def compare_seg_with_gt(max_n_images=10, epoch=0):
data_gen_validation = SegmentationBatchGeneratorDavid(
all_patients,
BATCH_SIZE,
validation_patients,
PATCH_SIZE=INPUT_PATCH_SIZE,
mode="test",
ignore=[81, 10, 47],
losses=None,
num_batches=None,
seed=10,
)
data_gen_validation = seg_channel_selection_generator(data_gen_validation, [2])
data_gen_validation = center_crop_seg_generator(data_gen_validation, OUTPUT_PATCH_SIZE)
data, seg, idx = data_gen_validation.next()
seg = np.array(seg)
seg_pred = get_segmentation(data)
plt.figure(figsize=(6, 20))
n_images = np.min((seg_pred.shape[0], max_n_images))
for i in range(n_images):
seg_pred[i][0, :6] = np.array([0, 1, 2, 3, 4, 5])
seg[i, 0, 0, :6] = np.array([0, 1, 2, 3, 4, 5])
plt.subplot(n_images, 2, 2 * i + 1)
plt.imshow(seg[i, 0])
plt.subplot(n_images, 2, 2 * i + 2)
plt.imshow(seg_pred[i])
plt.savefig(os.path.join(results_dir, "some_segmentations_ep_%d.png" % epoch))
def compare_seg_with_gt(max_n_images=10, epoch=0):
data_gen_validation = SegmentationBatchGeneratorDavid(
all_patients,
BATCH_SIZE,
validation_patients,
PATCH_SIZE=OUTPUT_PATCH_SIZE,
mode="test",
ignore=[81],
losses=None,
num_batches=None,
seed=10)
data_gen_validation = seg_channel_selection_generator(
data_gen_validation, [2])
data_gen_validation = center_crop_seg_generator(data_gen_validation,
OUTPUT_PATCH_SIZE)
data, seg, idx = data_gen_validation.next()
seg = np.array(seg)
seg_pred = get_segmentation(data)
plt.figure(figsize=(6, 20))
n_images = np.min((seg_pred.shape[0], max_n_images))
for i in range(n_images):
seg_pred[i][0, :6] = np.array([0, 1, 2, 3, 4, 5])
seg[i, 0, 0, :6] = np.array([0, 1, 2, 3, 4, 5])
plt.subplot(n_images, 2, 2 * i + 1)
plt.imshow(seg[i, 0])
plt.subplot(n_images, 2, 2 * i + 2)
plt.imshow(seg_pred[i])
plt.savefig(
os.path.join(results_dir, "some_segmentations_ep_%d.png" % epoch))
class_frequencies2 *= len(class_frequencies) '''d, s, l = data_gen_train.next() plt.figure(figsize=(12, 5)) plt.subplot(1, 3, 1) plt.imshow(d[0,0], cmap="gray") plt.subplot(1, 3, 2) d1=elastic_transform_2d(d[0,0], 550., 20.) plt.imshow(d1, cmap="gray") plt.subplot(1, 3, 3) plt.imshow(d[0,0]-d1) plt.show() plt.close()''' data_gen_validation = SegmentationBatchGeneratorDavid(all_patients, BATCH_SIZE, validation_patients, PATCH_SIZE=INPUT_PATCH_SIZE, mode="test", ignore=[81], losses=None, num_batches=None, seed=None) data_gen_validation = seg_channel_selection_generator(data_gen_validation, [2]) data_gen_validation = center_crop_seg_generator(data_gen_validation, OUTPUT_PATCH_SIZE) data_gen_validation = Multithreaded_Generator(data_gen_validation, 2, 10) data_gen_validation._start() net = build_UNet(25, BATCH_SIZE, num_output_classes=num_classes, base_n_filters=16, input_dim=INPUT_PATCH_SIZE, pad="valid") output_layer_for_loss = net["output_flattened"] n_batches_per_epoch = 300 # n_batches_per_epoch = np.floor(n_training_samples/float(BATCH_SIZE)) n_test_batches = 30 # n_test_batches = np.floor(n_val_samples/float(BATCH_SIZE)) x_sym = T.tensor4() seg_sym = T.ivector()