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# Number plate recognition system
## Install system dependencies:
* -- `sudo apt-get install tesseract-ocr-all`
* -- `mkdir db`
Number Plate recognition, also called License Plate realization or recognition using image processing methods is a potential research area in smart cities and the Internet of Things. An exponential increase in the number of vehicles necessitates the use of automated systems to maintain vehicle information for various purposes.
![ANPR](ANPR.jpg)
## Implementation:
In the proposed algorithm an efficient method for recognition of Indian vehicle number plates has been devised. We are able to deal with noisy, low illuminated, cross angled, non-standard font number plates. This work employs several image processing techniques such as, morphological transformation, Gaussian smoothing, Gaussian thresholding and Sobel edge detection method in the pre-processing stage, afterwhich number plate segmentation, contours are applied by border following and contours are filtered based on character dimensions and spatial localization. Finally we apply Optical Character Recognition (OCR) to recognize the extracted characters. The detected texts are stored in the database, further which they are sorted and made available for searching.
This project will work efficiently in recognizing owner's vehicle in small Institutions/Housing societies/Apartments. We can further modify the code to use it in other areas where ANPR is necessary.

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# -*- coding: utf-8 -*-
# -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.#
#* File Name : anpr_yolo_v8.py
#* Purpose :
#* Creation Date : 21-04-2025
#* Last Modified : Thu 01 May 2025 05:48:26 PM UTC
#* Created By : Yaay Nands
#_._._._._._._._._._._._._._._._._._._._._.#
import glob
import logging
import os
import pprint as pp
import signal
import time
import traceback
# Async imports
import asyncio
import aiosqlite
from datetime import datetime
from concurrent.futures import ThreadPoolExecutor
import functools
import aiofiles
from aiohttp import ClientSession, FormData, ConnectionTimeoutError, BasicAuth
from lib import anpr as anprm
from lib.consts import (
DB_RECORDINGS, VIDEO_OUT_FULL, VIDEO_OUT_ANAL, MODEL_NAME, RTSP_URL,
CONTOUR_MOTION_THRESHOLD, MAX_CLIP_TIME, MIN_TIME, CLASS_LABELS, CONFIG_JSON, AUTH_USER, AUTH_PASSWORD,
STATIONARY_THRESHOLD, FAST_LEARNING_RATE, GLOBAL_LEARNING_RATE, DIFF_THRESHOLD, MOTION_AREA_THRESHOLD
)
BASE_PATH = '.'
LOCATION_ID = 1
HAS_CONFIG = False
if os.path.exists(CONFIG_JSON):
with open(CONFIG_JSON, "r") as f:
config = json.load(f)
HAS_CONFIG = True
else:
config = {}
SQL = {
'INSERT_NUMBERS':
'INSERT INTO numberplates (model, filename, ts, ) VALUES (? , ?, ?)',
'UPDATE_NUMBERS':
'UPDATE numberplates set anal_complete=?, numberplates=? \
WHERE model=? AND filename=? AND ts=?',
'SELECT_UNPROCESSED':
'SELECT model, filename, ts FROM numberplates WHERE anal_complete=0;',
}
MODEL_NAME="YOLOv8"
daily_log_ts = time.strftime("%Y-%m-%d-%H:%M")
logging.basicConfig(
filename= f"logs/main-{daily_log_ts}.log",
level=logging.DEBUG,
format='%(asctime)s - %(name)s - %(levelname)s - %(funcName)s:%(lineno)d - %(message)s'
)
logger = logging.getLogger("anpr_async_loop")
async def anpr_thread_loop():
async with aiosqlite.connect(DB_RECORDINGS) as db:
logger.debug(db.__str__())
db.row_factory = aiosqlite.Row
