added code

lprecognition.py

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+
+# coding: utf-8
+
+# In[1]:
+
+from skimage.io import imread
+import numpy as np
+from skimage.filters import threshold_otsu
+import matplotlib.pyplot as plt
+#get_ipython().magic(u'matplotlib inline')
+
+
+# In[2]:
+
+car_image = imread("c5.jpg", as_grey=True)
+# it should be a 2 dimensional array
+print(car_image.shape)
+
+
+# In[3]:
+
+gray_car_image = car_image * 255
+fig, (ax1, ax2) = plt.subplots(1, 2)
+ax1.imshow(gray_car_image, cmap="gray")
+threshold_value = threshold_otsu(gray_car_image)
+binary_car_image = gray_car_image > threshold_value
+ax2.imshow(binary_car_image, cmap="gray")
+plt.show()
+
+
+# In[4]:
+
+from skimage import measure
+from skimage.measure import regionprops
+import matplotlib.pyplot as plt
+import matplotlib.patches as patches
+
+# this gets all the connected regions and groups them together
+label_image = measure.label(binary_car_image)
+fig, (ax1) = plt.subplots(1)
+ax1.imshow(gray_car_image, cmap="gray");
+
+# regionprops creates a list of properties of all the labelled regions
+for region in regionprops(label_image):
+    if region.area < 50:
+        #if the region is so small then it's likely not a license plate
+        continue
+
+    # the bounding box coordinates
+    minRow, minCol, maxRow, maxCol = region.bbox
+    rectBorder = patches.Rectangle((minCol, minRow), maxCol-minCol, maxRow-minRow, edgecolor="red", linewidth=2, fill=False)
+    ax1.add_patch(rectBorder)
+    # let's draw a red rectangle over those regions
+
+plt.show()
+
+
+# In[15]:
+
+from skimage import measure
+from skimage.measure import regionprops
+import matplotlib.pyplot as plt
+import matplotlib.patches as patches
+
+# this gets all the connected regions and groups them together
+label_image = measure.label(binary_car_image)
+
+# getting the maximum width, height and minimum width and height that a license plate can be
+plate_dimensions = (0.06*label_image.shape[0], 0.10*label_image.shape[0], 0.15*label_image.shape[1], 0.4*label_image.shape[1])
+min_height, max_height, min_width, max_width = plate_dimensions
+plate_objects_cordinates = []
+plate_like_objects = []
+fig, (ax1) = plt.subplots(1)
+ax1.imshow(gray_car_image, cmap="gray");
+
+# regionprops creates a list of properties of all the labelled regions
+for region in regionprops(label_image):
+    if region.area < 50:
+        #if the region is so small then it's likely not a license plate
+        continue
+
+    # the bounding box coordinates
+    min_row, min_col, max_row, max_col = region.bbox
+    region_height = max_row - min_row
+    region_width = max_col - min_col
+    # ensuring that the region identified satisfies the condition of a typical license plate
+    if region_height >= min_height and region_height <= max_height and region_width >= min_width and region_width <= max_width and region_width > region_height:
+        plate_like_objects.append(binary_car_image[min_row:max_row,
+                                  min_col:max_col])
+        plate_objects_cordinates.append((min_row, min_col,
+                                              max_row, max_col))
+        rectBorder = patches.Rectangle((min_col, min_row), max_col-min_col, max_row-min_row, edgecolor="red", linewidth=2, fill=False)
+        ax1.add_patch(rectBorder)
+    # let's draw a red rectangle over those regions
+
+plt.show()
+
+
+# In[66]:
+
+from skimage.transform import resize
+
+license_plate = np.invert(plate_like_objects[0])
+
+labelled_plate = measure.label(license_plate)
+
+fig, ax1 = plt.subplots(1)
+ax1.imshow(license_plate, cmap="gray")
+
+character_dimensions = (0.35*license_plate.shape[0], 0.70*license_plate.shape[0], 0.04*license_plate.shape[1], 0.70*license_plate.shape[1])
