# Manual settings bridge = CvBridge() orig_size = (720, 576) depth_orig = (558, 375) target_size = (64, 64) crop = ((55, 431), (107, 665)) # Create Flapnet objects fn_struct = flapnet.Structure() fn_losses = flapnet.LossFunction() fn_preproc = flapnet.Preprocessing() fn_post = flapnet.Postprocessing() img_proc = grapof.ImageProcessing() ts = grapof.TopicsSubscription() geo = grapof.Geometry() fn = flapnet.Functions(shape_img=(64, 64, 3)) def callback(image_msg): # define global variable global cam_disp global cv_image # convert image to a compatible format cv_image = bridge.imgmsg_to_cv2(image_msg, desired_encoding='bgr8') cv_image = cv_image[crop[0][0]:crop[0][1], crop[1][0]:crop[1][1]] cam_disp = fn_preproc.image_preproc(cv_image, target_size) # ROS init rospy.init_node('camera_flap_detection', anonymous=True)
mpl.rcParams['figure.figsize'] = (12, 12) import os import flapnet import numpy as np import matplotlib as mpl import pandas as pd import tensorflow as tf from tensorflow.python.keras import models mpl.rcParams['axes.grid'] = False mpl.rcParams['figure.figsize'] = (12, 12) model_path = '/home/aleks/nn_ftw.hdf5' testset_size = 0.1 fn = flapnet.Functions() fn_struct = flapnet.Structure() fn_losses = flapnet.LossFunction() # Da/home/aleks/nn_results/Gtaset folders init dataset_name = os.path.join('tissue_dataset', 'cyst_dataset') img_dir = os.path.join(dataset_name, "train") label_dir = os.path.join(dataset_name, "label") df_train = pd.read_csv(os.path.join(dataset_name, 'cyst_dataset.csv')) # Load filenames of labels and tra ining objects x_train_filenames, x_val_filenames, y_train_filenames, y_val_filenames = fn.load_filenames( df_train=df_train, img_dir=img_dir, label_dir=label_dir, test_size=testset_size)
#---------------------------- PARAMETERS ---------------------------# ##################################################################### img_shape = (64, 64, 3) # Input image shape batch_size = 30 # Batch size for training (decrease in case of memory error) epochs = 200 # Training epochs testset_size = 0.1 # Percentage of training set data (0.10 = 10%) dropout_rate = 0.4 # Droput rate num_filters = 32 # Number of neurons in first layer (subsequent layers have x2 neurons) learning_rate = 0.001 # Learning rate tuning for optimizer adam_opt = tf.keras.optimizers.Adam(lr=learning_rate) kfold = KFold(n_splits=6, shuffle=True, random_state=42) # FlapNet class init fn = flapnet.Functions(img_shape, batch_size, epochs) fn_struct = flapnet.Structure() fn_losses = flapnet.LossFunction() # Dataset folders init # dataset_name = os.path.join('tissue_dataset', 'dataset_baseline') # img_dir = os.path.join(dataset_name, "train") # label_dir = os.path.join(dataset_name, "labels") # df_train = pd.read_csv(os.path.join(dataset_name,'label_map_00.csv')) # # dataset_name = os.path.join('tissue_dataset', 'dataset_ready_aug_02') img_dir = os.path.join(dataset_name, "train") label_dir = os.path.join(dataset_name, "labels") df_train = pd.read_csv(os.path.join(dataset_name, 'ready_dataset_aug_02.csv')) #####################################################################