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136 | @dataclass
class DetectionBaseModel(BaseModel):
ontology: DetectionOntology
@abstractmethod
def predict(self, input: str | np.ndarray | Image.Image) -> sv.Detections:
pass
def sahi_predict(self, input: str | np.ndarray | Image.Image) -> sv.Detections:
slicer = sv.InferenceSlicer(callback=self.predict)
return slicer(load_image(input, return_format="cv2"))
def _record_confidence_in_files(
self,
annotations_directory_path: str,
images: Dict[str, np.ndarray],
annotations: Dict[str, sv.Detections],
) -> None:
Path(annotations_directory_path).mkdir(parents=True, exist_ok=True)
for image_name, _ in images.items():
detections = annotations[image_name]
yolo_annotations_name, _ = os.path.splitext(image_name)
confidence_path = os.path.join(
annotations_directory_path,
"confidence-" + yolo_annotations_name + ".txt",
)
confidence_list = [str(x) for x in detections.confidence.tolist()]
save_text_file(lines=confidence_list, file_path=confidence_path)
print("Saved confidence file: " + confidence_path)
def label(
self,
input_folder: str,
extension: str = ".jpg",
output_folder: str = None,
human_in_the_loop: bool = False,
roboflow_project: str = None,
roboflow_tags: str = ["autodistill"],
sahi: bool = False,
record_confidence: bool = False,
nms_settings: NmsSetting = NmsSetting.NONE,
) -> sv.DetectionDataset:
"""
Label a dataset with the model.
"""
if output_folder is None:
output_folder = input_folder + "_labeled"
os.makedirs(output_folder, exist_ok=True)
images_map = {}
detections_map = {}
if sahi:
slicer = sv.InferenceSlicer(callback=self.predict)
files = glob.glob(input_folder + "/*" + extension)
progress_bar = tqdm(files, desc="Labeling images")
# iterate through images in input_folder
for f_path in progress_bar:
progress_bar.set_description(desc=f"Labeling {f_path}", refresh=True)
image = cv2.imread(f_path)
f_path_short = os.path.basename(f_path)
images_map[f_path_short] = image.copy()
if sahi:
detections = slicer(image)
else:
detections = self.predict(image)
if nms_settings == NmsSetting.CLASS_SPECIFIC:
detections = detections.with_nms()
if nms_settings == NmsSetting.CLASS_AGNOSTIC:
detections = detections.with_nms(class_agnostic=True)
detections_map[f_path_short] = detections
dataset = sv.DetectionDataset(
self.ontology.classes(), images_map, detections_map
)
dataset.as_yolo(
output_folder + "/images",
output_folder + "/annotations",
min_image_area_percentage=0.01,
data_yaml_path=output_folder + "/data.yaml",
)
if record_confidence is True:
self._record_confidence_in_files(
output_folder + "/annotations", images_map, detections_map
)
split_data(output_folder, record_confidence=record_confidence)
if human_in_the_loop:
roboflow.login()
rf = roboflow.Roboflow()
workspace = rf.workspace()
workspace.upload_dataset(output_folder, project_name=roboflow_project)
print("Labeled dataset created - ready for distillation.")
return dataset
|