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