signal_received = asyncio.Event()
loop = asyncio.get_running_loop()
for signame in {'SIGINT', 'SIGTERM'}:
loop.add_signal_handler(getattr(signal, signame), lambda s=signame: signal_received.set())
tasks = [
#asyncio.create_task(motion_capture_and_record(db, signal_received)),
asyncio.create_task(capture_number_plate(db, signal_received)),
]
#if HAS_CONFIG and config['do_anal']:
# tasks.append(asyncio.create_task(do_analysis(db, signal_received)))
try:
await asyncio.gather(*tasks)
except asyncio.CancelledError:
logger.error("Closing loop anpr_monitor_loop")
#break
except Exception as e:
logger.critical(f"Error in anpr_monitor loop:")
err_type = type(e).__name__
line_no = e.__traceback__.tb_lineno
logger.critical(f"traceback:{traceback.format_tb(e.__traceback__)} ")
logger.critical(f"Error {err_type} occurred on line: {line_no}")
finally:
logger.info("finally shutting down")
for task in tasks:
task.cancel()
await db.commit()
await db.close()
async def motion_capture_and_record(db, signal_received):
logger.debug("motion_capture_and_record task running")
loop = asyncio.get_running_loop()
# logger.debug(f"CONTOUR_MOTION_THRESHOLD: {CONTOUR_MOTION_THRESHOLD}")
with ThreadPoolExecutor() as pool:
previous_frame = None
motion_detected = False
output_file = None
start_time = 0
recording = False
recording_process = None
# startrecord_stderr = ''
# startrecord_stdout = ''
loop_in_pool = functools.partial(loop.run_in_executor, pool)
cap = await loop_in_pool(lambda: cv2.VideoCapture(RTSP_URL))
cap.set(cv2.CAP_PROP_BUFFERSIZE, 2)
if not cap.isOpened():
logger.critical("Error: Could not open RTSP stream.")
return
ret, frame = cap.read()
if not ret:
logger.critical("Error: Unable to capture video.")
cap.release()
sys.exit(-1)
# Convert the first frame to grayscale and blur to reduce noise
gray_init = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray_init = cv2.GaussianBlur(gray_init, (21, 21), 0)
# Initialize the background model as a floating point image for blending
background = gray_init.astype("float")
# Create an image to count how many consecutive frames a pixel is stationary
stationary_counter = np.zeros_like(gray_init, dtype=np.uint8)
while not signal_received.is_set():
try:
try:
ret, frame = await loop_in_pool(lambda: cap.read())
except Exception as e:
logger.debug(f"Error capturing frame: {e}")
continue
if not ret or not is_valid_frame(frame):
logger.debug("Failed to capture from IP camera")
break
if not cap.isOpened():
logger.debug("RTSP stream disconnected. Reconnecting")
cap.release()
cap = await loop_in_pool(lambda: cv2.VideoCapture(RTSP_URL))
cap.set(cv2.CAP_PROP_BUFFERSIZE, 2)
# Preprocess: convert to grayscale and apply Gaussian blur
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray_blurred = cv2.GaussianBlur(gray, (21, 21), 0)
# Compute absolute difference between the current background and the new frame
diff = cv2.absdiff(cv2.convertScaleAbs(background), gray_blurred)
_, motion_mask = cv2.threshold(diff, DIFF_THRESHOLD, 255, cv2.THRESH_BINARY)
# Invert motion mask to get stationary areas (low difference)
stationary_mask = cv2.bitwise_not(motion_mask)
# Update the stationary counter: add 1 where stationary, reset where motion is detected
stationary_counter = cv2.add(stationary_counter, (stationary_mask // 255))
stationary_counter[motion_mask == 255] = 0
# Determine where pixels have been stationary long enough
update_mask = (stationary_counter >= STATIONARY_THRESHOLD).astype(np.float32)
