309 lines
10 KiB
Python
309 lines
10 KiB
Python
import cv2 as cv
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import numpy as np
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import matplotlib.pyplot as plt
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import argparse
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import typing
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import logging
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from pathlib import Path
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from dataclasses import dataclass
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def _uniform_car_plate(img: cv.typing.MatLike) -> cv.typing.MatLike:
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"""
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Uniform the image size to 512x512 while maintaining aspect ratio, padding with black.
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:param img: The image in BGR format to be uniformed.
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:return: The uniformed image in BGR format.
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"""
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UNI_HW: int = 512
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# Calculate the new width and height for given image
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h, w = img.shape[:2]
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scale = min(UNI_HW / w, UNI_HW / h)
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new_w = int(w * scale)
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new_h = int(h * scale)
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# Resize the image
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resized_img = cv.resize(img, (new_w, new_h))
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# Create a black canvas of size UNI_HW x UNI_HW
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padded_img = np.zeros((UNI_HW, UNI_HW, 3), dtype=np.uint8)
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# Calculate position to paste the resized image (centered)
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y_offset = (UNI_HW - new_h) // 2
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x_offset = (UNI_HW - new_w) // 2
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# Paste the resized image onto the canvas
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padded_img[y_offset:y_offset+new_h, x_offset:x_offset+new_w] = resized_img
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# Return the padded image
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return padded_img
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def extract_car_plate(img: cv.typing.MatLike) -> typing.Optional[cv.typing.MatLike]:
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"""Extract the car plate part from given image.
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:param img: The image containing car plate in BGR format.
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:return: The image of binary car plate in U8 format if succeed, otherwise None.
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"""
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# Reference: https://www.cnblogs.com/linuxAndMcu/p/19144795
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# Resize the image to make following step works about finding proper contours.
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img = _uniform_car_plate(img)
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# Convert to grayscale image
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gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
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# Histogram balance to increase contrast
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hist_gray = cv.equalizeHist(gray)
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# Apply Gaussian blur to reduce noise
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blurred = cv.GaussianBlur(hist_gray, (5, 5), 0)
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# Edge detection using Canny
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edges = cv.Canny(blurred, 50, 150)
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# cv.imshow('contours', edges)
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# k = cv.waitKey(0)
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# return None
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# Morphological operations to connect broken edges
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kernel_close = cv.getStructuringElement(cv.MORPH_RECT, (5, 5))
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closed = cv.morphologyEx(edges, cv.MORPH_CLOSE, kernel_close)
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kernel_open = cv.getStructuringElement(cv.MORPH_RECT, (3, 3))
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opened = cv.morphologyEx(closed, cv.MORPH_OPEN, kernel_open)
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kernel_dilate = cv.getStructuringElement(cv.MORPH_RECT, (3, 3))
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dilated = cv.dilate(edges, kernel_dilate, iterations=2)
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cv.imshow('contours', opened)
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k = cv.waitKey(0)
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return None
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# Find contours
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contours, _ = cv.findContours(closed, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
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if not contours:
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logging.error("No contours found")
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return None
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# List all contours
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logging.debug(f'Total {len(contours)} contours.')
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for i, contour in enumerate(contours):
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logging.debug(f'Contour[{i}] has {contour.shape[0]} points.')
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cv.drawContours(img, contours, -1, (0, 0, 255), 3)
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cv.imshow('contours', img)
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k = cv.waitKey(0)
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return None
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# Filter contours
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candidates: list[cv.typing.MatLike] = []
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MIN_AREA: float = 2000
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MAX_AREA: float = 100000
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MIN_ASPECT_RATIO: float = 2.5
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MAX_ASPECT_RATIO: float = 6.0
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for i, contour in enumerate(contours):
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# Calculate the area
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area = cv.contourArea(contour)
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if area < MIN_AREA or area > MAX_AREA:
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logging.debug(f'Contour[{i}] failed at area limit. The area of this contour is {area}.')
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continue
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# Get bounding rectangle
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bouding_rect = cv.boundingRect(contour)
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(x, y, w, h) = bouding_rect
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# Calaulate aspect ratio
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aspect_ratio = w / h
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if aspect_ratio < MIN_ASPECT_RATIO or aspect_ratio > MAX_ASPECT_RATIO:
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logging.debug(f'Contour[{i}] failed at aspect ratio limit. The aspect ratio of this contour is {aspect_ratio}.')
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continue
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# Get the convex hull of contour
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hull = cv.convexHull(contour)
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# Compute the occupation of contour area in convex hull area
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hull_area = cv.contourArea(hull)
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solidity = area / hull_area
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# Filter more regular contour
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if solidity > 0.6:
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# Extra check for the rectangle fill rate
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fill_ratio = area / (w * h)
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if fill_ratio > 0.3:
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logging.debug(f'Contour[{i}] is perfect.')
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candidates.append(contour)
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continue
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else:
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logging.debug(f'Contour[{i}] failed at rectangle fill ratio limit. The fill ratio of this contour is {fill_ratio}')
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else:
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logging.debug(f'Contour[{i}] failed at solidity limit. The solidity of this contour is {solidity}.')
