Loading imgutils/generic/yolo.py +72 −0 Original line number Diff line number Diff line Loading @@ -226,6 +226,30 @@ def _xy_postprocess(x, y, old_size: Tuple[float, float], new_size: Tuple[float, def _end2end_postprocess(output, conf_threshold: float, iou_threshold: float, old_size: Tuple[float, float], new_size: Tuple[float, float], labels: List[str]) \ -> List[Tuple[Tuple[int, int, int, int], str, float]]: """ Post-process the output of an end-to-end object detection model. This function filters detections based on confidence, applies non-maximum suppression, and transforms coordinates back to the original image size. :param output: Raw output from the end-to-end object detection model. :type output: np.ndarray :param conf_threshold: Confidence threshold for filtering detections. :type conf_threshold: float :param iou_threshold: IoU threshold for non-maximum suppression (not used in this function). :type iou_threshold: float :param old_size: Original image dimensions (width, height). :type old_size: Tuple[float, float] :param new_size: Preprocessed image dimensions (width, height). :type new_size: Tuple[float, float] :param labels: List of class labels. :type labels: List[str] :return: List of detections, each in the format ((x0, y0, x1, y1), label, confidence). :rtype: List[Tuple[Tuple[int, int, int, int], str, float]] :raises AssertionError: If the output shape is not as expected. """ assert output.shape[-1] == 6 _ = iou_threshold # actually the iou_threshold has not been supplied to end2end post-processing detections = [] Loading @@ -242,6 +266,30 @@ def _end2end_postprocess(output, conf_threshold: float, iou_threshold: float, def _nms_postprocess(output, conf_threshold: float, iou_threshold: float, old_size: Tuple[float, float], new_size: Tuple[float, float], labels: List[str]) \ -> List[Tuple[Tuple[int, int, int, int], str, float]]: """ Post-process the output of an NMS-based object detection model. This function applies confidence thresholding, non-maximum suppression, and transforms coordinates back to the original image size. :param output: Raw output from the NMS-based object detection model. :type output: np.ndarray :param conf_threshold: Confidence threshold for filtering detections. :type conf_threshold: float :param iou_threshold: IoU threshold for non-maximum suppression. :type iou_threshold: float :param old_size: Original image dimensions (width, height). :type old_size: Tuple[float, float] :param new_size: Preprocessed image dimensions (width, height). :type new_size: Tuple[float, float] :param labels: List of class labels. :type labels: List[str] :return: List of detections, each in the format ((x0, y0, x1, y1), label, confidence). :rtype: List[Tuple[Tuple[int, int, int, int], str, float]] :raises AssertionError: If the output shape is not as expected. """ assert output.shape[0] == 4 + len(labels) # the output should be like [4+cls, box_cnt] # cls means count of classes Loading Loading @@ -323,6 +371,30 @@ def _yolo_postprocess(output, conf_threshold: float, iou_threshold: float, def _rtdetr_postprocess(output, conf_threshold: float, iou_threshold: float, old_size: Tuple[int, int], new_size: Tuple[int, int], labels: List[str]) \ -> List[Tuple[Tuple[int, int, int, int], str, float]]: """ Post-process the output from an RT-DETR (Real-Time DEtection TRansformer) model. This function handles the specific output format of RT-DETR models and applies the necessary post-processing steps. :param output: Raw output from the RT-DETR model. :type output: np.ndarray :param conf_threshold: Confidence threshold for filtering detections. :type conf_threshold: float :param iou_threshold: IoU threshold for non-maximum suppression. :type iou_threshold: float :param old_size: Original image dimensions (width, height). :type old_size: Tuple[int, int] :param new_size: Preprocessed image dimensions (width, height) (not used in this function). :type new_size: Tuple[int, int] :param labels: List of class labels. :type labels: List[str] :return: List of detections, each in the format ((x0, y0, x1, y1), label, confidence). :rtype: List[Tuple[Tuple[int, int, int, int], str, float]] :raises AssertionError: If the output shape is not as expected. """ assert output.shape[-1] == 4 + len(labels) # the size rtdetr using is [0.0, 1.0] _ = new_size Loading test/detect/test_head.py +0 −2 Original line number Diff line number Diff line from pprint import pprint import pytest from imgutils.detect import detection_similarity Loading Loading
