Add OBB to docs, tests, fix typos

docs/source/creators/creators_add_metadata_to_model.rst

@@ -53,6 +53,7 @@ Availeble detector types:
 - :code:`YOLO_v9`
 - :code:`YOLO_Ultralytics`
 - :code:`YOLO_Ultralytics_segmentation`
+- :code:`YOLO_Ultralytics_obb`
 
 =======
 Example

test/manual_test_map_processor_obb_yolo_ultralytics.py

@@ -0,0 +1,60 @@
+import os
+from pathlib import Path
+from test.test_utils import create_default_input_channels_mapping_for_rgb_bands, create_rlayer_from_file, init_qgis
+from unittest.mock import MagicMock
+
+from deepness.common.processing_overlap import ProcessingOverlap, ProcessingOverlapOptions
+from deepness.common.processing_parameters.detection_parameters import DetectionParameters, DetectorType
+from deepness.common.processing_parameters.map_processing_parameters import ProcessedAreaType
+from deepness.processing.map_processor.map_processor_detection import MapProcessorDetection
+from deepness.processing.models.detector import Detector
+
+# Files and model from github issue: https://github.com/PUTvision/qgis-plugin-deepness/discussions/101
+
+HOME_DIR = Path(__file__).resolve().parents[1]
+EXAMPLE_DATA_DIR = os.path.join(HOME_DIR, 'examples', 'manually_downloaded')
+MODEL_FILE_PATH = os.path.join(EXAMPLE_DATA_DIR, 'yolo11m-obb.onnx')
+
+RASTER_FILE_PATH = os.path.join(HOME_DIR, 'examples', 'yolov7_planes_detection_google_earth', 'google_earth_planes_lawica.png')
+
+INPUT_CHANNELS_MAPPING = create_default_input_channels_mapping_for_rgb_bands()
+
+
+def test_map_processor_obb_yolo_ultralytics():
+    qgs = init_qgis()
+
+    rlayer = create_rlayer_from_file(RASTER_FILE_PATH)
+    model_wrapper = Detector(MODEL_FILE_PATH)
+
+    params = DetectionParameters(
+        resolution_cm_per_px=50,
+        tile_size_px=model_wrapper.get_input_size_in_pixels()[0],  # same x and y dimensions, so take x
+        batch_size=1,
+        local_cache=False,
+        processed_area_type=ProcessedAreaType.ENTIRE_LAYER,
+        mask_layer_id=None,
+        input_layer_id=rlayer.id(),
+        input_channels_mapping=INPUT_CHANNELS_MAPPING,
+        processing_overlap=ProcessingOverlap(ProcessingOverlapOptions.OVERLAP_IN_PERCENT, percentage=15),
+        model=model_wrapper,
+        confidence=0.5,
+        iou_threshold=0.4,
+        detector_type=DetectorType.YOLO_ULTRALYTICS_OBB,
+    )
+
+    map_processor = MapProcessorDetection(
+        rlayer=rlayer,
+        vlayer_mask=None,
+        map_canvas=MagicMock(),
+        params=params,
+    )
+
+    map_processor.run()
+
+    assert len(map_processor.get_all_detections()) == 2
+
+
+
+if __name__ == '__main__':
+    test_map_processor_obb_yolo_ultralytics()
+    print('Done')

