0 
import sys
from os import listdir, sep
import numpy as np
import pickle
from PIL import Image
import cv2
from sklearn.ensemble import RandomForestClassifier
import numpy as np
from PIL import Image


DEFAULT_IMAGE_SIZE = (45,45)
SPLIT_POINT_COEFF = 0.8

class PredictionService:


        def __init__(self):
             #self.dataset = datasetDIR
             #self.testImage = testImage
             pass

        def getImageVector(self, image):
            try:

                #NOTE:from docs
                #When translating a color image to black and white (mode “L”), the library uses the ITU-R 601-2 luma transform:
                #L = R * 299/1000 + G * 587/1000 + B * 114/1000

                imageGrayscale = Image.open(image).convert('L')
                #resize image to default image size - 45 x 45
                imageGrayscale = imageGrayscale.resize(DEFAULT_IMAGE_SIZE, Image.ANTIALIAS)
                #
                imageNP = np.array(imageGrayscale)
                imgList = []
                for line in imageNP:
                    for value in line:
                        imgList.append(value)
                #imgList is 2025 long vector
                return imgList
            except Exception as e:
                print("Error : {}".format(e))
                return None

        def addImagesToSet(self, rootPath, imageList, label, completeImageList = [], labelList = []):
            dashes = ['-','/','-','\\']
            counter = 0
            for image in imageList:
                print('[{}] Images loading...'.format(dashes[counter]))
                counter = (counter + 1) % len(dashes)
                completeImageList.append(self.getImageVector(rootPath + image))
                labelList.append(label)


        def getTrainingAndTestData(self, directoryPath):

            dirList = listdir(directoryPath)
            xTrain, yTrain, xTest, yTest = [], [], [], []
            try:
                if len(dirList) < 1:
                    return None

                imageDirPath = None

                counter = 1
                for directory in dirList:

                    imageDir = listdir('{}/{}'.format(directoryPath, directory))
                    splitPoint = int(SPLIT_POINT_COEFF * len(imageDir))

                    print('[{}] Loading dataset - {} images'.format(counter, directory))
                    counter += 1

                    trainImages, testImages = imageDir[:splitPoint], imageDir[splitPoint:]
                    imageDirPath = directoryPath + sep + directory + sep
                    self.addImagesToSet(imageDirPath, trainImages, directory, xTrain, yTrain)
                    self.addImagesToSet(imageDirPath, testImages, directory, xTest, yTest)

            except Exception as e:
                print('Error: {}'.format(e))
                return [],[],[],[]

            return xTrain, yTrain, xTest, yTest

        def trainModel(self, trainDatasetDir):
            train_dataset_dir = ('C:/Users/MUTHU/Documents/GitHub/Handwritten-math-symbols-recognition/Dataset')
            print('Training.....')
            xTrain, yTrain, xTest , yTest = self.getTrainingAndTestData(trainDatasetDir)
            if [] not in (xTrain, yTrain, xTest , yTest):
                randomForestClassifier = RandomForestClassifier()
                randomForestClassifier.fit(xTrain,yTrain)
                accuracyScore = randomForestClassifier.score(xTrain,yTrain)
                # save classifier
                pickle.dump(randomForestClassifier,open("Model/math_recognition_model.pkl",'wb'))
                print("Model Accuracy Score : {}".format(accuracyScore))
                testAccuracyScore = randomForestClassifier.score(xTest,yTest)
                print("Model Accuracy Score (Test) : {}".format(testAccuracyScore))
            else :
                print("An error occurred.")

        def predict(self, imagePath):
            try:
                image = [self.getImageVector(imagePath)]
                # load saved model
                try:
                    decisionTreeClassifierModel= pickle.load(open("Model/random_forest_classifier.pkl",'rb'))
                    modelPrediction = decisionTreeClassifierModel.predict(image)
                    print(modelPrediction)
                    print("Recognized expression:" +  str(modelPrediction[0]))
                except FileNotFoundError as modelFileError:
                    print("Error : {}".format(modelFileError))
                    self.trainModel(datasetDir)
                    self.predict(imagePath)

            except FileNotFoundError as fileError:
                print("Error : {}".format(fileError))
            except Exception as e:
                print("Error : {}".format(e))

请帮助我使用 pycharam 解决这个问题

4

1 回答 1

1

您需要在项目中单独导入它。为此，您可以参考以下链接： https ://www.jetbrains.com/pycharm/guide/tips/install-and-import/

还可以从菜单选项将您的 pycharm 更新到其最新版本 2020.1.5 转到帮助 -> 检查更新 -> 选项将在下方右侧弹出以进行更新

于 2021-01-15T06:41:20.637 回答