Automatic License Plate Recognition (ALPR) or Automatic Number Plate Recognition (ANPR) software that works with any camera.
Home Page: https://platerecognizer.com/
License: MIT License
Describe the bug
There is an Error when you upload .asf video format. It appears to be an issue with ffmpegcv
Log output:
skew_correction | INFO [app.21] => Api Server Startup
video_editor | DEBUG [video-editor.140] => Processing video: Sample-1Video.asf
video_editor | DEBUG [video-editor.143] => enabled_actions: visualization
video_editor | DEBUG [video-editor.149] => CONFIG frames_enabled: False
video_editor | DEBUG [video-editor.150] => CONFIG visualization_enabled: True
video_editor | DEBUG [video-editor.151] => CONFIG blur_enabled: False
video_editor | ERROR [video-editor.288] => Error:
video_editor | Traceback (most recent call last):
video_editor | File "/app/video_editor.py", line 286, in process_video_route
video_editor | process_video(file, action)
video_editor | File "/app/video_editor.py", line 169, in process_video
video_editor | cap = ffmpegcv.VideoCapture(video_path)
video_editor | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
video_editor | File "/usr/local/lib/python3.11/site-packages/ffmpegcv/__init__.py", line 151, in VideoCapture
video_editor | return FFmpegReader.VideoReader(
video_editor | ^^^^^^^^^^^^^^^^^^^^^^^^^
video_editor | File "/usr/local/lib/python3.11/site-packages/ffmpegcv/ffmpeg_reader.py", line 59, in VideoReader
video_editor | videoinfo = get_info(filename)
video_editor | ^^^^^^^^^^^^^^^^^^
video_editor | File "/usr/local/lib/python3.11/site-packages/ffmpegcv/video_info.py", line 25, in get_info
video_editor | outinfo['count'] = int(vinfo['nb_read_packets' if do_scan_the_whole
video_editor | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
video_editor | KeyError: 'nb_frames'
video_editor | INFO [werkzeug.96] => 172.19.0.1 - - [20/Oct/2023 06:05:46] "POST /process-video HTTP/1.1" 500 -
^CGracefully stopping... (press Ctrl+C again to force)
Aborting on container exit...To Reproduce
Steps to reproduce the behaviour:
Expected behaviour
The video should process okay
Describe the bug
After setting up ParkPow behind a reverse Proxy all request going through the Proxy are getting a CSFR error
To Reproduce
Steps to reproduce the behavior:
Expected behavior
Have ParkPow available over TLS
Desktop (please complete the following information):
Additional context
Log entries
2023-05-23 13:39:55 [WARNING] [/opt/pysetup/.venv/lib/python3.11/site-packages/django/utils/log.py:241]: Forbidden (Origin checking failed - null does not match any trusted origins.): /accounts/login/
() {72 vars in 1485 bytes} [Tue May 23 13:39:55 2023] POST /accounts/login/ => generated 1019 bytes in 3 msecs (HTTP/1.1 403) 7 headers in 229 bytes (1 switches on core 2)
It would be great to have an API for license plate recognition from live video feed or a recorded MP4 formatted.
Is it possible to get a license plate with a lower confidence value if no readings appear? Like I'm fine with an LPR reading with a score of 0.5, etc.
There is no way to reach you guys. After filling out the contact form and submitting the page returns with a captcha error and I believe my messages are not being sent.
If i use the self hosted docker image do I still need an API key or can I use it for free. What is the licensing mechanism for docker image.
Hi, thanks for the library.
From the curl examples, it looks like the regions should be specified as form POST data
Would this be the right way?
The API response (via python or curl) doesn't tell me if it actually used the regions, because even if I enter random characters in the region, it still proceeds to detect plates without any region feedback in the response, so I don't know if I am doing it right.
payload = {'regions':['kr','us']}
# payload = {'regions':'crouchingtigerhiddendragon'} # this works as well
response = requests.post(
'https://api.platerecognizer.com/v1/plate-reader/',
files=dict(upload=fp),
data=payload,
headers={'Authorization': 'Token ' + self.apikey})Thanks
Describe the bug
FFMPEGCV in video-editor script cannot process grayscale videos.
To Reproduce
Steps to reproduce the behavior:
KeyError: 'nb_frames' error on this line cap = ffmpegcv.VideoCapture(video_path).Additional context
Using cv2.VideoCapture instead works fine. Consider switching back to cv2 for reading frames?
