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model.py
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import gc
from random import randint
import cv2
import numpy as np
from keras import Sequential
from keras import backend as K
from keras.applications.resnet_v2 import ResNet101V2
from keras.applications.vgg19 import VGG19
from keras.layers import Flatten, Dense, Conv2D, BatchNormalization, Activation, MaxPooling2D, ZeroPadding2D, Dropout
from keras.models import load_model
from scipy.spatial import distance
from sklearn.model_selection import train_test_split
width = 224
height = 224
epochNumber = 40
nRowSetter = 2000
whichPhoto = randint(1, (int(nRowSetter * 0.3) - 1))
def mainModel(csv, img, mode):
cv2.imshow("template", cv2.imread("Template.png"))
trainX, testX, trainY, testY = train_test_split(img, csv, test_size=0.3, random_state=42)
if mode == 3:
evScoreVGG, imHolderVGG, imHolderVGGEarly = runKerasVGGModel(trainX, testX, trainY, testY, mode)
print("VGG |", evScoreVGG[1])
cv2.imshow("VGG", imHolderVGG)
else:
evScoreVGG, imHolderVGG, imHolderVGGEarly = runKerasVGGModel(trainX, testX, trainY, testY, mode)
print("VGG |", evScoreVGG[1])
cv2.imshow("VGG", imHolderVGG)
cv2.imshow("VGGEarly", imHolderVGGEarly)
del (evScoreVGG, imHolderVGG)
gc.collect()
cv2.waitKey()
evScoreAlexNet, imHolderAlexNet, imHolderAlexNetEarly = runKerasAlexNetModel(trainX, testX, trainY, testY, mode)
print("AlexNet |", evScoreAlexNet[1])
cv2.imshow("AlexNet", imHolderAlexNet)
cv2.imshow("AlexNetEarly", imHolderAlexNetEarly)
del (evScoreAlexNet, imHolderAlexNet)
gc.collect()
evScoreOwn, imHolderOwn, imHolderOwnEarly = runOwnModel(trainX, testX, trainY, testY, mode)
print("Own |", evScoreOwn[1])
cv2.imshow("Own", imHolderOwn)
cv2.imshow("OwnEarly", imHolderOwnEarly)
del (evScoreOwn, imHolderOwn)
gc.collect()
evScoreResNet, imHolderResnet,imHolderResnetEarly = runResnetKerasModel(trainX, testX, trainY, testY, mode)
print("Resnet |", evScoreResNet[1])
cv2.imshow("Resnet", imHolderResnet)
cv2.imshow("ResnetEarly", imHolderResnetEarly)
del(evScoreResNet,imHolderResnet)
gc.collect()
def createKerasResnetModel():
base_model = ResNet101V2(weights='imagenet',
include_top=False,
input_shape=(width, height, 3)
)
base_model.trainable = False
model = Sequential([
base_model,
Conv2D(164, kernel_size=(7, 7)),
Flatten(),
Dense(164, activation="softmax"),
])
return model
def createKerasVGG19Model():
base_model = VGG19(weights='imagenet',
include_top=False,
input_shape=(width, height, 3),
)
base_model.trainable = False
model = Sequential([
base_model,
Conv2D(164, (3, 3), name="firstBreak", activation='ReLU'),
MaxPooling2D(pool_size=(2, 2), strides=(2, 2)),
Conv2D(164, (2, 2), name="secondBreak", activation='ReLU'),
Flatten(),
Dense(164, activation="softmax"),
])
return model
def createOwnModel():
model = Sequential([
Conv2D(96, (11, 11), input_shape=(width, height, 3), ),
BatchNormalization(),
Activation('relu'),
MaxPooling2D(pool_size=(2, 2)),
# 2. layer
Conv2D(128, (5, 5)),
BatchNormalization(),
Activation('relu'),
MaxPooling2D(pool_size=(2, 2)),
# 3. layer
ZeroPadding2D((1, 1)),
Conv2D(64, (4, 4)),
BatchNormalization(),
Activation('relu'),
MaxPooling2D(pool_size=(3, 3)),
Conv2D(64, (4, 4)),
BatchNormalization(),
Activation('relu'),
MaxPooling2D(pool_size=(2, 2)),
Conv2D(64, (3, 3)),
BatchNormalization(),
