BagOfWordsNoEqual.py

import os
import cv2 as cv
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import MiniBatchKMeans
from sklearn import svm
from sklearn import metrics


def BOWSift(train_x,train_y,test_x,test_y):


    #Starting Question 1
    POI=[]
    k=100 #feel free to change this hyperparmeter to test different sizes
    mbk=MiniBatchKMeans(k)
    bins=100 #feel free to change this hyperparmeter to test different sizes
    c_array=[1,10,50,200]

    sift = cv.xfeatures2d.SIFT_create()


    for train_x_img in train_x:
        img=cv.imread(train_x_img)
        imgGray=cv.cvtColor(img,cv.COLOR_BGR2GRAY)
        _, des = sift.detectAndCompute(imgGray,None)
        POI.append(des)
        mbk.partial_fit(des)

    #building histogram
    hist=[]
    for des in POI:
        pred=mbk.predict(des)
        his,_=np.histogram(pred,bins=bins)
        hist.append(his)

    #SVM

    for c in c_array: #trying out diffrent values for C

        linear_svm=svm.LinearSVC(C=c,max_iter=5000,dual=False,random_state=20)
        linear_svm.fit(hist,train_y)

       #starting the testing phase
        POI_test=[]
        for picture in test_x:
            img = cv.imread(picture)
            imgGray=cv.cvtColor(img,cv.COLOR_BGR2GRAY)
            _,des=sift.detectAndCompute(imgGray,None)
            POI_test.append(des)

        hist_test=[]
        for des in POI_test:
            pred=mbk.predict(des)
            his,_=np.histogram(pred,bins=bins)
            hist_test.append(his)

        predictions=linear_svm.predict(hist_test)

        print(f" Class report for classifier {linear_svm},\n{metrics.classification_report(test_y,predictions)}")


from tensorflow.keras.applications.vgg16 import preprocess_input
from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.layers import Input
def BOWDeepLearning(train_x,train_y,test_x,test_y):

    input_tensor = Input(shape=(None, None, 3))
    model = VGG16(input_tensor=input_tensor, weights='imagenet', include_top=False)
    print(model.summary())

    im = plt.imread(train_x[0])  # read an image file
    im = np.expand_dims(im, axis=0)
    im = preprocess_input(im)
    pred = model.predict(im)

    # Starting Question 1
    POI = []
    bins=100 #feel free to change this hyperparmeter to test different sizes
    k=100 #feel free to change this hyperparmeter to test different sizes
    mbk = MiniBatchKMeans(k)
    c_array = [1, 10, 50]


    for train_x_img in train_x:
        im = cv.imread(train_x_img)
        im = np.expand_dims(im, axis=0)
        im = preprocess_input(im)
        des=model.predict(im)
        #print(des.shape)
        des=des.reshape([1024,32])
        #print(des.shape)
        POI.append(des)
        mbk.partial_fit(des)

    # building histogram
    hist = []
    for des in POI:
        pred = mbk.predict(des)
        his, _ = np.histogram(pred, bins=bins)
        hist.append(his)

    # SVM


    for c in c_array:  # trying out diffrent values for C

        linear_svm = svm.LinearSVC(C=c, max_iter=5000, dual=False, random_state=20)
        linear_svm.fit(hist, train_y)

        # starting the testing phase
        POI_test = []
        for picture in test_x:
            im = cv.imread(picture)
            im = np.expand_dims(im, axis=0)
            im = preprocess_input(im)
            des=model.predict(im)
            des = des.reshape([1024,32])
            POI_test.append(des)

        hist_test = []
        for des in POI_test:
            pred = mbk.predict(des)
            his, _ = np.histogram(pred, bins=bins)#200 normally
            hist_test.append(his)

        predictions = linear_svm.predict(hist_test)

        print(f" Class report for classifier {linear_svm},\n{metrics.classification_report(test_y, predictions)}")


if __name__ == '__main__':


