do_prediction*: refactor into separate module

tests/test_tiling.py

@@ -0,0 +1,108 @@
+import pytest
+import cv2
+import numpy as np
+from matplotlib import pyplot as plt
+
+from eynollah.utils.tiling import do_prediction, do_prediction_new_concept
+
+@pytest.mark.parametrize(
+    "height,width",
+    [
+        (448, 448),
+        (672, 672),
+        (1088, 832),
+        (1152, 896),
+    ])
+def test_tiling_idem(image_resources, height, width):
+    infile = image_resources[0]
+    class PseudoModel:
+        def predict(self, images, **kwargs):
+            return images
+        @property
+        def input_shape(self):
+            return None, height, width, None
+    model = PseudoModel()
+    in_img = cv2.imread(infile)
+    outimg = do_prediction(in_img, model, patches=True, is_enhancement=True,
+                           marginal_of_patch_percent=0)
+    assert in_img.shape == outimg.shape
+    assert np.all(in_img == outimg)
+    outimg = do_prediction(in_img, model, patches=True, is_enhancement=True,
+                           marginal_of_patch_percent=0.1)
+    assert in_img.shape == outimg.shape
+    assert np.all(in_img == outimg)
+    outimg = do_prediction(in_img, model, patches=True, is_enhancement=True,
+                           marginal_of_patch_percent=0.2)
+    assert in_img.shape == outimg.shape
+    assert in_img.dtype == outimg.dtype
+    assert np.all(in_img == outimg)
+
+@pytest.mark.parametrize(
+    "height,width",
+    [
+        (448, 448),
+        (672, 672),
+        (1088, 832),
+        (1152, 896),
+    ])
+def test_tiling_min(image_resources, height, width):
+    infile = image_resources[0]
+    class PseudoModel:
+        def predict(self, images, **kwargs):
+            M = images.min(axis=(1, 2, 3))
+            return 1. * (images == M)
+        @property
+        def input_shape(self):
+            return None, height, width, None
+    model = PseudoModel()
+    in_img = cv2.imread(infile)
+    outimg = do_prediction(in_img, model, patches=True,
+                           marginal_of_patch_percent=0)
+    assert in_img.shape[:2] == outimg.shape
+    assert np.any(outimg)
+    outimg = do_prediction(in_img, model, patches=True,
+                           marginal_of_patch_percent=0.1)
+    assert in_img.shape[:2] == outimg.shape
+    assert np.any(outimg)
+    outimg = do_prediction(in_img, model, patches=True,
+                           marginal_of_patch_percent=0.2)
+    assert in_img.shape[:2] == outimg.shape
+    assert np.any(outimg)
+
+@pytest.mark.parametrize(
+    "height,width",
+    [
+        (448, 448),
+        (672, 672),
+        (1088, 832),
+        (1152, 896),
+    ])
+def test_tiling_min_conf(image_resources, height, width):
+    infile = image_resources[0]
+    class PseudoModel:
+        def predict(self, images, **kwargs):
+            M = images.min(axis=(1, 2, 3))
+            return 1. * (images == M)
+        @property
+        def input_shape(self):
+            return None, height, width, None
+    model = PseudoModel()
+    in_img = cv2.imread(infile)
+    outimg, conf = do_prediction_new_concept(
+        in_img, model, patches=True,
+        marginal_of_patch_percent=0)
+    assert in_img.shape[:2] == outimg.shape
+    assert np.any(outimg)
+    assert np.sum(conf) < conf.size
+    outimg, conf = do_prediction_new_concept(
+        in_img, model, patches=True,
+        marginal_of_patch_percent=0.1)
+    assert in_img.shape[:2] == outimg.shape
+    assert np.any(outimg)
+    assert np.sum(conf) < conf.size
+    outimg, conf = do_prediction_new_concept(
+        in_img, model, patches=True,
+        marginal_of_patch_percent=0.2)
+    assert in_img.shape[:2] == outimg.shape
+    assert np.any(outimg)
+    assert np.sum(conf) < conf.size