License Plate Recognition
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Readme
License Plate Detection Tutorial
 Run on Google Colab
|
 View source on GitHub
|
 Download notebook
|
Install Application Dependecies
!wget -nc https://raw.githubusercontent.com/georgia-tech-db/license-plate-recognition/main/requirements.txt
!pip -q --no-color install -r requirements.txt
File requirements.txt already there; not retrieving.
[1m[[0mnotice[1m][0m A new release of pip available: 22.2.2 -> 22.3.1
[1m[[0mnotice[1m][0m To update, run: pip install --upgrade pip
Start EVA server
We are reusing the start server notebook for launching the EVA server.
!wget -nc "https://raw.githubusercontent.com/georgia-tech-db/eva/master/tutorials/00-start-eva-server.ipynb"
%run 00-start-eva-server.ipynb
cursor = connect_to_server()
File 00-start-eva-server.ipynb already there; not retrieving.
nohup eva_server > eva.log 2>&1 &
[1m[[0m[34;49mnotice[0m[1;39;49m][0m[39;49m A new release of pip available: [0m[31;49m22.2.2[0m[39;49m -> [0m[32;49m22.3.1[0m
[1m[[0m[34;49mnotice[0m[1;39;49m][0m[39;49m To update, run: [0m[32;49mpython -m pip install --upgrade pip[0m
Note: you may need to restart the kernel to use updated packages.
Register License Plate Extraction UDF
!wget -nc "https://raw.githubusercontent.com/georgia-tech-db/license-plate-recognition/main/ocr_extractor.py"
cursor.execute("DROP UDF LicensePlateExtractor;")
response = cursor.fetch_all()
print(response)
cursor.execute("""CREATE UDF IF NOT EXISTS LicensePlateExtractor
INPUT (frame NDARRAY UINT8(3, ANYDIM, ANYDIM))
OUTPUT (labels NDARRAY STR(ANYDIM), bboxes NDARRAY FLOAT32(ANYDIM, 4),
scores NDARRAY FLOAT32(ANYDIM))
TYPE Classification
IMPL 'license_plate_extractor.py';
""")
response = cursor.fetch_all()
print(response)
File ocr_extractor.py already there; not retrieving.
@status: ResponseStatus.SUCCESS
@batch:
0
0 UDF LicensePlateExtractor successfully dropped
@query_time: 0.021678909892216325
@status: ResponseStatus.SUCCESS
@batch:
0
0 UDF LicensePlateExtractor successfully added to the database.
@query_time: 5.78774525411427
Download Images or Videos for License Plate Recognition
!wget -nc "https://raw.githubusercontent.com/georgia-tech-db/license-plate-recognition/main/test_image_1.png"
!wget -nc "https://raw.githubusercontent.com/georgia-tech-db/license-plate-recognition/main/test_image_2.png"
!wget -nc "https://raw.githubusercontent.com/femioladeji/License-Plate-Recognition-Nigerian-vehicles/master/test_images/car10.jpg"
!wget -nc "https://raw.githubusercontent.com/femioladeji/License-Plate-Recognition-Nigerian-vehicles/master/test_images/car6.jpg"
!wget -nc "https://im.ezgif.com/tmp/ezgif-1-c32008dd2a-jpg/ezgif-frame-001.jpg"
!wget -nc "https://i.ytimg.com/vi/p91epBEfISk/maxresdefault.jpg"
# DOWNLOAD ADDITIONAL IMAGES IF NEEDED AND LOAD THEM HERE
########################################################
###
### IMAGES
###
########################################################
cursor.execute('DROP TABLE IF EXISTS MyImages')
response = cursor.fetch_all()
print(response)
cursor.execute('LOAD IMAGE "test_image_1.png" INTO MyImages;')
response = cursor.fetch_all()
print(response)
cursor.execute('LOAD IMAGE "test_image_2.png" INTO MyImages;')
response = cursor.fetch_all()
print(response)
cursor.execute('LOAD IMAGE "car10.jpg" INTO MyImages;')
response = cursor.fetch_all()
print(response)
cursor.execute('LOAD IMAGE "car6.jpg" INTO MyImages;')
response = cursor.fetch_all()
print(response)
cursor.execute('LOAD IMAGE "ezgif-frame-001.jpg" INTO MyImages;')
response = cursor.fetch_all()
print(response)
cursor.execute('LOAD IMAGE "maxresdefault.jpg" INTO MyImages;')
response = cursor.fetch_all()
print(response)
cursor.execute("""SELECT LicensePlateExtractor(data)
FROM MyImages""")
response = cursor.fetch_all()
print(response)
########################################################
###
### VIDEOS
###
########################################################
!wget -nc "https://raw.githubusercontent.com/apoorva-dave/LicensePlateDetector/master/video12.mp4"
# DOWNLOAD ADDITIONAL VIDEOS IF NEEDED AND LOAD THEM HERE
cursor.execute('DROP TABLE IF EXISTS MyVideos')
response2 = cursor.fetch_all()
print(response2)
cursor.execute('LOAD VIDEO "video12.mp4" INTO MyVideos;')
response2 = cursor.fetch_all()
print(response2)
cursor.execute("""SELECT LicensePlateExtractor(data)
FROM MyVideos
WHERE id = 10""")
response2 = cursor.fetch_all()
print(response2)
File test_image_1.png already there; not retrieving.