# Create an effective learning rate for each pixel:
# Pixels meeting the stationary criteria use fast_learning_rate,
# otherwise, they use the global (slow) learning rate.
effective_learning_rate = GLOBAL_LEARNING_RATE + update_mask * (FAST_LEARNING_RATE - GLOBAL_LEARNING_RATE)
# Update the background model for all pixels using the effective learning rate
background = (1 - effective_learning_rate) * background + effective_learning_rate * gray_blurred
motion_detected = False
# Motion detection: if enough pixels are marked as moving, declare motion
motion_pixels = cv2.countNonZero(motion_mask)
if motion_pixels > MOTION_AREA_THRESHOLD:
motion_detected = True
delta = time.time() - start_time
if motion_detected and not recording:
start_time = time.time()
logger.debug(f"Motion detected! Starting recording... {start_time}")
timestamp = time.strftime("%Y%m%d-%H%M%S")
output_file = f"motion_{timestamp}.mp4"
recording_process = await ffmpeg_start_recording(os.path.join(VIDEO_OUT_FULL, output_file))
recording = True
# If no motion is detected and we're recording, stop recording
elif ((not motion_detected and recording and delta > MIN_TIME) or
(motion_detected and recording and delta > MAX_CLIP_TIME)):
logger.debug("No motion detected or MAX_CLIP_TIME exceeded. Stopping recording...")
if recording_process:
await ffmpeg_stop_recording(recording_process)
await asyncio.sleep(0.1)
# logger.debug(f"startrecord_stderr: {startrecord_stderr}")
# logger.debug(f"startrecord_stdout: {startrecord_stdout}")
# if startrecord_stderr:
# with open(f"logs/ffmpeg-{output_file}-err.log") as fe:
# fe.write(str(startrecord_stderr))
# if startrecord_stdout:
# with open(f"logs/ffmpeg-{output_file}-out.log") as fo:
# fo.write(str(startrecord_stdout))
cursor = await db.execute(SQL["INSERT_NUMBERS"], (MODEL_NAME, output_file, timestamp))
await cursor.fetchone()
logger.debug(f"inserted recording with id: {cursor.lastrowid}")
recording_process = None
recording = False
except asyncio.CancelledError:
logger.debug("Closing loop motion_capture_and_record")
await db.commit()
break
except Exception as e:
logger.critical(f"Error in produce_anal_file:")
err_type = type(e).__name__
line_no = e.__traceback__.tb_lineno
logger.critical(f"Error {err_type} occurred on line: {line_no}")
async def capture_number_plate(db, signal_received):
#loop = asyncio.get_running_loop()
logger.debug("capture_number_plate task running")
with ThreadPoolExecutor() as pool:
while not signal_received.is_set():
try:
cursor = await db.execute(SQL["SELECT_UNPROCESSED"])
res = await cursor.fetchall()
logger.debug("found unprocessed records: %i ", len(res))
for record in res:
#logger.debug("processing record: %s"%json.dumps(record))
plates = anprm.infer(filename)
cursor = await db.execute(SQL["UPDATE_NUMBERS"],
(True, 'NOMARS', record['model'],
record['filename'],
record['ts']))
res = await cursor.fetchone()
except asyncio.CancelledError:
logger.debug("Closing loop capture_number_plate")
#break
except Exception as e:
logger.critical(f"traceback:{traceback.format_tb(e.__traceback__)} ")
logger.critical(f"Error in capture_number_plate logic:")
err_type = type(e).__name__
line_no = e.__traceback__.tb_lineno
logger.critical(f"Error {err_type} occurred on line: {line_no}")
if __name__ == '__main__':
try:
asyncio.run(anpr_thread_loop(), debug=True)
except RuntimeError as e:
logger.debug(f"Error in asyncio.run(main()) loop: {str(e)}")
print("Error in asyncio.run(main()) loop: ", str(e))

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import glob
import os
import pytesseract
import re
import torch
import ultralytics
from ultralytics import YOLO
from PIL import Image
ultralytics.checks()
from xml.etree import ElementTree as ET
if torch.cuda.is_available():
device = torch.device('cuda')
else:
device = torch.device('cpu')
BASE_PATH = '..'
def convert_bbox_to_yolo(
size: tuple[float, float], box: tuple[float, float, float, float]
) -> tuple[float, float, float, float]:
"""Convert bounding box from absolute coordinates to relative coordinates.
:param size: Tuple of (width, height) of the image.
:param box: Tuple of (xmin, ymin, xmax, ymax) for the bounding box.
:return: Tuple of (x_center, y_center, width, height) in relative
coordinates.
