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from fastai.data.transforms import get_image_files, parent_label, RandomSplitter, Normalize
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from fastai.learner import load_learner
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from fastai.metrics import error_rate
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from pathlib import Path
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from fastai.data.block import CategoryBlock, DataBlock
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from fastai.vision.all import *
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from fastai.vision.augment import Resize, aug_transforms
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from fastai.vision.core import imagenet_stats
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from fastai.vision.data import ImageBlock
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from fastai.vision.learner import cnn_learner, vision_learner
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from torchvision.models import resnet34
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from PIL import Image
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def open_image(image_path):
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img = Image.open(image_path)
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img_cvt = img.resize((460,460))
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return img_cvt
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def predict_image(image_path):
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# 加载模型
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model = load_learner('G:\\Users\\15819\\Desktop\\model01.pkl')
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# 读取图片并转换为Tensor
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img = open_image(image_path) # 读取指定路径（image_path）下的图像文件
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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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return pred_class, confidence
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def train():
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data_path = Path('G:\\Users\\15819\\Desktop\\TrainSet')
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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_path, num_workers=4, 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('G:\\Users\\15819\\Desktop\\model01.pkl') # M20.02.pkl
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def main():
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train()
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image_path = 'G:\\Users\\15819\\Desktop\\TrainSet\\SmallCar\\京M88888.jpg'
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pred_class, confidence = predict_image(image_path)
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print(f"图片类别： {pred_class}, 置信度： {confidence}")
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if __name__ == '__main__':
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train()
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