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))
