eltonfernando
commented
2 weeks ago in vision/datasets/voc_dataset.py create new method
def _gen_random_background(self):
return (np.array([[10, 30, 40, 50]], dtype=np.float32), np.array([0], dtype=np.int64), np.array([0], dtype=np.uint8))change method def _get_annotation(self, image_id): loaed anotation, before return add
if len(boxes) == 0:
return self._gen_random_background()this should be the result
def _get_annotation(self, image_id):
annotation_file = self.root / f"Annotations/{image_id}.xml"
objects = ET.parse(annotation_file).findall("object")
if len(objects) == 0:
return self._gen_random_background()
raise Exception(f"xml sem box {annotation_file}")
boxes = []
labels = []
is_difficult = []
for object in objects:
class_name = object.find("name").text.lower().strip()
# we're only concerned with clases in our list
if class_name in self.class_dict:
bbox = object.find("bndbox")
# VOC dataset format follows Matlab, in which indexes start from 0
x1 = float(bbox.find("xmin").text) - 1
y1 = float(bbox.find("ymin").text) - 1
x2 = float(bbox.find("xmax").text) - 1
y2 = float(bbox.find("ymax").text) - 1
boxes.append([x1, y1, x2, y2])
labels.append(self.class_dict[class_name])
is_difficult_str = object.find("difficult").text
is_difficult.append(int(is_difficult_str) if is_difficult_str else 0)
if len(boxes) == 0:
return self._gen_random_background()
return (np.array(boxes, dtype=np.float32), np.array(labels, dtype=np.int64), np.array(is_difficult, dtype=np.uint8))
def _gen_random_background(self):
return (np.array([[10, 30, 40, 50]], dtype=np.float32), np.array([0], dtype=np.int64), np.array([0], dtype=np.uint8))This works for training with background image, let me know if your model improved with this
For yolo templates I can put images without objects by adding an empty annotation file. Is there any way to do this with ssd or mobilinetv1?