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Project aimed at presenting a model to find a vacant parking spot in real time and ensure car safety using Deep Learning (Parking spot Classification and Face recognition).

Home Page: http://dalmia.github.io/WannaPark/

License: GNU General Public License v3.0

Python 76.10% Shell 0.27% C 0.96% Batchfile 0.03% Jupyter Notebook 3.91% Java 18.74%
android-development classification computer-vision deep-learning face-recognition machine-learning python supervised-learning

wannapark's Introduction

WannaPark - Your Personal Parking Buddy

A Real-time car parking system model using Deep learning applied on CCTV camera images, developed for the competition IdeaQuest, held among the summer interns of Qualcomm. We also propose a novel method for internal navigation and prevention of Car thefts (all details are not released yet).

Description

  • android - Contains the code for the Android App for WannaPark and a utility scanner app.

           

  • face_comparison - Contains the code to implement face comparison using Histogram of Gradients (HOG) for extracting the faces from the 2 images, Pose estimation to align the extracted face images as close to the center as possible and finally, using the VGGNet-16 (with weights=imagenet) as a feature extractor for the two images. The similarity comparison is done by calculating the euclidean distance between the extracted features of the two images. Compare different images using images placed in images/test_images.

Input Entry       Input Exit

Entry Image       Exit Image 1

Output Entry       Output Exit

Output Entry Image       Output Exit Image 1

Distance: 11.616581

Input Entry       Input Exit

Entry Image       Exit Image 2

Output Entry       Output Exit

Entry Image       Output Exit Image 2

Distance: 25.926846
  • images - Contains the various images used.

    • final_detection - Contains the image for parking lot vacancy detection.
    • final_entry - Contains the image considered as the one during entry in the parking lot.
    • final_exit - Contains the image considered as the one during exit from the parking lot.
    • test_images - Contains images which can be used for seeing different results (Don't forget to change the extension of file names to .jpg).
  • parking_lot_detection - Contains the code to implement parking lot vacancy detection. It uses the PKLot dataset for training where each image is of size 54x32. It uses the pre-trained convolutional layers from VGGNet-F (As this is the only version of VGGNet which would work with images of dimension 54x32) and then fine-tunes the dense layer for the required task. It achieved an accuracy of ~ 94%. Details on using the code are present in the folder.

Input       Output

     

  • server - Contains the server side code implemented in Python which connects the Android Application, a scanner app, the face_comparison module and the parking_lot_detection module.

We used neuralPlateRecognition for Number plate extraction from the image.

Contributors

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wannapark's Issues

Error in loading the model

Error in loading the saved optimizer state. As a result, your model is starting with a freshly initialized optimizer.
warnings.warn('Error in loading the saved optimizer '

parking_lot

hey i also tried to run train_detection.py i am facing this error while train the PKLot dataset.
here is the error:-
Traceback (most recent call last):
File "train_detection.py", line 95, in
validX[counter_test] = img
IndexError: index 216649 is out of bounds for axis 0 with size 216649

Problem in executing the test_images.py

I was trying to execute your test_images.py script with PKLot dataset , but i was getting an error.
Here is the error....
/home/rashmi/.virtualenvs/dl4cv/lib/python3.6/site-packages/h5py/init.py:36: FutureWarning: Conversion of the second argument of issubdtype from float to np.floating is deprecated. In future, it will be treated as np.float64 == np.dtype(float).type.
from ._conv import register_converters as register_converters
Using TensorFlow backend.
/home/rashmi/.virtualenvs/dl4cv/lib/python3.6/importlib/bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6
return f(*args, **kwds)
2018-03-28 16:27:40.455187: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
2018-03-28 16:27:40.678485: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:892] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2018-03-28 16:27:40.678921: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Found device 0 with properties:
name: GeForce GTX 1050 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.506
pciBusID: 0000:01:00.0
totalMemory: 3.94GiB freeMemory: 2.08GiB
2018-03-28 16:27:40.678938: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1120] Creating TensorFlow device (/device:GPU:0) -> (device: 0, name: GeForce GTX 1050 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1)
/home/rashmi/.virtualenvs/dl4cv/lib/python3.6/site-packages/keras/models.py:291: UserWarning: Error in loading the saved optimizer state. As a result, your model is starting with a freshly initialized optimizer.
warnings.warn('Error in loading the saved optimizer '
8/8 [==============================] - 35s 4s/step
[ INFO:0] Initialize OpenCL runtime...
Traceback (most recent call last):
File "test_images.py", line 69, in
im
= cv2.rectangle(im
,(y,x+2),(y+y_shift,x+x_shift-2),(255*(predictions[i]),255*(1-predictions[i]),0),2)
TypeError: Scalar value for argument 'color' is not numeric

PKLot database structure

Hey, first of all great project! I tried to recreate your parking_lot_detection python scripts, but got some problems in train_detection.
I downloaded the following database here PKLot.tar.gz but it seems to have a different structure compared to your database.

Could you give me an example, what your directory looked like or maybe even upload it somewhere to share it? I could offer you some temporary cloud storage.

Thanks in advance!

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