python深色版本 import os import torch import cv2 import numpy as np from ultralytics import YOLO from ultralytics.yolo.utils.general import non_max_suppression, scale_coords from ultralytics.yolo.utils.plots import Annotator def load_model(weights_path, device): model = YOLO(weights_path) mode...
return iou def non_max_suppression(self, prediction, origin_h, origin_w, conf_thres=0.5, nms_thres=0.4): """ description: Removes detections with lower object confidence score than 'conf_thres' and performs Non-Maximum Suppression to further filter detections. param: prediction: detections, (x...
编写一个推理脚本infer_yolov8.py: python深色版本 import os import torch import cv2 import numpy as np from ultralytics import YOLO from ultralytics.yolo.utils.general import non_max_suppression, scale_coords from ultralytics.yolo.utils.plots import Annotator def load_model(weights_path, device)...
(dw, dh) def non_max_suppression( prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False, labels=(), max_det=300, nm=0, # number of masks ): """Non-Maximum Suppression (NMS) on inference results to reject overlapping detections Returns: list of...
masks = []#TODO:filter by classesp = ops.non_max_suppression(preds[0],self.args.conf,self.args.iou, agnostic=self.args.agnostic_nms, max_det=self.args.max_det, nm=32) proto = preds[1][-
在dt[2]时间性能记录块中,调用non_max_suppression函数对预测结果进行非极大值抑制处理。该函数会根据置信度阈值(conf_thres)、IoU阈值(iou_thres)、类别列表(classes)、是否使用类别不可知的NMS(agnostic_nms)以及最大检测数(max_det)等参数进行NMS操作,过滤掉重叠度高的边界框。
(0)import matplotlib.pyplot as pltfrom tqdm import trangefrom PIL import Imagefrom ultralytics.nn.tasks import attempt_load_weightsfrom ultralytics.utils.torch_utils import intersect_dictsfrom ultralytics.utils.ops import xywh2xyxy, non_max_suppressionfrom pytorch_grad_cam import GradCAMPlusPlus,...
class_ids.append(maxClassIndex)#Apply NMS (Non-maximum suppression)result_boxes = cv2.dnn.NMSBoxes(boxes, scores, 0.25, 0.45, 0.5) detections=[]#Iterate through NMS results to draw bounding boxes and labelsforiinrange(len(result_boxes)): ...
non_max_suppression函数 执行非极大值抑制,去除重叠的检测框。 def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.35, classes=None, agnostic=False, multi_label=False, labels=(), max_det=300, nm=0 # number of masks ): """ Perform Non-Maximum Suppression (NMS) on the boxes ...
(labels_pro, dtype=np.int32) return boxes_pro, scores_pro, labels_pro def non_max_suppression( boxes: ndarray, scores: ndarray, labels: ndarray, conf_thres: float = 0.25, iou_thres: float = 0.65, ) -> Tuple[ndarray, ndarray, ndarray]: # indices = cv2.dnn.NMSBoxesBatched(boxes, ...