使用方式
microsoftml.resize_image(cols: [str, dict, list], width: int = 224,
height: int = 224, resizing_option: ['IsoPad', 'IsoCrop',
'Aniso'] = 'IsoCrop', **kargs)
說明
使用指定的調整尺寸方法將影像調整到指定尺寸。
詳細資料
resize_image 使用指定的尺寸調整方法，將圖片調整到指定的高度與寬度。 此轉換的輸入變數必須是影像，通常是轉換的 load_image 結果。
論點
科爾斯
一個字串或變數名稱清單，要轉換。 若 dict，鍵代表將建立的新變數名稱。
width
指定縮放後影像的寬度（像素單位）。 預設值是224。
高度
指定縮放後影像的高度（像素單位）。 預設值是224。
resizing_option
指定了要用的調整尺寸方法。 請注意，所有方法皆使用雙線性插值。 可用選項包括：
"IsoPad"：影像會重新調整大小，以保持長寬比。 如有需要，影像會以黑色填充以符合新的寬度或高度。"IsoCrop"：影像會重新調整大小，以保持長寬比。 如有需要，圖片會被裁切以符合新的寬度或高度。"Aniso"：影像被拉伸至新的寬度與高度，但不保留長寬比。
預設值為 "IsoPad"。
卡格族
額外參數送入計算引擎。
Returns
一個定義轉換的物件。
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