什么网站做美式软装设计方案信息流推广的竞价机制是

目录

推理代码，source可以是文件名，路径，

预测可视化：

预测可视化加nms

训练自己的数据集，

训练一段时间报错：dill库

解决方法：

---

推理代码，source可以是文件名，路径，

保存结果：

from ultralytics import YOLOv10# model = YOLOv10.from_pretrained('jameslahm/yolov10{n/s/m/b/l/x}')
# or
# wget https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10{n/s/m/b/l/x}.pt
model = YOLOv10('yolov10s.pt')# model.val(data='coco.yaml', batch=256)source = 'http://images.cocodataset.org/val2017/000000039769.jpg'
source = 'F:\data\qijun\dao\pics_re_1'
model.predict(source=source, save=True)

预测可视化：

import cv2
import time
# import torch
from ultralytics import YOLOv10cv2.namedWindow('window', cv2.WINDOW_NORMAL)
cv2.resizeWindow('window', 640, 480)model = YOLOv10('yolov10s.pt')# 打开摄像头
cap = cv2.VideoCapture(0)# 检查摄像头是否打开
if not cap.isOpened():print("无法打开摄像头")exit()# 获取视频帧的宽度和高度
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
print(width, height)# 计时器和FPS初始化
prev_time = 0
fps = 0while True:# 读取帧ret, frame = cap.read()if not ret:print("无法读取帧")break# 改变输入图像尺寸，加快推理速度# frame = cv2.resize(frame, (width // 4, height // 4))# frame = cv2.resize(frame,(128,128) )prev_time = time.time()# 将帧传递给模型进行预测，并明确指定使用CPUresults = model(frame, device='0')curr_time = time.time()# 获取预测结果并绘制在帧上for result in results:boxes = result.boxes.xyxy.cpu().numpy()confidences = result.boxes.conf.cpu().numpy()class_ids = result.boxes.cls.cpu().numpy().astype(int)for i in range(len(boxes)):box = boxes[i]x1, y1, x2, y2 = map(int, box[:4])confidence = confidences[i]class_id = class_ids[i]label = result.names[class_id]cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)cv2.putText(frame, f'{label} {confidence:.2f}', (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (36, 255, 12), 1)fps =  (curr_time - prev_time)cv2.putText(frame, f'FPS: {fps:.2f}', (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)cv2.imshow('window', frame)if cv2.waitKey(1) & 0xFF == ord('q'):break# 释放摄像头并关闭窗口
cap.release()
cv2.destroyAllWindows()