File test_image_2.png already there; not retrieving.
File car10.jpg already there; not retrieving.
File car6.jpg already there; not retrieving.
File ezgif-frame-001.jpg already there; not retrieving.
File maxresdefault.jpg already there; not retrieving.
@status: ResponseStatus.SUCCESS
@batch:
0
0 Table Successfully dropped: MyImages
@query_time: 0.03773099696263671
@status: ResponseStatus.SUCCESS
@batch:
0
0 Number of loaded IMAGE: 1
@query_time: 0.05547582497820258
@status: ResponseStatus.SUCCESS
@batch:
0
0 Number of loaded IMAGE: 1
@query_time: 0.014967684866860509
@status: ResponseStatus.SUCCESS
@batch:
0
0 Number of loaded IMAGE: 1
@query_time: 0.014217793010175228
@status: ResponseStatus.SUCCESS
@batch:
0
0 Number of loaded IMAGE: 1
@query_time: 0.01373684499412775
@status: ResponseStatus.SUCCESS
@batch:
0
0 Number of loaded IMAGE: 1
@query_time: 0.09569284389726818
@status: ResponseStatus.SUCCESS
@batch:
0
0 Number of loaded IMAGE: 1
@query_time: 0.022398902103304863
@status: ResponseStatus.SUCCESS
@batch:
licenseplateextractor.labels \
0 [TN4805566]
1 [INAQ3044]
2 [EGB62644]
3 [LEM446A4]
4 [4II982A7A, 770L7726]
5 [LPD7SRV616, IPD8NBZ548]
licenseplateextractor.bboxes \
0 [[[432, 648], [723, 648], [723, 698], [432, 698]]]
1 [[[298, 238], [398, 238], [398, 264], [298, 264]]]
2 [[[178, 430], [300, 430], [300, 462], [178, 462]]]
3 [[[263, 465], [383, 465], [383, 501], [263, 501]]]
4 [[[2263, 398], [2347, 398], [2347, 436], [2263, 436]], [[216.02985749985467, 829.1194299994187],...
5 [[[236, 372], [344, 372], [344, 396], [236, 396]], [[986, 536], [1100, 536], [1100, 560], [986, ...
licenseplateextractor.scores
0 [0.765264012834225]
1 [0.5979598335637649]
2 [0.8205181373232121]
3 [0.37683450003837526]
4 [0.005999226930693703, 0.041628318390644674]
5 [0.3380678860228084, 0.8426790601376755]
@query_time: 6.209830478997901
File video12.mp4 already there; not retrieving.
