def get_som_clf(self):
dir_list = ['..', '..', 'logger', 'final']
som_path = utils.join_list(dir_list + [Log.FINAL_SOM.value + ".pkl"])
NN_path = utils.join_list(dir_list + [Log.FINAL_CLF.value + ".pkl"])
som = joblib.load(som_path)
clf = joblib.load(NN_path)
return som, clf
def read_data(path):
"""returns path X,Y of data where X[i] is an image and Y[i] is its label"""
data = {}
val = {"no": -1, "undetermined": 0, "yes": 1}
print("started reading data")
start_time = time.time()
labels = []
for (dirpath, dirnames, filenames) in os.walk(path):
labels.extend(dirnames)
break
label_val = assign_vals(labels)
for (dirpath, dirnames, filenames) in os.walk(path):
label = utils.get_dirname(dirpath)
if label not in label_val:
continue
data[label] = filenames
X = []
Y = []
for label in data:
for img_name in data[label]:
path_to_img = utils.join_list([path, label, img_name])
X.append(cv2.imread(path_to_img, 0))
Y.append(label_val[label])
elapsed_time = time.time() - start_time
minutes = elapsed_time / 60
seconds = elapsed_time % 60
print("finished reading data in %d min and %d seconds" %
(minutes, seconds))
X = np.array(X)
Y = np.array(Y)
return X, Y
def visit(self, node, scope: Scope):
self.visit(node.expr, scope)
self.update_type(node.expr.inferenced_type)
type_list = []
for var in node.casevars:
child = scope.create_child()
self.visit(var, child)
type_list.append(var.inferenced_type)
node_type = join_list(type_list)
node.inferenced_type = node_type
def visit(self, node, scope: Scope):
self.visit(node.expr, scope)
var_types = []
for var in node.casevars:
child = scope.next_child()
self.visit(var, child)
var_types.append(var.inferenced_type)
node_inferred = node.inferenced_type
auto = join_list(var_types)
if is_subset(auto, node_inferred):
node.inferenced_type = self.compare_types(node_inferred, auto)
def read_data(path):
"""returns path X,Y of data where X[i] is an image and Y[i] is its label"""
data = {}
print("started reading data")
start_time = time.time()
labels = []
for (dirpath, dirnames, filenames) in walk(path):
labels.extend(dirnames)
break
label_val = assign_vals(labels)
cnt_all = 0
for (dirpath, dirnames, filenames) in walk(path):
label = utils.get_dirname(dirpath)
if label not in label_val: continue
data[label] = filenames
cnt_all += len(filenames)
X = []
Y = []
cnt = lst = 0
for label in data:
for img_name in data[label]:
path_to_img = utils.join_list([path, label, img_name])
X.append(cv2.imread(path_to_img, 0))
Y.append(label_val[label])
cnt += 1
p = int(cnt * 100.0 / cnt_all)
if p == lst + 5:
print('%d%% done' % p, cnt)
lst = p
elapsed_time = time.time() - start_time
minutes = elapsed_time / 60
seconds = elapsed_time % 60
print("finished reading data in %d min and %d seconds" %
(minutes, seconds))
#X = np.array(X)
Y = np.array(Y)
return X, Y
(cnt, minutes, seconds))
return res_imgs, res_labels
def test(imgs, labels, path_to_trained_som, path_to_trained_network, som_m,
som_n):
surf = cv2.xfeatures2d.SURF_create(10000)
print('reading som')
som = joblib.load(path_to_trained_som)
print('reading network')
clf = joblib.load(path_to_trained_network)
print("found %d images" % (len(imgs)))
print('compressing images')
imgs, labels = transform_data(imgs, labels, som, som_m, som_n)
prediction = clf.predict(imgs)
accuracy = accuracy_score(labels, prediction) * 100
return accuracy
if __name__ == "__main__":
# ToDo: change the logger child directory to reflect the right cached SOM
dir_list = [
'..', 'logger',
'on Sun Jun 18 13-53-06 2017 working on goal with MLP and SOM is [20, 20] and [5, 5] hidden layer'
]
som_path = utils.join_list(dir_list + [Log.SOM.value + ".pkl"])
NN_path = utils.join_list(dir_list + [Log.CLF.value + ".pkl"])
# ToDo: use the testing data path instead
data_path = utils.join_parent("goal_test")
imgs, labels = read_data(data_path)
print(test(imgs, labels, som_path, NN_path, 20, 20))
def load_clfs():
dir_list = ['..', '..', 'logger', 'final']
clfs, fclfs = DT.read_clfs(utils.join_list(dir_list))
return clfs, fclfs