我有一個已經(jīng)訓(xùn)練好的模型，我想對目錄中的圖像進(jìn)行二元分類預(yù)測。我有超過 100,000 張圖像，因此為了提高效率，我想進(jìn)行批量預(yù)測。如何對圖像進(jìn)行批量預(yù)測，獲取預(yù)測結(jié)果，并在根據(jù)類別進(jìn)行預(yù)測后將圖像存儲在兩個單獨的文件夾中？這就是我的代碼到目前為止的樣子......model_filepath = r"C:\Users\model_200.h5"model = tf.keras.models.load_model(model_filepath)test_dir = r"C:\Users\image_testing_folder"batch_size = 64IMG_HEIGHT = 200IMG_WIDTH = 200test_image_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1./255)test_image_gen = test_image_generator.flow_from_directory(directory=str(test_dir), batch_size=batch_size, shuffle=False, target_size=(IMG_HEIGHT, IMG_WIDTH), )predictions = (model.predict(test_image_gen) > 0.5).astype("int32")predictions一種解決方案是將預(yù)測與圖像文件路徑聯(lián)系起來，然后使用 Shutil.move() 將原始圖像移動到目標(biāo)文件夾。我該怎么做？有沒有比使用 ImageDataGenerator 和 .flow_from_directory 更好的方法來進(jìn)行批量預(yù)測？
1 回答
滄海一幻覺
TA貢獻(xiàn)1824條經(jīng)驗 獲得超5個贊
您可以創(chuàng)建自定義數(shù)據(jù)集，以便還可以輕松檢索文件名：
import tensorflow as tf
from tensorflow.keras.layers import *
from tensorflow.keras import Sequential
from glob2 import glob
from shutil import copy
import numpy as np
files = glob('group1\\*\\*.jpg')
imsize = 64
def load(file_path):
img = tf.io.read_file(file_path)
img = tf.image.decode_png(img, channels=3)
img = tf.image.convert_image_dtype(img, tf.float32)
img = tf.image.resize(img, size=(imsize, imsize))
return img, file_path
ds = tf.data.Dataset.from_tensor_slices(files).\
take(100).\
shuffle(100).\
map(load).batch(4)
model = Sequential()
model.add(Conv2D(8, (3, 3), input_shape=(imsize, imsize, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(units=32, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(units=2, activation='sigmoid'))
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.build(input_shape=(imsize, imsize, 3))
categories = np.array(['cats', 'dogs'])
target_dir = 'newpics'
for cat in categories:
os.makedirs(os.path.join(target_dir, cat), exist_ok=True)
for images, filenames in ds:
preds = model(images)
targets = categories[np.argmax(preds, axis=1)]
for file, destination in zip(filenames, targets):
copy(file.numpy().decode(), os.path.join(target_dir, destination,
os.path.basename(file.numpy().decode())
))
print(file.numpy().decode(), '-->', os.path.join(target_dir, destination,
os.path.basename(file.numpy().decode())
))
group1\cats\cat.4051.jpg --> newpics\cats\cat.4051.jpg
group1\cats\cat.4091.jpg --> newpics\dogs\cat.4091.jpg
group1\cats\cat.4055.jpg --> newpics\cats\cat.4055.jpg
group1\cats\cat.4041.jpg --> newpics\cats\cat.4041.jpg
group1\cats\cat.4090.jpg --> newpics\cats\cat.4090.jpg
group1\cats\cat.4071.jpg --> newpics\dogs\cat.4071.jpg
group1\cats\cat.4082.jpg --> newpics\cats\cat.4082.jpg
group1\cats\cat.4037.jpg --> newpics\cats\cat.4037.jpg
group1\cats\cat.4005.jpg --> newpics\cats\cat.4005.jpg
您需要更改的只是全局模式和文件夾。
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