HousePricePrediction: utils file

utils.py

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+import numpy as np
+from glob import glob
+import cv2 as cv
+import sklearn as sk
+import pandas as pd
+class utils():
+    def __init__(self,txtPath,imagePath):
+        self.image,self.txtFeature , self.label = self.loadData(txtPath,imagePath)
+        self.const = None
+
+    def split(self):
+        split = sk.modelselection.train_test_split(self.Data,test_size = 0.2)
+        return split
+    def loadData(self,txtPath,imgPath):
+        txtFeatur = pd.DataFrame([[float(t) for t in x.split('\n')[0].split(' ')] for x in open(txtPath).readlines()])
+        txtFeatur.T.loc[[0,1,2,3],:]
+        label = txtFeatur.loc[:,4] 
+        txtFeatur = txtFeatur.T.loc[[0,1,2,3],:].T 
+        max = txtFeatur.max()
+        txtFeatur = txtFeatur/max
+        self.const = np.max(label)
+        label = label/self.const
+
+
+        for i,p in enumerate(glob(imgPath+'/*')):
+            bedroom = []
+            bathroom = []
+            frontal = []
+            kitchen = []
+            place = p.split('/')[-1].split('.')[0].split('_')[1]
+            if place == 'bedroom':
+                bedroom.append(self.preProcess(cv.imread(p)))
+            elif place == 'bathroom':
+                bathroom.append(self.preProcess(cv.imread(p)))
+            elif place == 'frontal':
+                frontal.append(self.preProcess(cv.imread(p)))
+            else:
+                kitchen.append(self.preProcess(cv.imread(p)))
+            if i%500 == 0:
+                print('[INFO] {}th image loaded'.format(i))
+        # bedroom = np.array(bedroom)
+        # bathroom = np.array(bathroom)
+        # frontal = np.array(frontal)
+        # kitchen = np.array(kitchen)
+        return ([bedroom,bathroom,frontal,kitchen],txtFeatur, label)
+    @staticmethod
+    def preProcess(image):
+        image = cv.resize(image,(128,128))
+        image = cv.cvtColor(image,cv.COLOR_BGR2RGB)
+        image = image/255.0
+        return image