modify

.gitignore

@@ -9,6 +9,7 @@ runs/
 .vscode/
 build/
 result/
+onnx/
 *.pyc
 pretrained_model/
 # 不忽略下面指定的文件类型

onnx_infer.py

@@ -1,160 +0,0 @@
-import onnxruntime
-import numpy as np
-import cv2
-import copy
-import os
-import argparse
-from PIL import Image, ImageDraw, ImageFont
-import time
-
-def allFilePath(rootPath,allFIleList):  #遍历文件
-    fileList = os.listdir(rootPath)
-    for temp in fileList:
-        if os.path.isfile(os.path.join(rootPath,temp)):
-            allFIleList.append(os.path.join(rootPath,temp))
-        else:
-            allFilePath(os.path.join(rootPath,temp),allFIleList)
-
-
-def my_letter_box(img,size=(640,640)):  #对应yolo中的letterbox前处理
-    h,w,c = img.shape
-    r = min(size[0]/h,size[1]/w)
-    new_h,new_w = int(h*r),int(w*r)
-    top = int((size[0]-new_h)/2)
-    left = int((size[1]-new_w)/2)
-    
-    bottom = size[0]-new_h-top
-    right = size[1]-new_w-left
-    img_resize = cv2.resize(img,(new_w,new_h))
-    img = cv2.copyMakeBorder(img_resize,top,bottom,left,right,borderType=cv2.BORDER_CONSTANT,value=(114,114,114))
-    return img,r,left,top
-
-def xywh2xyxy(boxes):   #xywh坐标变为 左上 ，右下坐标 x1,y1  x2,y2
-    xywh =copy.deepcopy(boxes)
-    xywh[:,0]=boxes[:,0]-boxes[:,2]/2
-    xywh[:,1]=boxes[:,1]-boxes[:,3]/2
-    xywh[:,2]=boxes[:,0]+boxes[:,2]/2
-    xywh[:,3]=boxes[:,1]+boxes[:,3]/2
-    return xywh
- 
-def my_nms(boxes,iou_thresh):         #自己实现的nms
-    index = np.argsort(boxes[:,4])[::-1]
-    keep = []
-    while index.size >0:
-        i = index[0]
-        keep.append(i)
-        x1=np.maximum(boxes[i,0],boxes[index[1:],0])
-        y1=np.maximum(boxes[i,1],boxes[index[1:],1])
-        x2=np.minimum(boxes[i,2],boxes[index[1:],2])
-        y2=np.minimum(boxes[i,3],boxes[index[1:],3])
-        
-        w = np.maximum(0,x2-x1)
-        h = np.maximum(0,y2-y1)
-
-        inter_area = w*h
-        union_area = (boxes[i,2]-boxes[i,0])*(boxes[i,3]-boxes[i,1])+(boxes[index[1:],2]-boxes[index[1:],0])*(boxes[index[1:],3]-boxes[index[1:],1])
-        iou = inter_area/(union_area-inter_area)
-        idx = np.where(iou<=iou_thresh)[0]
-        index = index[idx+1]
-    return keep
-
-def restore_box(boxes,r,left,top):  #返回原图上面的坐标
-    boxes[:,[0,2]]-=left
-    boxes[:,[1,3]]-=top
-
-    boxes[:,[0,2]]/=r
-    boxes[:,[1,3]]/=r
-    return boxes
-
-def detect_pre_precessing(img,img_size):  #检测前处理
-    img,r,left,top=my_letter_box(img,img_size)
-    # cv2.imwrite("1.jpg",img)
-    img =img[:,:,::-1].transpose(2,0,1).copy().astype(np.float32)
-    img=img/255
-    img=img.reshape(1,*img.shape)
-    return img,r,left,top
-
-def post_precessing(dets,r,left,top,conf_thresh=0.4,iou_thresh=0.5,num_class=8):#检测后处理
-    score= np.max(dets[:,:,4:4+num_class],axis=-1,keepdims=True)
-    new_dets = np.concatenate((dets,score),axis=-1)
-    choice = new_dets[:,:,-1]>conf_thresh
-    new_dets=new_dets[choice]
-    score=score[choice]
-    # dets[:,5:5+num_class]*=dets[:,4:5]
-    box = new_dets[:,:4]
-    boxes = xywh2xyxy(box)
-    # score= np.max(dets[:,5:5+num_class],axis=-1,keepdims=True)
