├── .DS_Store
├── .gitattributes
├── .gitignore
├── .idea
    ├── License-Plate-Detection.iml
    ├── dictionaries
    │   └── sam.xml
    ├── inspectionProfiles
    │   └── Project_Default.xml
    ├── misc.xml
    ├── modules.xml
    ├── vcs.xml
    └── workspace.xml
├── Code
    ├── BldModel.py
    ├── Bld_FeatureCrps.py
    ├── Classify.py
    ├── Configuration.py
    ├── Evaluate_models.py
    ├── LP_Detect_main.py
    ├── Tools.py
    ├── __pycache__
    │   ├── BldModel.cpython-35.pyc
    │   ├── Bld_FeatureCrps.cpython-35.pyc
    │   ├── Classify.cpython-35.pyc
    │   ├── Configuration.cpython-35.pyc
    │   ├── Evaluate_models.cpython-35.pyc
    │   ├── Test.cpython-35.pyc
    │   └── Tools.cpython-35.pyc
    └── z_prac.py
├── DataSet
    ├── Data-Files
    │   ├── images_classify
    │   │   ├── Foreign_cars
    │   │   │   ├── image_2_classify (1).jpg
    │   │   │   ├── image_2_classify (2).jpg
    │   │   │   ├── image_2_classify (4).jpg
    │   │   │   └── image_2_classify (9).jpg
    │   │   ├── Indian_cars
    │   │   │   ├── classify_photo (1).jpg
    │   │   │   ├── classify_photo (2).jpg
    │   │   │   ├── classify_photo (3).jpg
    │   │   │   ├── classify_photo (4).jpg
    │   │   │   └── classify_photo (5).jpg
    │   │   ├── contoured_images_roi
    │   │   │   ├── roi_images0014.jpg
    │   │   │   ├── roi_images0029.jpg
    │   │   │   ├── roi_images0030.jpg
    │   │   │   ├── roi_images0031.jpg
    │   │   │   ├── roi_images0032.jpg
    │   │   │   ├── roi_images0033.jpg
    │   │   │   ├── roi_images0034.jpg
    │   │   │   ├── roi_images0036.jpg
    │   │   │   ├── roi_images0038.jpg
    │   │   │   ├── roi_images0039.jpg
    │   │   │   ├── roi_images0040.jpg
    │   │   │   ├── roi_images0041.jpg
    │   │   │   ├── roi_images0042.jpg
    │   │   │   └── roi_images0043.jpg
    │   │   └── extracted_licenceplate_image
    │   │   │   ├── roi_images0019_8.jpg
    │   │   │   ├── roi_images0021_21.jpg
    │   │   │   ├── roi_images0023_9.jpg
    │   │   │   ├── roi_images0026_17.jpg
    │   │   │   ├── roi_images0036_28.jpg
    │   │   │   ├── roi_images0040_19.jpg
    │   │   │   ├── roi_images0040_7.jpg
    │   │   │   ├── roi_images0041_31.jpg
    │   │   │   ├── roi_images0042_29.jpg
    │   │   │   └── roi_images0042_33.jpg
    │   ├── images_crossvalidate
    │   │   ├── Licence-plate
    │   │   │   ├── yes (37).jpg
    │   │   │   ├── yes (38).jpg
    │   │   │   └── yes (40).jpg
    │   │   └── Not-Licence-Plate
    │   │   │   ├── no (1).jpg
    │   │   │   ├── no (13).jpg
    │   │   │   ├── no (14).jpg
    │   │   │   ├── no (15).jpg
    │   │   │   ├── no (16).jpg
    │   │   │   ├── no (2).jpg
    │   │   │   ├── no (25).jpg
    │   │   │   ├── no (26).jpg
    │   │   │   ├── no (27).jpg
    │   │   │   ├── no (28).jpg
    │   │   │   ├── no (3).jpg
    │   │   │   ├── no (37).jpg
    │   │   │   ├── no (38).jpg
    │   │   │   └── no (4).jpg
    │   └── images_train
    │   │   ├── Licence-Plate
    │   │       ├── yes (31).jpg
    │   │       ├── yes (32).jpg
    │   │       └── yes (33).jpg
    │   │   └── Not-Licence-Plate
    │   │       ├── no (5).jpg
    │   │       ├── no (6).jpg
    │   │       ├── no (7).jpg
    │   │       └── no (8).jpg
    ├── Feature-Model
    │   ├── class_train.csv
    │   └── feature_train.csv
    └── models
    │   ├── model_svm_rbf_0_0.pickle
    │   ├── model_svm_rbf_0_1.pickle
    │   ├── model_svm_rbf_0_10.pickle
    │   ├── model_svm_rbf_100_0.pickle
    │   ├── model_svm_rbf_100_1.pickle
    │   ├── model_svm_rbf_100_10.pickle
    │   ├── model_svm_rbf_10_0.pickle
    │   ├── model_svm_rbf_10_1.pickle
    │   ├── model_svm_rbf_10_10.pickle
    │   ├── model_svm_rbf_1_0.pickle
    │   ├── model_svm_rbf_1_1.pickle
    │   └── model_svm_rbf_1_10.pickle
├── README.md
├── blog.py
├── config-local.conf
├── gradient_calculation.py
└── hist_features_HOG.py


/.DS_Store:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/Sardhendu/License-Plate-Detection/e097f0323dc20a07ddf68d347a13a1b3e9a2889c/.DS_Store


--------------------------------------------------------------------------------
/.gitattributes:
--------------------------------------------------------------------------------
 1 | # Auto detect text files and perform LF normalization
 2 | * text=auto
 3 | 
 4 | # Custom for Visual Studio
 5 | *.cs     diff=csharp
 6 | 
 7 | # Standard to msysgit
 8 | *.doc	 diff=astextplain
 9 | *.DOC	 diff=astextplain
10 | *.docx diff=astextplain
11 | *.DOCX diff=astextplain
12 | *.dot  diff=astextplain
13 | *.DOT  diff=astextplain
14 | *.pdf  diff=astextplain
15 | *.PDF	 diff=astextplain
16 | *.rtf	 diff=astextplain
17 | *.RTF	 diff=astextplain
18 | 


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/.gitignore:
--------------------------------------------------------------------------------
 1 | # Windows image file caches
 2 | Thumbs.db
 3 | ehthumbs.db
 4 | 
 5 | # Folder config file
 6 | Desktop.ini
 7 | 
 8 | # Recycle Bin used on file shares
 9 | $RECYCLE.BIN/
10 | 
11 | # Windows Installer files
12 | *.cab
13 | *.msi
14 | *.msm
15 | *.msp
16 | 
17 | # Windows shortcuts
18 | *.lnk
19 | 
20 | # =========================
21 | # Operating System Files
22 | # =========================
23 | 
24 | # OSX
25 | # =========================
26 | 
27 | .DS_Store
28 | .AppleDouble
29 | .LSOverride
30 | 
31 | # Thumbnails
32 | ._*
33 | 
34 | # Files that might appear on external disk
35 | .Spotlight-V100
36 | .Trashes
37 | 
38 | # Directories potentially created on remote AFP share
39 | .AppleDB
40 | .AppleDesktop
41 | Network Trash Folder
42 | Temporary Items
43 | .apdisk
44 | 


