GitPlanet

Cheap and reliable Node.js hosting starts at $3/month, and $1/month static HTML hosting

Created with love in Canada, visit hostnodejs.com today

Feel like to post an Ad? Learn Details
All Projects → ashislaha → CarDetection-Keras

ashislaha / CarDetection-Keras

Licence: other
"Car Detection" is trained in Keras using Tensorflow as back-end. It's taking an image as input & gives a binary decision whether a car is present in the image or not.

Programming Languages

python
139335 projects - #7 most used programming language

Labels

cardetectioncar-detection-tensorflowcar-detection-mlmodelcar-detection-dnn

Projects that are alternatives of or similar to CarDetection-Keras

car-detection-yolo
Autonomous driving - car detection using the very powerful YOLO model
Stars: ✭ 73 (+305.56%)
Mutual labels:  cardetection

Creating CarDetection mlmodel using Keras from scratch.

"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).

prerequisite:

(1). Install pip:

(package management system used to install and manage software packages written in python).

sudo python -m ensurepip

OR

sudo easy_install pip

(2). Install tensor flow:

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.

(3). Install Keras

$ 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 creation:

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'

CNN TRAIN RESULTS :

	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)

DNN TRAIN RESULTS :

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)

Convert the .h5 file into .mlmodel:

 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)

CNN mlmodel specification:

cnn_spec

DNN mlmodel specification:

dnn_spec

You can use .mlmodel to your xcode.

Test your model:

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'

Predictions program:

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")

Output of predictions:

screen shot 2018-03-25 at 1 59 14 pm

screen shot 2018-03-25 at 2 00 03 pm

video:

https://www.youtube.com/watch?v=0nMcfBg_so0&t=18s

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].