-    index = np.argmax(new_dets[:,4:4+num_class],axis=-1).reshape(-1,1)
-    output = np.concatenate((boxes,score,index),axis=1) 
-    reserve_=my_nms(output,iou_thresh) 
-    output=output[reserve_] 
-    output = restore_box(output,r,left,top)
-    return output
-
-def Detect(img,session_detect,r,left,top,num_class=2):
-    y_onnx = session_detect.run([session_detect.get_outputs()[0].name], {session_detect.get_inputs()[0].name: img})[0]
-    y_onnx = y_onnx.transpose((0,2,1))
-    outputs = post_precessing(y_onnx,r,left,top,num_class=num_class)
-    detect_dict =[]
-    for result in outputs:
-        result_dict = {}
-        rect=[int(result[0]),int(result[1]),int(result[2]),int(result[3])]
-        label = int(result[5])
-        obj_score=result[4]
-        result_dict["rect"]=rect
-        result_dict["label"]=label
-        result_dict["obj_score"]=round(obj_score,4)
-        detect_dict.append(result_dict)
-        
-    return detect_dict
-
-def  init_detect_model(model_path):
-     session_detect = onnxruntime.InferenceSession(model_path, providers=providers )
-     return session_detect
-
-
-
-
-
-if __name__ == "__main__":
-    
-    parser = argparse.ArgumentParser()
-    first_stage_detect_model_path =r"runs/detect/train2/weights/best.onnx"
-    fiest_state_detect_image_size = 640
-    image_path = "imgs"
-    output='result1'
-    opt = parser.parse_args()
-    file_list = []
-    allFilePath(image_path,file_list)
-    providers =  ['CUDAExecutionProvider']
-    clors = [(255,0,0),(0,255,0),(0,0,255),(255,255,0),(0,255,255),(0,0,0),(255,255,255),(255,0,255)]
-    img_size = (640,640)
-    session_detect = init_detect_model(first_stage_detect_model_path)
-  
-    if not os.path.exists(output):
-        os.mkdir(output)
-    save_path = output
-    count = 0
-    begin = time.time()
-    for pic_ in file_list:
-        count+=1
-        print(count,pic_,end=" ")
-        img=cv2.imread(pic_)
-        img0 = copy.deepcopy(img)
-        img,r,left,top = detect_pre_precessing(img,img_size) #检测前处理
-        outputs = Detect(img,session_detect,r,left,top,num_class=2) #得到检测结果
-        for output in outputs:
-            # label =int(output[5])
-            rect = output['rect']
-            cv2.rectangle(img0,(int(rect[0]),int(rect[1])),(int(rect[2]),int(rect[3])),clors[output['label']],2)
-            
-        img_name = os.path.basename(pic_)
-        save_img_path = os.path.join(save_path,img_name)
-        cv2.imwrite(save_img_path,img0)
-        # cv2.imwrite('haha.jpg',img0)
-        print(len(outputs))
-       
-    print(f"总共耗时{time.time()-begin} s")
-    
-
-        

readme/README.md

@@ -6,8 +6,6 @@
    ```
    label x y w h  
    ```
-   **自己的数据集**可以通过lablme 软件,create polygons标注车牌四个点即可，然后通过json2yolo.py 将数据集转为yolo格式，即可训练
-
 2. **修改ultralytics/datasets/yolov8-plate.yaml    train和val路径,换成你的数据路径**
 
    ```
@@ -20,7 +18,8 @@
    1: double
 
    ```
-3. **训练
+3. **训练**
+
    ```
    yolo task=detect mode=train model=yolov8s.yaml  data=./ultralytics/cfg/datasets/plate.yaml epochs=120 batch=32 imgsz=640 pretrained=False optimizer=SGD 
    ```