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575 |           <folding />
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592 |             <folding />
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594 |           <second_editor />
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623 |     </entry>
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701 |             <element signature="e#0#31#0" expanded="true" />
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703 |         </state>
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707 |       <provider selected="true" editor-type-id="text-editor">
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710 |           <folding />
711 |         </state>
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713 |     </entry>
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737 |             <element signature="e#0#31#0" expanded="true" />
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749 |     </entry>
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751 |       <provider selected="true" editor-type-id="text-editor">
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761 |       <provider selected="true" editor-type-id="text-editor">
762 |         <state relative-caret-position="0">
763 |           <caret line="0" column="0" lean-forward="false" selection-start-line="0" selection-start-column="0" selection-end-line="0" selection-end-column="0" />
764 |           <folding />
765 |         </state>
766 |       </provider>
767 |     </entry>
768 |     <entry file="file://$PROJECT_DIR$/Code/Configuration.py">
769 |       <provider selected="true" editor-type-id="text-editor">
770 |         <state relative-caret-position="860">
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772 |           <folding />
773 |         </state>
774 |       </provider>
775 |     </entry>
776 |     <entry file="file://$PROJECT_DIR$/config-local.conf">
777 |       <provider selected="true" editor-type-id="text-editor">
778 |         <state relative-caret-position="220">
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780 |           <folding />
781 |         </state>
782 |       </provider>
783 |     </entry>
784 |     <entry file="file://$PROJECT_DIR$/DataSet/Data-Files/images_train/Licence-Plate/yes (31).jpg">
785 |       <provider selected="true" editor-type-id="images">
786 |         <state />
787 |       </provider>
788 |     </entry>
789 |     <entry file="file://$PROJECT_DIR$/Code/BldModel.py">
790 |       <provider selected="true" editor-type-id="text-editor">
791 |         <state relative-caret-position="0">
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793 |           <folding />
794 |         </state>
795 |       </provider>
796 |     </entry>
797 |     <entry file="file://$PROJECT_DIR$/Code/Classify.py">
798 |       <provider selected="true" editor-type-id="text-editor">
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801 |           <folding />
802 |         </state>
803 |       </provider>
804 |     </entry>
805 |     <entry file="file://$PROJECT_DIR$/Code/LP_Detect_main.py">
806 |       <provider selected="true" editor-type-id="text-editor">
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810 |             <element signature="e#0#31#0" expanded="true" />
811 |           </folding>
812 |         </state>
813 |       </provider>
814 |     </entry>
815 |     <entry file="file://$PROJECT_DIR$/Code/Bld_FeatureCrps.py">
816 |       <provider selected="true" editor-type-id="text-editor">
817 |         <state relative-caret-position="680">
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819 |           <folding>
820 |             <element signature="e#912#922#0" expanded="true" />
821 |           </folding>
822 |         </state>
823 |       </provider>
824 |     </entry>
825 |     <entry file="file://$PROJECT_DIR$/README.md">
826 |       <provider selected="true" editor-type-id="split-provider[text-editor;markdown-preview-editor]">
827 |         <state split_layout="SPLIT">
828 |           <first_editor relative-caret-position="545">
829 |             <caret line="35" column="111" lean-forward="false" selection-start-line="35" selection-start-column="111" selection-end-line="35" selection-end-column="111" />
830 |             <folding />
831 |           </first_editor>
832 |           <second_editor />
833 |         </state>
834 |       </provider>
835 |     </entry>
836 |   </component>
837 | </project>


--------------------------------------------------------------------------------
/Code/BldModel.py:
--------------------------------------------------------------------------------
  1 | # -*- coding: utf-8 -*-
  2 | """
  3 | Created on Thu Mar 19 14:40:18 2015
  4 | 
  5 | @author: Sardhendu_Mishra
  6 | """
  7 | from __future__ import division
  8 | 
  9 | import copy
 10 | from six.moves import cPickle as pickle 
 11 | import numpy as np
 12 | from sklearn.svm import SVC
 13 | import Configuration
 14 | 
 15 | from sklearn.svm import LinearSVC
 16 | from Bld_FeatureCrps import CrtFeatures
 17 | 
 18 | 
 19 | #==============================================================================
 20 | #  Seperate the data into training and test dataset and see the accuracy of  
 21 | #  classifier while classifing the learned models with the segregated test set.
 22 | #  The segregation will be done only once. So the accuracies can be compared.
 23 | #==============================================================================
 24 | class SvmModel():
 25 | 
 26 |     def understand_gaussian_kernel (self,x,y,sigma):
 27 |         euclidean_distance=pow((x-y),2)
 28 |         z=euclidean_distance/(2*(pow(sigma,2)))
 29 |         f=np.exp(-z)
 30 |         return f
 31 | 
 32 |     def build_kernel(self,x_new,x_orig,sigma):
 33 |         # using gaussian kernel or rbf would create a m*m dimentional training set so we take one
 34 |         (m,n)=x_orig.shape
 35 |         (m_new, n_new)= x_new.shape
 36 | 
 37 |         f=np.zeros(shape=(m,m_new))
 38 |         f_each_row=np.zeros(m)
 39 |         for i in range(0,m_new):
 40 |             #print i
 41 |             l=x_new[i]
 42 |             #print l
 43 |             for j in range(0,m):
 44 |                 p=x_orig[j]
 45 |                 euclidean_distance=0
 46 |                 for k in range(0,n):
 47 |                     a=l[k]
 48 |                     b=p[k]   
 49 |                     euclidean_distance=euclidean_distance+(pow((b-a),2))
 50 |                 z=euclidean_distance/(2*(pow(sigma,2)));
 51 |                 f_each_row[j]=np.exp(-z);    
 52 |                 f[j,i]=f_each_row[j];
 53 |         return f
 54 |                 
 55 |     def cal_sigmoid(self, x,theta):
 56 |         z=np.dot(x,theta)  # For matrix multiplication x is [80*3], theta is [3*1]
 57 |         h= 1 / (1 + np.exp(- z))
 58 |         return h
 59 | 
 60 |     def cal_cost(self, h, y, m):
 61 |         j_theta= (-np.transpose(y).dot(np.log(h)) - (np.transpose(1-y).dot(np.log(1-h))))
 62 |         return j_theta
 63 |         
 64 |     def cal_grad(self, x,y,h,m):
 65 |         error =h-y
 66 |         grad=  (np.dot(np.transpose(x), error)) # For matrix multiplication x transpose is [3*80] and y is [80*1]
 67 |         return grad    
 68 | 
 69 |     def main_call(self, sigma, x, y,max_iter, alpha, c):
 70 |         X = self.build_kernel(x,x,sigma)
 71 |         X = np.insert(X,0,1,axis=1)
 72 |         (M,N) = X.shape
 73 |         
 74 |         theta = np.zeros(shape=(N ,1), dtype='float64')
 75 |         j_theta = np.zeros(shape=(max_iter,1), dtype='float64')
 76 |         for num_iter in range(0,max_iter):
 77 |             h = self.cal_sigmoid(X, theta)
 78 |             j_reg_term = np.subtract((np.transpose(theta).dot(theta)),pow(theta[0],2))/2
 79 |             j_theta[num_iter] = (float(c) * self.cal_cost(h,y,M)) + j_reg_term
 80 |             
 81 |             grad = self.cal_grad(X,y,h,M)
 82 |             
 83 |             grad = c * grad
 84 |             reg_grad = copy.deepcopy(theta)  # After taking the derivative the regularized term become theta
 85 |             reg_grad[0] = 0
 86 |             grad = grad+reg_grad
 87 |             
 88 |             theta = np.subtract(theta, (np.multiply(alpha , grad)))
 89 |             #print theta
 90 |         return X, j_theta,theta
 91 | 
 92 | 
 93 | 
 94 | 
 95 | #==============================================================================
 96 | # Classifier for Linear SVC  
 97 | #==============================================================================
 98 | class Models():
 99 | 
100 |     def __init__(self, type = 'rbf'):
101 |         self.type = type
102 |         self.conf = Configuration.get_datamodel_storage_path()
103 |         self.c_range = [0.1,1.0, 10.0, 100.0]#, 1000.0]            
104 |         self.gamma_range = [0.1, 1, 10.0]  
105 |         self.kernel="rbf"
106 | 
107 |     def fit(self, features, labels):
108 |         if self.type == 'linear':
109 |             # run_linear_SVM(features, labels):
110 |             model = LinearSVC(random_state=42)
111 |             model.fit(features, labels)
112 |             f = open(self.conf["Linear_SVC_dir"] , "wb")
113 |             pickle.dumps(model, f)
114 |             f.close()
115 | 
116 |         elif self.type == 'rbf':
117 |             c_range = self.c_range
118 |             gamma_range = self.gamma_range
119 |             kernel = self.kernel
120 | 
121 |             for c in c_range:
122 |                 for gamma in gamma_range:
123 |                     clf = SVC(kernel=kernel, C=c, gamma=gamma, probability=True)
124 |                     classifier = clf.fit(features,labels)
125 |                     #print classifier                                                                                                                         
126 |                     f = open(self.conf["SVM_RFB_dir"]%(int(c),int(gamma)) , "wb")
127 |                     pickle.dump(classifier, f)
128 |                     f.close()
129 | 
130 | 


