Update classifier.py

classifier.py

@@ -9,10 +9,17 @@ from tensorflow.keras.preprocessing.image import ImageDataGenerator
 from tensorflow.keras.layers import Dense, Flatten, MaxPooling2D, Dropout, Conv2D
 from tensorflow.keras.models import load_model
 
-# Loads the data required for detecting the license plates from cascade classifier.
-plate_cascade = cv2.CascadeClassifier('indian_license_plate.xml')
 
-def detect_plate(img, text=''): # the function detects and perfors blurring on the number plate.
+
+def my_function():
+
+
+
+  # Loads the data required for detecting the license plates from cascade classifier.
+  plate_cascade = cv2.CascadeClassifier('indian_license_plate.xml')
+
+
+  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.
@@ -26,8 +33,10 @@ def detect_plate(img, text=''): # the function detects and perfors blurring on t
 
     return plate_img, plate # returning the processed image.
 
-# Testing the above function
-def display(img_, title=''):
+
+
+  # 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)
@@ -36,11 +45,131 @@ def display(img_, title=''):
     plt.title(title)
     plt.show()
 
-display(img, 'input image')
+  img = cv2.imread('car3.jpg')
+
+
+  # Getting plate prom the processed image
+  output_img, plate = detect_plate(img)
+
+
+  # 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
 
-def show_results():
+
+
+  # Let's see the segmented characters
+  char = segment_characters(plate)
+
+
+
+
+  # Using the Model that we Trained on Kaggle Dataset earlier
+  model = load_model('licence_trained_model.h5', compile=False)
+
+
+
+  # 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):
@@ -60,7 +189,16 @@ def show_results():
 
     return plate_number
 
-print(show_results())
+  print(show_results())
+
+my_function()
+
+
+
+
+
+
+
 
 
 
@@ -78,17 +216,13 @@ def run():
 
         # Check if any file is uploaded
         if uploaded_files:
-            for img in uploaded_files:
-                st.write("filename:", img.name)
+            for uploaded_file in uploaded_files:
+                st.write("filename:", uploaded_file.name)
         # Close the form
         submitted = st.form_submit_button('Recognize')
     
     if submitted:
-
-        
-
-
-
+        my_function()
         
         
 if __name__ == '__main__':