test/test_nms_with_rotation_function.py

@@ -0,0 +1,77 @@
+from unittest.mock import MagicMock
+import numpy as np
+
+from deepness.processing.models.detector import Detection, Detector
+from deepness.processing.processing_utils import BoundingBox
+from test.test_utils import init_qgis
+
+
+def test_nms_with_rotation_function():
+    detections = [
+        Detection(bbox=BoundingBox(x_min=90, x_max=173, y_min=338, y_max=422, rot=0), conf=0.9426471, clss=0),
+        Detection(bbox=BoundingBox(x_min=297, x_max=380, y_min=340, y_max=426, rot=np.pi/2), conf=0.9370736, clss=0),
+        Detection(bbox=BoundingBox(x_min=481, x_max=512, y_min=404, y_max=501, rot=-np.pi/2), conf=0.88433576, clss=0),
+        Detection(bbox=BoundingBox(x_min=479, x_max=563, y_min=411, y_max=497, rot=0), conf=0.94546044, clss=0),
+        Detection(bbox=BoundingBox(x_min=307, x_max=381, y_min=341, y_max=427, rot=np.pi/2), conf=0.94528174, clss=0),
+        Detection(bbox=BoundingBox(x_min=659, x_max=741, y_min=484, y_max=512, rot=-np.pi/2), conf=0.8914648, clss=0),
+        Detection(bbox=BoundingBox(x_min=657, x_max=741, y_min=482, y_max=512, rot=0), conf=0.86605114, clss=0),
+        Detection(bbox=BoundingBox(x_min=90, x_max=173, y_min=339, y_max=422, rot=np.pi/2), conf=0.9531542, clss=0),
+        Detection(bbox=BoundingBox(x_min=225, x_max=308, y_min=533, y_max=616, rot=-np.pi/2), conf=0.94643426, clss=0),
+        Detection(bbox=BoundingBox(x_min=297, x_max=380, y_min=340, y_max=427, rot=0), conf=0.9433237, clss=0),
+        Detection(bbox=BoundingBox(x_min=406, x_max=490, y_min=602, y_max=688, rot=0), conf=0.93923676, clss=0),
+        Detection(bbox=BoundingBox(x_min=480, x_max=512, y_min=406, y_max=502, rot=0), conf=0.82953537, clss=0),
+        Detection(bbox=BoundingBox(x_min=587, x_max=675, y_min=670, y_max=761, rot=0), conf=0.9486014, clss=0),
+        Detection(bbox=BoundingBox(x_min=308, x_max=381, y_min=339, y_max=427, rot=0), conf=0.94426584, clss=0),
+        Detection(bbox=BoundingBox(x_min=407, x_max=490, y_min=604, y_max=689, rot=0), conf=0.94282913, clss=0),
+        Detection(bbox=BoundingBox(x_min=660, x_max=744, y_min=481, y_max=567, rot=0), conf=0.942073, clss=0),
+        Detection(bbox=BoundingBox(x_min=479, x_max=563, y_min=411, y_max=498, rot=0), conf=0.9392239, clss=0),
+        Detection(bbox=BoundingBox(x_min=781, x_max=820, y_min=729, y_max=817, rot=0), conf=0.9145942, clss=0),
+        Detection(bbox=BoundingBox(x_min=534, x_max=613, y_min=802, y_max=819, rot=0), conf=0.67225057, clss=0),
+        Detection(bbox=BoundingBox(x_min=850, x_max=932, y_min=546, y_max=627, rot=0), conf=0.9545207, clss=0),
+        Detection(bbox=BoundingBox(x_min=780, x_max=862, y_min=733, y_max=813, rot=0), conf=0.947471, clss=0),
+        Detection(bbox=BoundingBox(x_min=659, x_max=744, y_min=480, y_max=567, rot=0), conf=0.9352352, clss=0),
+        Detection(bbox=BoundingBox(x_min=616, x_max=673, y_min=667, y_max=760, rot=0), conf=0.88940847, clss=0),
+    ]
+
+    bboxes = []
+    confs = []
+    for detection in detections:
+        bboxes.append(detection.bbox.get_xyxy_rot())
+        confs.append(detection.conf)
+
+    picks = Detector.non_max_suppression_fast(np.array(bboxes), np.array(confs), 0.2, with_rot=True)
+
+    detections = [d for i, d in enumerate(detections) if i in picks]
+
+    assert len(detections) == 11
+
+    inter = []
+    for i in range(len(detections)):
+        for j in range(i+1, len(detections)):
+            if i != j:
+                inter.append(detections[i].bbox.calculate_intersection_over_smaler_area(detections[j].bbox))
+
+    assert np.all(np.array(inter) < 0.2)
+
+def test_nms_human_case_with_rotation():
+    detections = [
+        Detection(bbox=BoundingBox(x_min=10, x_max=80, y_min=20, y_max=150, rot=0), conf=0.99999, clss=0),
+        Detection(bbox=BoundingBox(x_min=0, x_max=90, y_min=100, y_max=190, rot=0), conf=0.44444, clss=0),
+        Detection(bbox=BoundingBox(x_min=0, x_max=100, y_min=0, y_max=200, rot=0), conf=0.88888, clss=0),
+    ]
+
+    bboxes = []
+    confs = []
+    for detection in detections:
+        bboxes.append(detection.bbox.get_xyxy_rot())
+        confs.append(detection.conf)
+
+    picks = Detector.non_max_suppression_fast(np.array(bboxes), np.array(confs), 0.2, with_rot=True)
+
+    detections = [d for i, d in enumerate(detections) if i in picks]
+    assert len(detections) == 1
+
+if __name__ == '__main__':
+    test_nms_with_rotation_function()
+    test_nms_human_case_with_rotation()
+