ai-school/mv-and-ip/car_plate.py

import cv2 as cv
import numpy as np
import matplotlib.pyplot as plt
import argparse
import typing
import logging
from pathlib import Path
from dataclasses import dataclass

def _uniform_car_plate(img: cv.typing.MatLike) -> cv.typing.MatLike:
    """
    Uniform the image size to 512x512 while maintaining aspect ratio, padding with black.

    :param img: The image in BGR format to be uniformed.
    :return: The uniformed image in BGR format.
    """
    UNI_HW: int = 512
    
    # Calculate the new width and height for given image
    h, w = img.shape[:2]
    scale = min(UNI_HW / w, UNI_HW / h)
    new_w = int(w * scale)
    new_h = int(h * scale)
    
    # Resize the image
    resized_img = cv.resize(img, (new_w, new_h))
    
    # Create a black canvas of size UNI_HW x UNI_HW
    padded_img = np.zeros((UNI_HW, UNI_HW, 3), dtype=np.uint8)
    
    # Calculate position to paste the resized image (centered)
    y_offset = (UNI_HW - new_h) // 2
    x_offset = (UNI_HW - new_w) // 2
    
    # Paste the resized image onto the canvas
    padded_img[y_offset:y_offset+new_h, x_offset:x_offset+new_w] = resized_img
    
    # Return the padded image
    return padded_img


def extract_car_plate(img: cv.typing.MatLike) -> typing.Optional[cv.typing.MatLike]:
    """Extract the car plate part from given image.

    :param img: The image containing car plate in BGR format.
    :return: The image of binary car plate in U8 format if succeed, otherwise None.
    """
    # Reference: https://www.cnblogs.com/linuxAndMcu/p/19144795

    # Resize the image to make following step works about finding proper contours.
    img = _uniform_car_plate(img)
    
    # Convert to grayscale image
    gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)

    # Histogram balance to increase contrast
    hist_gray = cv.equalizeHist(gray)
    
    # Apply Gaussian blur to reduce noise
    blurred = cv.GaussianBlur(hist_gray, (5, 5), 0)
    
    # Edge detection using Canny
    edges = cv.Canny(blurred, 50, 150)

    # cv.imshow('contours', edges)
    # k = cv.waitKey(0)
    # return None
    
    # Morphological operations to connect broken edges
    kernel_close = cv.getStructuringElement(cv.MORPH_RECT, (5, 5))
    closed = cv.morphologyEx(edges, cv.MORPH_CLOSE, kernel_close)
    kernel_open = cv.getStructuringElement(cv.MORPH_RECT, (3, 3))
    opened = cv.morphologyEx(closed, cv.MORPH_OPEN, kernel_open)
    kernel_dilate = cv.getStructuringElement(cv.MORPH_RECT, (3, 3))
    dilated = cv.dilate(edges, kernel_dilate, iterations=2)

    cv.imshow('contours', opened)
    k = cv.waitKey(0)
    return None
    
    # Find contours
    contours, _ = cv.findContours(closed, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
    if not contours:
        logging.error("No contours found")
        return None
    # List all contours
    logging.debug(f'Total {len(contours)} contours.')
    for i, contour in enumerate(contours):
        logging.debug(f'Contour[{i}] has {contour.shape[0]} points.')
    
    cv.drawContours(img, contours, -1, (0, 0, 255), 3)
    cv.imshow('contours', img)
    k = cv.waitKey(0)
    return None

    # Filter contours
    candidates: list[cv.typing.MatLike] = []
    MIN_AREA: float = 2000
    MAX_AREA: float = 100000
    MIN_ASPECT_RATIO: float = 2.5
    MAX_ASPECT_RATIO: float = 6.0

    for i, contour in enumerate(contours):
        # Calculate the area
        area = cv.contourArea(contour)
        if area < MIN_AREA or area > MAX_AREA:
            logging.debug(f'Contour[{i}] failed at area limit. The area of this contour is {area}.')
            continue

        # Get bounding rectangle
        bouding_rect = cv.boundingRect(contour)
        (x, y, w, h) = bouding_rect
        # Calaulate aspect ratio
        aspect_ratio = w / h
        if aspect_ratio < MIN_ASPECT_RATIO or aspect_ratio > MAX_ASPECT_RATIO:
            logging.debug(f'Contour[{i}] failed at aspect ratio limit. The aspect ratio of this contour is {aspect_ratio}.')
            continue

        # Get the convex hull of contour
        hull = cv.convexHull(contour)
        
        # Compute the occupation of contour area in convex hull area
        hull_area = cv.contourArea(hull)
        solidity = area / hull_area

        # Filter more regular contour
        if solidity > 0.6:
            # Extra check for the rectangle fill rate
            fill_ratio = area / (w * h)
            if fill_ratio > 0.3:
                logging.debug(f'Contour[{i}] is perfect.')
                candidates.append(contour)
                continue
            else:
                logging.debug(f'Contour[{i}] failed at rectangle fill ratio limit. The fill ratio of this contour is {fill_ratio}')
        else:
            logging.debug(f'Contour[{i}] failed at solidity limit. The solidity of this contour is {solidity}.')

    if len(candidates) == 0:
        logging.error("No candidate contour")
        return None
    
    cv.drawContours(img, contours, -1, (0, 0, 255), 3)
    cv.imshow('contours', img)
    k = cv.waitKey(0)
    return None
    


