv', 'w') as writeFile:
writer = csv.writer(writeFile)
writer.writerow(['filename', 'groundtruth'])
for i in range(0, 5):
imsave(
os.getcwd() +
"/tests/classification/testimages/test/image/image" + str(i) +
".jpg", X_true)
writer.writerow(["image" + str(i) + ".jpg", str(1)])
configs = {
"use_algorithm": "Classification",
"path": "./tests/classification/testimages/",
"batchsize": 1,
"iterations_over_dataset": 1,
"num_classes": 2,
"evaluation": False
}
pipeline = GetPipeLine(configs)
pipeline.run()
K.clear_session()
# Make sure the networks has changed something
assert (
os.path.isfile(os.getcwd() +
"/tests/classification/testimages/results/results.csv"))
#Delete created test data
if os.path.exists(os.getcwd() +
"/tests/classification/testimages") and os.path.isdir(
os.getcwd() + "/tests/classification/testimages"):
shutil.rmtree(os.getcwd() + "/tests/classification/testimages")