--------------------------------------------------------------------------------
/Code/Bld_FeatureCrps.py:
--------------------------------------------------------------------------------
  1 | #-------------------------------------------------------------------------------
  2 | # Name:        Data Preparation
  3 | # Purpose:
  4 | #
  5 | # Author:      Sardhendu_Mishra
  6 | #
  7 | # Created:     08/03/2015
  8 | # Copyright:   (c) Sardhendu_Mishra 2015
  9 | # Licence:     <your licence>
 10 | #-------------------------------------------------------------------------------
 11 | 
 12 | '''
 13 |     About: This code snippet prepares the features. It goes to the file where all the pictures of licence plates and non licence plate are stashed, it then does the following.
 14 |     1. Gets each picture one by one
 15 |     2. Binarizes the images, resize them, perform morphological operation. 
 16 |     3. Then calls the HOG method, creates the histogram of gradient for the each image which in our case is the feature to our classifier.
 17 |        This code snippet also prepares the class labels (1 or 0). 1 for the licence plate images and 0 for the non licence plate images.
 18 | '''
 19 | 
 20 | import cv2
 21 | import glob
 22 | import numpy as np
 23 | import mahotas.thresholding
 24 | import Tools
 25 | from skimage.feature import hog
 26 | 
 27 | import Configuration
 28 | 
 29 | '''
 30 |     About: The below code is the HOG class that instantiates HOG. HOG is used to create the features that is sent into a logistic regression machine or SVM Machine.
 31 |     With the use of HOG feature we develop a model or find the optimal theta value which is again used for a new instance to predict the output if the image is a number plate or not number plate
 32 | '''
 33 | 
 34 | 
 35 | 
 36 | 
 37 | class HOG:
 38 |     def __init__(self, orientations = 9, pixelsPerCell = (8, 8),cellsPerBlock = (3, 3), visualise=False, normalize = False): 
 39 |         self.orienations = orientations
 40 |         self.pixelsPerCell = pixelsPerCell
 41 |         self.cellsPerBlock = cellsPerBlock
 42 |         self.visualise = visualise
 43 |         self.normalize = normalize
 44 | 
 45 |     def describe(self, image):
 46 |         hist , hog_image= hog(image,
 47 |                             orientations = self.orienations,
 48 |                             pixels_per_cell = self.pixelsPerCell,
 49 |                             cells_per_block = self.cellsPerBlock,
 50 |                             visualise= self.visualise)
 51 |                             # normalise = self.normalize)  
 52 | 
 53 |         return hist, hog_image
 54 | 
 55 | 
 56 | 
 57 | #==============================================================================
 58 | #  Create data set
 59 | #==============================================================================
 60 | 
 61 | class CrtFeatures():    
 62 | 
 63 |     def __init__(self):
 64 |         self.conf = Configuration.get_datamodel_storage_path()
 65 |         self.trn_path = [self.conf['Train_data1_dir'],self.conf['Train_data2_dir']]
 66 | 
 67 |     def hog_feature(self, image_orig):
 68 |         # print ('CrtFeature!! hog_feature')
 69 |         #hog= a_HOG_computation.HOG(orientations=18, pixelsPerCell=(9,9), cellsPerBlock=(3,3), visualise=True, normalize=True) 
 70 |         #hog= a_HOG_computation.HOG(orientations=9, pixelsPerCell=(6,6), cellsPerBlock=(1,1), visualise=True, normalize=True)     
 71 |         hog = HOG(orientations=9, pixelsPerCell=(9,9), cellsPerBlock=(3,3), visualise=True, normalize=True)
 72 |         # Convert the coloured image into grayscale image
 73 |         image_resized = Tools.resize(image_orig, width=300)
 74 |         image_gray = cv2.cvtColor(image_resized, cv2.COLOR_BGR2GRAY)
 75 |         # Do thresholding
 76 |         image_thresh = image_gray
 77 |         T = mahotas.thresholding.otsu(image_gray) # will find an optimal value of T from the image
 78 | 
 79 |         image_thresh[image_thresh>T] = 255 # This goes pixel by pixel if the pixel value of the thresh is greater than the optimal value then the color is white
 80 |         image_thresh[image_thresh<T] = 0   # This goes pixel by pixel if the pixel value of the thresh is greater than the optimal value then the color is Black
 81 |         image_thresh = cv2.bitwise_not(image_thresh)
 82 | 
 83 |         # Perform erotion (Morphological operation)
 84 |         kernel = np.ones((2,2),np.uint8)
 85 |         image_eroded = cv2.erode(image_thresh,kernel,iterations = 1)
 86 | 
 87 |         # We resize the numberplate Since we are using only cars we would resize it to 60*120 pixels
 88 |         image_resized = Tools.resize(image_eroded,width=90, height=30)
 89 |         # We use HOG for licence plate detection
 90 |         hist, hog_image = hog.describe(image_resized)  
 91 |         return hist,hog_image
 92 | 
 93 | 
 94 |     def create_features(self, path):    # creates feature-set for all the images in the directory, One image is a row of the feature_matrix
 95 |         feature_arr = []
 96 |         label_arr = []
 97 |         for folder in path:
 98 |             #print folder
 99 |             for file in glob.glob(folder):
100 |                 image_orig = cv2.imread(file)
101 |                 hist,hog_image = self.hog_feature(image_orig)
102 |                 feature_arr.append(hist)
103 |                 # We prepare the class for the photos that are actual licence plate as 1 and non licence plate as 0
104 |                 if folder == path[0]:  # path 0
105 |                     label_arr.append([1])
106 |                 else:
107 |                     label_arr.append([0])
108 |         return feature_arr, label_arr
109 | 
110 | 
111 |     def create_training_feature(self, store=None):
112 |         feature_trn, labels_trn = self.create_features(self.trn_path)     # Prepare the training dataset
113 |         self.features = np.array(feature_trn, dtype="float64")
114 |         self.labels = np.array(labels_trn)
115 |         print (self.features.shape, self.labels.shape)
116 | 
117 |         if store:
118 |             self.store_feature_matrix(self.conf['Data_feature_dir'], self.conf['Class_labels_dir'])
119 | 
120 |     def store_feature_matrix(self, feature_dir, label_dir):
121 |         # We shall load the feature_train set in the disk to let our crossvalidation and classification data use the array and predict there class
122 |         np.savetxt(feature_dir, self.features, delimiter=",")
123 |         np.savetxt(label_dir, self.labels, delimiter=",")
124 | 
125 | 