Activation('relu'),
Conv2D(164, (4, 4)),
BatchNormalization(),
Activation('relu'),
Flatten(),
Dense(164, activation="softmax"),
])
return model
def createKerasAlexNetModel():
model = Sequential([
# 1. layer
Conv2D(96, (11, 11), input_shape=(width, height, 3),
padding='same'),
BatchNormalization(),
Activation('relu'),
MaxPooling2D(pool_size=(2, 2)),
# 2. layer
Conv2D(128, (5, 5), padding='same'),
BatchNormalization(),
Activation('relu'),
MaxPooling2D(pool_size=(2, 2)),
# 3. layer
ZeroPadding2D((1, 1)),
Conv2D(128, (3, 3), padding='same'),
BatchNormalization(),
Activation('relu'),
MaxPooling2D(pool_size=(2, 2)),
# 4. layer
ZeroPadding2D((1, 1)),
Conv2D(128, (3, 3), padding='same'),
BatchNormalization(),
Activation('relu'),
# 5. layer
ZeroPadding2D((1, 1)),
Conv2D(128, (3, 3), padding='same'),
BatchNormalization(),
Activation('relu'),
MaxPooling2D(pool_size=(2, 2)), # 21
# 6. layer
Flatten(),
Dense(3072),
BatchNormalization(),
Activation('relu'),
Dropout(0.5),
# 7. layer
Dense(4096),
BatchNormalization(),
Activation('relu'),
Dropout(0.5),
# 8. layer
Dense(164),
BatchNormalization(), # 32
Activation('softmax'),
])
return model
def runKerasAlexNetModel(trainX, testX, trainY, testY, mode):
modelKeras = load_model("pretrained/AlexNet.h5")
tempArrHolder = []
tempOut = [modelKeras.layers[32].output]
functors = [K.function([modelKeras.input], [out]) for out in tempOut]
for x in testX:
x = x[np.newaxis, ...]
tempArrHolder.append(functors[0](x))
tempArrHolder = np.reshape(tempArrHolder, newshape=(-1, 164))
tempImg = makePredictionWithImg(tempArrHolder, mode, testX, testY)
evScore = modelKeras.evaluate(testX, testY)
predictedValue = modelKeras.predict(testX, mode)
imgHolder = makePredictionWithImg(predictedValue, mode, testX, testY)
return evScore, imgHolder, tempImg
def runKerasVGGModel(trainX, testX, trainY, testY, mode):
modelKeras = load_model("pretrained/VGG.h5")
modelKeras.summary()
tempArrHolder = []
tempOut = [modelKeras.layers[3].output]
functors = [K.function([modelKeras.input], [out]) for out in tempOut]
for x in testX:
x = x[np.newaxis, ...]
tempArrHolder.append(functors[0](x))
tempArrHolder = np.reshape(tempArrHolder, newshape=(-1, 164))
tempImg = makePredictionWithImg(tempArrHolder, mode, testX, testY)
evScore = modelKeras.evaluate(testX, testY)
predictedValue = modelKeras.predict(testX, mode)
imgHolder = makePredictionWithImg(predictedValue, mode, testX, testY)
return evScore, imgHolder, tempImg
def runOwnModel(trainX, testX, trainY, testY, mode):
tempArrHolder = []
modelKeras = load_model("pretrained/Own.h5")
tempOut = [modelKeras.layers[22].output]
functors = [K.function([modelKeras.input], [out]) for out in tempOut]
for x in testX:
x = x[np.newaxis, ...]
tempArrHolder.append(functors[0](x))
tempArrHolder = np.reshape(tempArrHolder, newshape=(-1, 164))
tempImg = makePredictionWithImg(tempArrHolder, mode, testX, testY)
evScore = modelKeras.evaluate(testX, testY)
predictedValue = modelKeras.predict(testX, mode)
imgHolder = makePredictionWithImg(predictedValue, mode, testX, testY)
return evScore, imgHolder, tempImg
def runResnetKerasModel(trainX, testX, trainY, testY, mode):
modelKeras = load_model("pretrained/ResNet.h5")
tempArrHolder = []
tempOut = [modelKeras.layers[2].output]
functors = [K.function([modelKeras.input], [out]) for out in tempOut]
for x in testX:
x = x[np.newaxis, ...]
