利用Xception网络判断图片是不是狗，入门用的

最新推荐文章于 2023-11-13 07:54:39 发布

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最新推荐文章于 2023-11-13 07:54:39 发布

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分类专栏：机器学习算法

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本文介绍使用Xception深度学习模型来判断一张图片是否为狗的方法。通过加载预训练权重，设置狗的ImageNet类别ID，并对输入图片进行预处理，实现对图片中狗的精确识别。

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利用Xception网络判断图片是不是狗，更多办法，查阅

https://keras-cn.readthedocs.io/en/latest/other/application/

from keras.applications import *
import cv2
import numpy as np

model_pre = xception.Xception(weights='imagenet')
Dogs = set(['n02085620', 'n02085782', 'n02085936', 'n02086079', 'n02086240', 'n02086646', 'n02086910', 'n02087046', 'n02087394', 'n02088094', 'n02088238',
            'n02088364', 'n02088466', 'n02088632', 'n02089078', 'n02089867', 'n02089973', 'n02090379', 'n02090622', 'n02090721', 'n02091032', 'n02091134',
            'n02091244', 'n02091467', 'n02091635', 'n02091831', 'n02092002', 'n02092339', 'n02093256', 'n02093428', 'n02093647', 'n02093754', 'n02093859',
            'n02093991', 'n02094114', 'n02094258', 'n02094433', 'n02095314', 'n02095570', 'n02095889', 'n02096051', 'n02096177', 'n02096294', 'n02096437',
            'n02096585', 'n02097047', 'n02097130', 'n02097209', 'n02097298', 'n02097474', 'n02097658', 'n02098105', 'n02098286', 'n02098413', 'n02099267',
            'n02099429', 'n02099601', 'n02099712', 'n02099849', 'n02100236', 'n02100583', 'n02100735', 'n02100877', 'n02101006', 'n02101388', 'n02101556',
            'n02102040', 'n02102177', 'n02102318', 'n02102480', 'n02102973', 'n02104029', 'n02104365', 'n02105056', 'n02105162', 'n02105251', 'n02105412',
            'n02105505', 'n02105641', 'n02105855', 'n02106030', 'n02106166', 'n02106382', 'n02106550', 'n02106662', 'n02107142', 'n02107312', 'n02107574',
            'n02107683', 'n02107908', 'n02108000', 'n02108089', 'n02108422', 'n02108551', 'n02108915', 'n02109047', 'n02109525', 'n02109961', 'n02110063',
            'n02110185', 'n02110341', 'n02110627', 'n02110806', 'n02110958', 'n02111129', 'n02111277', 'n02111500', 'n02111889', 'n02112018', 'n02112137',
            'n02112350', 'n02112706', 'n02113023', 'n02113186', 'n02113624', 'n02113712', 'n02113799', 'n02113978'])

def pred_dog(imgpath):
    img = cv2.imread(imgpath)
    img = img[:, :, ::-1]
    img = cv2.resize(img, (299, 299))
    X = np.expand_dims(img, axis=0)
    X = xception.preprocess_input(X)
    preds = model_pre.predict(X)
    dps = xception.decode_predictions(preds, 5)
    is_dog = False
    features = dps[0]
    for i, val in enumerate(features):
        category = val[0]
        if category in Dogs:
            is_dog = True
            break
    if not is_dog:
        print(imgpath, is_dog)
    return is_dog