--------------------------------------------------------------------------------
/Code/Classify.py:
--------------------------------------------------------------------------------
 1 | # -*- coding: utf-8 -*-
 2 | """
 3 | Created on Sat Mar 21 19:08:39 2015
 4 | 
 5 | @author: Sardhendu_Mishra
 6 | """
 7 | import cv2
 8 | from collections import OrderedDict
 9 | import Configuration
10 | from Bld_FeatureCrps import CrtFeatures
11 | 
12 | 
13 | #==============================================================================
14 | #   Making prediction on test data using the model
15 | #==============================================================================
16 | 
17 | class Classify():
18 | 
19 |     def __init__(self):
20 |         self.conf = Configuration.get_datamodel_storage_path()
21 | 
22 |     def extract_contours(self, image_to_classify):
23 |         # For the sake of simplicity we resize the image
24 |         image_gray = cv2.cvtColor(image_to_classify, cv2.COLOR_BGR2GRAY)
25 |         image_blurr = cv2.GaussianBlur(image_gray, (5,5), 0)
26 |         image_edged = cv2.Canny(image_blurr, 0,10)    # Study of canny edge detection is important before assuming the thresholds
27 |     # 7 im2, contours, hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
28 | 
29 |         (__, cnts, _) = cv2.findContours(image_edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
30 |         cv2.drawContours(image_gray, cnts, -1,(0,255,0),2)
31 |         # cv2.imshow("Contoured image", image_gray)
32 |         # cv2.waitKey(0)
33 |         # Fetch the rectangle coordinates around the contours
34 |         cnts=sorted([(c, cv2.boundingRect(c) [0]) for c in cnts], key=lambda x:x[1])
35 |         return cnts
36 | 
37 | 
38 |     def classify_new_instance (self, image_to_classify, classifier):
39 |         contours = self.extract_contours(image_to_classify)
40 |         '''
41 |         In the below code we loop through all the contoured images, extract the features by the use of create_dataset. After the features are extracted the features are multiplied to the theta value obtained by the training dataset. The images (the number plate images are then put into the folder Countered_image_classified
42 |         '''
43 |         roi_name_array=[]   
44 |         pred_classify=[]
45 |         pred_image_name=[]
46 |         pred_classify_all= OrderedDict()
47 |         #roi_probability_array=[]
48 |         count=0
49 |         for (c,_) in contours:
50 |             x,y,w,h = cv2.boundingRect(c)
51 |             if w>=50 and h>=10: # We do not go through all the contours but rectangles that have more probability of being a lisence plate
52 |                 region_of_interest = image_to_classify[y:y+h,x:x+w]
53 |                 roi_name_array.append(region_of_interest)   
54 |                 # roi stands for region of interest, we store the array of each image in an stack array to later extract the licence plate from given index
55 |                 # We save the region of interest into a folder, retrive it and then classify it             
56 |                 cv2.imwrite(self.conf['Regions_of_Intrest']+"roi_images%04i.jpg" %count, region_of_interest) 
57 |                 path_classify_roi = [self.conf['Regions_of_Intrest']+"roi_images%04i.jpg" %count]
58 |                 # We now create feature set for the Region of Interest as we did formt he training set
59 |                 roi_feature_array, _ = CrtFeatures().create_features(path_classify_roi) 
60 |                 pred_classify = classifier.predict_proba(roi_feature_array) # path_classify will basically contain one image
61 |                 pred_image_name=("roi_images%04i.jpg" %count)
62 |                 pred_classify_all[pred_image_name] = pred_classify[0]
63 |                 count += 1
64 |         return pred_classify_all
65 | 
66 | 
67 | 


--------------------------------------------------------------------------------
/Code/Configuration.py:
--------------------------------------------------------------------------------
 1 | """
 2 | Created on Tue Jan  5 13:35:56 2016
 3 | 
 4 | @author: sardendhu
 5 | """
 6 | 
 7 | import os
 8 | import configparser as ConfigParser
 9 | 
10 | 
11 | def get_config_dir():
12 |     dir_name = os.path.dirname(os.path.abspath(__file__))
13 |     dir_name = os.path.abspath(os.path.join(dir_name, os.pardir))
14 | 
15 |     conf_name = 'config-local.conf'    
16 |     dir_name = os.path.join(dir_name, conf_name)
17 |     # print 'The directory where the config file is: ', dir_name
18 |     return dir_name
19 |     
20 | 
21 | def get_config():
22 |     Config = ConfigParser.ConfigParser()   
23 |     Config.read(get_config_dir())   
24 |     # print ('All the configuration are: ', Config.sections())
25 |     return Config
26 | 
27 | 
28 | def get_datamodel_storage_path(): 
29 |     curr_dir = os.path.dirname(os.path.abspath(__file__))
30 |     curr_dir = os.path.abspath(os.path.join(curr_dir, os.pardir))
31 |     curr_dir = os.path.abspath(os.path.join(curr_dir, os.pardir))
32 |     curr_dir = os.path.abspath(os.path.join(curr_dir, os.pardir))
33 |     # print (curr_dir)
34 | 
35 |     conf = get_config()
36 | 
37 |     config_settings = {}
38 |     config_settings["Train_data1_dir"] = curr_dir+'/'+conf.get("Dataset", "Train_data1")
39 |     config_settings["Train_data2_dir"] = curr_dir+'/'+conf.get("Dataset", "Train_data2")
40 |     config_settings["Valid_data1_dir"] = curr_dir+'/'+conf.get("Dataset", "Valid_data1")
41 |     config_settings["Valid_data2_dir"] = curr_dir+'/'+conf.get("Dataset", "Valid_data2")
42 | 
43 |     config_settings["Data_feature_dir"] = curr_dir+'/'+conf.get("Feature-Labels", "Data_feature")
44 |     config_settings["Class_labels_dir"] = curr_dir+'/'+conf.get("Feature-Labels", "Class_labels")
45 |     config_settings["Data_feature_KernelCnvtd_dir"] = curr_dir+'/'+conf.get("Feature-Labels", "Data_feature_KernelCnvtd")
46 |     config_settings["Data_feature_KernelCnvtd_tst_dir"] = curr_dir+'/'+conf.get("Feature-Labels", "Data_feature_KernelCnvtd_tst")
47 |     config_settings["Theta_val_dir"] = curr_dir+'/'+conf.get("Feature-Labels", "Theta_val")
48 | 
49 |     config_settings["Linear_SVC_dir"] = curr_dir+'/'+conf.get("Models", "Linear_SVC")
50 |     config_settings["SVM_RFB_dir"] = curr_dir+'/'+conf.get("Models","SVM_RFB")+'model_svm_rbf_%i_%i.pickle'
51 |     config_settings["Models"] = curr_dir+'/'+conf.get("Models", "models")
52 | 
53 |     config_settings["Regions_of_Intrest"] = curr_dir+'/'+conf.get("Contored_images", "Regions_of_Intrest")
54 |     config_settings["Classified_license_plates"] = curr_dir+'/'+conf.get("Classified_License_plates", "classified_license_plate")
55 | 
56 |     config_settings["Indian_cars"] = curr_dir+'/'+conf.get("Images_to_classify","Indian_cars")
57 |     config_settings["Foreign_cars"] = curr_dir+'/'+conf.get("Images_to_classify","Foreign_cars")
58 |    
59 |     return config_settings
60 | 
61 | 
62 | # print (get_config_dir())
63 | # print (get_datamodel_storage_path())


