def create_dataset(**kwargs):
yolo = YOLO(**dict(kwargs))
input_path = os.path.expanduser(kwargs.get('input_path', ''))
output_path = os.path.expanduser(kwargs.get('output_path', ''))
class_name = kwargs.get('class_name', '')
class_names = yolo._get_class()
if class_name not in class_names:
yolo.close_session()
return
class_name = class_name.replace(' ', '_')
output_path = output_path + '/' + class_name + '_dataset/'
try:
os.makedirs(output_path)
except OSError:
pass
for root, _, files in os.walk(input_path):
label = os.path.basename(root).lower()
if len(files) > 0:
try:
os.makedirs(output_path + label)
except:
pass
for file in files:
input_file = root + '/' + file
try:
image = Image.open(input_file)
except:
continue
else:
_, images = yolo.detect_image(image)
for image in images:
output_file = output_path + label + '/' + str(uuid4()) + \
'.png'
image.save(output_file)
yolo.close_session()
# create instance of the flask class
app = Flask(__name__)
app.config["DEBUG"] = True
# initialize instance of class YOLO in memory
yolo_m1 = YOLO()
# yolo_m2 = YOLO()
# set model paths
yolo_m1.model_path = 'model_data/final_model.h5'
# yolo_m2.model_path ='model_data/model_2/with_6_categories.h5'
# set anchors and txt_paths
yolo_m1.anchors_path = 'model_data/new_anchors.txt'
yolo_m1.classes_path = 'model_data/new_classes.txt'
yolo_m1.class_names = yolo_m1._get_class()
# yolo_m2.anchors_path = 'model_data/model_2/txts/anchors.txt'
# yolo_m2.classes_path = 'model_data/model_2/txts/classes.txt'
yolo_m1.anchors = yolo_m1._get_anchors()
# load models in memory
yolo_m1.boxes, yolo_m1.scores, yolo_m1.classes = yolo_m1.generate()
print("model 1 loaded successfully...")
# yolo_m2.boxes, yolo_m2.scores, yolo_m2.classes = yolo_m2.generate()
# print("model 2 loaded successfully...")
def detection_function():
om = ''
predictions = {}
directory = yolo_m1.test_images_directory