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if len(candidates) == 0:
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logging.error("No candidate contour")
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return None
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cv.drawContours(img, contours, -1, (0, 0, 255), 3)
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cv.imshow('contours', img)
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k = cv.waitKey(0)
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return None
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# Step 6: Find the most likely license plate contour
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# License plates are typically rectangular with specific aspect ratios
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max_area = 0
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best_contour = None
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for contour in contours:
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area = cv.contourArea(contour)
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if area < 500: # Filter out small contours
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continue
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# Approximate the contour
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peri = cv.arcLength(contour, True)
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approx = cv.approxPolyDP(contour, 0.02 * peri, True)
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# Look for quadrilateral shapes (4 corners)
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if len(approx) == 4:
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x, y, w, h = cv.boundingRect(contour)
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aspect_ratio = float(w) / h if h > 0 else 0
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# Typical license plate aspect ratio is between 2 and 5
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if 2 <= aspect_ratio <= 5 and area > max_area:
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max_area = area
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best_contour = approx
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if best_contour is None:
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# If no perfect quadrilateral found, try with largest rectangular contour
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for contour in contours:
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area = cv.contourArea(contour)
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if area < 500:
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continue
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x, y, w, h = cv.boundingRect(contour)
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aspect_ratio = float(w) / h if h > 0 else 0
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if 1.5 <= aspect_ratio <= 6 and area > max_area:
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max_area = area
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rect = cv.minAreaRect(contour)
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box = cv.boxPoints(rect)
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best_contour = np.int0(box)
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if best_contour is None:
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logging.error("No valid contour found")
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return None
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# Step 7: Perspective transformation to get front view
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# Order points: top-left, top-right, bottom-right, bottom-left
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pts = best_contour.reshape(4, 2)
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rect = np.zeros((4, 2), dtype="float32")
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# Sum and difference of coordinates to find corners
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s = pts.sum(axis=1)
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rect[0] = pts[np.argmin(s)] # Top-left has smallest sum
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rect[2] = pts[np.argmax(s)] # Bottom-right has largest sum
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diff = np.diff(pts, axis=1)
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rect[1] = pts[np.argmin(diff)] # Top-right has smallest difference
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rect[3] = pts[np.argmax(diff)] # Bottom-left has largest difference
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# Calculate width and height of new image
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width_a = np.linalg.norm(rect[0] - rect[1])
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width_b = np.linalg.norm(rect[2] - rect[3])
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max_width = max(int(width_a), int(width_b))
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height_a = np.linalg.norm(rect[0] - rect[3])
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height_b = np.linalg.norm(rect[1] - rect[2])
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max_height = max(int(height_a), int(height_b))
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# Destination points for perspective transform
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dst_pts = np.array([
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[0, 0],
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[max_width - 1, 0],
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[max_width - 1, max_height - 1],
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[0, max_height - 1]
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], dtype="float32")
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# Get perspective transform matrix and apply it
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M = cv.getPerspectiveTransform(rect, dst_pts)
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warped = cv.warpPerspective(img, M, (max_width, max_height))
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# Step 8: Convert warped image to grayscale
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warped_gray = cv.cvtColor(warped, cv.COLOR_BGR2GRAY)
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# Step 9: Apply adaptive thresholding for better binarization
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# First, enhance contrast
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clahe = cv.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
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enhanced = clahe.apply(warped_gray)
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# Apply Otsu's thresholding to get binary image
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_, binary = cv.threshold(enhanced, 0, 255, cv.THRESH_BINARY_INV + cv.THRESH_OTSU)
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# Step 10: Clean up the binary image with morphological operations
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kernel_clean = cv.getStructuringElement(cv.MORPH_RECT, (2, 2))
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cleaned = cv.morphologyEx(binary, cv.MORPH_CLOSE, kernel_clean)
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# Ensure the output is in the correct format (U8)
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result = cleaned.astype(np.uint8)
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return result
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@dataclass
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class Cli:
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input_file: Path
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"""The path to input file"""
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output_file: Path
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"""The path to output file"""
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@staticmethod
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def from_cmdline() -> "Cli":
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# Build parser
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parser = argparse.ArgumentParser(
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prog="Car Plate Extractor",
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description="Extract the car plate part from given image.",
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)
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parser.add_argument(
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"-i",
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"--in",
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required=True,
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type=str,
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action="store",
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dest="input_file",
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metavar="in.jpg",
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help="""The path to input image containing car plate.""",
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)
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parser.add_argument(
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"-o",
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"--out",
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required=True,
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type=str,
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action="store",
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dest="output_file",
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metavar="out.png",
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help="""The path to output image for extracted car plate.""",
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)
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# Parse argument from cmdline and return
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args = parser.parse_args()
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return Cli(Path(args.input_file), Path(args.output_file))
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def main():
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# Setup logging format
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logging.basicConfig(format="[%(levelname)s] %(message)s", level=logging.DEBUG)
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# Get user request
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cli = Cli.from_cmdline()
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# Load file
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in_img = cv.imread(str(cli.input_file), cv.IMREAD_COLOR)
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if in_img is None:
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logging.error(f"Fail to load image {cli.input_file}")
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return
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# Save extracted file if possible
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out_img = extract_car_plate(in_img)
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if out_img is not None:
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cv.imwrite(str(cli.output_file), out_img)
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if __name__ == "__main__":
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main()
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