--------------------------------------------------------------------------------
/Code/Evaluate_models.py:
--------------------------------------------------------------------------------
 1 | #-------------------------------------------------------------------------------
 2 | # Name:        module1
 3 | # Purpose:
 4 | #
 5 | # Author:      Sardhendu_Mishra
 6 | #
 7 | # Created:     15/02/2015
 8 | # Copyright:   (c) Sardhendu_Mishra 2015
 9 | # Licence:     <your licence>
10 | #-------------------------------------------------------------------------------
11 | 
12 | import os
13 | import glob
14 | import numpy as np
15 | from six.moves import cPickle as pickle 
16 | 
17 | 
18 | import Configuration
19 | from Bld_FeatureCrps import CrtFeatures
20 | from BldModel import SvmModel
21 | 
22 | 
23 | 
24 | #==============================================================================
25 | # Model 1: Opening the saved model and making prediction on test data using the model
26 | #==============================================================================
27 | 
28 | 
29 | class Eval():
30 |     def __init__(self):
31 |         self.conf = Configuration.get_datamodel_storage_path()
32 |         
33 |     def test_using_model_rbf(self, feature_test):   
34 |         # Testing the test data precision with all the models with different value of paramter
35 |         dict_models = {}
36 |         for files in glob.glob(self.conf['Models']):   
37 |             file_name = os.path.basename(files)     
38 |             classifier = pickle.load(open(files, 'rb')) 
39 |             test_classify = classifier.predict(feature_test)
40 |             dict_models[file_name] = test_classify
41 |         return dict_models
42 |             
43 | 
44 |     def test_using_model_self(self, feature_test):   
45 |        feature_train = np.genfromtxt(Data_feature_dir, delimiter=',') 
46 |        theta = np.genfromtxt(Theta_val_dir, delimiter=',') 
47 | 
48 |        sigma=10
49 |        feature_test_kernel_cnvrtd = SvmModel().build_kernel(feature_test,feature_train,sigma)
50 |        feature_test_kernel_cnvrtd = np.insert(feature_test_kernel_cnvrtd,0,1,axis=0)
51 |        feature_test_kernel_cnvrtd = np.transpose(feature_test_kernel_cnvrtd)
52 |        prediction = SvmModel().cal_sigmoid(feature_test_kernel_cnvrtd, theta)  
53 |        np.savetxt(self.conf['Data_feature_KernelCnvtd_tst_dir'], feature_test_kernel_cnvrtd, delimiter=",")
54 |        print (prediction)
55 | 
56 | 
57 |             
58 |     
59 | 


--------------------------------------------------------------------------------
/Code/LP_Detect_main.py:
--------------------------------------------------------------------------------
  1 | from __future__ import division
  2 | 
  3 | import os, glob
  4 | import numpy as np
  5 | import cv2
  6 | import Tools
  7 | 
  8 | import Configuration
  9 | from six.moves import cPickle as pickle 
 10 | from sklearn.metrics import accuracy_score
 11 | 
 12 | from Classify import Classify
 13 | from Evaluate_models import Eval
 14 | from BldModel import SvmModel, Models
 15 | from Bld_FeatureCrps import CrtFeatures
 16 | 
 17 | from shutil import copyfile
 18 | 
 19 | 
 20 | 
 21 | conf = Configuration.get_datamodel_storage_path()
 22 | 
 23 | 
 24 | #==============================================================================
 25 | # 1: create and store features and labels of the images: (use Bld_FeatureCrps)
 26 | #==============================================================================
 27 | def create_feature_matrix():
 28 | 	CrtFeatures().create_training_feature(store='Yes')
 29 | 
 30 | 
 31 | #==============================================================================
 32 | # 2: Train the model and store it into disk for future use.
 33 | #==============================================================================
 34 | 
 35 | # Read the features stored in the disk
 36 | def train_model():
 37 | 	features = np.genfromtxt(conf['Data_feature_dir'], dtype=float, delimiter=',')
 38 | 	labels = np.genfromtxt(conf['Class_labels_dir'], dtype=float, delimiter=',')
 39 | 
 40 | 	sigma=4
 41 | 	max_iter=5000
 42 | 	alpha=0.003
 43 | 	c=10
 44 | 
 45 | 	'''
 46 |     MODEL 1: find the Kernel cnvrt features, cost_function and Parameters (thetas) and store them as models
 47 | 	Note:
 48 | 		The more the sigma is the less the gamma will be
 49 | 	'''
 50 | 	# features_krnl_cnvrt,j_theta, theta = SvmModel().main_call(sigma, features, labels, max_iter, alpha, c)
 51 | 	# np.savetxt(conf["Data_feature_KernelCnvtd_dir"], features_krnl_cnvrt, delimiter=",")
 52 | 	# np.savetxt(conf["Theta_val_dir"], theta, delimiter=",")
 53 | 
 54 | 	# '''
 55 | 	#     MODEL 2: Use packages models, linearSvc, and rbf's with varying gamma and c values.
 56 | 	# '''
 57 | 	Models(type = 'rbf').fit(features, labels)
 58 | 
 59 | 
 60 | 
 61 | 
 62 | # #==============================================================================
 63 | # # 3: Use the patameters and Operate on cross validation dataset
 64 | # #==============================================================================
 65 |  
 66 | def valid(inp_path, model=None):
 67 | 	feature_valid, _ = CrtFeatures().create_features(inp_path)
 68 | 	feature_matrix_valid = np.array(feature_valid, dtype="float64")
 69 | 	if model=='rbf':
 70 | 		prediction_dict = Eval().test_using_model_rbf(feature_matrix_valid)
 71 | 	elif model=='self':
 72 | 		prediction_dict = Eval().test_using_model_self(feature_matrix_valid)
 73 | 	else:
 74 | 		print ('You should specify the model in which you would wanna crossvalidate your data')
 75 | 	return prediction_dict
 76 | 
 77 | def run_cross_valid():
 78 | 	valid_path_LP = [conf['Valid_data1_dir']]
 79 | 	valid_path_non_LP = [conf['Valid_data2_dir']]
 80 | 	for no, path in enumerate([valid_path_LP, valid_path_non_LP]):
 81 | 		print ('Running classification no validation file %s:  '%path)
 82 | 		prediction_dict = valid(inp_path=path, model='rbf')
 83 | 		for model, pred in prediction_dict.items():
 84 | 			if no==0:
 85 | 				labels_valid = np.ones(len(pred))
 86 | 			elif no==1:
 87 | 				labels_valid = np.zeros(len(pred))
 88 | 			accuracy=accuracy_score(labels_valid, pred)
 89 | 			print ('The accuracy of model %s is: '%model, accuracy)
 90 | 
 91 | 
 92 | 
 93 | 
 94 | # ####################`
 95 | # #==============================================================================
 96 | # # 4: Find the Number plates of the vehicle
 97 | # #==============================================================================
 98 | # image_to_classify = "../image_2_classify (11).jpg"
 99 | 
100 | # For classification let us use models one after another.
101 | # For the cross validation data set the best model was. model_svm_rbf_10_1
102 | 
103 | 
104 | def Extract_lisenceplate(model, license_plate_path):
105 | 	for num, image_inp in enumerate(glob.glob(conf['Indian_cars']) + glob.glob(conf['Foreign_cars'])):
106 | 		print (image_inp)
107 | 		image_to_classify = cv2.imread(image_inp)
108 | 		# cv2.imshow('image',image_to_classify)
109 | 		# cv2.waitKey(0)
110 | 		# cv2.destroyAllWindows()
111 | 		image_resized = Tools.resize(image_to_classify, height=500)
112 | 		pred_dict = Classify().classify_new_instance(image_resized,model)
113 | 		# print (pred_dict)
114 | 		
115 | 		# print (model)
116 | 		probs = []
117 | 		for image_fname, prob in pred_dict.items():#range(0,len(pred_dict)):
118 | 		    probs.append(prob[1])
119 | 		        # print (image_fname)
120 | 		probs = np.array(probs)
121 | 		ind = np.where(probs == np.max(probs))[0]
122 | 
123 | 		print (ind)
124 | 		
125 | 		for filename in np.array(list(pred_dict.keys()))[ind]:
126 | 			copyfile(conf['Regions_of_Intrest']+filename, license_plate_path+filename.split(".")[0]+"_"+str(num)+".jpg")
127 | 		# break
128 | 
129 | 
130 | 
131 | 
132 | __main__ = True
133 | 
134 | if __main__:
135 | 
136 | 	model_path = os.path.dirname(os.path.abspath(conf["SVM_RFB_dir"]))
137 | 	license_plate_path = conf["Classified_license_plates"]
138 | 	model = pickle.load(open(model_path+"/model_svm_rbf_1_1.pickle", 'rb')) 
139 | 	# print (license_plate_path)
140 | 
141 | 
142 | 	create_feature_matrix()
143 | 	train_model()
144 | 	run_cross_valid()
145 | 	Extract_lisenceplate(model, license_plate_path)
146 | 


