Merge pull request #163 from PUTvision/fix/shifted_channels

Fix shifted values, updated test for them

src/deepness/metadata.txt

@@ -6,7 +6,7 @@
 name=Deepness: Deep Neural Remote Sensing
 qgisMinimumVersion=3.22
 description=Inference of deep neural network models (ONNX) for segmentation, detection and regression
-version=0.6.2
+version=0.6.3
 author=PUT Vision
 [email protected]
 

src/deepness/processing/map_processor/map_processor_segmentation.py

@@ -87,8 +87,7 @@ def _create_result_message(self, result_img: np.ndarray) -> str:
             total_area = number_of_pixels_in_processing_area * self.params.resolution_m_per_px**2
 
             for channel_id in range(layer_sizes):
-                # See note in the class description why are we adding/subtracting 1 here
-                pixels_count = counts_map.get(channel_id, 0)
+                pixels_count = counts_map.get(channel_id + 1, 0) # we add 1 to avoid 0 values, find the MADD1 code for explanation
                 area = pixels_count * self.params.resolution_m_per_px**2
 
                 if total_area > 0 and not np.isnan(total_area) and not np.isinf(total_area):
@@ -110,8 +109,7 @@ def _create_vlayer_from_mask_for_base_extent(self, mask_img) -> Callable:
         for output_id, layer_sizes in enumerate(self._get_indexes_of_model_output_channels_to_create()):
             output_vlayers = []
             for channel_id in range(layer_sizes):
-                # See note in the class description why are we adding/subtracting 1 here
-                local_mask_img = np.uint8(mask_img[output_id] == channel_id)
+                local_mask_img = np.uint8(mask_img[output_id] == (channel_id + 1)) # we add 1 to avoid 0 values, find the MADD1 code for explanation
 
                 contours, hierarchy = cv2.findContours(local_mask_img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
                 contours = processing_utils.transform_contours_yx_pixels_to_target_crs(
@@ -177,10 +175,10 @@ def _process_tile(self, tile_img_batched: np.ndarray) -> np.ndarray:
                 result = np.expand_dims(result, axis=1)
 
             if (result.shape[1] == 1):
-                result = (result != 0).astype(int)
+                result = (result != 0).astype(int) + 1 # we add 1 to avoid 0 values, find the MADD1 code for explanation
             else:
                 shape = result.shape
-                result = np.argmax(result, axis=1).reshape(shape[0], 1, shape[2], shape[3]) + 1
+                result = np.argmax(result, axis=1).reshape(shape[0], 1, shape[2], shape[3]) + 1 # we add 1 to avoid 0 values, find the MADD1 code for explanation
 
             assert len(result.shape) == 4
             assert result.shape[1] == 1

test/test_map_processor_segmentation.py

@@ -144,10 +144,10 @@ def test_generic_processing_test__specified_extent_from_vlayer_one_channel():
 
     # just check a few pixels
     assert all(result_img.ravel()[[365, 41234, 59876, 234353, 111222, 134534, 223423, 65463, 156451]] ==
-               np.asarray([0, 1, 1, 1, 1, 0, 0, 1, 0]))
+               np.asarray([0, 2, 2, 2, 2, 0, 0, 2, 0]))
 
     # and counts of different values
-    np.testing.assert_allclose(np.unique(result_img, return_counts=True)[1], np.array([178063, 206029]), atol=3)
+    np.testing.assert_allclose(np.unique(result_img, return_counts=True)[1], np.array([166903,  11159, 206030]), atol=3)
 
 def test_generic_processing_test__specified_extent_from_vlayer_two_channels():
     qgs = init_qgis()
@@ -188,10 +188,10 @@ def test_generic_processing_test__specified_extent_from_vlayer_two_channels():
 
     # just check a few pixels
     assert all(result_img.ravel()[[365, 41234, 59876, 234353, 111222, 134534, 223423, 65463, 156451]] ==
-               np.asarray([0, 1, 1, 1, 1, 0, 0, 1, 0]))
+               np.asarray([0, 2, 2, 2, 2, 0, 0, 2, 0]))
 
     # and counts of different values
-    np.testing.assert_allclose(np.unique(result_img, return_counts=True)[1], np.array([356126, 412058]), atol=3)
+    np.testing.assert_allclose(np.unique(result_img, return_counts=True)[1], np.array([333806,  22318, 412060]), atol=3)
 
 
 def test_generic_processing_test__specified_extent_from_vlayer_crs3857_one_channel():
@@ -233,9 +233,9 @@ def test_generic_processing_test__specified_extent_from_vlayer_crs3857_one_chann
 
     # just check a few pixels
     assert all(result_img.ravel()[[365, 41234, 59876, 234353, 111222, 134534, 223423, 65463, 156451]] ==
-               np.asarray([0, 0, 1, 1, 1, 0, 0, 1, 0]))
+               np.asarray([0, 0, 2, 2, 2, 0, 0, 2, 0]))
     # and counts of different values
-    np.testing.assert_allclose(np.unique(result_img, return_counts=True)[1], np.array([193853, 203797]), atol=3)
+    np.testing.assert_allclose(np.unique(result_img, return_counts=True)[1], np.array([182693,  11159, 203798]), atol=3)
 
 def test_generic_processing_test__specified_extent_from_vlayer_crs3857_two_channels():
     qgs = init_qgis()
@@ -279,9 +279,9 @@ def test_generic_processing_test__specified_extent_from_vlayer_crs3857_two_chann
 
     # just check a few pixels
     assert all(result_img.ravel()[[365, 41234, 59876, 234353, 111222, 134534, 223423, 65463, 156451]] ==
-               np.asarray([0, 0, 1, 1, 1, 0, 0, 1, 0]))
+               np.asarray([0, 0, 2, 2, 2, 0, 0, 2, 0]))
 
-    np.testing.assert_allclose(np.unique(result_img, return_counts=True)[1], np.array([387706, 407594]), atol=3)
+    np.testing.assert_allclose(np.unique(result_img, return_counts=True)[1], np.array([365386,  22318, 407596]), atol=3)
 
 
 def test_generic_processing_test__specified_extent_from_active_map_extent_one_channel():

test/test_map_processor_segmentation_sigmoid.py

@@ -1,6 +1,7 @@
 from test.test_utils import (create_default_input_channels_mapping_for_rgba_bands, create_rlayer_from_file,
                              create_vlayer_from_file, get_dummy_fotomap_area_crs3857_path, get_dummy_fotomap_area_path,
-                             get_dummy_fotomap_small_path, get_dummy_segmentation_model_path, get_dummy_sigmoid_model_path, init_qgis)
+                             get_dummy_fotomap_small_path, get_dummy_segmentation_model_path,
+                             get_dummy_sigmoid_model_path, init_qgis)
 from unittest.mock import MagicMock
 
 import matplotlib.pyplot as plt
@@ -56,8 +57,8 @@ def test_sigmoid_model_processing__entire_file():
     result_img = map_processor.get_result_img()
 
     assert result_img.shape == (1, 561, 829)
-    non_zero_pixels = np.count_nonzero(result_img)
-    assert abs(non_zero_pixels - 25002) < 50  # number of RED pixels in the image
+    pixels = np.unique(result_img, return_counts=True)
+    assert abs(pixels[1][1] - 25002) < 50  # number of RED pixels in the image
 
     # you should see only the part of RASTER_FILE_PATH that is pure red pixels. Use snippet below for debugging
     # from matplotlib import pyplot as plt