microsoftml.resize_image：調整影像大小

發行項
05/04/2023

使用方式

microsoftml.resize_image(cols: [str, dict, list], width: int = 224,
    height: int = 224, resizing_option: ['IsoPad', 'IsoCrop',
    'Aniso'] = 'IsoCrop', **kargs)

Description

使用指定的調整大小方法，將影像大小調整成指定的維度。

詳細資料

resize_image 會使用指定的調整大小方法，將影像大小調整為指定的高度和寬度。此轉換的輸入變數必須是影像，通常是 load_image 轉換的結果。

引數

cols

要轉換的字元字串或變數名稱清單。如果是 dict，則索引鍵代表要建立的新變數名稱。

width

指定縮放影像的寬度 (以像素為單位)。預設值為 224。

身高

指定縮放影像的高度 (以像素為單位)。預設值為 224。

resizing_option

指定要使用的調整大小方法。請注意，所有方法都使用雙線性插補。選項包括：

"IsoPad"：影像會調整大小以保留外觀比例。如有需要，可用黑色填補影像，以因應新的寬度或高度。
"IsoCrop"：影像會調整大小以保留外觀比例。如有需要，可裁切影像以因應新的寬度或高度。
"Aniso"：影像會延展到新的寬度和高度，而不會保留外觀比例。

預設值是 "IsoPad"。

kargs

傳送至計算引擎的其他引數。

傳回

定義轉換的物件。

另請參閱

load_image, extract_pixels, featurize_image.

範例

'''
Example with images.
'''
import numpy
import pandas
from microsoftml import rx_neural_network, rx_predict, rx_fast_linear
from microsoftml import load_image, resize_image, extract_pixels
from microsoftml.datasets.image import get_RevolutionAnalyticslogo

train = pandas.DataFrame(data=dict(Path=[get_RevolutionAnalyticslogo()], Label=[True]))

# Loads the images from variable Path, resizes the images to 1x1 pixels
# and trains a neural net.
model1 = rx_neural_network("Label ~ Features", data=train, 
            ml_transforms=[            
                    load_image(cols=dict(Features="Path")), 
                    resize_image(cols="Features", width=1, height=1, resizing="Aniso"), 
                    extract_pixels(cols="Features")], 
            ml_transform_vars=["Path"], 
            num_hidden_nodes=1, num_iterations=1)

# Featurizes the images from variable Path using the default model, and trains a linear model on the result.
# If dnnModel == "AlexNet", the image has to be resized to 227x227.
model2 = rx_fast_linear("Label ~ Features ", data=train, 
            ml_transforms=[            
                    load_image(cols=dict(Features="Path")), 
                    resize_image(cols="Features", width=224, height=224), 
                    extract_pixels(cols="Features")], 
            ml_transform_vars=["Path"], max_iterations=1)

# We predict even if it does not make too much sense on this single image.
print("\nrx_neural_network")
prediction1 = rx_predict(model1, data=train)
print(prediction1)

print("\nrx_fast_linear")
prediction2 = rx_predict(model2, data=train)
print(prediction2)

輸出：

Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
Beginning processing data.
Rows Read: 1, Read Time: 0, Transform Time: 0
Beginning processing data.
Beginning processing data.
Rows Read: 1, Read Time: 0, Transform Time: 0
Beginning processing data.
Beginning processing data.
Rows Read: 1, Read Time: 0.001, Transform Time: 0
Beginning processing data.
Using: AVX Math

***** Net definition *****
  input Data [3];
  hidden H [1] sigmoid { // Depth 1
    from Data all;
  }
  output Result [1] sigmoid { // Depth 0
    from H all;
  }
***** End net definition *****
Input count: 3
Output count: 1
Output Function: Sigmoid
Loss Function: LogLoss
PreTrainer: NoPreTrainer
___________________________________________________________________
Starting training...
Learning rate: 0.001000
Momentum: 0.000000
InitWtsDiameter: 0.100000
___________________________________________________________________
Initializing 1 Hidden Layers, 6 Weights...
Estimated Pre-training MeanError = 0.707823
Iter:1/1, MeanErr=0.707823(0.00%), 0.01M WeightUpdates/sec
Done!
Estimated Post-training MeanError = 0.707499
___________________________________________________________________
Not training a calibrator because it is not needed.
Elapsed time: 00:00:00.0820600
Elapsed time: 00:00:00.0090292
Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
Beginning processing data.
Rows Read: 1, Read Time: 0, Transform Time: 0
Beginning processing data.
Beginning processing data.
Rows Read: 1, Read Time: 0, Transform Time: 0
Beginning processing data.
Beginning processing data.
Rows Read: 1, Read Time: 0, Transform Time: 0
Beginning processing data.
Using 2 threads to train.
Automatically choosing a check frequency of 2.
Auto-tuning parameters: L2 = 5.
Auto-tuning parameters: L1Threshold (L1/L2) = 1.
Using model from last iteration.
Not training a calibrator because it is not needed.
Elapsed time: 00:00:01.0852660
Elapsed time: 00:00:00.0132126

rx_neural_network
Beginning processing data.
Rows Read: 1, Read Time: 0, Transform Time: 0
Beginning processing data.
Elapsed time: 00:00:00.0441601
Finished writing 1 rows.
Writing completed.
  PredictedLabel     Score  Probability
0          False -0.028504     0.492875

rx_fast_linear
Beginning processing data.
Rows Read: 1, Read Time: 0.001, Transform Time: 0
Beginning processing data.
Elapsed time: 00:00:00.5196788
Finished writing 1 rows.
Writing completed.
  PredictedLabel  Score  Probability
0          False    0.0          0.5

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