fixes and more tests

config/datasets/semantic-segmentation/segmentation_brain_complex.yaml

@@ -14,4 +14,4 @@ train:
   target: ./data/stroke/train
 infer:
   source: https://api.blackhole.ai.innopolis.university/public-datasets/rtk/complex_infer.zip
-  target: /data/complex/infer
+  target: ./data/complex/infer

examples/metrics_ct_brain_hemorrhage_detection.sh

@@ -6,7 +6,7 @@ if [ -z "$data_path" ]; then
 fi
 
 if [ -z "$output" ]; then
-	output="../innofw/logs/infer/segmentation/semantic-segmentation/SK_180822_qmciwj41_unet_brain_rtk/"
+	output="../innofw/logs/infer/segmentation/semantic-segmentation/AK_081224_gwVOeQ_unet_brain_rtk/"
 	output+="$(ls $output | tail -n 1)"
 	echo "Using default output path $output"
 fi

examples/metrics_ct_brain_hemorrhage_segmentation.sh

@@ -6,7 +6,7 @@ if [ -z "$data_path" ]; then
 fi
 
 if [ -z "$output" ]; then
-	output="../innofw/logs/infer/segmentation/semantic-segmentation/SK_180822_qmciwj41_unet_brain_rtk/"
+	output="../innofw/logs/infer/segmentation/semantic-segmentation/AK_081224_gwVOeQ_unet_brain_rtk/"
 	output+="$(ls $output -tr| tail -n 1)"
 	echo "Using default output path $output"
 fi

examples/metrics_ct_mri_complexing.sh

@@ -7,7 +7,7 @@ if [ -z "$data_path" ]; then
 fi
 
 if [ -z "$output" ]; then
-	output="../innofw/logs/infer/segmentation/semantic-segmentation/SK_100923_unet_brain_complex.yaml/"
+	output="../innofw/logs/infer/segmentation/semantic-segmentation/AK_081224_Yjc97FX_unet_brain_complex.yaml/"
 	output+="$(ls $output -tr | tail -n 1)"
 fi
 python innofw/utils/data_utils/rtk/CT_complexing_metrics.py -i $data_path -o $output

innofw/utils/data_utils/preprocessing/CT_hemorrhage_contrast_metrics.py

@@ -71,6 +71,7 @@ def hemorrhage_contrast_metrics(input_path: str):
         metrics = calculate_metrics(raw_image, image)
         plt.suptitle("\n".join([f"{k}:{np.round(v, 2)}" for k, v in metrics.items()]))
         plt.show()
+        plt.close("all")
 
 
 def callback(arguments):

innofw/utils/data_utils/preprocessing/CT_hemorrhage_contrast_rtk.py

@@ -23,7 +23,7 @@ def hemorrhage_contrast(input_path: str, output_folder: str = None):
         except TypeError:
             raise ValueError(f"Wrong path to save: {output_folder}")
     if urlparse(input_path):
-        default_path = "innofw/data/rtk/infer/"
+        default_path = "./data/rtk/infer/"
         path = {"source": input_path, "target": default_path}
     else:
         path = {"source": input_path, "target": input_path}

innofw/utils/data_utils/rtk/CT_hemorrhage_metrics.py

@@ -62,7 +62,7 @@ def compute_metrics(gt_boxes, pr_boxes, iou_threshold=0.5):
     return metrics
 
