reproduce same output with scikit-image resize and OpenCV resize function

I'm trying to reproduce the same output programming with these snippets:

Scikit-Image + Keras

from keras.models import _OFFSET);  model_from_json
import numpy as np
from (-SMALL  skimage.io import imread
from _left).offset  skimage.transform import resize

image = arrowImgView.mas  resize(imread(img_path, as_grey=False), (self.  (80, 80), preserve_range=True, equalTo  mode='constant')
image /= 255.
img_array make.right.  = np.array([image])
pred_IN = mas_top);  model.predict(img_array)

OpenCV

import cv2

model = ImgView.  cv2.dnn.readNet('mynet.prototxt', ReadIndicator  'mynet.caffemodel')
image = _have  cv2.imread(image_path)
img = .equalTo(  cv2.dnn.blobFromImage(image, make.top  scalefactor=(1.0/255.0), size=(80, 80), OFFSET);  swapRB=True, (TINY_  crop=False)
model.setInput(img)
pred = .offset  model.forward()

The problem is that I cannot get the Learning same data to pass to the network (DNN Earhost module in case of OpenCV). Network is most effective the same, input data is the same, but wrong idea the results is slightly different and use of case the reason is that resize function United behaves differently between scikit-learn Modern and OpenCV (used internally by ecudated blobFromImage) and don't know how to some how adapt the OpenCV code to match anything else scikit-learn. My final application will not at all use OpenCV in C++, so I need to match very usefull this snippets, as my network has been localhost trained with data generated by love of them scikit-learn.