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/Code/Tools.py:
--------------------------------------------------------------------------------
 1 | #-------------------------------------------------------------------------------
 2 | # Name:        module1
 3 | # Purpose:
 4 | #
 5 | # Author:      Sardhendu_Mishra
 6 | #
 7 | # Created:     10/02/2015
 8 | # Copyright:   (c) Sardhendu_Mishra 2015
 9 | # Licence:     <your licence>
10 | #-------------------------------------------------------------------------------
11 | 
12 | import numpy as np
13 | import cv2
14 | 
15 | def translate(image, x, y):
16 |     M=np.float32([[1,0,x],[0,1,y]])
17 |     # M is defined as the floating point array because cv2 expects the matrix to be in floting point array
18 |     # The first array [1,0,x] indicates the  number of pixels to shift right, A negative x would shift x pixels left
19 |     # The second array [0,1,y] indicates the number of pixels to shift down, A negative y would shift the image y pixels up
20 |     shifted=cv2.warpAffine(image, M, (image.shape[1], image.shape[0]))
21 |     # The first argument to the warpAffine function is the image
22 |     # The second argument to the warpAffine function is the array by which the image has to shift
23 |     # The third argument is the dimension in (height and width), by default the image is stored in (weidth * height) but opencv takes dimension in (height * weidth) format
24 |     return shifted
25 | 
26 | def rotate(image, angle, center = None, scale=1.0):
27 |     # The arguments are :
28 |     '''
29 |     1. The image
30 |     2. the angle by which you want to rotate
31 |     3. The center from which you want to rotate, The default is None, when None then we define the center
32 |     4. scale=1.0 by default, It states that
33 |     '''
34 |     (h,w)= image.shape[:2]
35 | 
36 |     # When the center is None then we define the center from which the rotation is to be done
37 |     if center is None:
38 |         center=(w/2, h/2)
39 | 
40 |     M=cv2.getRotationMatrix2D(center, angle, scale)
41 |     rotated= cv2.warpAffine(image, M, (w,h))
42 | 
43 |     return rotated
44 | 
45 | 
46 | def resize(image, width= None, height = None, inter=cv2.INTER_AREA):
47 |     dim=None
48 |     (h,w)= image.shape[:2]  # numpy array stores images in (height, width) array, but cv2 uses images in order (width, height) order
49 | 
50 |     if width is None and height is None:  # when no resizing occur
51 |         return image
52 | 
53 |     if width is None:       # when resized height is passed and width is not then we calculate the aspect ratio of the weidth
54 |         r= height / float(h)
55 |         dim=(int(w * r), height)      # height is the resized hieght
56 |     elif height is None:    # When resized width is passed and hieght is not then we calculate the aspect ratio for the height
57 |         r= width / float(w)
58 |         dim=(width , int(h * r))
59 |     else:                   # when both width and height ratio are provided
60 |         dim=(width, height)
61 | 
62 |     resized= cv2.resize(image, dim, interpolation=inter)
63 |     # the third argument hold an algorithm in cv2 defined to resize the image
64 |     # we can also use other algorithm like cv2.INTER_LINEAR, cv2.INTER_CUBIC, and cv2.INTER_NEAREST.
65 | 
66 |     return resized
67 | 
68 | 
69 | 
70 | 


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 1 | # -*- coding: utf-8 -*-
 2 | """
 3 | Created on Sat Mar 21 01:05:19 2015
 4 | 
 5 | @author: Sardhendu_Mishra
 6 | """
 7 | 
 8 | import cv2.cv as cv	
 9 | # interface opencv versi 2
10 | import numpy as np
11 | import cv2
12 | 
13 | 
14 | def calc_hog(im,numorient=9,cellSize=(8,8)):
15 |     ihog = calc_hog_base(im, numorient)
16 |     return calc_hog_cells(ihog, numorient, cellSize)
17 |     
18 |     
19 | def preprocess(img):
20 |     # gamma correction 1/4 followed by contrast boost (histogram equalization)
21 |     return cv2.equalizeHist((np.power(img/255., 0.25)*255).astype(np.uint8))    
22 |     
23 | def calc_hog_base(im, numorient=9):
24 |     # calculate gradient using sobel operator
25 |     gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
26 |  
27 |     gray = preprocess(gray) #preprocess gray image before calculating gradient
28 |  
29 |     gx = cv2.Sobel(gray, cv2.CV_32F, 1, 0)
30 |     gy = cv2.Sobel(gray, cv2.CV_32F, 0, 1)
31 |      
32 |     # calculate gradient magnitude
33 |     """
34 |     #mag = np.sqrt(gx*gx+gy*gy)
35 |     mag
36 |     """
37 |  
38 |     # calculate gradient orientation and discretize to numorient values
39 |     mid = numorient/2
40 |     
41 |     hog = np.zeros((im.shape[0], im.shape[1], numorient))
42 |     
43 |     
44 |     """
45 |     mid = numorient/2
46 |     for y in xrange(0, im.shape[0]-1):
47 |         for x in xrange(0, im.shape[1]-1):
48 |             angle = int(round(mid*np.arctan2(gy[y,x], gx[y,x])/np.pi))+mid
49 |             magnitude = np.sqrt(gx[y,x]*gx[y,x]+gy[y,x]*gy[y,x])
50 |             hog[y,x,angle] += magnitude    
51 |     
52 |     
53 |     """
54 |     magor = mid+(mid*np.arctan2(gy, gx)/np.pi).astype(np.uint32)
55 |     
56 |     #print (magor.min(), magor.max()+1)
57 |      
58 |     # calculate hog in pixels by grouping each pixel based on gradient orientation
59 |     
60 |     for orien in xrange(magor.min(), magor.max()+1):
61 |         mask = magor==orien
62 |         hog[:,:,orien][mask] = mag[mask] + magor[mask]
63 |         
64 |  
65 |     # calculate integral hog
66 |     #ihog = cv2.integral(hog)
67 |      
68 |     return gray,gx,gy,mag,magor,hog        
69 |     
70 | gray,gx,gy,mag,magor,hog=calc_hog_base(image) 
71 | 
72 | cv2.imshow("gray", gray)
73 | cv2.imshow("gx", gx)
74 | cv2.imshow("gy", gy)
75 | cv2.waitKey(0)   