    # Step 6: Find the most likely license plate contour
    # License plates are typically rectangular with specific aspect ratios
    max_area = 0
    best_contour = None
    
    for contour in contours:
        area = cv.contourArea(contour)
        if area < 500:  # Filter out small contours
            continue
            
        # Approximate the contour
        peri = cv.arcLength(contour, True)
        approx = cv.approxPolyDP(contour, 0.02 * peri, True)
        
        # Look for quadrilateral shapes (4 corners)
        if len(approx) == 4:
            x, y, w, h = cv.boundingRect(contour)
            aspect_ratio = float(w) / h if h > 0 else 0
            
            # Typical license plate aspect ratio is between 2 and 5
            if 2 <= aspect_ratio <= 5 and area > max_area:
                max_area = area
                best_contour = approx
    
    if best_contour is None:
        # If no perfect quadrilateral found, try with largest rectangular contour
        for contour in contours:
            area = cv.contourArea(contour)
            if area < 500:
                continue
            
            x, y, w, h = cv.boundingRect(contour)
            aspect_ratio = float(w) / h if h > 0 else 0
            
            if 1.5 <= aspect_ratio <= 6 and area > max_area:
                max_area = area
                rect = cv.minAreaRect(contour)
                box = cv.boxPoints(rect)
                best_contour = np.int0(box)
    
    if best_contour is None:
        logging.error("No valid contour found")
        return None
    
    # Step 7: Perspective transformation to get front view
    # Order points: top-left, top-right, bottom-right, bottom-left
    pts = best_contour.reshape(4, 2)
    rect = np.zeros((4, 2), dtype="float32")
    
    # Sum and difference of coordinates to find corners
    s = pts.sum(axis=1)
    rect[0] = pts[np.argmin(s)]  # Top-left has smallest sum
    rect[2] = pts[np.argmax(s)]  # Bottom-right has largest sum
    
    diff = np.diff(pts, axis=1)
    rect[1] = pts[np.argmin(diff)]  # Top-right has smallest difference
    rect[3] = pts[np.argmax(diff)]  # Bottom-left has largest difference
    
    # Calculate width and height of new image
    width_a = np.linalg.norm(rect[0] - rect[1])
    width_b = np.linalg.norm(rect[2] - rect[3])
    max_width = max(int(width_a), int(width_b))
    
    height_a = np.linalg.norm(rect[0] - rect[3])
    height_b = np.linalg.norm(rect[1] - rect[2])
    max_height = max(int(height_a), int(height_b))
    
    # Destination points for perspective transform
    dst_pts = np.array([
        [0, 0],
        [max_width - 1, 0],
        [max_width - 1, max_height - 1],
        [0, max_height - 1]
    ], dtype="float32")
    
    # Get perspective transform matrix and apply it
    M = cv.getPerspectiveTransform(rect, dst_pts)
    warped = cv.warpPerspective(img, M, (max_width, max_height))

    # Step 8: Convert warped image to grayscale
    warped_gray = cv.cvtColor(warped, cv.COLOR_BGR2GRAY)
    
    # Step 9: Apply adaptive thresholding for better binarization
    # First, enhance contrast
    clahe = cv.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
    enhanced = clahe.apply(warped_gray)
    
    # Apply Otsu's thresholding to get binary image
    _, binary = cv.threshold(enhanced, 0, 255, cv.THRESH_BINARY_INV + cv.THRESH_OTSU)
    
    # Step 10: Clean up the binary image with morphological operations
    kernel_clean = cv.getStructuringElement(cv.MORPH_RECT, (2, 2))
    cleaned = cv.morphologyEx(binary, cv.MORPH_CLOSE, kernel_clean)
    
    # Ensure the output is in the correct format (U8)
    result = cleaned.astype(np.uint8)
    
    return result

@dataclass
class Cli:
    input_file: Path
    """The path to input file"""
    output_file: Path
    """The path to output file"""

    @staticmethod
    def from_cmdline() -> "Cli":
        # Build parser
        parser = argparse.ArgumentParser(
            prog="Car Plate Extractor",
            description="Extract the car plate part from given image.",
        )
        parser.add_argument(
            "-i",
            "--in",
            required=True,
            type=str,
            action="store",
            dest="input_file",
            metavar="in.jpg",
            help="""The path to input image containing car plate.""",
        )
        parser.add_argument(
            "-o",
            "--out",
            required=True,
            type=str,
            action="store",
            dest="output_file",
            metavar="out.png",
            help="""The path to output image for extracted car plate.""",
        )

        # Parse argument from cmdline and return
        args = parser.parse_args()
        return Cli(Path(args.input_file), Path(args.output_file))


def main():
    # Setup logging format
    logging.basicConfig(format="[%(levelname)s] %(message)s", level=logging.DEBUG)

    # Get user request
    cli = Cli.from_cmdline()

    # Load file
    in_img = cv.imread(str(cli.input_file), cv.IMREAD_COLOR)
    if in_img is None:
        logging.error(f"Fail to load image {cli.input_file}")
        return
    # Save extracted file if possible
    out_img = extract_car_plate(in_img)
    if out_img is not None:
        cv.imwrite(str(cli.output_file), out_img)


if __name__ == "__main__":
    main()