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SHUMAILA AFROZ SIDDIQUI answered on June 25, 2022 Popularity 9/10 Helpfulness 1/10

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# Import the necessary packages
import argparse
import cv2
import numpy as np
from keras.models import load_model
from tensorflow.keras.utils import img_to_array

import functools
 
# Construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True, help="Path to the image")
ap.add_argument("-m", "--model", required=True, help="Path to the pre-trained model")
args = vars(ap.parse_args())

###############################################
# This takes two stages
# The first stage is to segment characters
# The second stage is to recognise characters
###############################################

###############################################
# The first stage
###############################################

# Read the image and convert to grayscale
image = cv2.imread(args["image"])
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Show the original image
cv2.imshow("License Plate", image)

# Apply Gaussian blurring and thresholding 
# to reveal the characters on the license plate
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
thresh = cv2.adaptiveThreshold(blurred, 255,
	cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 45, 15)

# Perform connected components analysis on the thresholded images and
# initialize the mask to hold only the components we are interested in
_, labels = cv2.connectedComponents(thresh)
mask = np.zeros(thresh.shape, dtype="uint8")

# Set lower bound and upper bound criteria for characters
total_pixels = image.shape[0] * image.shape[1]
lower = total_pixels // 70 # heuristic param, can be fine tuned if necessary
upper = total_pixels // 20 # heuristic param, can be fine tuned if necessary

# Loop over the unique components
for (i, label) in enumerate(np.unique(labels)):
	# If this is the background label, ignore it
	if label == 0:
		continue
 
	# Otherwise, construct the label mask to display only connected component
	# for the current label
	labelMask = np.zeros(thresh.shape, dtype="uint8")
	labelMask[labels == label] = 255
	numPixels = cv2.countNonZero(labelMask)
 
	# If the number of pixels in the component is between lower bound and upper bound, 
	# add it to our mask
	if numPixels > lower and numPixels < upper:
		mask = cv2.add(mask, labelMask)

# Find contours and get bounding box for each contour
cnts, _ = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
boundingBoxes = [cv2.boundingRect(c) for c in cnts]

# Sort the bounding boxes from left to right, top to bottom
# sort by Y first, and then sort by X if Ys are similar
def compare(rect1, rect2):
    if abs(rect1[1] - rect2[1]) > 10:
        return rect1[1] - rect2[1]
    else:
        return rect1[0] - rect2[0]
boundingBoxes = sorted(boundingBoxes, key=functools.cmp_to_key(compare) )


###############################################
# The second stage
###############################################

# Define constants
TARGET_WIDTH = 128
TARGET_HEIGHT = 128

chars = [
    '0','1','2','3','4','5','6','7','8','9','A','B','C','D','E','F','G',
    'H','I','J','K','L','M','N','O','P','Q','R','S','T','U','V','W','X','Y','Z'
    ]

# Load the pre-trained convolutional neural network
model = load_model(args["model"], compile=False)


vehicle_plate = ""
# Loop over the bounding boxes
for rect in boundingBoxes:

    # Get the coordinates from the bounding box
    x,y,w,h = rect

    # Crop the character from the mask
    # and apply bitwise_not because in our training data for pre-trained model
    # the characters are black on a white background
    crop = mask[y:y+h, x:x+w]
    crop = cv2.bitwise_not(crop)

    # Get the number of rows and columns for each cropped image
    # and calculate the padding to match the image input of pre-trained model
    rows = crop.shape[0]
    columns = crop.shape[1]
    paddingY = (TARGET_HEIGHT - rows) // 2 if rows < TARGET_HEIGHT else int(0.17 * rows)
    paddingX = (TARGET_WIDTH - columns) // 2 if columns < TARGET_WIDTH else int(0.45 * columns)
    
    # Apply padding to make the image fit for neural network model
    crop = cv2.copyMakeBorder(crop, paddingY, paddingY, paddingX, paddingX, cv2.BORDER_CONSTANT, None, 255)

    # Convert and resize image
    crop = cv2.cvtColor(crop, cv2.COLOR_GRAY2RGB)     
    crop = cv2.resize(crop, (TARGET_WIDTH, TARGET_HEIGHT))

    # Prepare data for prediction
    crop = crop.astype("float") / 255.0
    crop = img_to_array(crop)
    crop = np.expand_dims(crop, axis=0)

    # Make prediction
    prob = model.predict(crop)[0]
    idx = np.argsort(prob)[-1]
    vehicle_plate += chars[idx]

    # Show bounding box and prediction on image
    cv2.rectangle(image, (x,y), (x+w,y+h), (0, 255, 0), 2)
    cv2.putText(image, chars[idx], (x,y+15), 0, 0.8, (0, 0, 255), 2)

# Show final image
cv2.imshow('Final', image)
print("Vehicle plate: " + vehicle_plate)
cv2.waitKey(0)

Popularity 9/10 Helpfulness 1/10 Language python

Source: Grepper

Tags: python

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Contributed on Jun 25 2022

SHUMAILA AFROZ SIDDIQUI

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