New utility feature class filtering for Object Detectors 2D (#467)

* Added initial version of class filtering

* added parameter conditions for all implemented learners

* added parameter conditions for all implemented learners

* final implementation of class filtering for object detectors. added inference demo for yolov5.

* correction inference demo for yolov5.

* small fixes to pass tests

* added License Copyright to pass test

* Update src/opendr/perception/object_detection_2d/utils/class_filter_wrapper.py

Co-authored-by: Kostas Tsampazis <27914645+tsampazk@users.noreply.github.com>

* Update projects/python/perception/object_detection_2d/yolov5/filtered_inference_demo.py

Co-authored-by: Kostas Tsampazis <27914645+tsampazk@users.noreply.github.com>

* Update projects/python/perception/object_detection_2d/yolov5/filtered_inference_demo.py

Co-authored-by: Kostas Tsampazis <27914645+tsampazk@users.noreply.github.com>

* Update projects/python/perception/object_detection_2d/yolov5/filtered_inference_demo.py

Co-authored-by: Kostas Tsampazis <27914645+tsampazk@users.noreply.github.com>

* Update projects/python/perception/object_detection_2d/yolov5/filtered_inference_demo.py

Co-authored-by: Kostas Tsampazis <27914645+tsampazk@users.noreply.github.com>

* Update src/opendr/perception/object_detection_2d/utils/class_filter_wrapper.py

Co-authored-by: Kostas Tsampazis <27914645+tsampazk@users.noreply.github.com>

* Update src/opendr/perception/object_detection_2d/utils/class_filter_wrapper.py

Co-authored-by: Kostas Tsampazis <27914645+tsampazk@users.noreply.github.com>

* Removed filtering support for RetinaFace, since it exclusively detects face objects.

---------

Co-authored-by: Kostas Tsampazis <27914645+tsampazk@users.noreply.github.com>

projects/python/perception/object_detection_2d/yolov5/filtered_inference_demo.py

@@ -0,0 +1,61 @@
+# Copyright 2020-2023 OpenDR European Project
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+
+import cv2
+import torch
+import numpy as np
+
+from opendr.engine.data import Image
+from opendr.perception.object_detection_2d import YOLOv5DetectorLearner
+from opendr.perception.object_detection_2d import draw_bounding_boxes
+
+from opendr.perception.object_detection_2d.utils.class_filter_wrapper import FilteredLearnerWrapper
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--device", help="Device to use (cpu, cuda)", type=str, default="cuda", choices=["cuda", "cpu"])
+    parser.add_argument("--classes", help="Classes of interest to detect", type=str, nargs="+", default=['person'])
+    args = parser.parse_args()
+
+    yolo = YOLOv5DetectorLearner(model_name='yolov5s', device=args.device)
+
+    for f in 'zidane.jpg', 'bus.jpg':
+        torch.hub.download_url_to_file('https://ultralytics.com/images/' + f, f)  # download 2 images
+    im1 = Image.open('zidane.jpg')  # OpenDR image
+    im2 = cv2.imread('bus.jpg')  # OpenCV image (BGR to RGB)
+
+    # By default, only objects of the 'person' class are of interest
+    filtered_yolo = FilteredLearnerWrapper(yolo, allowed_classes=args.classes)
+
+    # detection before filtering
+    print("No filtering...")
+    results = yolo.infer(im1)
+    draw_bounding_boxes(im1.opencv(), results, yolo.classes, show=True, line_thickness=3)
+
+    # detection after filtering
+    print("With filtering...")
+    filtered_results = filtered_yolo.infer(im1)
+    draw_bounding_boxes(im1.opencv(), filtered_results, filtered_yolo.classes, show=True, line_thickness=3)
+
+    # detection before filtering
+    print("No filtering...")
+    results = yolo.infer(im2)
+    draw_bounding_boxes(np.copy(im2), results, yolo.classes, show=True, line_thickness=3)
+
+    # detection after filtering
+    print("With filtering...")
+    filtered_results = filtered_yolo.infer(im2)
+    draw_bounding_boxes(np.copy(im2), filtered_results, filtered_yolo.classes, show=True, line_thickness=3)