    path = 'spatial_envelope_256x256_static_8outdoorcategories'
    coast=[]
    forest=[]
    highway=[]
    insidecity=[]
    mountain=[]
    opencountry=[]
    street=[]
    tallbuilding=[]
    # r=root, d=directories, f = files
    for r, d, f in os.walk(path):
        for file in f:
            if 'coast' in file:
                coast.append(os.path.join(r, file))
            if 'forest' in file:
                forest.append(os.path.join(r, file))
            if 'highway' in file:
                highway.append(os.path.join(r, file))
            if 'insidecity' in file:
                insidecity.append(os.path.join(r, file))
            if 'mountain' in file:
                mountain.append(os.path.join(r, file))
            if 'opencountry' in file:
                opencountry.append(os.path.join(r, file))
            if 'street' in file:
                street.append(os.path.join(r, file))
            if 'tallbuilding' in file:
                tallbuilding.append(os.path.join(r, file))
    train_x=[]
    train_y=[]
    test_x=[]
    test_y=[]
    minPictures=(min(len(coast),len(forest),len(highway),len(insidecity),len(mountain),len(opencountry),len(street),len(tallbuilding)))
    print(f'coast: {len(coast)}, forest: {len(forest)}, highway:  {len(highway)}, insidecity: {len(insidecity)}, mountain: {len(mountain)}, opencountry: {len(opencountry)}, street: {len(street)}, tallbuilding {len(tallbuilding)} ')
    #print(f'the least amount of pictures from all of the categories is : {minPictures}')
    ratios=0.8 # the ratio of Database split to Train/test Set

    ratio=int(minPictures*ratios) # the ratios isnt balanced here!

    for i in range (len(coast)):
            if i<int(len(coast)*ratios):
                train_x.append(coast[i])
                train_y.append('coast')
            else:
                test_x.append(coast[i])
                test_y.append('coast')

    for i in  range (len(forest)):
            if i<(len(forest)*ratios):
                train_x.append(forest[i])
                train_y.append('forest')
            else:
                test_x.append(forest[i])
                test_y.append('forest')

    for i in range (len(highway)):
            if i < (len(highway)*ratios):
                train_x.append(highway[i])
                train_y.append('highway')
            else:
                test_x.append(highway[i])
                test_y.append('highway')

    for i in range (len(insidecity)):
            if i < (len(insidecity)*ratios):
                train_x.append(insidecity[i])
                train_y.append('insidecity')
            else:
                test_x.append(insidecity[i])
                test_y.append('insidecity')

    for i in range(len(mountain)):
            if i < (len(mountain)*ratios):
                train_x.append(mountain[i])
                train_y.append('mountain')
            else:
                test_x.append(mountain[i])
                test_y.append('mountain')

    for i in range(len(opencountry)):
            if i < (len(opencountry)*ratios):
                train_x.append(opencountry[i])
                train_y.append('opencountry')
            else:
                test_x.append(opencountry[i])
                test_y.append('opencountry')

    for i in range(len(street)):
            if i < (len(street)*ratios):
                train_x.append(street[i])
                train_y.append('street')
            else:
                test_x.append(street[i])
                test_y.append('street')

    for i in range(len(tallbuilding)):
            if i < (len(tallbuilding)*ratios):
                train_x.append(tallbuilding[i])
                train_y.append('tallbuilding')
            else:
                test_x.append(tallbuilding[i])
                test_y.append('tallbuilding')




    print(f'train_x and train_y sizes are:  {len(train_x)},{len(train_y)}') #making sure the math done right and we got 260*8*0.8=1664 pictures in the training set
    print(f'test_x and test_y sizes are:  {len(test_x)},{len(test_y)}') #making sure the math done right and we got 260*8*0.2=416 pictures in the testing set
    #now the Dataset is ready, we lost some data along the way but each class have equal amounts of pictures in training,testing set
    #BOWSift(train_x,train_y,test_x,test_y)
    #BOWDeepLearning(train_x,train_y,test_x,test_y)