Hi,
I have docker installed in my arm macine.
# uname -m armv7l
I am using raspberry pi docker image. If I run docker then,
# docker run --rm -t -p 8080:8080 -v license:/license -e TOKEN=bc44e48030a5735cdd428e7a231dca19b3d33d03 -e LICENSE_KEY=xxxxx platerecognizer/alpr-raspberry-pi PlateRecognizer v1.20.0 License Key: xxxxx Usage since 2021-04-09: 106/2500 calls. Loading...
issue with connecting localhost:
`# curl -F 'upload=@/tmp/car.jpg' http://localhost:8080/v1/plate-reader/
curl: (7) Failed to connect to localhost port 8080: Connection refused
`
Only 1 line is tailed from the docker logs command, so when there are multiple cameras (2 in my case), there's a good chance that over a shorter timeout interval (including the default of 20s) that a camera will be shown as offline when it is actively logging, because the line that was tailed over that interval never happened to be a certain camera
https://github.com/parkpow/deep-license-plate-recognition/blob/master/stream/stream_monitor.py#L78
IMO it would be great if this monitor was part of the Stream image itself and did not rely on having to parse docker logs.
Example of the issue:
Debug output from stream_monitor.py:
2022-08-23 15:04:38,235 DEBUG root Thread-1 : log_line: ['INFO', 'egress-north', '2022-08-23T15:04:33+00']
2022-08-23 15:04:38,235 DEBUG root Thread-1 : No new logs detected
127.0.0.1 - - [23/Aug/2022 15:04:38] "GET / HTTP/1.1" 200 -
2022-08-23 15:04:38,241 DEBUG root Thread-2 : state:
2022-08-23 15:04:38,241 DEBUG root Thread-2 : {'container_active': False, 'last_log_times': {'egress-north': datetime.datetime(2022, 8, 23, 15, 4, 34, 40497), 'ingress-north': datetime.datetime(2022, 8, 23, 15, 4, 23, 282175)}, 'container_running': True}
2022-08-23 15:04:38,241 DEBUG root Thread-2 : Comparing times: [2022-08-23 15:04:34.040497] and [2022-08-23 15:04:18.241586]
2022-08-23 15:04:38,241 DEBUG root Thread-2 : Comparing times: [2022-08-23 15:04:23.282175] and [2022-08-23 15:04:18.241586]
2022-08-23 15:04:38,241 DEBUG root Thread-2 : response
2022-08-23 15:04:38,241 DEBUG root Thread-2 : {'active': True, 'cameras': {'egress-north': {'status': 'running'}, 'ingress-north': {'status': 'running'}}}
127.0.0.1 - - [23/Aug/2022 15:04:39] "GET / HTTP/1.1" 200 -
2022-08-23 15:04:39,286 DEBUG root Thread-2 : state:
2022-08-23 15:04:39,286 DEBUG root Thread-2 : {'container_active': False, 'last_log_times': {'egress-north': datetime.datetime(2022, 8, 23, 15, 4, 34, 40497), 'ingress-north': datetime.datetime(2022, 8, 23, 15, 4, 23, 282175)}, 'container_running': True}
2022-08-23 15:04:39,286 DEBUG root Thread-2 : Comparing times: [2022-08-23 15:04:34.040497] and [2022-08-23 15:04:19.286954]
2022-08-23 15:04:39,287 DEBUG root Thread-2 : Comparing times: [2022-08-23 15:04:23.282175] and [2022-08-23 15:04:19.286954]
2022-08-23 15:04:39,287 DEBUG root Thread-2 : response
2022-08-23 15:04:39,287 DEBUG root Thread-2 : {'active': True, 'cameras': {'egress-north': {'status': 'running'}, 'ingress-north': {'status': 'running'}}}
2022-08-23 15:04:40,237 DEBUG root Thread-1 : Captures: 10
2022-08-23 15:04:40,320 DEBUG root Thread-1 : docker_log: INFO:egress-north:2022-08-23T15:04:38+00:00: Health Score: 70%
2022-08-23 15:04:40,320 DEBUG root Thread-1 : log_line: ['INFO', 'egress-north', '2022-08-23T15:04:38+00']
127.0.0.1 - - [23/Aug/2022 15:04:40] "GET / HTTP/1.1" 200 -
