(test_x, test_y) = (np.load(".\\npy\\NRR_x_9339in2334.npy"), np.load(".\\npy\\NRR_y_9339in2334.npy")) train_x = np.concatenate([train_x_90, train_x_180], axis=0) train_y = np.concatenate([train_y_90, train_y_180], axis=0) #print("test x shape {}".format(test_x.shape)) test_data_start = training_num + 1 input_size = (256, 256, 4) for i in range(len(name)): # print("Train data shape {}\n{}".format(train_x.shape, train_y.shape)) print("Building model.") input_shape = (256, 256, 3) model_select = model.UNet_DtoU5(block=model.RDBlocks, name="unet_2RD-5", input_size=input_shape, block_num=2) print("Loading data.") if i == 0: print("EX high data") excel_file = ".\\result\\data\\20201118_256_51984_UNet(2RDB8-DtoU-5)_CETrainData_iou.xlsx" total_num = 51984 (train_x, train_y) = data.extract_high_result( np.load(".\\npy\\V1_start1_total51984_size256_x.npy"), np.load(".\\npy\\V1_start1_total51984_size256_y.npy"), excel_file, total_num, threshold=0.8) excel_file = ".\\result\\data\\20201118_256_51984_UNet(2RDB8-DtoU-5)_CE_iou.xlsx" total_num = 12996 (test_x, test_y) = data.extract_high_result(
cv2.drawContours(result[index], contours, contour, 1, -1) result[index] = np.expand_dims(cv2.dilate(result[index], kernel, iterations=1), axis=-1) return result if __name__ == "__main__": os.environ["CUDA_VISIBLE_DEVICES"] = "-1" (train_x, train_y) = (np.load(".\\npy\\V3.2_x_1in7131.npy"), np.load(".\\npy\\V3.2_y_1in7131.npy")) model_select = model.UNet_DtoU5(block=model.RDBlocks, input_size=(256, 256, 4), n_layers_per_block=8, block_num=2) model_select.load_weights( ".\\result\model_record\V3.2_test\\20200525_256(50%)_7131_V3.2_UNet(2RDB8-DtoU-5)_CE\\20200525_256(50%)_7131_V3.2_UNet(2RDB8-DtoU-5)_CE.h5" ) test_flag = 1 batch = 3 epoch = 50 print("model building.") # model_build = model.model(model=model_select, name="20200525_256(50%)_7131_V3.2_UNet(2RDB8-DtoU-5)_CE", size=(256, 256, 4)) print("model building.") model_build = model.model( model=model_select, name="20200525_256(50%)_7131_V3.2_UNet(2RDB8-DtoU-5)_CE", size=(256, 256, 4))