 
-def processing(input_path, output_folder, task="detection"):
+def process_metrics(input_path, output_folder, task="detection"):
 
     dataset = DicomCocoDatasetRTK(data_dir=input_path, transform=transform)
     outs = os.listdir(output_folder)
@@ -107,6 +107,7 @@ def processing(input_path, output_folder, task="detection"):
         patch = Patch(facecolor="red", edgecolor="r", label="pathology")
         f.legend(handles=[patch], loc="lower center")
         plt.show()
+        plt.close("all")
 
 
 def result_bbox(masks, image):
@@ -168,15 +169,12 @@ def draw_bboxes(image, bboxes):
     Returns:
     np.ndarray: Изображение с отрисованными bounding boxes.
     """
-
     color = [255, 0, 0]
 
     for bbox in bboxes:
         if not bbox:
             break
-
         x_min, y_min, x_max, y_max = bbox
-
         cv2.rectangle(image, (x_min, y_min), (x_max, y_max), color, 2)
 
     return image
@@ -185,7 +183,7 @@ def draw_bboxes(image, bboxes):
 def callback(arguments):
     """Callback function for arguments"""
     try:
-        processing(arguments.input, arguments.output, arguments.task)
+        process_metrics(arguments.input, arguments.output, arguments.task)
     except KeyboardInterrupt:
         print("You exited")
 

tests/unit/datamodules/lightning_datamodules/test_rtk.py

@@ -1,3 +1,4 @@
+from unittest.mock import patch
 import os
 import shutil
 
@@ -14,8 +15,10 @@
 )
 from innofw.core.datasets.coco_rtk import DicomCocoDatasetRTK
 from innofw.utils.data_utils.preprocessing.CT_hemorrhage_contrast_rtk import (
-    hemorrhage_contrast,
+    hemorrhage_contrast, transform as resize_transform
 )
+from innofw.utils.data_utils.rtk.CT_hemorrhage_metrics import process_metrics
+from innofw.utils.data_utils.preprocessing.CT_hemorrhage_contrast_metrics import hemorrhage_contrast_metrics
 
 rtk_complex = "https://api.blackhole.ai.innopolis.university/public-datasets/rtk/complex_infer.zip"
 rtk_segm = "https://api.blackhole.ai.innopolis.university/public-datasets/rtk/infer.zip"
@@ -47,7 +50,7 @@ def test_DicomCocoDataModuleRTK(transform):
         shutil.rmtree(target_dir)
 
     path = {"source": rtk_segm, "target": target_dir}
-    dm = DicomCocoDataModuleRTK(infer=path, transform=transform)
+    dm = DicomCocoDataModuleRTK(infer=path, transform=resize_transform)
     dm.setup_infer()
     ds = dm.predict_dataloader()
     for batch in ds:
@@ -97,7 +100,8 @@ def test_datamodule_description():
         assert key in ds
 
 
-def test_hemor_contrast(tmp_path_factory):
+@patch("matplotlib.pyplot.show")
+def test_hemor_contrast(mock_show, tmp_path_factory):
     target_dir = "./data/rtk/infer"
     if os.path.exists(target_dir):
         shutil.rmtree(target_dir)
@@ -108,3 +112,29 @@ def test_hemor_contrast(tmp_path_factory):
     assert len(content) % 3 == 0
     assert np.any([x.endswith("npy") for x in content])
     assert np.any([x.endswith("png") for x in content])
+
+    hemorrhage_contrast_metrics(out_)
+    assert mock_show.call_count > 0
+
+
+@pytest.mark.parametrize("task", ["segmentation", "detection"])
+@patch("matplotlib.pyplot.show")
+def test_segm_detection_pipeline_metrics(mock_show, tmp_path_factory, task):
+
+    # just to imitate data loading
+    target_dir = tmp_path_factory.mktemp("target_dir")
+    path = {"source": rtk_segm, "target": target_dir}
+    dm = DicomCocoDataModuleRTK(infer=path, transform=resize_transform)
+    dm.setup_infer()
+    ds = dm.predict_dataloader()
+    ds.transform = resize_transform
+
+    samples_number = len(ds)
+
+    out_dir = tmp_path_factory.mktemp("out")
+    for i in range(samples_number):
+        random_numpy = np.random.randint(0, 1, [256,256,1])
+        np.save(os.path.join(out_dir, f"{i}.npy"), random_numpy)
+
+    process_metrics(input_path=target_dir, output_folder=out_dir)
+    assert mock_show.call_count > 0