imgutils/generic/yolo.py +72 −0 Original line number Diff line number Diff line Loading @@ -226,6 +226,30 @@ def _xy_postprocess(x, y, old_size: Tuple[float, float], new_size: Tuple[float, def _end2end_postprocess(output, conf_threshold: float, iou_threshold: float, old_size: Tuple[float, float], new_size: Tuple[float, float], labels: List[str]) \ -> List[Tuple[Tuple[int, int, int, int], str, float]]: """ Post-process the output of an end-to-end object detection model. This function filters detections based on confidence, applies non-maximum suppression, and transforms coordinates back to the original image size. :param output: Raw output from the end-to-end object detection model. :type output: np.ndarray :param conf_threshold: Confidence threshold for filtering detections. :type conf_threshold: float :param iou_threshold: IoU threshold for non-maximum suppression (not used in this function). :type iou_threshold: float :param old_size: Original image dimensions (width, height). :type old_size: Tuple[float, float] :param new_size: Preprocessed image dimensions (width, height). :type new_size: Tuple[float, float] :param labels: List of class labels. :type labels: List[str] :return: List of detections, each in the format ((x0, y0, x1, y1), label, confidence). :rtype: List[Tuple[Tuple[int, int, int, int], str, float]] :raises AssertionError: If the output shape is not as expected. """ assert output.shape[-1] == 6 _ = iou_threshold # actually the iou_threshold has not been supplied to end2end post-processing detections = [] Loading @@ -242,6 +266,30 @@ def _end2end_postprocess(output, conf_threshold: float, iou_threshold: float, def _nms_postprocess(output, conf_threshold: float, iou_threshold: float, old_size: Tuple[float, float], new_size: Tuple[float, float], labels: List[str]) \ -> List[Tuple[Tuple[int, int, int, int], str, float]]: """ Post-process the output of an NMS-based object detection model. This function applies confidence thresholding, non-maximum suppression, and transforms coordinates back to the original image size. :param output: Raw output from the NMS-based object detection model. :type output: np.ndarray :param conf_threshold: Confidence threshold for filtering detections. :type conf_threshold: float :param iou_threshold: IoU threshold for non-maximum suppression. :type iou_threshold: float :param old_size: Original image dimensions (width, height). :type old_size: Tuple[float, float] :param new_size: Preprocessed image dimensions (width, height). :type new_size: Tuple[float, float] :param labels: List of class labels. :type labels: List[str] :return: List of detections, each in the format ((x0, y0, x1, y1), label, confidence). :rtype: List[Tuple[Tuple[int, int, int, int], str, float]] :raises AssertionError: If the output shape is not as expected. """ assert output.shape[0] == 4 + len(labels) # the output should be like [4+cls, box_cnt] # cls means count of classes Loading Loading @@ -323,6 +371,30 @@ def _yolo_postprocess(output, conf_threshold: float, iou_threshold: float, def _rtdetr_postprocess(output, conf_threshold: float, iou_threshold: float, old_size: Tuple[int, int], new_size: Tuple[int, int], labels: List[str]) \ -> List[Tuple[Tuple[int, int, int, int], str, float]]: """ Post-process the output from an RT-DETR (Real-Time DEtection TRansformer) model. This function handles the specific output format of RT-DETR models and applies the necessary post-processing steps. :param output: Raw output from the RT-DETR model. :type output: np.ndarray :param conf_threshold: Confidence threshold for filtering detections. :type conf_threshold: float :param iou_threshold: IoU threshold for non-maximum suppression. :type iou_threshold: float :param old_size: Original image dimensions (width, height). :type old_size: Tuple[int, int] :param new_size: Preprocessed image dimensions (width, height) (not used in this function). :type new_size: Tuple[int, int] :param labels: List of class labels. :type labels: List[str] :return: List of detections, each in the format ((x0, y0, x1, y1), label, confidence). :rtype: List[Tuple[Tuple[int, int, int, int], str, float]] :raises AssertionError: If the output shape is not as expected. """ assert output.shape[-1] == 4 + len(labels) # the size rtdetr using is [0.0, 1.0] _ = new_size Loading
test/detect/test_head.py +0 −2 Original line number Diff line number Diff line from pprint import pprint import pytest from imgutils.detect import detection_similarity Loading