src/opendr/perception/object_detection_2d/utils/class_filter_wrapper.py

@@ -0,0 +1,124 @@
+# Copyright 2020-2023 OpenDR European Project
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from opendr.engine.target import BoundingBoxList
+from opendr.perception.object_detection_2d.centernet.centernet_learner import CenterNetDetectorLearner
+from opendr.perception.object_detection_2d.detr.detr_learner import DetrLearner
+from opendr.perception.object_detection_2d.ssd.ssd_learner import SingleShotDetectorLearner
+from opendr.perception.object_detection_2d.yolov3.yolov3_learner import YOLOv3DetectorLearner
+from opendr.perception.object_detection_2d.yolov5.yolov5_learner import YOLOv5DetectorLearner
+from opendr.perception.object_detection_2d.nanodet.nanodet_learner import NanodetLearner
+
+
+class FilteredLearnerWrapper:
+    def __init__(self, learner, allowed_classes=None):
+        self.learner = learner
+        self.allowed_classes = allowed_classes if allowed_classes is not None else []
+
+        if isinstance(self.learner,
+                      (CenterNetDetectorLearner, YOLOv3DetectorLearner, YOLOv5DetectorLearner, NanodetLearner,
+                       SingleShotDetectorLearner)):
+            self.classes = self.learner.classes
+        if isinstance(self.learner, DetrLearner):
+            self.classes = [
+                "N/A", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat",
+                "traffic light", "fire hydrant", "N/A", "stop sign", "parking meter", "bench", "bird", "cat", "dog",
+                "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "N/A", "backpack", "umbrella", "N/A",
+                "N/A", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite",
+                "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "N/A",
+                "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange",
+                "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed",
+                "N/A", "dining table", "N/A", "N/A", "toilet", "N/A", "tv", "laptop", "mouse", "remote", "keyboard",
+                "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "N/A", "book", "clock", "vase",
+                "scissors", "teddy bear", "hair drier", "toothbrush",
+            ]
+
+        # Verify that allowed classes are in the detector's class list
+        invalid_classes = [cls for cls in self.allowed_classes if cls not in self.classes]
+        if invalid_classes:
+            raise ValueError(
+                f"The following classes are not detected by this detector: {', '.join(invalid_classes)}")
+
+    def infer(self, img=None, threshold=None, keep_size=None, input=None, conf_threshold=None, iou_threshold=None,
+              nms_max_num=None, size=None, custom_nms=None, nms_thresh=None, nms_topk=None, post_nms=None,
+              extract_maps=None):
+
+        # match variable names
+        if isinstance(self.learner, NanodetLearner) and input is not None:
+            img = input
+
+        if img is None:
+            raise ValueError(
+                "An image input is required. Please provide a valid image.")
+
+        if isinstance(self.learner, CenterNetDetectorLearner):
+            if threshold is None:
+                threshold = 0.2
+            if keep_size is None:
+                keep_size = True
+            boxes = self.learner.infer(img, threshold=threshold, keep_size=keep_size)
+
+        elif isinstance(self.learner, YOLOv3DetectorLearner):
+            if threshold is None:
+                threshold = 0.1
+            if keep_size is None:
+                keep_size = True
+            boxes = self.learner.infer(img, threshold=threshold, keep_size=keep_size)
+
+        elif isinstance(self.learner, YOLOv5DetectorLearner):
+            if size is None:
+                size = 640
+            boxes = self.learner.infer(img, size)
+
+        elif isinstance(self.learner, DetrLearner):
+            boxes = self.learner.infer(img)
+
+        elif isinstance(self.learner, NanodetLearner):
+            if conf_threshold is None:
+                conf_threshold = 0.35
+            if iou_threshold is None:
+                iou_threshold = 0.6
+            if nms_max_num is None:
+                nms_max_num = 100
+            boxes = self.learner.infer(img, conf_threshold, iou_threshold, nms_max_num)
+
+        elif isinstance(self.learner, SingleShotDetectorLearner):
+            if threshold is None:
+                threshold = 0.2
+            if keep_size is None:
+                keep_size = False
+            if nms_thresh is None:
+                nms_thresh = 0.45
+            if nms_topk is None:
+                nms_topk = 400
+            if post_nms is None:
+                post_nms = 100
+            if extract_maps is None:
+                extract_maps = False
+            boxes = self.learner.infer(img, threshold, keep_size, custom_nms,
+                                       nms_thresh, nms_topk, post_nms, extract_maps)
+        else:
+            raise ValueError(
+                "Filtering has not been implemented for the specified detector class."
+            )
+
+        if not self.allowed_classes:
+            return boxes
+        filtered_boxes = BoundingBoxList(
+            [box for box in boxes if self.classes[int(box.name)] in self.allowed_classes])
+        return filtered_boxes
+
+    def __getattr__(self, attr):
+        return getattr(self.learner, attr)