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| import cv2 | |
| from keras.models import load_model | |
| from keras.utils.generic_utils import CustomObjectScope | |
| from models.unets import Unet2D | |
| from models.deeplab import Deeplabv3, relu6, BilinearUpsampling, DepthwiseConv2D | |
| from models.FCN import FCN_Vgg16_16s | |
| from utils.learning.metrics import dice_coef, precision, recall | |
| from utils.BilinearUpSampling import BilinearUpSampling2D | |
| from utils.io.data import load_data, save_results, save_rgb_results, save_history, load_test_images, DataGen | |
| # settings | |
| input_dim_x = 224 | |
| input_dim_y = 224 | |
| color_space = 'rgb' | |
| path = './data/Medetec_foot_ulcer_224/' | |
| weight_file_name = '2019-12-19 01%3A53%3A15.480800.hdf5' | |
| pred_save_path = '2019-12-19 01%3A53%3A15.480800/' | |
| data_gen = DataGen(path, split_ratio=0.0, x=input_dim_x, y=input_dim_y, color_space=color_space) | |
| x_test, test_label_filenames_list = load_test_images(path) | |
| # ### get unet model | |
| # unet2d = Unet2D(n_filters=64, input_dim_x=input_dim_x, input_dim_y=input_dim_y, num_channels=3) | |
| # model = unet2d.get_unet_model_yuanqing() | |
| # model = load_model('./azh_wound_care_center_diabetic_foot_training_history/' + weight_file_name | |
| # , custom_objects={'recall':recall, | |
| # 'precision':precision, | |
| # 'dice_coef': dice_coef, | |
| # 'relu6':relu6, | |
| # 'DepthwiseConv2D':DepthwiseConv2D, | |
| # 'BilinearUpsampling':BilinearUpsampling}) | |
| # ### get separable unet model | |
| # sep_unet = Separable_Unet2D(n_filters=64, input_dim_x=input_dim_x, input_dim_y=input_dim_y, num_channels=3) | |
| # model, model_name = sep_unet.get_sep_unet_v2() | |
| # model = load_model('./azh_wound_care_center_diabetic_foot_training_history/' + weight_file_name | |
| # , custom_objects={'dice_coef': dice_coef, | |
| # 'relu6':relu6, | |
| # 'DepthwiseConv2D':DepthwiseConv2D, | |
| # 'BilinearUpsampling':BilinearUpsampling}) | |
| # ### get VGG16 model | |
| # model, model_name = FCN_Vgg16_16s(input_shape=(input_dim_x, input_dim_y, 3)) | |
| # with CustomObjectScope({'BilinearUpSampling2D':BilinearUpSampling2D}): | |
| # model = load_model('./azh_wound_care_center_diabetic_foot_training_history/' + weight_file_name | |
| # , custom_objects={'dice_coef': dice_coef}) | |
| # ### get mobilenetv2 model | |
| model = Deeplabv3(input_shape=(input_dim_x, input_dim_y, 3), classes=1) | |
| model = load_model('./training_history/' + weight_file_name | |
| , custom_objects={'recall':recall, | |
| 'precision':precision, | |
| 'dice_coef': dice_coef, | |
| 'relu6':relu6, | |
| 'DepthwiseConv2D':DepthwiseConv2D, | |
| 'BilinearUpsampling':BilinearUpsampling}) | |
| for image_batch, label_batch in data_gen.generate_data(batch_size=len(x_test), test=True): | |
| prediction = model.predict(image_batch, verbose=1) | |
| save_results(prediction, 'rgb', path + 'test/predictions/' + pred_save_path, test_label_filenames_list) | |
| break | |