+min_height, max_height, min_width, max_width = character_dimensions
+
+characters = []
+counter=0
+column_list = []
+for regions in regionprops(labelled_plate):
+    y0, x0, y1, x1 = regions.bbox
+    region_height = y1 - y0
+    region_width = x1 - x0
+
+    if region_height > min_height and region_height < max_height and region_width > min_width and region_width < max_width:
+        roi = license_plate[y0:y1, x0:x1]
+
+        # draw a red bordered rectangle over the character.
+        rect_border = patches.Rectangle((x0, y0), x1 - x0, y1 - y0, edgecolor="red",linewidth=2, fill=False)
+        ax1.add_patch(rect_border)
+
+        # resize the characters to 20X10 and then append each character into the characters list
+        resized_char = resize(roi, (20, 10))
+        characters.append(resized_char)
+
+        # this is just to keep track of the arrangement of the characters
+        column_list.append(x1)
+print(column_list)
+plt.show()
+
+
+# In[38]:
+
+import os
+import numpy as np
+from sklearn.svm import SVC
+from sklearn.model_selection import cross_val_score
+from sklearn.externals import joblib
+from skimage.io import imread
+from skimage.filters import threshold_otsu
+
+letters = [
+            '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D',
+            'E', 'F', 'G', 'H', 'J', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T',
+            'U', 'V', 'W', 'X', 'Y', 'Z'
+        ]
+
+def read_training_data(training_directory):
+    image_data = []
+    target_data = []
+    for each_letter in letters:
+        for each in range(10):
+            image_path = os.path.join(training_directory, each_letter, each_letter + '_' + str(each) + '.jpg')
+            # read each image of each character
+            img_details = imread(image_path, as_grey=True)
+            # converts each character image to binary image
+            binary_image = img_details < threshold_otsu(img_details)
+            flat_bin_image = binary_image.reshape(-1)
+            image_data.append(flat_bin_image)
+            target_data.append(each_letter)
+    return (np.array(image_data), np.array(target_data))
+
+def cross_validation(model, num_of_fold, train_data, train_label):
+    accuracy_result = cross_val_score(model, train_data, train_label,cv=num_of_fold)
+    print("Cross Validation Result for "+ str(num_of_fold)+ " -fold")
+
+    print(accuracy_result * 100)
+
+
+current_dir = os.path.dirname(os.path.realpath('__file__'))
+
+training_dataset_dir = os.path.join(current_dir, 'train')
+
+image_data, target_data = read_training_data(training_dataset_dir)
+
+
+svc_model = SVC(kernel='linear', probability=True)
+
+cross_validation(svc_model, 4, image_data, target_data)
+
+# let's train the model with all the input data
+svc_model.fit(image_data, target_data)
+
+
+# In[67]:
+
+classification_result = []
+for each_character in characters:
+    # converts it to a 1D array
+    each_character = each_character.reshape(1, -1);
+    result = svc_model.predict(each_character)
+    classification_result.append(result)
+
+print(classification_result)
+
+plate_string = ''
+for eachPredict in classification_result:
+    plate_string += eachPredict[0]
+
+
+
+column_list_copy = column_list[:]
+column_list.sort()
+rightplate_string = ''
+for each in column_list:
+    rightplate_string += plate_string[column_list_copy.index(each)]
+
+print(rightplate_string)
+
+
+# In[47]:
+
+import pandas as pd
+df=pd.read_csv('sdata.csv')
+dfs=pd.read_csv('mh.csv')
+
+
+# In[68]:
+v=''
+w=''
+for x in df['Code']:
+    if plate_string[0:2]==str(x):
+        v=v+x
+g=df[df['Code']==v]
+print(g['State'])
+plate_string1=rightplate_string[0:2]+'-'+rightplate_string[2:4]
+for y in dfs['code'] :
+    if plate_string1[0:5]==str(y):
+        w=w+y
+h=dfs[dfs['code']==str(w)]
+print(h['area'])
+
+
+# In[ ]:
+
+
+

