brain_tumor_segmentation_models.py 文件源码

def predict_image(self, test_img):
        """
        predicts classes of input image
        :param test_img: filepath to image to predict on
        :return: segmented result
        """
        # imgs = io.imread(test_img).astype('float').reshape(5, 216, 160)
        imgs = mpimg.imread(test_img).astype('float')
        imgs = rgb2gray(imgs).reshape(5, 216, 160)

        plist = []

        # create patches_to_predict from an entire slice
        for img in imgs[:-1]:
            if np.max(img) != 0:
                img /= np.max(img)
            p = extract_patches_2d(img, (33, 33))
            plist.append(p)
        patches_to_predict = np.array(
            zip(np.array(plist[0]), np.array(plist[1]), np.array(plist[2]), np.array(plist[3])))

        # predict classes of each pixel based on model
        full_pred = self.model.predict_classes(patches_to_predict)
        fp1 = full_pred.reshape(184, 128)
        return fp1