import sys

class CatchOutErr:

def __init__(self):

self.value = ''

def write(self, txt):

self.value += txt

catchOutErr = CatchOutErr()

sys.stderr = catchOutErr

from keras.preprocessing import image

from keras.models import load_model

import os

import numpy as np

import io

import lime

from lime import lime_image

from skimage.segmentation import mark_boundaries

from PIL import Image

def transform_img_fn(img):

img = Image.open(io.BytesIO(bytes(img)))

imgR = img.resize((224, 224))

x = image.img_to_array(imgR)

x = np.expand_dims(x, axis=0)

return (x, img)

def getTopPrediction(prediction):

maxP = 0

for i in range(1, len(prediction)):

if prediction[maxP] < prediction[i]:

maxP = i

return maxP

def getTopXpredictions(prediction, topLabels):

prediction_class = []

for i in range(0, len(prediction[0])):

prediction_class.append((i, prediction[0][i]))

prediction_class.sort(key=lambda x: x[1], reverse=True)

return prediction_class[:topLabels]

def loadModel(modelPath):

try:

model = load_model(modelPath)

except Exception as ex:

return None

return model

def explain(model, img, topLabels, numSamples, numFeatures, hideRest, hideColor, positiveOnly):

img, oldImg = transform_img_fn(img)

img = img*(1./255)

prediction = model.predict(img)

explainer = lime_image.LimeImageExplainer()

img = np.squeeze(img)

explanation = explainer.explain_instance(img, model.predict, top_labels=topLabels, hide_color=hideColor, num_samples=numSamples)

temp, mask = explanation.get_image_and_mask(getTopPrediction(prediction[0]), positive_only=positiveOnly, num_features=numFeatures, hide_rest=hideRest)

tempMask = mask * 255

temp = Image.fromarray(np.uint8(tempMask))

temp = temp.resize((oldImg.width, oldImg.height))

temp = image.img_to_array(temp)

temp = temp * 1./255

temp = temp.astype(np.int64)

temp = np.squeeze(temp)

oldImgArr = image.img_to_array(oldImg)

oldImgArr = oldImgArr * (1./255)

oldImgArr = oldImgArr.astype(np.float64)

imgExplained = mark_boundaries(oldImgArr, temp)

imgFinal = np.uint8(imgExplained*255)

img = Image.fromarray(imgFinal)

imgByteArr = io.BytesIO()

img.save(imgByteArr, format='JPEG')

imgByteArr = imgByteArr.getvalue()

return imgByteArr

def explain_raw(model, img, topLabels, numSamples, numFeatures, hideRest, hideColor, positiveOnly):

img, oldImg = transform_img_fn(img)

img = img*(1./255)

prediction = model.predict(img)

explainer = lime_image.LimeImageExplainer()

img = np.squeeze(img)

explanation = explainer.explain_instance(img, model.predict, top_labels=topLabels, hide_color=hideColor, num_samples=numSamples)

temp, mask = explanation.get_image_and_mask(getTopPrediction(prediction[0]), positive_only=positiveOnly, num_features=numFeatures, hide_rest=hideRest)

imgExplained = mark_boundaries(temp, mask)

img = Image.fromarray(np.uint8(imgExplained*255))

imgByteArr = io.BytesIO()

img.save(imgByteArr, format='JPEG')

imgByteArr = imgByteArr.getvalue()

return imgByteArr

def explain_multiple(model, img, topLabels, numSamples, numFeatures, hideRest, hideColor, positiveOnly):

img, oldImg = transform_img_fn(img)

img = img*(1./255)

prediction = model.predict(img)

explainer = lime_image.LimeImageExplainer()

img = np.squeeze(img)

explanation = explainer.explain_instance(img, model.predict, top_labels=topLabels, hide_color=hideColor, num_samples=numSamples)

topClasses = getTopXpredictions(prediction, topLabels)

explanations = []

for cl in topClasses:

temp, mask = explanation.get_image_and_mask(cl[0], positive_only=positiveOnly, num_features=numFeatures, hide_rest=hideRest)

imgExplained = mark_boundaries(temp, mask)

img = Image.fromarray(np.uint8(imgExplained*255))

imgByteArr = io.BytesIO()

img.save(imgByteArr, format='JPEG')

imgByteArr = imgByteArr.getvalue()

explanations.append((cl[0], cl[1], imgByteArr))

return (explanations, len(explanations))