tempArrHolder.append(functors[0](x))
tempArrHolder = np.reshape(tempArrHolder, newshape=(-1, 164))
tempImg = makePredictionWithImg(tempArrHolder, mode, testX, testY)
evScore = modelKeras.evaluate(testX, testY)
predictedValue = modelKeras.predict(testX, mode)
imgHolder = makePredictionWithImg(predictedValue, mode, testX, testY)
return evScore, imgHolder, tempImg
def makePredictionWithImg(predictedValue, mode, testX, testY):
simPic = [distance.cosine(predictedValue[whichPhoto], img) for img in predictedValue]
print("Index of the photo : ", whichPhoto)
kipkop = sorted(range(len(simPic)), key=lambda k: simPic[k])
if (mode == 2):
bipbop = createBipBop(kipkop, testY)
else:
bipbop = sorted(range(len(simPic)), key=lambda k: simPic[k])[1:6]
# add if bipbop[n] == 0 print noFound.jpg
firstCon = cv2.hconcat([testX[whichPhoto], testX[bipbop[0]]])
secCon = cv2.hconcat([testX[bipbop[1]], testX[bipbop[2]]])
thrdCon = cv2.hconcat([testX[bipbop[3]], testX[bipbop[4]]])
sumCon = cv2.vconcat([firstCon, secCon])
sumCon2 = cv2.vconcat([sumCon, thrdCon])
return sumCon2
def createBipBop(fullbop, testY): # name veriables like normal human beign at some point
tempCounter = 0
bipbop = [0, 0, 0, 0, 0]
for x in range(1, len(fullbop)):
tempClass = belongedClass(fullbop[x], testY, bipbop)
for y in range(len(bipbop)):
if (bipbop[tempClass] == 0):
bipbop[tempClass] = fullbop[x]
tempCounter += 1
if (tempCounter == 4):
break
return bipbop
def belongedClass(temp, testY, bipbop):
valueToReturn = -1
temp = testY.iloc[temp]
# overLayer = [10,15,16,17,52,59,71,89,90,97,102,131,132]
# fullBody = [1,2,3,21,22,58,64,65,94,95,98,151,160,161,]
accesories = [24, 25, 26, 27, 28, 29, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 51, 73, 74, 75, 84, 103, 112, 113,
114, 115, 116, 117, 118, 122, 123, 124, 128, 137, 138, 139, 142, 143, 144, 154, 155, 157, 158, 159]
top = [0, 9, 11, 12, 13, 14, 48, 60, 62, 63, 67, 72, 85, 86, 87, 88, 99, 120, 125, 130, 133, 147, 148, 149, 150,
152,
163]
sumOfFullLayer = [0, 1, 2, 3, 10, 15, 16, 17, 21, 22, 23, 52, 58, 59, 64, 65, 71, 89, 90, 94, 95, 97, 98, 102, 126,
127, 131, 132, 145, 146, 151, 160, 161]
down = [0, 4, 5, 6, 7, 8, 18, 19, 20, 53, 54, 55, 56, 57, 61, 66, 68, 69, 70, 91, 92, 93, 96, 100, 101, 119, 121,
129, 134, 135, 153, 162]
shoes = [30, 31, 32, 33, 34, 35, 36, 37, 49, 50, 76, 77, 78, 79, 80, 81, 82, 83, 104, 105, 106, 107, 108, 109, 110,
111, 136, 140, 141, 156]
for x in range(len(accesories)):
if (accesories[x] == temp):
valueToReturn = 0
for x in range(len(top)):
if (top[x] == temp):
valueToReturn = 1
for x in range(len(sumOfFullLayer)):
if (sumOfFullLayer[x] == temp):
valueToReturn = 2
for x in range(len(down)):
if (down[x] == temp):
valueToReturn = 3
for x in range(len(shoes)):
if (shoes[x] == temp):
valueToReturn = 4
if (valueToReturn == -1):
print("You forgot to implement : ", temp)
return valueToReturn