Example #1
0
    print "Learning step: " + str(learning_step_current_nr) + "..."

    # Get image from training set.
    img_train_name = img_train_names[it]

    # Load the image and labels of image with name 'img_train_name'
    img_train = np.load(TRAINING_AND_TESTING_DATA_ROOT + "img_npy/" + img_train_name)
    labels_train = np.load(
        TRAINING_AND_TESTING_DATA_ROOT + "labels_npy/" + img_train_name.replace("_img.npy", "_labels.npy")
    )

    # Preprocess the data before loading it into FCN.
    img_train_FCN_ready = lib.rgb_to_bgr(img_train)
    img_train_FCN_ready = lib.subtract_bgr_channel_means(
        img=img_train_FCN_ready,
        blue_mean=channel_means["blue_mean"],
        green_mean=channel_means["green_mean"],
        red_mean=channel_means["red_mean"],
    )
    img_train_FCN_ready = img_train_FCN_ready.transpose(2, 0, 1).reshape(1, 3, 500, 500)
    labels_train_FCN_ready = labels_train.reshape(1, 1, 500, 500)

    # Load image and labels into FCN.
    solver.net.blobs["data"].data[...] = img_train_FCN_ready
    solver.net.blobs["label"].data[...] = labels_train_FCN_ready

    # Make one training step.
    solver.step(1)

    # Measure and temporarily store the loss every 'loss_interval' training steps.
    if it % loss_interval == 0:
        loss_temp = float(solver.net.blobs["loss"].data)
Example #2
0
    # Print current learning step.
    print "Learning step: " + str(learning_step_current_nr) + '...'

    # Get image from training set.
    img_train_name = img_train_names[it]

    # Load the image and labels of image with name 'img_train_name'
    img_train = np.load(TRAINING_AND_TESTING_DATA_ROOT + 'img_npy/' +
                        img_train_name)
    labels_train = np.load(TRAINING_AND_TESTING_DATA_ROOT + 'labels_npy/' +
                           img_train_name.replace('_img.npy', '_labels.npy'))

    # Preprocess the data before loading it into FCN.
    img_train_FCN_ready = lib.rgb_to_bgr(img_train)
    img_train_FCN_ready = lib.subtract_bgr_channel_means(img=img_train_FCN_ready,
                                                         blue_mean=channel_means['blue_mean'],
                                                         green_mean=channel_means['green_mean'],
                                                         red_mean=channel_means['red_mean'])
    img_train_FCN_ready = img_train_FCN_ready.transpose(2, 0, 1).reshape(1, 3, 500, 500)
    labels_train_FCN_ready = labels_train.reshape(1, 1, 500, 500)

    # Load image and labels into FCN.
    solver.net.blobs['data'].data[...] = img_train_FCN_ready
    solver.net.blobs['label'].data[...] = labels_train_FCN_ready

    # Make one training step.
    solver.step(1)

    # Measure and temporarily store the loss every 'loss_interval' training steps.
    if it % loss_interval == 0:
        loss_temp = float(solver.net.blobs['loss'].data)
        loss_current.append(loss_temp)
    labels_predicted = np.argmax(scores, 2).astype(np.uint8)
    lib.plot_sat_image_and_labels(img=img_cropped,
                                  lab=labels_predicted,
                                  alpha=0.5,
                                  plot_img=True,
                                  store_img=False,
                                  plot_with='pyplot',
                                  dir_name=RESULTS_ROOT,
                                  file_name='test_image_x_' + area_name + RUN_NAME + str(number_of_training_steps),
                                  cmap=cmap1)

    img = lib.rgb_to_bgr(img)
    blue_mean = channel_means['blue_mean']
    green_mean = channel_means['green_mean']
    red_mean = channel_means['red_mean']
    img = lib.subtract_bgr_channel_means(img, blue_mean, green_mean, red_mean)
    tiles_of_image = lib.image_to_tiles(img=img,
                                        hgh=500,
                                        wdt=500)

    net.predict([tiles_of_image[0, 0, :, :, :]], oversample=False)
    caffe_out = net.blobs['score'].data[0, :, :, :]
    asdf = np.argmax(caffe_out, 0).astype(np.uint8)

    lib.plot_sat_image_and_labels(img=img_cropped,
                                  lab=asdf,
                                  alpha=0.5,
                                  plot_img=False,
                                  store_img=True,
                                  plot_with='Image',
                                  dir_name=RESULTS_ROOT,