reinstate ensemble combination and in-memory prediction

sbb_binarize/cli.py

@@ -15,4 +15,4 @@ from .sbb_binarize import SbbBinarizer
 def main(model_dir, input_image, output_image):
     binarizer = SbbBinarizer()
     binarizer.load_model(model_dir)
-    binarizer.binarize_image(image_path=input_image, save_path=output_image)
+    binarizer.binarize_image_file(image_path=input_image, save_path=output_image)

sbb_binarize/sbb_binarize.py

@@ -5,7 +5,7 @@ import math
 import os
 import sys
 from pathlib import Path
-from typing import Union, List, Any
+from typing import Union, List, Tuple, Any
 
 import cv2
 import numpy as np
@@ -24,37 +24,53 @@ from mpire.utils import make_single_arguments
 class SbbBinarizer:
     def __init__(self) -> None:
         super().__init__()
-        self.model: Any = None
-        self.model_height: int = 0
-        self.model_width: int = 0
-        self.n_classes: int = 0
+        self.models: List[Tuple[Any, int, int, int]] = []
 
     def load_model(self, model_dir: Union[str, Path]):
         model_dir = Path(model_dir)
-        self.model = load_model(str(model_dir.absolute()), compile=False)
-        self.model_height = self.model.layers[len(self.model.layers) - 1].output_shape[1]
-        self.model_width = self.model.layers[len(self.model.layers) - 1].output_shape[2]
-        self.n_classes = self.model.layers[len(self.model.layers) - 1].output_shape[3]
+        model_paths = list(model_dir.glob('*.h5')) or list(model_dir.glob('*/'))
+        for path in model_paths:
+            model = load_model(str(path.absolute()), compile=False)
+            height = model.layers[len(model.layers) - 1].output_shape[1]
+            width = model.layers[len(model.layers) - 1].output_shape[2]
+            classes = model.layers[len(model.layers) - 1].output_shape[3]
+            self.models.append((model, height, width, classes))
 
-    def binarize_image(self, image_path: Path, save_path: Path):
+    def binarize_image_file(self, image_path: Path, save_path: Path):
         if not image_path.exists():
             raise ValueError(f"Image not found: {str(image_path)}")
 
         # noinspection PyUnresolvedReferences
         img = cv2.imread(str(image_path))
+
+        full_image = self.binarize_image(img)
+
+        Path(save_path).parent.mkdir(parents=True, exist_ok=True)
+        # noinspection PyUnresolvedReferences
+        cv2.imwrite(str(save_path), full_image)
+
+    def binarize_image(self, img: np.ndarray) -> np.ndarray:
+        img_last = False
+        for model, model_height, model_width, _ in self.models:
+            img_res = self.binarize_image_by_model(img, model, model_height, model_width)
+            img_last = img_last + (img_res == 0)
+        img_last = (~img_last).astype(np.uint8) * 255
+        return img_last
+
+    def binarize_image_by_model(self, img: np.ndarray, model: Any, model_height: int, model_width: int) -> np.ndarray:
+        # Padded images must be multiples of model size
         original_image_height, original_image_width, image_channels = img.shape
 
-        # Padded images must be multiples of model size
-        padded_image_height = math.ceil(original_image_height / self.model_height) * self.model_height
-        padded_image_width = math.ceil(original_image_width / self.model_width) * self.model_width
+        padded_image_height = math.ceil(original_image_height / model_height) * model_height
+        padded_image_width = math.ceil(original_image_width / model_width) * model_width
         padded_image = np.zeros((padded_image_height, padded_image_width, image_channels))
         padded_image[0:original_image_height, 0:original_image_width, :] = img[:, :, :]
 
         image_batch = np.expand_dims(padded_image, 0)  # Create the batch dimension
         patches = tf.image.extract_patches(
             images=image_batch,
-            sizes=[1, self.model_height, self.model_width, 1],
-            strides=[1, self.model_height, self.model_width, 1],
+            sizes=[1, model_height, model_width, 1],
+            strides=[1, model_height, model_width, 1],
             rates=[1, 1, 1, 1],
             padding='SAME'
         )
@@ -62,13 +78,13 @@ class SbbBinarizer:
         number_of_horizontal_patches = patches.shape[1]
         number_of_vertical_patches = patches.shape[2]
         total_number_of_patches = number_of_horizontal_patches * number_of_vertical_patches
-        target_shape = (total_number_of_patches, self.model_height, self.model_width, image_channels)
+        target_shape = (total_number_of_patches, model_height, model_width, image_channels)
         # Squeeze all image patches (n, m, width, height, channels) into a single big batch (b, width, height, channels)
         image_patches = tf.reshape(patches, target_shape)
         # Normalize the image to values between 0.0 - 1.0
         image_patches = image_patches / float(255.0)
 
-        predicted_patches = self.model.predict(image_patches)
+        predicted_patches = model.predict(image_patches, verbose=0)
         # We have to manually call garbage collection and clear_session here to avoid memory leaks.
         # Taken from https://medium.com/dive-into-ml-ai/dealing-with-memory-leak-issue-in-keras-model-training-e703907a6501
         gc.collect()
@@ -83,13 +99,11 @@ class SbbBinarizer:
             grayscale_patches,
             padded_image_height,
             padded_image_width,
-            self.model_height,
-            self.model_width
+            model_height,
+            model_width
         )
         full_image = full_image_with_padding[0:original_image_height, 0:original_image_width]
-        Path(save_path).parent.mkdir(parents=True, exist_ok=True)
-        # noinspection PyUnresolvedReferences
-        cv2.imwrite(str(save_path), full_image)
+        return full_image
 
     def _patches_to_image(self, patches: np.ndarray, image_height: int, image_width: int, patch_height: int, patch_width: int):
         height = math.ceil(image_height / patch_height) * patch_height