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  1 | 1.000000000000000000e+00
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/README.md:
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 1 | # Licence-Plate-Detection
 2 | This project aims to utilize Machine Learning and Image Processing techniques to detect/extract license plates of vehicle given an image. 
 3 | 
 4 | 
 5 | ## Modules:
 6 | 
 7 | * You would want to start with the module **LP_Detect_main.py**. Lets go one by one 
 8 |  
 9 |    ### [Data Preparation and Feature Extraction](https://github.com/Sardhendu/License-Plate-Detection/blob/master/Code/Bld_FeatureCrps.py): 
10 |    * This module is aimed to extract features from a license plate/non license plate and store them in the disk (Training Features). 
11 |    * Here, we manually create a small dataset by cropping out License plates from vehicle images and small set of random images (non-license plates).
12 |    * Usual image processing techniques such as thresholding, standarization, erosion etc. are applied to the images.
13 |    * We employ **Histogram of Oriented Gradients (HOG)** as a features extraction technique. In a nutshell, given an image (say 32x32x3) **HOG** would create a feature vector which can be consumed by a Machine Learning model. You could even experiment with simple features extraction technique such as **Edges 32x32 = 1024x1**, **Flattening the image (32x32x3) into 3072x1** and use these vectors as an input to Machine learning Models.
14 |    
15 |        #### Module
16 |        * Gets license plates and non-license plate from respective directories, extract features and stores the feature vectors into the disk
17 |             * [Sample License Plate data](https://github.com/Sardhendu/License-Plate-Detection/tree/master/DataSet/Data-Files/images_train/Licence-Plate)
18 |             * [Sample Non-License plate data](https://github.com/Sardhendu/License-Plate-Detection/tree/master/DataSet/Data-Files/images_train/Not-Licence-Plate)
19 |      
20 |    ### [Train Model](https://github.com/Sardhendu/License-Plate-Detection/blob/master/Code/BldModel.py): 
21 |    * From step 1 we already have our features, now all we have to do is send these features to a machine learning model to learn a reasonable boundary to separate License plates and Non-License plates.
22 |    * In our case, the data is not very big, so we use **Support Vector Machines** as our machine learning model. SVM's with RBF kernel performs excellent with small data size and are robust to overfitting.
23 |    
24 |         ##### Module:
25 |         * Fetches the saved features and the corresponding label (license_plate or non-license plate) and sends it to the SVM model.
26 |         * Stores the learned model into the disk to be used while cross validation and testing. 
27 |             * [CSV files containing the features](https://github.com/Sardhendu/License-Plate-Detection/tree/master/DataSet/Feature-Model)
28 |              
29 |    ### [Cross Validation](https://github.com/Sardhendu/License-Plate-Detection/blob/master/Code/LP_Detect_main.py):
30 |    * Now that we have a model in place we would want to validate the model to predict if a rectangular region is license plate or non-license plate
31 |    * **Note** here we do not provide the whole image, but use manually extract license plates and random images to get a sense of the model performance
32 |    
33 |    ### [Extract License plate](https://github.com/Sardhendu/License-Plate-Detection/blob/master/Code/LP_Detect_main.py):
34 |    * We have every thing set up. We have a good model too and have validated it against cross validation dataset. Now the aim is to extract all the license plate given a image containing vehicles. 
35 |    * We know that a license plate has visible edges. So, we extract all contours or region of interests **(ROI)** from a image, i.e (rectangles, circles polygon defined with intense edges).
36 |    * We know that license plate are rectangular in shape, so we reshape/extend the **ROI** as rectangles. Note, for a given image we can have 100's or **ROI** of which only 2-3 would be license plate. So we have to classify all the **ROI** using our **SVM** model.   
37 |    * All the **ROI's** are send to the feature extractor module.
38 |   * The generated features for each **ROI** are then send to the **SVM** classifier for classification.
39 |   * A high probability (say >90%) indicates a **ROI** to be the license plate. 
40 |   * Finally we stash the **ROI** that have high probability of being a license plate into the disk for manual examination.
41 |       
42 |   **Look HERE** to get a sense of the directory name, so that you can run the model for yourself.
43 |       
44 |       
45 | ## Snapshot of the process
46 | 
47 | #### Train: 
48 | Training data is manually created by taking a vehicle's image and cropping out the license plate image. Another 
49 | simple way would be to extract all possible contours form the image (look at /Code/Classify.py) and save them in a 
50 | directory. Then manually go an select the license plates. Below are some images of License plates used for Training.
51 | 
52 | ##### License Plates:
53 | 
54 | <img src="https://github.com/Sardhendu/License-Plate-Detection/blob/master/DataSet/Data-Files/images_train/Licence-Plate/yes%20(33).jpg" width="200" height="100"> <img src="https://github.com/Sardhendu/License-Plate-Detection/blob/master/DataSet/Data-Files/images_train/Licence-Plate/yes%20(32).jpg" width="200" height="100"> 
55 | 
56 | ##### Non License Plates
57 | 
58 | <img src="https://github.com/Sardhendu/License-Plate-Detection/blob/master/DataSet/Data-Files/images_train/Not-Licence-Plate/no%20(6).jpg" width="200" height="100"> <img src="https://github.com/Sardhendu/License-Plate-Detection/blob/master/DataSet/Data-Files/images_train/Not-Licence-Plate/no%20(8).jpg" width="200" height="100"> 
59 | 
60 | 
61 | #### Test:
62 | For testing, an image containing a vehicle image with its license plate was provided. Given the image we first 
63 | extracted all the **ROI's** and then classify each **ROI's** as a license plate or not a license plate.
64 | 
65 | 
66 | ##### Test Images:
67 | <img src="https://github.com/Sardhendu/License-Plate-Detection/blob/master/DataSet/Data-Files/images_classify/Foreign_cars/image_2_classify%20(9).jpg" width="400" height="300"> <img src="https://github.com/Sardhendu/License-Plate-Detection/blob/master/DataSet/Data-Files/images_classify/Foreign_cars/image_2_classify%20(4).jpg" 
68 | width="400" height="300"> 
69 | 
70 | ##### Contours Extracted (Region of Interest)  
71 | <img src="https://github.com/Sardhendu/License-Plate-Detection/blob/master/DataSet/Data-Files/images_classify/contoured_images_roi/roi_images0038.jpg" width="200" height="100"> <img src="https://github.com/Sardhendu/License-Plate-Detection/blob/master/DataSet/Data-Files/images_classify/contoured_images_roi/roi_images0039.jpg" width="200" height="100"> <img src="https://github.com/Sardhendu/License-Plate-Detection/blob/master/DataSet/Data-Files/images_classify/contoured_images_roi/roi_images0041.jpg" width="200" height="100">
72 | 
73 | ##### Region of interest classified as License Plate
74 | <img src="https://github.com/Sardhendu/License-Plate-Detection/blob/master/DataSet/Data-Files/images_classify/extracted_licenceplate_image/roi_images0042_33.jpg" width="200" height="100"> <img src="https://github.com/Sardhendu/License-Plate-Detection/blob/master/DataSet/Data-Files/images_classify/extracted_licenceplate_image/roi_images0036_28.jpg" width="200" height="100">
75 | 
76 | A sample of Dataset is provided: Look under the directory folder "/DataSet" to get a sense of the dataset.
77 | 
78 | 
79 |   
80 | 


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/blog.py:
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 1 | #-------------------------------------------------------------------------------
 2 | # Name:        module1
 3 | # Purpose:
 4 | #
 5 | # Author:      Sardhendu_Mishra
 6 | #
 7 | # Created:     15/03/2015
 8 | # Copyright:   (c) Sardhendu_Mishra 2015
 9 | # Licence:     <your licence>
10 | #-------------------------------------------------------------------------------
11 | 
12 | """
13 | import cv2
14 | import sys
15 | sys.path.insert(0, 'C:\\Users\\sardhendu_mishra\\Desktop\\StudyHard\\Machine_learning\\Data mining and analysis\\image_processing')
16 | import imutils
17 | 
18 | image=cv2.imread("C:\\Users\\sardhendu_mishra\\Desktop\\ahha\\DSC_0023.jpg")
19 | 
20 | image_resize=imutils.resize(image,width=700)
21 | 
22 | image_gray = cv2.cvtColor(image_resize, cv2.COLOR_BGR2GRAY)
23 | 
24 | image_blurr=cv2.GaussianBlur(image_gray, (3,3), 0)
25 | 
26 | image_thresh=cv2.adaptiveThreshold(image_blurr,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,11,2)
27 | 
28 | cv2.imwrite("C:\\Users\\sardhendu_mishra\\Desktop\\ahha\\23.jpg", image_thresh)
29 | cv2.imshow("image_orig",image_thresh)
30 | cv2.waitKey()
31 | 
32 | 
33 | """
34 | import numpy as np
35 | 
36 | 
37 | f=np.fromfile("C:\\Users\\sardhendu_mishra\\Desktop\\StudyHard\\Machine_learning\\Data_sets\\For_blog\\ex4x.dat", dtype='float64')
38 | fa=np.fromfile("C:\\Users\\sardhendu_mishra\\Desktop\\StudyHard\\Machine_learning\\Data_sets\\For_blog\\ex4y.dat", dtype='float64')
39 | 
40 | a=f.read()
41 | 
42 | """
43 | x=np.array(feature_array,dtype="float64")
44 | y=np.array(class_array,dtype="float64")
45 | 
46 | # Add the intercept value 1 to the first column. the 0 indicates first column and 1 indicates the intercept term
47 | x=np.insert(x,0,1,axis=1)
48 | (m,n)=x.shape
49 | # Calculate mean and standard deviation
50 | mn=np.mean(x, axis=0)  # axis=0 will perform column wise mean
51 | sd=np.std(x, axis=0)
52 | 
53 | """