"""
scale_width = 1.0 / size[0]
scale_height = 1.0 / size[1]
center_x = (box[0] + box[2]) / 2.0
center_y = (box[1] + box[3]) / 2.0
box_width = box[2] - box[0]
box_height = box[3] - box[1]
rel_center_x = center_x * scale_width
rel_center_y = center_y * scale_height
rel_width = box_width * scale_width
rel_height = box_height * scale_height
return (rel_center_x, rel_center_y, rel_width, rel_height)
def xml_to_txt(input_xml: str, output_txt: str, class_mapping: dict[str, int]):
"""Parse an XML file and write to a .txt file in YOLO format.
:param input_xml: Path to the input XML file.
:param output_txt: Path to the output .txt file.
:param class_mapping: Dictionary mapping class names to class.
"""
tree = ET.parse(input_xml)
root = tree.getroot()
width = int(root.find(".//size/width").text)
height = int(root.find(".//size/height").text)
with open(output_txt, "w", encoding="utf-8") as txt_file:
for obj in root.iter("object"):
cell_name = obj.find("name").text
cell_id = class_mapping.get(cell_name, -1)
if cell_id == -1:
continue
xmlbox = obj.find("bndbox")
box = (
float(xmlbox.find("xmin").text),
float(xmlbox.find("ymin").text),
float(xmlbox.find("xmax").text),
float(xmlbox.find("ymax").text),
)
bbox = convert_bbox_to_yolo((width, height), box)
txt_file.write(f"{cell_id} {' '.join([str(a) for a in bbox])}\n")
def download_kaggle_data():
import kagglehub
if not os.path.exists(f"{BASE_PATH}/data/indian-number-plates-dataset"):
# Download latest version
path = kagglehub.dataset_download("dataclusterlabs/indian-number-plates-dataset")
print("Path to dataset files:", path)
def convert_to_yolo_format(data_path):
import glob
files = glob.glob(data_path + '/*.xml')
print(len(files))
for xml_fil in files:
if not os.path.isdir(xml_fil):
txt_fil = xml_fil.split('.')[:-1]
txt_fil = '.'.join(txt_fil) + '.txt'
xml_to_txt(xml_fil, txt_fil, class_mapping = {'number_plate': '0'})
files = glob.glob(data_path + '/*.txt')
print(len(files))
def train_model():
from ultralytics.data.dataset import YOLODataset
dataset = YOLODataset(img_path=f"{BASE_PATH}/data/train/images", data={"names": {0: "person"}}, task="detect")
dataset.get_labels()
model = YOLO(f"{BASE_PATH}/models/yolov8n.pt")
results = model.train(data=f'{BASE_PATH}/data/data.yaml', epochs=50, imgsz=1728)
model.export(format='onnx', dynamic=True,
#path = "../models/yolov8n_anpr.onnx",
simplify=True, device=device)
def infer(filename):
model2 = YOLO(f"{BASE_PATH}/models/train25/best.pt")
test_result = model2.predict(source=filename)
#onnx_model = YOLO("../models/train25/best.onnx")
#model_2 = YOLO('/kaggle/input/weights/best(2).pt')
#testfiles = glob.glob('../data/TEST/*')
#import pdb; pdb.set_trace()
#test_result = model2.predict(source=testfiles[4])
number_plates = dict()
for i, res in enumerate(test_result):
res_img = res.plot()
plate_im = Image.fromarray(res_img)
np_text = pytesseract.image_to_string(plate_im, lang='eng')
plate = str("".join(re.split("[^a-zA-Z0-9]*", np_text)))
number_plates[i] = plate.upper()
return number_plates
#download_kaggle_data()
#data_path = '../data/train/labels'
#convert_to_yolo_format(data_path)
#data_path = '../data/TEST/labels'
#convert_to_yolo_format(data_path)
#train_model()

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DB_RECORDINGS = "db/numberplates.sqlite3"
VIDEO_OUT_FULL = "videos/full/"
VIDEO_OUT_ANAL = "videos/analysis"
MODEL_NAME = "models/train25/best.pt"
RTSP_URL = "rtsp://admin:C4sinfra12@192.168.1.10:554/rtsp"
SCREEN_RESOLUTION = (1280, 720) # Resolution of the RTSP stream
BITRATE = 1000000 # Bitrate for video recording
CONTOUR_MOTION_THRESHOLD = 800 # Sensitivity for motion detection
MAX_CLIP_TIME = 20 # Maximum recording time in seconds
MIN_TIME = 10 # Minimum recording time in seconds
CLASS_LABELS = ["dumping", "not"]
CONFIG_JSON = "config.json"
AUTH_USER = 'c4suser'
AUTH_PASSWORD = 'c4sinfra!'