预测可视化加nms

import cv2
import timeimport numpy as np
import torchfrom img_reader import ImgReader
# import torch
from ultralytics import YOLOv10# cv2.namedWindow('window', cv2.WINDOW_NORMAL)
# cv2.resizeWindow('window', 640, 480)# model = YOLOv10('yolov10s.pt')
model = YOLOv10('runs/train/exp2/weights/best.pt')# 计时器和FPS初始化
prev_time = 0
fps = 0f_type='img'
source = r'B:\project\qijun\data\dataSet-coins\images\train'# file_reader = ImgReader(source, f_type=f_type)f_type='cam'
source=0
f_type='mp4'
source = r"B:\project\qijun\data\test\shuiguo1.mp4"
file_reader = ImgReader(source, f_type=f_type)for img_i in range(file_reader.total_frames):img_o, img_index, img_file = file_reader.get_img()if max(img_o.shape[:2]) > 1500:x_scale = 1500 / max(img_o.shape[:2])img_o = cv2.resize(img_o, None, fx=x_scale, fy=x_scale, interpolation=cv2.INTER_AREA)img=img_oframe=img_o.copy()if img_file is not None:print(img_file)# 改变输入图像尺寸，加快推理速度# frame = cv2.resize(frame, (width // 4, height // 4))# frame = cv2.resize(frame,(128,128) )prev_time = time.time()# 将帧传递给模型进行预测，并明确指定使用CPUresults = model(frame, device='0')curr_time = time.time()# 获取预测结果并绘制在帧上for result in results:boxes = result.boxes.xyxy.cpu().numpy()confidences = result.boxes.conf.cpu().numpy()class_ids = result.boxes.cls.cpu().numpy().astype(int)for i in range(len(boxes)):box = boxes[i]x1, y1, x2, y2 = map(int, box[:4])confidence = confidences[i]class_id = class_ids[i]label = result.names[class_id]cv2.rectangle(img, (x1, y1), (x2, y2), (0, 0, 255), 3)# cv2.putText(img, f'{label} {confidence:.2f}', (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (36, 255, 12), 1)final_boxes = []final_confidences = []final_class_ids = []# 对每个类别单独进行NMSunique_classes = set(class_ids)for cls in unique_classes:cls_indices = (class_ids == cls)# 提取当前类别的boxes, confidencesboxes_cls = torch.tensor(boxes[cls_indices])confidences_cls = torch.tensor(confidences[cls_indices])# 对当前类别进行NMSkeep_indices = torch.ops.torchvision.nms(boxes_cls, confidences_cls, iou_threshold=0.5)  # 设置你的IoU阈值num_filtered = len(boxes_cls) - len(keep_indices)if num_filtered>0:print(f"Class {cls}: {num_filtered} boxes filtered out by NMS")# 过滤当前类别的boxes, confidences, class_idsfinal_boxes.append(boxes_cls[keep_indices].numpy())final_confidences.append(confidences_cls[keep_indices].numpy())final_class_ids.append([cls] * len(keep_indices))# 合并所有类别的结果final_boxes = np.concatenate(final_boxes, axis=0)final_confidences = np.concatenate(final_confidences, axis=0)final_class_ids = np.concatenate(final_class_ids, axis=0)for i in range(len(final_boxes)):box = final_boxes[i]x1, y1, x2, y2 = map(int, box[:4])confidence = final_confidences[i]class_id = final_class_ids[i]label = result.names[class_id]cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)cv2.putText(img, f'{label} {confidence:.2f}', (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (36, 255, 12), 1)# for result in results:#     boxes = result.boxes.xyxy.cpu().numpy()#     confidences = result.boxes.conf.cpu().numpy()#     class_ids = result.boxes.cls.cpu().numpy().astype(int)##     for i in range(len(boxes)):#         box = boxes[i]#         x1, y1, x2, y2 = map(int, box[:4])#         confidence = confidences[i]#         class_id = class_ids[i]#         label = result.names[class_id]#         cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)#         cv2.putText(img, f'{label} {confidence:.2f}', (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (36, 255, 12), 1)fps =  (curr_time - prev_time)cv2.putText(img, f'{img_i} FPS: {fps:.2f}', (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)cv2.imshow('window', img)waitkey=0if f_type == 'cam':waitkey=2if cv2.waitKey(waitkey) & 0xFF == ord('q'):break

训练自己的数据集，

原版标签是txt格式

我下载了完整代码，自己修改数据集

https://download.csdn.net/download/qq_38408785/89356134

from ultralytics import YOLOv10if __name__ == '__main__':model = YOLOv10('ultralytics/cfg/models/v10/yolov10n.yaml')model.load('yolov10n.pt') # loading pretrain weightsmodel.train(data='data/NEU-DET.yaml',cache=False,imgsz=640,epochs=200,batch=16,close_mosaic=10,device='0',optimizer='SGD', # using SGDproject='runs/train',name='exp',)

训练一段时间报错：dill库

  File "D:\ProgramData\miniconda3\envs\py310\lib\pickle.py", line 603, in saveself.save_reduce(obj=obj, *rv)File "D:\ProgramData\miniconda3\envs\py310\lib\pickle.py", line 717, in save_reducesave(state)File "D:\ProgramData\miniconda3\envs\py310\lib\site-packages\dill\_dill.py", line 388, in saveStockPickler.save(self, obj, save_persistent_id)File "D:\ProgramData\miniconda3\envs\py310\lib\pickle.py", line 560, in savef(self, obj)  # Call unbound method with explicit selfFile "D:\ProgramData\miniconda3\envs\py310\lib\site-packages\dill\_dill.py", line 1186, in save_module_dictStockPickler.save_dict(pickler, obj)File "D:\ProgramData\miniconda3\envs\py310\lib\pickle.py", line 972, in save_dictself._batch_setitems(obj.items())File "D:\ProgramData\miniconda3\envs\py310\lib\pickle.py", line 997, in _batch_setitemssave(k)File "D:\ProgramData\miniconda3\envs\py310\lib\site-packages\dill\_dill.py", line 388, in saveStockPickler.save(self, obj, save_persistent_id)File "D:\ProgramData\miniconda3\envs\py310\lib\pickle.py", line 539, in savepid = self.persistent_id(obj)File "D:\ProgramData\miniconda3\envs\py310\lib\site-packages\torch\serialization.py", line 622, in persistent_idstorage_type = normalize_storage_type(type(obj))File "D:\ProgramData\miniconda3\envs\py310\lib\site-packages\torch\serialization.py", line 226, in normalize_storage_typereturn getattr(torch, storage_type.__name__)
AttributeError: module 'torch' has no attribute 'str'

解决方法：

pip install dill -U

升级为dill-0.3.8 后报错没有了。