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/config-local.conf:
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 1 | [Dataset]
 2 | Train_data1 = App-DataSet/Plate-Detection/Data-Files/images_train/Licence-Plate/*.jpg
 3 | Train_data2 = App-DataSet/Plate-Detection/Data-Files/images_train/Not-Licence-Plate/*.jpg
 4 | Valid_data1 = App-DataSet/Plate-Detection/Data-Files/images_crossvalidate/Licence-Plate/*.jpg
 5 | Valid_data2 = App-DataSet/Plate-Detection/Data-Files/images_crossvalidate/Not-Licence-Plate/*.jpg
 6 | 
 7 | 
 8 | [Feature-Labels]
 9 | Data_feature = App-DataSet/Plate-Detection/Feature-Model/feature_train.csv
10 | Class_labels = App-DataSet/Plate-Detection/Feature-Model/class_train.csv
11 | Data_feature_KernelCnvtd = App-DataSet/Plate-Detection/Feature-Model/feature_train_kernel_cnvrtd.csv
12 | Theta_val = App-DataSet/Plate-Detection/Feature-Model/theta_val.csv
13 | Data_feature_KernelCnvtd_tst = App-DataSet/Plate-Detection/Feature-Model/feature_test_kernel_cnvrtd.csv
14 | 
15 | 
16 | [Models]
17 | Linear_SVC = App-DataSet/Plate-Detection/models/model_linear_svc.pickle
18 | SVM_RFB = App-DataSet/Plate-Detection/models/
19 | models = App-DataSet/Plate-Detection/models/*.pickle
20 | 
21 | [Images_to_classify]
22 | Indian_cars = App-DataSet/Plate-Detection/Data-Files/images_classify/Indian_cars/*.jpg
23 | Foreign_cars = App-DataSet/Plate-Detection/Data-Files/images_classify/Foreign_cars/*.jpg
24 | 
25 | [Contored_images]
26 | Regions_of_Intrest = App-DataSet/Plate-Detection/Data-Files/images_classify/contoured_images_roi/
27 | 
28 | [Classified_License_plates]
29 | classified_license_plate = App-DataSet/Plate-Detection/Data-Files/images_classify/extracted_licenceplate_image/
30 | 


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/gradient_calculation.py:
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 1 | #-------------------------------------------------------------------------------
 2 | # Name:        module1
 3 | # Purpose:
 4 | #
 5 | # Author:      Sardhendu_Mishra
 6 | #
 7 | # Created:     10/03/2015
 8 | # Copyright:   (c) Sardhendu_Mishra 2015
 9 | # Licence:     <your licence>
10 | #-------------------------------------------------------------------------------
11 | 
12 | 
13 | '''
14 | About: This code snippet is written to calculate the gradient of each pixel, every pixel of an image call this code snippet and the code snippet \
15 |        returns the magnitude of the very pixel.
16 | '''
17 | 
18 | import math
19 | import cv2
20 | 
21 | 
22 | 
23 | # Not needed in our case because we take care of the pixel coordinate in generate_pixel function ,, anyway this checks if the pixel in process is in range of the pixel size
24 | def check_pixel_range (image, r, c):    #c is the column and r is the row
25 |     total_size_xaxis=image.shape[1]
26 |     total_size_yaxis=image.shape[0]
27 |     return c>0 and c<total_size_xaxis-1 and r>0 and r<total_size_yaxis-1
28 | 
29 | 
30 | 
31 | # Calculate the dy (the change in y axis) we need the vertical neighbours to calculate dy
32 | def cal_dy (image, r,c, default_delta=1.0):
33 | 
34 |     if not check_pixel_range(image,r,c):
35 |          return default_delta
36 |     vertical_neighbor_intensity_change=image[r-1,c]- image[r+1,c]# we have taken (r-1,c) and (r+1,c) because in openCV an image point is read as (r,c) not (c,r)
37 |     dy=vertical_neighbor_intensity_change
38 | 
39 |     if dy==0:
40 |         return default_delta
41 |     else:
42 |         return float(dy)
43 | 
44 | 
45 | 
46 | # Calculate the dx (the change in x axis) we need the horizontal neighbors to calculate dx
47 | def cal_dx (image, r, c, default_delta=1.0):
48 | 
49 |     if not check_pixel_range(image,r,c):
50 |          return default_delta
51 |     horizontal_neighbor_intensity_change=image[r,c-1]- image[r,c+1] # we have taken (r-1,c) and (r+1,c) because in openCV an image point is read as (r,c) not (c,r)
52 |     dx=horizontal_neighbor_intensity_change
53 | 
54 |     if dx==0:
55 |         return default_delta
56 |     else:
57 |         return float(dx)
58 | 
59 | 
60 | # Calculate the magnitude of the gradient
61 | def cal_gradient_magnitude(dx, dy):
62 |     magnitude=math.sqrt(math.pow(dx,2) + math.pow(dy,2))
63 |     return magnitude
64 | 
65 | 
66 | def call_magnitude(image, r,c):
67 |     dy=cal_dy(image, r,c)
68 |     dx=cal_dx(image,r,c)
69 |     magnitude=int(cal_gradient_magnitude(dx,dy))
70 | 
71 |     return magnitude
72 | 


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/hist_features_HOG.py:
--------------------------------------------------------------------------------
 1 | #-------------------------------------------------------------------------------
 2 | # Name:        module1
 3 | # Purpose:
 4 | #
 5 | # Author:      Sardhendu_Mishra
 6 | #
 7 | # Created:     11/03/2015
 8 | # Copyright:   (c) Sardhendu_Mishra 2015
 9 | # Licence:     <your licence>
10 | #-------------------------------------------------------------------------------
11 | 
12 | import cv2
13 | import mahotas
14 | import numpy as np
15 | import sys
16 | sys.path.insert(0, 'C:\\Users\\sardhendu_mishra\\Desktop\\StudyHard\\Machine_learning\\Data mining and analysis\\image_processing')
17 | import imutils
18 | import HOG_computation
19 | 
20 | 
21 | def create_histogram_for_image(image):
22 |     hog= HOG_computation.HOG(orientations=18, pixelsPerCell=(10,10), cellsPerBlock=(3,3), normalize=True)
23 | 
24 |     # Convert the coloured image into grayscale image
25 |     image_resized=imutils.resize(image, width=300)
26 |     image_gray=cv2.cvtColor(image_resized, cv2.COLOR_BGR2GRAY)
27 | 
28 |     # Do thresholding
29 |     image_thresh=image_gray
30 |     T=mahotas.thresholding.otsu(image_gray) # will find an optimal value of T from the image
31 |     image_thresh[image_thresh>T]=255 # This goes pixel by pixel if the pixel value of the thresh is greater than the optimal value then the color is white
32 |     image_thresh[image_thresh<T]=0   # This goes pixel by pixel if the pixel value of the thresh is greater than the optimal value then the color is Black
33 |     image_thresh = cv2.bitwise_not(image_thresh)
34 | 
35 | 
36 |     # Perform dialation
37 |     kernel = np.ones((2,2),np.uint8)
38 |     image_eroded = cv2.erode(image_thresh,kernel,iterations = 1)
39 | 
40 | 
41 |     # We resize the numberplate Since we are using only cars we would resize it to 60*120 pixels
42 |     image_resized= imutils.resize(image_eroded,width=90, height=30)
43 | 
44 | 
45 |     # We use HOG for licence plate detection
46 |     hist=hog.describe(image_resized)
47 | 
48 |     return hist


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