@status: ResponseStatus.SUCCESS
@batch:
0
0 Table Successfully dropped: MyVideos
@query_time: 0.035812282003462315
@status: ResponseStatus.SUCCESS
@batch:
0
0 Number of loaded VIDEO: 1
@query_time: 0.09962842799723148
@status: ResponseStatus.SUCCESS
@batch:
licenseplateextractor.labels \
0 [TSO7FX353L]
licenseplateextractor.bboxes \
0 [[[361, 765], [685, 765], [685, 841], [361, 841]]]
licenseplateextractor.scores
0 [0.5929218894477851]
@query_time: 5.568858061917126
Annotate Model Output on Image
import cv2
from pprint import pprint
from matplotlib import pyplot as plt
from pathlib import Path
def annotate_image_ocr(detections, input_image_path, frame_id):
color1=(0, 255, 150)
color2=(255, 49, 49)
white=(255, 255, 255)
thickness=4
frame = cv2.imread(input_image_path)
height, width = frame.shape[:2]
if frame_id == 0:
frame= cv2.copyMakeBorder(frame, 0, 100, 0, 100, cv2.BORDER_CONSTANT,value=white)
print(detections)
plate_id = 0
df = detections
df = df[['licenseplateextractor.bboxes', 'licenseplateextractor.labels']][df.index == frame_id]
x_offset = width * 0.1
y_offset = height * 0.4
if df.size:
dfLst = df.values.tolist()
for bbox, label in zip(dfLst[plate_id][0], dfLst[plate_id][1]):
x1, y1, x2, y2 = bbox
x1, y1, x2, y2 = int(x1[0]), int(x1[1]), int(x2[0]), int(x2[1])
# object bbox
cv2.rectangle(frame, (x1, y1), (x2, y2), color1, thickness)
# object label
cv2.putText(frame, label, (int(x1), int(y1 - height * 0.1)), cv2.FONT_HERSHEY_SIMPLEX, (6 * width)/2400, color2,
int((12 * width)/2400), cv2.LINE_AA)
# Show every frame
plt.imshow(frame)
plt.show()
p = Path(input_image_path)
output_path = "{0}_{2}{1}".format(p.stem, p.suffix, "output")
cv2.imwrite(output_path, frame)
dataframe = response.batch.frames
annotate_image_ocr(dataframe, 'test_image_1.png', frame_id = 0)
annotate_image_ocr(dataframe, 'test_image_2.png', frame_id = 1)
annotate_image_ocr(dataframe, 'car10.jpg', frame_id = 2)
annotate_image_ocr(dataframe, 'car6.jpg', frame_id = 3)
annotate_image_ocr(dataframe, 'ezgif-frame-001.jpg', frame_id = 4)
annotate_image_ocr(dataframe, 'maxresdefault.jpg', frame_id = 5)
licenseplateextractor.labels \
0 [TN4805566]
1 [INAQ3044]
2 [EGB62644]
3 [LEM446A4]
4 [4II982A7A, 770L7726]
5 [LPD7SRV616, IPD8NBZ548]
licenseplateextractor.bboxes \
0 [[[432, 648], [723, 648], [723, 698], [432, 69...
1 [[[298, 238], [398, 238], [398, 264], [298, 26...
2 [[[178, 430], [300, 430], [300, 462], [178, 46...
3 [[[263, 465], [383, 465], [383, 501], [263, 50...
4 [[[2263, 398], [2347, 398], [2347, 436], [2263...
5 [[[236, 372], [344, 372], [344, 396], [236, 39...
licenseplateextractor.scores
0 [0.765264012834225]
1 [0.5979598335637649]
2 [0.8205181373232121]
3 [0.37683450003837526]
4 [0.005999226930693703, 0.041628318390644674]
5 [0.3380678860228084, 0.8426790601376755]
licenseplateextractor.labels \
0 [TN4805566]
1 [INAQ3044]
2 [EGB62644]
3 [LEM446A4]
4 [4II982A7A, 770L7726]
5 [LPD7SRV616, IPD8NBZ548]
licenseplateextractor.bboxes \
0 [[[432, 648], [723, 648], [723, 698], [432, 69...
1 [[[298, 238], [398, 238], [398, 264], [298, 26...
2 [[[178, 430], [300, 430], [300, 462], [178, 46...
3 [[[263, 465], [383, 465], [383, 501], [263, 50...
4 [[[2263, 398], [2347, 398], [2347, 436], [2263...
5 [[[236, 372], [344, 372], [344, 396], [236, 39...
licenseplateextractor.scores
0 [0.765264012834225]
1 [0.5979598335637649]
2 [0.8205181373232121]
3 [0.37683450003837526]
4 [0.005999226930693703, 0.041628318390644674]
5 [0.3380678860228084, 0.8426790601376755]
licenseplateextractor.labels \
0 [TN4805566]
1 [INAQ3044]
2 [EGB62644]
3 [LEM446A4]
4 [4II982A7A, 770L7726]
5 [LPD7SRV616, IPD8NBZ548]
licenseplateextractor.bboxes \
0 [[[432, 648], [723, 648], [723, 698], [432, 69...
1 [[[298, 238], [398, 238], [398, 264], [298, 26...
2 [[[178, 430], [300, 430], [300, 462], [178, 46...
3 [[[263, 465], [383, 465], [383, 501], [263, 50...
4 [[[2263, 398], [2347, 398], [2347, 436], [2263...
5 [[[236, 372], [344, 372], [344, 396], [236, 39...
licenseplateextractor.scores
0 [0.765264012834225]
1 [0.5979598335637649]
2 [0.8205181373232121]
3 [0.37683450003837526]
4 [0.005999226930693703, 0.041628318390644674]
5 [0.3380678860228084, 0.8426790601376755]
licenseplateextractor.labels \
0 [TN4805566]
1 [INAQ3044]
2 [EGB62644]
3 [LEM446A4]
4 [4II982A7A, 770L7726]
5 [LPD7SRV616, IPD8NBZ548]
licenseplateextractor.bboxes \
0 [[[432, 648], [723, 648], [723, 698], [432, 69...