mh.csv

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+code,area
+MH-01,"Mumbai (South) – RTO is located in Tardeo. BEST, MCGM and Mumbai Police vehicles are also registered here"
+MH-02,Mumbai (West) – RTO is located in Andheri
+MH-03,Mumbai (East) – RTO is located in Wadala. Branch in Worli
+MH-04,"Thane. Branch in Murphy Colony.
+Also MSRTC buses are registered here."
+MH-05,"Kalyan-Dombivali, Ulhasnagar, Ambernath and Badlapur – RTO is located in Kalyan"
+MH-06,"Raigad – RTO is located in Pen (does not include Navi Mumbai, Kharghar, Panvel, Khopoli)
+Also MSRTC buses are registered here."
+MH-07,"Sindhudurg – RTO is located in Sindhudurg Nagari
+Also MSRTC buses are registered here."
+MH-08,Ratnagiri
+MH-09,Kolhapur
+MH-10,Sangli-Miraj-Kupwad
+MH-11,"Satara
+Also MSRTC buses are registered here."
+MH-12,"Pune (city). Branch in Yerwada.
+Also MSRTC buses are registered here."
+MH-13,Solapur (city)
+MH-14,"Pimpri-Chinchwad
+Also MSRTC buses are registered here."
+MH-15,Nashik (city)
+MH-16,Ahmednagar District (South)
+MH-17,Ahmednagar District (North)
+MH-18,Dhule
+MH-19,Jalgaon
+MH-20,"Aurangabad – RTO is located on Station Road
+Also MSRTC buses are registered here."
+MH-21,Jalna (city)
+MH-22,Parbhani
+MH-23,Beed
+MH-24,Latur RTO located in Babhalgaon Road
+MH-25,Osmanabad
+MH-26,Nanded
+MH-27,Amravati
+MH-28,Buldhana
+MH-29,Yavatmal – RTO is located on Nagpur Road
+MH-30,Akola – RTO is located on Dabki Road - 444002
+MH-31,"Nagpur West - RTO is Located at Amravati Road
+Also MSRTC buses are registered here."
+MH-32,Wardha
+MH-33,Gadchiroli
+MH-34,Chandrapur
+MH-35,Gondia
+MH-36,Bhandara
+MH-37,Washim
+MH-38,Hingoli
+MH-39,Nandurbar
+MH-40,"Nagpur (Rural)
+Also MSRTC buses are registered here."
+MH-41,"Malegaon, Nashik District"
+MH-42,"Baramati, Pune"
+MH-43,Navi Mumbai – RTO at Vashi
+MH-44,Ambejogai RTO located on Latur Road
+MH-45,Akluj
+MH-46,Panvel also MSRTC buses are registered here
+MH-47,Mumbai North
+MH-48,Virar
+MH-49Z 5111,Nagpur East – RTO is located on Bhandara Road
+MH-50,Karad (Satara Rural) – RTO is located in Karad
+MH-51,Nashik Rural
+MH-52,Kanwati – RTO is located in Nanded Districts
+MH-53,Nanded South
+MH-54,Pune South
+MH-55,Pune North
+MH-56,Latur central
+MH-57,Latur East
+MH-58,Latur West
+MH-59,Aurangbad Rural
+MH-60,Sangli Rural
+MH-61,Nashik South
+MH-62,Nanded Rural
+MH-63,Nanded West
+MH-64,Nanded Central

sdata.csv

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+Code,State
+AN,Andaman and Nicobar Islands
+AP,Andhra Pradesh
+AR,Arunachal Pradesh
+AS,Assam
+BR,Bihar
+CG,Chhattisgarh
+CH,Chandigarh
+DD,Daman and Diu
+DL,Delhi
+DN,Dadra and Nagar Haveli
+GA,Goa
+GJ,Gujarat
+HR,Haryana
+HP,Himachal Pradesh
+JH,Jharkhand
+JK,Jammu and Kashmir
+KA,Karnataka
+KL,Kerala
+LD,Lakshadweep
+MH,Maharashtra
+ML,Meghalaya
+MN,Manipur
+MP,Madhya Pradesh
+MZ,Mizoram
+NL,Nagaland
+OD,Odisha
+PB,Punjab
+PY,Puducherry
+RJ,Rajasthan
+SK,Sikkim
+TN,Tamil Nadu
+TR,Tripura
+TS,Telangana
+UK,Uttarakhand
+UP,Uttar Pradesh
+WB,West Bengal