cleanup

nms.py

@@ -53,61 +53,8 @@ def box_iou(box_i,boxes):
 
 def non_max_suppresion_v8(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False,
                         labels=(), max_det=300):
-    # TODO: Test this for changed parameter number in yolov8!!! All that could be detected with last commit were persons!
-    #
-    # # Transpose and squeeze the output to match the expected shape
-    # # prediction = np.transpose(np.squeeze(prediction[0]))
-    # prediction = np.squeeze(prediction[0])  # eleminate batch id. only one batch can run un TPU anyway..., transpose is already done one step eralier
-    #
-    # # Get the number of rows in the outputs array
-    # rows = prediction.shape[0]
-    #
-    # # Lists to store the bounding boxes, scores, and class IDs of the detections
-    # boxes = []
-    # scores = []
-    # class_ids = []
-    #
-    # # Iterate over each row in the outputs array
-    # for i in range(rows):
-    #     # Extract the class scores from the current row
-    #     classes_scores = prediction[i][4:]
-    #
-    #     # Find the maximum score among the class scores
-    #     max_score = np.amax(classes_scores)
-    #
-    #     # If the maximum score is above the confidence threshold
-    #     if max_score >= conf_thres:
-    #         # Get the class ID with the highest score
-    #         class_id = np.argmax(classes_scores)
-    #
-    #         # Add the class ID, score, and box coordinates to the respective lists
-    #         class_ids.append(class_id)
-    #         scores.append(max_score)
-    #         boxes.append(xywh2xyxy(prediction[:, :4]))
-    #
-    # # Apply non-maximum suppression to filter out overlapping bounding boxes
-    # indices = nms(np.ndarray(boxes), np.ndarray(scores), iou_thres)
-    #
-    # if indices.shape[0] > max_det:  # limit detections
-    #     indices = indices[:max_det]
-    #
-    #     if (time.time() - t) > time_limit:
-    #         print(f'WARNING: NMS time limit {time_limit}s exceeded')
-    #         break  # time limit exceeded
-    #
-    #
-    # # Return the modified input image
-    # return input_image
 
     nc = prediction.shape[2] - 4  # number of classes
-    # xc = prediction[..., 4] > conf_thres  # candidates
-    # xc = []
-    # for xi, x in enumerate(prediction):  # image index, image inference
-    #     conf = np.amax(x[:, 4:], axis=1, keepdims=True)
-    #     if np.any(conf > conf_thres):
-    #         xc.append(True)
-    #     else:
-    #         xc.append(False)
     xc = np.amax(prediction[..., 4:], axis=2) > conf_thres
 
     # Checks