1 [[[298, 238], [398, 238], [398, 264], [298, 26...
2 [[[178, 430], [300, 430], [300, 462], [178, 46...
3 [[[263, 465], [383, 465], [383, 501], [263, 50...
4 [[[2263, 398], [2347, 398], [2347, 436], [2263...
5 [[[236, 372], [344, 372], [344, 396], [236, 39...
licenseplateextractor.scores
0 [0.765264012834225]
1 [0.5979598335637649]
2 [0.8205181373232121]
3 [0.37683450003837526]
4 [0.005999226930693703, 0.041628318390644674]
5 [0.3380678860228084, 0.8426790601376755]
licenseplateextractor.labels \
0 [TN4805566]
1 [INAQ3044]
2 [EGB62644]
3 [LEM446A4]
4 [4II982A7A, 770L7726]
5 [LPD7SRV616, IPD8NBZ548]
licenseplateextractor.bboxes \
0 [[[432, 648], [723, 648], [723, 698], [432, 69...
1 [[[298, 238], [398, 238], [398, 264], [298, 26...
2 [[[178, 430], [300, 430], [300, 462], [178, 46...
3 [[[263, 465], [383, 465], [383, 501], [263, 50...
4 [[[2263, 398], [2347, 398], [2347, 436], [2263...
5 [[[236, 372], [344, 372], [344, 396], [236, 39...
licenseplateextractor.scores
0 [0.765264012834225]
1 [0.5979598335637649]
2 [0.8205181373232121]
3 [0.37683450003837526]
4 [0.005999226930693703, 0.041628318390644674]
5 [0.3380678860228084, 0.8426790601376755]
licenseplateextractor.labels \
0 [TN4805566]
1 [INAQ3044]
2 [EGB62644]
3 [LEM446A4]
4 [4II982A7A, 770L7726]
5 [LPD7SRV616, IPD8NBZ548]
licenseplateextractor.bboxes \
0 [[[432, 648], [723, 648], [723, 698], [432, 69...
1 [[[298, 238], [398, 238], [398, 264], [298, 26...
2 [[[178, 430], [300, 430], [300, 462], [178, 46...
3 [[[263, 465], [383, 465], [383, 501], [263, 50...
4 [[[2263, 398], [2347, 398], [2347, 436], [2263...
5 [[[236, 372], [344, 372], [344, 396], [236, 39...
licenseplateextractor.scores
0 [0.765264012834225]
1 [0.5979598335637649]
2 [0.8205181373232121]
3 [0.37683450003837526]
4 [0.005999226930693703, 0.041628318390644674]
5 [0.3380678860228084, 0.8426790601376755]
Annotate Model Output on Video
def annotate_video(detections, input_video_path):
color1=(207, 248, 64)
color2=(255, 49, 49)
thickness=4
vcap = cv2.VideoCapture(input_video_path)
width = int(vcap.get(3))
height = int(vcap.get(4))
fps = vcap.get(5)
fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v') #codec
p = Path(input_video_path)
output_video_path = "{0}_{2}{1}".format(
p.stem,
p.suffix,
"output")
video=cv2.VideoWriter(output_video_path, fourcc, fps, (width,height))
frame_id = 0
# Capture frame-by-frame
# ret = 1 if the video is captured; frame is the image
ret, frame = vcap.read()
x_offset = width * 0.2
y_offset = height * 0.8
while ret:
df = detections
df = df[['licenseplateextractor.bboxes', 'licenseplateextractor.labels']][df.index == frame_id]
if df.size:
dfLst = df.values.tolist()
for bbox, label in zip(dfLst[frame_id][0], dfLst[frame_id][1]):
x1, y1, x2, y2 = bbox
x1, y1, x2, y2 = int(x1[0]), int(x1[1]), int(x2[0]), int(x2[1])
# object bbox
cv2.rectangle(frame, (x1, y1), (x2, y2), color1, thickness)
# object label
cv2.putText(frame, label, (int(x_offset), int(y_offset)), cv2.FONT_HERSHEY_SIMPLEX, 3, color2, thickness * 4, cv2.LINE_AA)
y_offset = y_offset + height * 0.2
video.write(frame)
# Show every fifth frame
if frame_id % 5 == 0:
plt.imshow(frame)
plt.show()
frame_id+=1
ret, frame = vcap.read()
video.release()
vcap.release()
dataframe2 = response2.batch.frames
annotate_video(dataframe2, "video12.mp4")
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