def test_train_and_compare():
weights_file = artifact_dir + "/weights.h5"
print(weights_file)
model_v4, test_func_v4 = anmccv4.train_model(dataset_dir)
try:
os.makedirs(artifact_dir)
except:
print("Error creating artifact_dir")
model_v4.save_weights(weights_file)
model_v5 = anmccv5.ComponentModelCTC_v5(weights_file)
test_func_v5 = model_v5._predictor
size = 32
tests = 20
for i in range(tests):
X_data = np.random.ranf([size, 1, model_v5.img_h, model_v5.img_w])
v4_out = test_func_v4([X_data])
v5_out = test_func_v5([X_data])
assert len(v4_out) == len(
v5_out), "Output lengths differ: \nv4: {}\nv5: {}".format(
len(v4_out), len(v5_out))
print("V4:\n{}".format(v4_out))
print("V5:\n{}".format(v5_out))
for i, v4v in enumerate(v4_out):
v5v = v5_out[i]
assert np.all(v4v == v5v)
def test_train_for_a_single_word():
model = anmccv5.ComponentModelCTC_v5(None)
model.train_model(dataset_dir)
save_file = "/tmp/small-classifier.h5"
model.save_weights(save_file)
model_reloaded = anmccv5.ComponentModelCTC_v5(save_file)
image_files_dir = os.path.expandvars(
"$HOME/Annex/Arabic/arabic-component-dataset-test-small/")
image_files = aui.recursive_file_list(image_files_dir)
assert len(image_files) > 0
for image_file in image_files:
assert os.path.exists(image_file)
img = cv2.imread(image_file, 0)
# cls = model.classify_image(img)
cls_reloaded = model_reloaded.classify_image(img)
mm = re.match(image_files_dir + r'([a-z]+)/cc.*png', image_file)
correct_cls = mm.group(1)
yield check_cls, correct_cls, cls_reloaded
def test_train_and_classify():
current_weights_file = artifact_dir + "/weights.h5"
if os.path.exists(current_weights_file):
model = anmccv5.ComponentModelCTC_v5(current_weights_file)
else:
model = anmccv5.ComponentModelCTC_v5(None)
model.train_model(dataset_dir=dataset_dir, nb_epoch=100)
if not os.path.exists(artifact_dir):
os.mkdir(artifact_dir)
model.save_weights(current_weights_file)
image_files_dir = dataset_dir
image_files = aui.recursive_file_list(image_files_dir)
image_files = np.random.choice(image_files, 200)
assert len(image_files) > 0
for image_file in image_files:
assert os.path.exists(image_file)
img = cv2.imread(image_file, 0)
cls = model.classify_image(img)
mm = re.match(image_files_dir + r'([a-z]+)/.*png', image_file)
correct_cls = mm.group(1)
yield check_cls_among, correct_cls, cls
def test_classify_image_with_pretrained():
model = anmccv5.ComponentModelCTC_v5(weights_file)
image_files_dir = os.path.expandvars(
"$HOME/Annex/Arabic/Avians/arabic-component-dataset-4170/")
image_files = aui.recursive_file_list(image_files_dir)
image_files = np.random.choice(image_files, 200)
assert len(image_files) > 0
for image_file in image_files:
assert os.path.exists(image_file)
img = cv2.imread(image_file, 0)
cls = model.classify_image(img)
mm = re.match(image_files_dir + r'([a-z]+)/cc.*png', image_file)
correct_cls = mm.group(1)
yield check_cls, correct_cls, cls
def test_init():
# Is it able to create a model
model = anmccv5.ComponentModelCTC_v5(weights_file)
assert model is not None