"Car Detection" is trained in Keras using Tensorflow as back-end. It's taking an image as input and it gives a binary decision whether a car is present in the image or not. I am using both Convolutional Neural Network (CNN) and Dense Neural Network(DNN) to train the model. After train, converting the .h5 model into .mlmodel to use in Xcode. (Please check the CarDetection-iOS project for that : https://github.com/ashislaha/CarDetection-iOS).
(package management system used to install and manage software packages written in python).
sudo python -m ensurepip
OR
sudo easy_install pip
A. Update virtual environment
$ sudo easy_install pip
$ pip install --upgrade virtualenv
B. Create a virtual environment:
$ virtualenv --system-site-packages targetDirectory # for Python 2.7
$ virtualenv --system-site-packages -p python3 targetDirectory # for Python 3.n
C. Activate virtual environment:
$ cd targetDirectory
$ source ./bin/activate # If using bash, sh, ksh, or zsh
D. Install tensor flow:
(targetDirectory)$ sudo pip install --upgrade tensorflow # for Python 2.7
(targetDirectory)$ sudo pip3 install --upgrade tensorflow # for Python 3.n
If it’s getting failed due to numpy or some other package then install numpy/that package
$ sudo pip install --ignore-installed numpy
That’s it, tensor flow is installed now.
$ sudo pip install keras
Install some other dependancies if not install:
$ sudo pip install Pillow # to install the image library
$ sudo pip install h5py # installing hierarchical data format
$ sudo pip install coremltools # installing core ml tools to convert .h5 to .mlmodel
Training Data set is uploaded in https://drive.google.com/drive/folders/0B0QC-w3ZqaT1RzlGeGtYeVE2cTA OR https://s3.ap-south-1.amazonaws.com/car-detection-images/Archive.zip .
Put the data set into your working directory to train the model. It contains around 1500 training data & 100 test data where both car & non-car images are present.
CNN gives 99.6% accuracy on Training data & 88.6% on Test data. DNN gives 92% accuracy on Training data & 87% on Test data.Used Metrics to train in CNN : loss='binary_crossentropy', metrics=['accuracy'], optimizer='adadelta'
Used Metrics to train in DNN : loss='binary_crossentropy', metrics=['accuracy'], optimizer='rmsprop'
1475/1475 [==============================] - 14s - loss: 0.6336 - acc: 0.6142 - val_loss: 0.6956 - val_acc: 0.9207
Epoch 2/25
1475/1475 [==============================] - 15s - loss: 0.5122 - acc: 0.7620 - val_loss: 0.8328 - val_acc: 0.6524
Epoch 3/25
1475/1475 [==============================] - 16s - loss: 0.4236 - acc: 0.8210 - val_loss: 0.2083 - val_acc: 0.9573
Epoch 4/25
1475/1475 [==============================] - 16s - loss: 0.3493 - acc: 0.8508 - val_loss: 0.4898 - val_acc: 0.8537
Epoch 5/25
1475/1475 [==============================] - 16s - loss: 0.2797 - acc: 0.8942 - val_loss: 0.5409 - val_acc: 0.8171
Epoch 6/25
1475/1475 [==============================] - 15s - loss: 0.2392 - acc: 0.9119 - val_loss: 0.5585 - val_acc: 0.8049
Epoch 7/25
1475/1475 [==============================] - 15s - loss: 0.2006 - acc: 0.9281 - val_loss: 0.5729 - val_acc: 0.7683
Epoch 8/25
1475/1475 [==============================] - 15s - loss: 0.1705 - acc: 0.9403 - val_loss: 0.3006 - val_acc: 0.9146
Epoch 9/25
1475/1475 [==============================] - 15s - loss: 0.1404 - acc: 0.9539 - val_loss: 0.0538 - val_acc: 0.9878
Epoch 10/25
1475/1475 [==============================] - 15s - loss: 0.1152 - acc: 0.9647 - val_loss: 0.2950 - val_acc: 0.9085
Epoch 11/25
1475/1475 [==============================] - 15s - loss: 0.0961 - acc: 0.9776 - val_loss: 0.2111 - val_acc: 0.9329
Epoch 12/25
1475/1475 [==============================] - 15s - loss: 0.0770 - acc: 0.9817 - val_loss: 0.3777 - val_acc: 0.8841
Epoch 13/25
1475/1475 [==============================] - 15s - loss: 0.0631 - acc: 0.9864 - val_loss: 0.2729 - val_acc: 0.9329
Epoch 14/25
1475/1475 [==============================] - 15s - loss: 0.0503 - acc: 0.9905 - val_loss: 0.4031 - val_acc: 0.8963
Epoch 15/25
1475/1475 [==============================] - 17s - loss: 0.0440 - acc: 0.9905 - val_loss: 0.3678 - val_acc: 0.8963
Epoch 16/25
1475/1475 [==============================] - 17s - loss: 0.0320 - acc: 0.9946 - val_loss: 0.1558 - val_acc: 0.9512
