예제 #1
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        # 2、初始化和启动线程
        tf.global_variables_initializer().run()
        tf.local_variables_initializer().run()
        coord = tf.train.Coordinator()
        threads = tf.train.start_queue_runners(sess=sess, coord=coord)
        model.restore(model_restore_name)

        #3、测试图片
        index = 0
        for i in range(test_data_number // batch_size):
            pics, pics_masks = sess.run([images, labels])  # 取出一个batchsize的图片
            pics = pics / 255
            # 3、计算耗时
            since = time.time()
            pre = model.predict(pics)
            seconds = time.time() - since

            pre_list = np.split(pre[0], batch_size, axis=0)
            pres = np.squeeze(pre_list, axis=0)

            for img, label, predict in zip(pics, pics_masks, pres):
                img = img * 255
                label = label * 255
                predict = predict * 255

                label = cv2.cvtColor(label, cv2.COLOR_GRAY2BGR)
                predict = cv2.cvtColor(predict, cv2.COLOR_GRAY2BGR)

                hstack = np.hstack((img, label, predict))
예제 #2
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        loss=bce_dice_loss,
        metrics=[bce_dice_loss, recall_score, precision_score, rocauc_score])
    ckpt = ModelCheckpoint('.model.hdf5',
                           save_best_only=True,
                           monitor='val_rocauc_score',
                           mode='max')

    model.fit_generator(train_generator,
                        steps_per_epoch=len(xtr) / 6,
                        epochs=50,
                        validation_data=val_generator,
                        validation_steps=len(xval) / batch_size,
                        callbacks=[ckpt])
    model.load_weights('.model.hdf5')

    preds_test = model.predict(X_test, verbose=1)

    preds_test_upsampled = []
    for i in range(len(preds_test)):
        preds_test_upsampled.append(
            cv2.resize(preds_test[i], (sizes_test[i][1], sizes_test[i][0])))

    test_ids = next(os.walk(test_path))[1]
    new_test_ids = []
    rles = []
    for n, id_ in enumerate(test_ids):
        rle = list(prob_to_rles(preds_test_upsampled[n]))
        rles.extend(rle)
        new_test_ids.extend([id_] * len(rle))
    sub = pd.DataFrame()
    sub['ImageId'] = new_test_ids