STATIONARY_THRESHOLD = 50 # Number of frames a pixel must remain stationary to be updated faster
FAST_LEARNING_RATE = 0.1 # Learning rate for pixels that have been stationary
GLOBAL_LEARNING_RATE = 0.01 # Slow, continuous update for all pixels
DIFF_THRESHOLD = 25 # Pixel intensity difference threshold for motion detection
MOTION_AREA_THRESHOLD = 5000 # Minimum number of changed pixels to declare motion

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import asyncio
import signal
import os
from .consts import RTSP_URL, VIDEO_OUT_FULL, VIDEO_OUT_ANAL
async def ffmpeg_start_recording(file):
ffmpeg_args = [
"-rtsp_transport", "tcp",
"-timeout", "5000000",
"-i", RTSP_URL,
"-c:v", "libx264",
"-pix_fmt", "yuv420p",
"-preset", "fast",
"-b:v", "1M",
"-f", "mp4",
"-an", file
]
process = await asyncio.create_subprocess_exec('ffmpeg', *ffmpeg_args,
stdout=asyncio.subprocess.DEVNULL,
stderr=asyncio.subprocess.DEVNULL
)
return process
async def ffmpeg_stop_recording(process):
# logger.debug("Inside ffmpeg_stop_recording")
if process:
# logger.debug(f"Sending SIGQUIT to recording_process: {process}")
process.send_signal(signal.SIGQUIT)
try:
await asyncio.wait_for(process.wait(), timeout=2)
except Exception as e:
# logger.debug(f"Got an exception while waiting for the process to finish: {e}")
process.kill()
await process.wait()
async def ffmpeg_create_anal_file(file, anal_file):
print("Inside ffmpeg_create_anal_file")
print(f"VIDEO_OUT_FULL: {VIDEO_OUT_FULL}, VIDEO_OUT_ANAL: {VIDEO_OUT_ANAL}, file:{file}, anal_file: {anal_file} ")
ffmpeg_args = [
"-y", "-i", os.path.join(VIDEO_OUT_FULL, file),
"-vf", "scale=456:256,setsar=1",
"-c:v", "libx264",
"-pix_fmt", "yuv420p",
"-an", "-movflags", "faststart",
os.path.join(VIDEO_OUT_ANAL, anal_file)
]
# logger.debug(f"writing to: {os.path.join(VIDEO_OUT_ANAL, anal_file)}")
# logger.debug(f"calling ffmpeg with {' '.join(ffmpeg_args)}")
process = await asyncio.create_subprocess_exec('ffmpeg', *ffmpeg_args,
stdout=asyncio.subprocess.PIPE,
stderr=asyncio.subprocess.PIPE
)
# monitor_task = asyncio.create_task(monitor_process(process))
# monitor_task.add_done_callback(
# lambda task: asyncio.create_task(cb_completed(row_id, anal_file))
# )
stdout, stderr = await process.communicate()
return stdout, stderr
# logger.debug(f"Process: {process} ended with return code: {process.returncode}")
# if stderr:
# logger.debug("stderr:")
# logger.debug(stderr.decode())
# if stdout:
# logger.debug("stdout:")
# logger.debug(stdout.decode())

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import sys
import glob
import os
import glob
import numpy as np
import cv2
from PIL import Image
import pytesseract
import re
def clean2_plate(plate):
gray_img = cv2.cvtColor(plate, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(gray_img, 110, 255, cv2.THRESH_BINARY)
#if cv2.waitKey(0) & 0xff == ord('q'):
# pass
num_contours,hierarchy = cv2.findContours(thresh.copy(),cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
if num_contours:
contour_area = [cv2.contourArea(c) for c in num_contours]
max_cntr_index = np.argmax(contour_area)
max_cnt = num_contours[max_cntr_index]
max_cntArea = contour_area[max_cntr_index]
x,y,w,h = cv2.boundingRect(max_cnt)
if not ratioCheck(max_cntArea,w,h):
return plate,None
final_img = thresh[y:y+h, x:x+w]
