Create app.py

app.py

@@ -0,0 +1,216 @@
+import matplotlib.pyplot as plt
+import numpy as np
+import cv2
+import tensorflow as tf
+from sklearn.metrics import f1_score 
+from tensorflow.keras import optimizers
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+from tensorflow.keras.layers import Dense, Flatten, MaxPooling2D, Dropout, Conv2D
+import streamlit as st
+
+
+# Loads the data required for detecting the license plates from cascade classifier.
+plate_cascade = cv2.CascadeClassifier('../input/ai-indian-license-plate-recognition-data/indian_license_plate.xml')
+# add the path to 'india_license_plate.xml' file.
+
+
+
+model = load_model('.h5', compile=False)
+
+
+
+
+
+
+
+def detect_plate(img, text=''): # the function detects and perfors blurring on the number plate.
+    plate_img = img.copy()
+    roi = img.copy()
+    plate_rect = plate_cascade.detectMultiScale(plate_img, scaleFactor = 1.2, minNeighbors = 7) # detects numberplates and returns the coordinates and dimensions of detected license plate's contours.
+    for (x,y,w,h) in plate_rect:
+        roi_ = roi[y:y+h, x:x+w, :] # extracting the Region of Interest of license plate for blurring.
+        plate = roi[y:y+h, x:x+w, :]
+        cv2.rectangle(plate_img, (x+2,y), (x+w-3, y+h-5), (51,181,155), 3) # finally representing the detected contours by drawing rectangles around the edges.
+    if text!='':
+        plate_img = cv2.putText(plate_img, text, (x-w//2,y-h//2), 
+                                cv2.FONT_HERSHEY_COMPLEX_SMALL , 0.5, (51,181,155), 1, cv2.LINE_AA)
+        
+    return plate_img, plate # returning the processed image.
+
+
+
+
+# Testing the above function
+def display(img_, title=''):
+    img = cv2.cvtColor(img_, cv2.COLOR_BGR2RGB)
+    fig = plt.figure(figsize=(10,6))
+    ax = plt.subplot(111)
+    ax.imshow(img)
+    plt.axis('off')
+    plt.title(title)
+    plt.show()
+
+# img = cv2.imread('../input/ai-indian-license-plate-recognition-data/car.jpg')
+display(img, 'input image')
+
+
+
+# Getting plate prom the processed image
+output_img, plate = detect_plate(img)
+
+
+
+display(output_img, 'detected license plate in the input image')
+
+
+
+display(plate, 'extracted license plate from the image')
+
+
+
+
+# Match contours to license plate or character template
+def find_contours(dimensions, img) :
+
+    # Find all contours in the image
+    cntrs, _ = cv2.findContours(img.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+
+    # Retrieve potential dimensions
+    lower_width = dimensions[0]
+    upper_width = dimensions[1]
+    lower_height = dimensions[2]
+    upper_height = dimensions[3]
+    
+    # Check largest 5 or  15 contours for license plate or character respectively
+    cntrs = sorted(cntrs, key=cv2.contourArea, reverse=True)[:15]
+    
+    ii = cv2.imread('contour.jpg')
+    
+    x_cntr_list = []
+    target_contours = []
+    img_res = []
+    for cntr in cntrs :
+        # detects contour in binary image and returns the coordinates of rectangle enclosing it
+        intX, intY, intWidth, intHeight = cv2.boundingRect(cntr)
+        
+        # checking the dimensions of the contour to filter out the characters by contour's size
+        if intWidth > lower_width and intWidth < upper_width and intHeight > lower_height and intHeight < upper_height :
+            x_cntr_list.append(intX) #stores the x coordinate of the character's contour, to used later for indexing the contours
+
+            char_copy = np.zeros((44,24))
+            # extracting each character using the enclosing rectangle's coordinates.
+            char = img[intY:intY+intHeight, intX:intX+intWidth]
+            char = cv2.resize(char, (20, 40))
+            
+            cv2.rectangle(ii, (intX,intY), (intWidth+intX, intY+intHeight), (50,21,200), 2)
+            plt.imshow(ii, cmap='gray')
+
+            # Make result formatted for classification: invert colors
+            char = cv2.subtract(255, char)
+
+            # Resize the image to 24x44 with black border
+            char_copy[2:42, 2:22] = char
+            char_copy[0:2, :] = 0
+            char_copy[:, 0:2] = 0
+            char_copy[42:44, :] = 0
+            char_copy[:, 22:24] = 0
+
+            img_res.append(char_copy) # List that stores the character's binary image (unsorted)
+            
+    # Return characters on ascending order with respect to the x-coordinate (most-left character first)
+            
+    plt.show()
+    # arbitrary function that stores sorted list of character indeces
+    indices = sorted(range(len(x_cntr_list)), key=lambda k: x_cntr_list[k])
+    img_res_copy = []
+    for idx in indices:
+        img_res_copy.append(img_res[idx])# stores character images according to their index
+    img_res = np.array(img_res_copy)
+
+    return img_res
+
+
+
+
+
+# Find characters in the resulting images
+def segment_characters(image) :
+
+    # Preprocess cropped license plate image
+    img_lp = cv2.resize(image, (333, 75))
+    img_gray_lp = cv2.cvtColor(img_lp, cv2.COLOR_BGR2GRAY)
+    _, img_binary_lp = cv2.threshold(img_gray_lp, 200, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
+    img_binary_lp = cv2.erode(img_binary_lp, (3,3))
+    img_binary_lp = cv2.dilate(img_binary_lp, (3,3))
+
+    LP_WIDTH = img_binary_lp.shape[0]
+    LP_HEIGHT = img_binary_lp.shape[1]
+
+    # Make borders white
+    img_binary_lp[0:3,:] = 255
+    img_binary_lp[:,0:3] = 255
+    img_binary_lp[72:75,:] = 255
+    img_binary_lp[:,330:333] = 255
+
+    # Estimations of character contours sizes of cropped license plates
+    dimensions = [LP_WIDTH/6,
+                       LP_WIDTH/2,
+                       LP_HEIGHT/10,
+                       2*LP_HEIGHT/3]
+    plt.imshow(img_binary_lp, cmap='gray')
+    plt.show()
+    cv2.imwrite('contour.jpg',img_binary_lp)
+
+    # Get contours within cropped license plate
+    char_list = find_contours(dimensions, img_binary_lp)
+
+    return char_list
+
+
+
+# Let's see the segmented characters
+char = segment_characters(plate)
+
+
+
+
+for i in range(10):
+    plt.subplot(1, 10, i+1)
+    plt.imshow(char[i], cmap='gray')
+    plt.axis('off')
+
+
+
+
+
+# Predicting the output
+def fix_dimension(img): 
+  new_img = np.zeros((28,28,3))
+  for i in range(3):
+    new_img[:,:,i] = img
+  return new_img
+  
+def show_results():
+    dic = {}
+    characters = '0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ'
+    for i,c in enumerate(characters):
+        dic[i] = c
+
+    output = []
+    for i,ch in enumerate(char): #iterating over the characters
+        img_ = cv2.resize(ch, (28,28), interpolation=cv2.INTER_AREA)
+        img = fix_dimension(img_)
+        img = img.reshape(1,28,28,3) #preparing image for the model
+        y_ = model.predict_classes(img)[0] #predicting the class
+        character = dic[y_] #
+        output.append(character) #storing the result in a list
+        
+    plate_number = ''.join(output)
+    
+    return plate_number
+
+print(show_results())
+
+
+