total = sum(np.array(max_precision)) print(total / len(max_precision)) return (total / len(max_precision)) if __name__ == '__main__': tic = time.clock() args = parse_args() print('=' * 50) print('Called with args:') print(args) if args.data_dir: config.data_dir = args.data_dir config.set_paths() if args.model: config.model = args.model util.set_img_format() model_module = util.get_model_class_instance() model = model_module.load() classes_in_keras_format = util.get_classes_in_keras_format() all_metrix = [] print('args.path:', os.listdir(args.path)) for cow_dir in os.listdir(args.path): root = args.path + str(cow_dir) store_label = predict(root) # print(store_label)
cnf_matrix = confusion_matrix(y_trues, predictions) util.plot_confusion_matrix(cnf_matrix, config.classes, normalize=False) util.plot_confusion_matrix(cnf_matrix, config.classes, normalize=True) if __name__ == '__main__': tic = time.clock() args = parse_args() print('=' * 50) print('Called with args:') print(args) if args.data_dir: config.data_dir = args.data_dir config.set_paths() if args.model: config.model = args.model util.set_img_format() model_module = util.get_model_class_instance() model = model_module.load() classes_in_keras_format = util.get_classes_in_keras_format() predict(args.path) if args.execution_time: toc = time.clock() print('Time: %s' % (toc - tic))
util.plot_confusion_matrix(cnf_matrix, config.classes, normalize=True) if __name__ == '__main__': tic = time.clock() #return the current cpu time args = parse_args() print('=' * 50) print('Called with args:') print(args) if args.data_dir: #user defined directory config.data_dir = args.data_dir #~ config.set_paths() #~ if args.model: #user defined model config.model = args.model #~ util.set_img_format() #channel_first or channels_last model_module = util.get_model_class_instance() #class model.resnet50 model = model_module.load( ) #creat base_model and load trained weight!(ResNet50) "G:\keras-transfer-learning-for-oxford102\trained\fine-tuned-resnet50-weights.h5" classes_in_keras_format = util.get_classes_in_keras_format( ) #get a dictory of classes predict(args.path) #we must input a path of directory including pictures if args.execution_time: # toc = time.clock() #record current time
config.classes, normalize=False) util.plot_confusion_matrix(cnf_matrix, config.classes, normalize=True) if __name__ == '__main__': tic = time.clock() args = parse_args() print('=' * 50) print('Called with args:') print(args) if args.data_dir: config.data_dir = args.data_dir config.set_paths(args.data_dir) if args.model: config.model = args.model util.set_img_format() model_module = util.get_model_class_instance() model = model_module.load() classes_in_keras_format = util.get_finetuned_classes_in_keras_format() predict(args.path) if args.execution_time: toc = time.clock() print('Time: %s' % (toc - tic))