120 lines
3.9 KiB
Python
120 lines
3.9 KiB
Python
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from plate_recognition.plateNet import myNet_ocr,myNet_ocr_color
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import torch
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import torch.nn as nn
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import cv2
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import numpy as np
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import os
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import time
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import sys
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def cv_imread(path): #可以读取中文路径的图片
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img=cv2.imdecode(np.fromfile(path,dtype=np.uint8),-1)
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return img
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def allFilePath(rootPath,allFIleList):
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fileList = os.listdir(rootPath)
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for temp in fileList:
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if os.path.isfile(os.path.join(rootPath,temp)):
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if temp.endswith('.jpg') or temp.endswith('.png') or temp.endswith('.JPG'):
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allFIleList.append(os.path.join(rootPath,temp))
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else:
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allFilePath(os.path.join(rootPath,temp),allFIleList)
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device = torch.device('cuda') if torch.cuda.is_available() else torch.device("cpu")
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color=['黑色','蓝色','绿色','白色','黄色']
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plateName=r"#京沪津渝冀晋蒙辽吉黑苏浙皖闽赣鲁豫鄂湘粤桂琼川贵云藏陕甘青宁新学警港澳挂使领民航危0123456789ABCDEFGHJKLMNPQRSTUVWXYZ险品"
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mean_value,std_value=(0.588,0.193)
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def decodePlate(preds):
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pre=0
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newPreds=[]
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index=[]
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for i in range(len(preds)):
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if preds[i]!=0 and preds[i]!=pre:
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newPreds.append(preds[i])
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index.append(i)
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pre=preds[i]
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return newPreds,index
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def image_processing(img,device):
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img = cv2.resize(img, (168,48))
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img = np.reshape(img, (48, 168, 3))
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# normalize
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img = img.astype(np.float32)
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img = (img / 255. - mean_value) / std_value
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img = img.transpose([2, 0, 1])
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img = torch.from_numpy(img)
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img = img.to(device)
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img = img.view(1, *img.size())
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return img
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def get_plate_result(img,device,model,is_color=False):
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input = image_processing(img,device)
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if is_color: #是否识别颜色
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preds,color_preds = model(input)
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color_preds = torch.softmax(color_preds,dim=-1)
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color_conf,color_index = torch.max(color_preds,dim=-1)
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color_conf=color_conf.item()
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else:
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preds = model(input)
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preds=torch.softmax(preds,dim=-1)
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prob,index=preds.max(dim=-1)
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index = index.view(-1).detach().cpu().numpy()
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prob=prob.view(-1).detach().cpu().numpy()
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# preds=preds.view(-1).detach().cpu().numpy()
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newPreds,new_index=decodePlate(index)
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prob=prob[new_index]
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plate=""
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for i in newPreds:
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plate+=plateName[i]
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# if not (plate[0] in plateName[1:44] ):
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# return ""
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if is_color:
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return plate,prob,color[color_index],color_conf #返回车牌号以及每个字符的概率,以及颜色，和颜色的概率
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else:
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return plate,prob
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def init_model(device,model_path,is_color = False):
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# print( print(sys.path))
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# model_path ="plate_recognition/model/checkpoint_61_acc_0.9715.pth"
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check_point = torch.load(model_path,map_location=device)
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model_state=check_point['state_dict']
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cfg=check_point['cfg']
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color_classes=0
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if is_color:
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color_classes=5 #颜色类别数
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model = myNet_ocr_color(num_classes=len(plateName),export=True,cfg=cfg,color_num=color_classes)
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model.load_state_dict(model_state,strict=False)
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model.to(device)
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model.eval()
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return model
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# model = init_model(device)
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if __name__ == '__main__':
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model_path = r"weights/plate_rec_color.pth"
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image_path ="images/tmp2424.png"
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testPath = r"/mnt/Gpan/Mydata/pytorchPorject/CRNN/crnn_plate_recognition/images"
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fileList=[]
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allFilePath(testPath,fileList)
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# result = get_plate_result(image_path,device)
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# print(result)
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is_color = False
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model = init_model(device,model_path,is_color=is_color)
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right=0
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begin = time.time()
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for imge_path in fileList:
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img=cv2.imread(imge_path)
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if is_color:
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plate,_,plate_color,_=get_plate_result(img,device,model,is_color=is_color)
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print(plate)
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else:
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plate,_=get_plate_result(img,device,model,is_color=is_color)
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print(plate,imge_path)
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