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"""
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模块作者：
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AI代码结构：刘钰廷、冯雅君
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代码优化整理：王昱博、冯昌盛
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AI模型训练/纠错：刘钰廷、冯雅君、冯昌盛
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代码整合/打包：王昱博
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模块用途：
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图像分类AI，用于区分车牌的具体类型
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"""
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import cv2
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from PIL import Image
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from pathlib import Path
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from fastai.vision.all import *
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from fastai.metrics import error_rate
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from fastai.learner import load_learner
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from torchvision.models import resnet34
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from fastai.vision.data import ImageBlock
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from fastai.vision.core import imagenet_stats
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from fastai.data.block import CategoryBlock, DataBlock
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from fastai.vision.augment import Resize, aug_transforms
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from fastai.vision.learner import cnn_learner, vision_learner
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from fastai.data.transforms import get_image_files, parent_label, RandomSplitter, Normalize
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class ClassificationAI:
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@staticmethod
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def ConvertImage(cv_img: cv2.Mat) -> Image.Image:
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return Image.fromarray(cv2.cvtColor(cv_img, cv2.COLOR_BGR2RGB)).resize((460, 460))
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@staticmethod
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def ConvertClassifyResult(cla: str) -> str:
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if cla == 'ForeignV':
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return '外籍车辆'
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elif cla == 'In-fieldV':
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return '场内车辆'
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elif cla == 'large-scaleNewenergyV':
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return '大型新能源车辆'
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elif cla == 'MediumLarge-sizedV':
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return '中/大型车辆'
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elif cla == 'MilitaryPoliceEmergencyV':
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return '军/警/应急车辆'
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elif cla == 'SmallCar':
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return '小型轿车'
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elif cla == 'SmallNewEnergyV':
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return '小型新能源轿车'
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else:
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return '未知'
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@classmethod
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def TrainAI(cls, data_set_path: str, export_path: str) -> None:
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blocks = (ImageBlock, CategoryBlock)
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batch_size = 32
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dls = DataBlock(
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blocks=blocks,
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get_items=get_image_files,
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splitter=RandomSplitter(),
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get_y=parent_label,
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item_tfms=Resize(460),
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batch_tfms=[*aug_transforms(size=224, min_scale=0.75), Normalize.from_stats(*imagenet_stats)]
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).dataloaders(data_set_path, num_workers=0, bs=batch_size)
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model = vision_learner(dls, resnet34, metrics=error_rate)
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model.fine_tune(5, freeze_epochs=3) # 5 - 训练的轮次， 3 - 冻结的轮次
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model.export(Path(export_path) / 'model.pkl')
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@classmethod
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def PredictImage(cls, image: cv2.Mat, model_path: str) -> tuple:
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# 加载模型
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model = load_learner(model_path)
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# 读取图片并转换为Tensor
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img = cls.ConvertImage(image) # 读取图像文件
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# 进行预测
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pred_class, pred_idx, outputs = model.predict(img)
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# 获取置信度
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# 检查输出张量的维度
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if outputs.dim() == 0:
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confidence = float(outputs)
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else:
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confidence = float(outputs[pred_idx])
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pred_class = cls.ConvertClassifyResult(pred_class)
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return pred_class, confidence
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