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python - 我如何知道图像分类器中图像的最佳重塑尺寸？

问题描述

当我尝试创建只有两个数字（7 和 10）的手写数字的图像数据集时，我尝试加载自定义图像（原始颜色：黑白，尺寸：251 x 54 请参见下面的示例）我得到了这个我的 load_img 函数错误如下： 

from tensorflow.keras.preprocessing.image import load_img
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.models import load_model


# load and prepare the image
def load_image(filename):
    # load the image
    img = load_img(filename, color_mode="grayscale",interpolation='nearest')
    # convert to array
    img = img_to_array(img)
    # reshape into a single sample with 1 channel
    img = img.reshape(2, 200, 50, 1)
    # prepare pixel data
    img = img.astype('float32')
    img = img / 255.0
    return img




# load an image and predict the class
def run_example():
    # load the image
    img = load_image('C:/Users/ADEM/Desktop/msi_youssef/PFE/dataset/10/kz.png')
    # load model
    model = load_model('C:/Users/ADEM/Desktop/msi_youssef/PFE/other_shit/first_try.h5')
    # predict the class
    digit = model.predict_classes(img)
    print(digit[0])


# entry point, run the example
run_example()

这是我得到的错误： 

ValueError                                Traceback (most recent call last)
<ipython-input-2-5427252e970b> in <module>
     32 
     33 # entry point, run the example
---> 34 run_example()

<ipython-input-2-5427252e970b> in run_example()
     23 def run_example():
     24     # load the image
---> 25     img = load_image('C:/Users/ADEM/Desktop/msi_youssef/PFE/dataset/10/kz.png')
     26     # load model
     27     model = load_model('C:/Users/ADEM/Desktop/msi_youssef/PFE/other_shit/final_model.h5')

<ipython-input-2-5427252e970b> in load_image(filename)
     11     img = img_to_array(img)
     12     # reshape into a single sample with 1 channel
---> 13     img = img.reshape(2, 200, 50, 1)
     14     # prepare pixel data
     15     img = img.astype('float32')

ValueError: cannot reshape array of size 13554 into shape (2,200,50,1)

请注意，在 final_model.h5 中，我将 img 平均大小设置为 200 ， 50
final_model.h5 的代码将在第一个答案中！

标签： pythonnumpytensorflowkerasclassification

解决方案

2D 卷积层需要输入为 -

if using channels_last: (batch_size, imageside1, imageside2, channels)
if using channels_first: (batch_size, channels, imageside1, imageside2)

在您的情况下，它将是 batch_size= 不指定， imageside1= 200， imageside1= 50， channels= 1（灰度图像）

因此load_image，通过以下更改修改您的功能

# load and prepare the image
def load_image(filename):
    # load the image with target size
    img = load_img(filename, color_mode="grayscale",interpolation='nearest',target_size=(200,50))
    # convert to array
    img = img_to_array(img)
    # reshape into a single sample with 1 channel
    # img = img.reshape(2, 200, 50, 1) --> This is not required now and why batch size argument as 2?
    # prepare pixel data
    img = img.astype('float32')
    img = img / 255.0
    return img

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