Epoch 17/25
1475/1475 [==============================] - 17s - loss: 0.0285 - acc: 0.9932 - val_loss: 0.3010 - val_acc: 0.9329
Epoch 18/25
1475/1475 [==============================] - 20s - loss: 0.0245 - acc: 0.9953 - val_loss: 0.3219 - val_acc: 0.9268
Epoch 19/25
1475/1475 [==============================] - 22s - loss: 0.0214 - acc: 0.9966 - val_loss: 0.4263 - val_acc: 0.9085
Epoch 20/25
1475/1475 [==============================] - 21s - loss: 0.0172 - acc: 0.9973 - val_loss: 0.4255 - val_acc: 0.9024
Epoch 21/25
1475/1475 [==============================] - 19s - loss: 0.0155 - acc: 0.9959 - val_loss: 0.2827 - val_acc: 0.9390
Epoch 22/25
1475/1475 [==============================] - 18s - loss: 0.0146 - acc: 0.9959 - val_loss: 0.3245 - val_acc: 0.9329
Epoch 23/25
1475/1475 [==============================] - 18s - loss: 0.0158 - acc: 0.9959 - val_loss: 0.2655 - val_acc: 0.9390
Epoch 24/25
1475/1475 [==============================] - 17s - loss: 0.0116 - acc: 0.9966 - val_loss: 0.3174 - val_acc: 0.9329
Epoch 25/25
1475/1475 [==============================] - 16s - loss: 0.0104 - acc: 0.9966 - val_loss: 0.3039 - val_acc: 0.9329
238/238 [==============================] - 0s
('\nTest accuracy:', 0.8865546223496189)
This is the accuray after trained by 50 epochs , almost 92% accurate on train data & on unknown test data 87% accurate
1639/1639 [==============================] - 1s - loss: 5.9528 - acc: 0.4997
Epoch 2/50
1639/1639 [==============================] - 1s - loss: 0.7847 - acc: 0.6516
Epoch 3/50
1639/1639 [==============================] - 1s - loss: 0.6361 - acc: 0.6840
Epoch 4/50
1639/1639 [==============================] - 1s - loss: 0.6202 - acc: 0.6901
Epoch 5/50
1639/1639 [==============================] - 1s - loss: 0.5706 - acc: 0.7035
Epoch 6/50
1639/1639 [==============================] - 1s - loss: 0.5586 - acc: 0.7212
Epoch 7/50
1639/1639 [==============================] - 1s - loss: 0.5272 - acc: 0.7364
Epoch 8/50
1639/1639 [==============================] - 1s - loss: 0.5192 - acc: 0.7572
Epoch 9/50
1639/1639 [==============================] - 1s - loss: 0.4962 - acc: 0.7608
Epoch 10/50
1639/1639 [==============================] - 1s - loss: 0.4754 - acc: 0.7749
Epoch 11/50
1639/1639 [==============================] - 1s - loss: 0.4548 - acc: 0.7877
Epoch 12/50
1639/1639 [==============================] - 1s - loss: 0.4559 - acc: 0.7993
Epoch 13/50
1639/1639 [==============================] - 1s - loss: 0.4350 - acc: 0.7999
Epoch 14/50
1639/1639 [==============================] - 1s - loss: 0.4194 - acc: 0.8212
Epoch 15/50
1639/1639 [==============================] - 1s - loss: 0.3927 - acc: 0.8170
Epoch 16/50
1639/1639 [==============================] - 1s - loss: 0.3953 - acc: 0.8231
Epoch 17/50
1639/1639 [==============================] - 1s - loss: 0.3828 - acc: 0.8286
Epoch 18/50
1639/1639 [==============================] - 1s - loss: 0.3720 - acc: 0.8383
Epoch 19/50
1639/1639 [==============================] - 1s - loss: 0.3543 - acc: 0.8408
Epoch 20/50
1639/1639 [==============================] - 1s - loss: 0.3486 - acc: 0.8475
Epoch 21/50
1639/1639 [==============================] - 1s - loss: 0.3400 - acc: 0.8481
Epoch 22/50
1639/1639 [==============================] - 1s - loss: 0.3153 - acc: 0.8542
Epoch 23/50
1639/1639 [==============================] - 1s - loss: 0.3149 - acc: 0.8633
Epoch 24/50
1639/1639 [==============================] - 1s - loss: 0.2994 - acc: 0.8700
Epoch 25/50
1639/1639 [==============================] - 1s - loss: 0.2977 - acc: 0.8658
Epoch 26/50
1639/1639 [==============================] - 1s - loss: 0.2908 - acc: 0.8713
Epoch 27/50
1639/1639 [==============================] - 1s - loss: 0.2725 - acc: 0.8761
Epoch 28/50
1639/1639 [==============================] - 1s - loss: 0.2640 - acc: 0.8914
Epoch 29/50
1639/1639 [==============================] - 1s - loss: 0.2467 - acc: 0.8883
Epoch 30/50
1639/1639 [==============================] - 1s - loss: 0.2375 - acc: 0.8932
Epoch 31/50
1639/1639 [==============================] - 1s - loss: 0.2549 - acc: 0.8883
Epoch 32/50
1639/1639 [==============================] - 1s - loss: 0.2557 - acc: 0.8926