return final_img,[x,y,w,h]
else:
return plate,None
def ratioCheck(area, width, height):
ratio = float(width) / float(height)
if ratio < 1:
ratio = 1 / ratio
if (area < 1063.62 or area > 73862.5) or (ratio < 3 or ratio > 6):
return False
return True
def isMaxWhite(plate):
avg = np.mean(plate)
if(avg>=115):
return True
else:
return False
def ratio_and_rotation(rect):
(x, y), (width, height), rect_angle = rect
if(width>height):
angle = -rect_angle
else:
angle = 90 + rect_angle
if angle>15:
return False
if height == 0 or width == 0:
return False
area = height*width
if not ratioCheck(area,width,height):
return False
else:
return True
#Detecting numberplate
def number_plate_detection(img):
img2 = cv2.GaussianBlur(img, (5,5), 0)
img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
img2 = cv2.Sobel(img2,cv2.CV_8U,1,0,ksize=3)
_,img2 = cv2.threshold(img2,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
element = cv2.getStructuringElement(shape=cv2.MORPH_RECT, ksize=(17, 3))
morph_img_threshold = img2.copy()
cv2.morphologyEx(src=img2, op=cv2.MORPH_CLOSE, kernel=element, dst=morph_img_threshold)
num_contours, hierarchy= cv2.findContours(morph_img_threshold,mode=cv2.RETR_EXTERNAL,method=cv2.CHAIN_APPROX_NONE)
cv2.drawContours(img2, num_contours, -1, (0,255,0), 1)
for i,cnt in enumerate(num_contours):
min_rect = cv2.minAreaRect(cnt)
if ratio_and_rotation(min_rect):
x,y,w,h = cv2.boundingRect(cnt)
plate_img = img[y:y+h,x:x+w]
if(isMaxWhite(plate_img)):
clean_plate, rect = clean2_plate(plate_img)
if rect:
fg=0
x1,y1,w1,h1 = rect
x,y,w,h = x+x1,y+y1,w1,h1
plate_im = Image.fromarray(clean_plate)
text = pytesseract.image_to_string(plate_im, lang='eng')
return text
print("HELLO!!")
print("Welcome to the Number Plate Detection System.\n")
array=[]
dir = os.path.dirname(__file__)
for img in glob.glob(dir+"/Images/*.jpeg") :
img=cv2.imread(img)
img2 = cv2.resize(img, (600, 600))
cv2.imshow("Image of car ",img2)
cv2.waitKey(1000)
cv2.destroyAllWindows()
number_plate=number_plate_detection(img)
res2 = str("".join(re.split("[^a-zA-Z0-9]*", number_plate)))
res2=res2.upper()
print(res2)
array.append(res2)
import pprint
pprint.pprint(array)
img = cv2.imread("./Search_Image/Car.jpeg")
number_plate=number_plate_detection(img)
import pdb; pdb.set_trace()
res2 = str("".join(re.split("[^a-zA-Z0-9]*", number_plate)))
print("The car number is:- ",res2)

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aiohttp
aiofiles
pytesseract
ultralytics

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SELECT count(1) FROM recordings;

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BEGIN;
CREATE TABLE numberplates(
model TEXT,
filename TEXT,
ts DATETIME DEFAULT (datetime('now', 'localtime')),
anal_complete BOOLEAN NOT NULL DEFAULT FALSE,
numberplates TEXT,
);
CREATE INDEX filename_idx ON numberplates (filename);
CREATE INDEX ts_idx ON numberplates (ts);
CREATE INDEX anal_complete_idx ON numberplates (anal_complete);
COMMIT;

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sql/dropall.sql Normal file
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DELETE FROM numberplates;

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sql/selectall.sql Normal file
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SELECT rowid, * FROM numberplates;