Epoch 33/50
1639/1639 [==============================] - 1s - loss: 0.2406 - acc: 0.8993
Epoch 34/50
1639/1639 [==============================] - 1s - loss: 0.2407 - acc: 0.8981
Epoch 35/50
1639/1639 [==============================] - 1s - loss: 0.2246 - acc: 0.9024
Epoch 36/50
1639/1639 [==============================] - 1s - loss: 0.2290 - acc: 0.9091
Epoch 37/50
1639/1639 [==============================] - 1s - loss: 0.2134 - acc: 0.9109
Epoch 38/50
1639/1639 [==============================] - 1s - loss: 0.2129 - acc: 0.9048
Epoch 39/50
1639/1639 [==============================] - 1s - loss: 0.2411 - acc: 0.9073
Epoch 40/50
1639/1639 [==============================] - 1s - loss: 0.1841 - acc: 0.9158
Epoch 41/50
1639/1639 [==============================] - 1s - loss: 0.2019 - acc: 0.9182
Epoch 42/50
1639/1639 [==============================] - 1s - loss: 0.1755 - acc: 0.9207
Epoch 43/50
1639/1639 [==============================] - 1s - loss: 0.2057 - acc: 0.9140
Epoch 44/50
1639/1639 [==============================] - 1s - loss: 0.1791 - acc: 0.9219
Epoch 45/50
1639/1639 [==============================] - 1s - loss: 0.1613 - acc: 0.9256
Epoch 46/50
1639/1639 [==============================] - 1s - loss: 0.1857 - acc: 0.9176
Epoch 47/50
1639/1639 [==============================] - 1s - loss: 0.1731 - acc: 0.9182
Epoch 48/50
1639/1639 [==============================] - 1s - loss: 0.1661 - acc: 0.9286
Epoch 49/50
1639/1639 [==============================] - 1s - loss: 0.1607 - acc: 0.9213
Epoch 50/50
1639/1639 [==============================] - 1s - loss: 0.1721 - acc: 0.9201
32/238 [===>..........................] - ETA: 0s('\nTest accuracy:', 0.87394957832929465)
import coremltools
DNN_ml_model = coremltools.converters.keras.convert('car_detection_keras_DNN_model.h5')
DNN_ml_model.author = 'Ashis Laha'
DNN_ml_model.description = 'Use for Car Detection'
DNN_ml_model.save('car_detection_keras_DNN.mlmodel')
print(DNN_ml_model)
DNN_ml_model = coremltools.converters.keras.convert('car_detection_keras_CNN_model.h5')
DNN_ml_model.author = 'Ashis Laha'
DNN_ml_model.description = 'Use for Car Detection'
DNN_ml_model.save('car_detection_keras_CNN.mlmodel')
print(DNN_ml_model)
You can use .mlmodel to your xcode.
If you want to test a random image whether car is present in the image or not :
goto "model" directory --> paste your image in "test" folder --> $ python predictions.py --file='test/your_image'
from keras.models import load_model
import numpy as np
import os,sys
from PIL import Image
import matplotlib.pyplot as plot
from optparse import OptionParser
mode = "CNN"
model = None
if mode == "DNN":
model = load_model('car_detection_keras_DNN_model.h5')
else:
model = load_model('car_detection_keras_CNN_model.h5')
row,column = 100,100
# take input to test it
parser = OptionParser()
parser.add_option("-f", "--file", dest="filename", help="write report to FILE", metavar="FILE")
(options, args) = parser.parse_args()
URL = options.filename
print("URL ------> %s" %URL)
img = Image.open(URL)
print("TESTING IMAGE -----> %s %s %s" %( img.bits, img.size, img.format))
# Resize the image
img = img.resize((row,column),Image.ANTIALIAS)
# Gray Scale Image
def grayscale(picture):
res= Image.new(picture.mode, picture.size)
width, height = picture.size
for i in range(0, width):
for j in range(0, height):
pixel=picture.getpixel((i,j))
avg=(pixel[0]+pixel[1]+pixel[2])/3
res.putpixel((i,j),(avg,avg,avg))
res.show()
return res
gray_image = grayscale(img)
# Normalize between 0 and 1
def normalize(picture):
width, height = picture.size
normalized_array = []
for j in range(0, height):
for i in range(0, width):
pixel = picture.getpixel((i,j))
normalized_array.append( pixel[0] / 255.0 )
return np.array(normalized_array)
X_test = normalize(gray_image)
if mode == "DNN":
X_test = X_test.reshape(1, row*column) # [row*column] - 1D input for DNN
else:
X_test = X_test.reshape(1, row, column, 1) # (1, row, column) 3D input for CNN
# Do predictions
classes = model.predict(X_test)
print(classes)
maxVal = classes[0].max()
indexVal = np.where(classes[0]==maxVal) # result is an array
if (indexVal[0] == 0):
print("\n......... It's CAR .........\n")
else:
print("\n.......... Not A Car .......\n")