2022-08-23 15:04:40,340 DEBUG root Thread-2 : state:
2022-08-23 15:04:40,340 DEBUG root Thread-2 : {'container_active': False, 'last_log_times': {'egress-north': datetime.datetime(2022, 8, 23, 15, 4, 40, 320456), 'ingress-north': datetime.datetime(2022, 8, 23, 15, 4, 23, 282175)}, 'container_running': True}
2022-08-23 15:04:40,340 DEBUG root Thread-2 : Comparing times: [2022-08-23 15:04:40.320456] and [2022-08-23 15:04:20.340253]
2022-08-23 15:04:40,340 DEBUG root Thread-2 : Comparing times: [2022-08-23 15:04:23.282175] and [2022-08-23 15:04:20.340253]
2022-08-23 15:04:40,340 DEBUG root Thread-2 : response
2022-08-23 15:04:40,340 DEBUG root Thread-2 : {'active': True, 'cameras': {'egress-north': {'status': 'running'}, 'ingress-north': {'status': 'running'}}}
127.0.0.1 - - [23/Aug/2022 15:04:41] "GET / HTTP/1.1" 200 -
2022-08-23 15:04:41,385 DEBUG root Thread-2 : state:
2022-08-23 15:04:41,385 DEBUG root Thread-2 : {'container_active': False, 'last_log_times': {'egress-north': datetime.datetime(2022, 8, 23, 15, 4, 40, 320456), 'ingress-north': datetime.datetime(2022, 8, 23, 15, 4, 23, 282175)}, 'container_running': True}
2022-08-23 15:04:41,386 DEBUG root Thread-2 : Comparing times: [2022-08-23 15:04:40.320456] and [2022-08-23 15:04:21.386020]
2022-08-23 15:04:41,386 DEBUG root Thread-2 : Comparing times: [2022-08-23 15:04:23.282175] and [2022-08-23 15:04:21.386020]
2022-08-23 15:04:41,386 DEBUG root Thread-2 : response
2022-08-23 15:04:41,386 DEBUG root Thread-2 : {'active': True, 'cameras': {'egress-north': {'status': 'running'}, 'ingress-north': {'status': 'running'}}}
2022-08-23 15:04:42,321 DEBUG root Thread-1 : Captures: 11
2022-08-23 15:04:42,408 DEBUG root Thread-1 : docker_log: INFO:egress-north:2022-08-23T15:04:38+00:00: Health Score: 70%
2022-08-23 15:04:42,408 DEBUG root Thread-1 : log_line: ['INFO', 'egress-north', '2022-08-23T15:04:38+00']
2022-08-23 15:04:42,408 DEBUG root Thread-1 : No new logs detected
127.0.0.1 - - [23/Aug/2022 15:04:42] "GET / HTTP/1.1" 200 -
2022-08-23 15:04:42,437 DEBUG root Thread-2 : state:
2022-08-23 15:04:42,438 DEBUG root Thread-2 : {'container_active': False, 'last_log_times': {'egress-north': datetime.datetime(2022, 8, 23, 15, 4, 40, 320456), 'ingress-north': datetime.datetime(2022, 8, 23, 15, 4, 23, 282175)}, 'container_running': True}
2022-08-23 15:04:42,438 DEBUG root Thread-2 : Comparing times: [2022-08-23 15:04:40.320456] and [2022-08-23 15:04:22.438102]
2022-08-23 15:04:42,438 DEBUG root Thread-2 : Comparing times: [2022-08-23 15:04:23.282175] and [2022-08-23 15:04:22.438102]
2022-08-23 15:04:42,438 DEBUG root Thread-2 : response
2022-08-23 15:04:42,438 DEBUG root Thread-2 : {'active': True, 'cameras': {'egress-north': {'status': 'running'}, 'ingress-north': {'status': 'running'}}}
127.0.0.1 - - [23/Aug/2022 15:04:43] "GET / HTTP/1.1" 200 -
2022-08-23 15:04:43,566 DEBUG root Thread-2 : state:
2022-08-23 15:04:43,567 DEBUG root Thread-2 : {'container_active': False, 'last_log_times': {'egress-north': datetime.datetime(2022, 8, 23, 15, 4, 40, 320456), 'ingress-north': datetime.datetime(2022, 8, 23, 15, 4, 23, 282175)}, 'container_running': True}
2022-08-23 15:04:43,567 DEBUG root Thread-2 : Comparing times: [2022-08-23 15:04:40.320456] and [2022-08-23 15:04:23.567227]
2022-08-23 15:04:43,567 DEBUG root Thread-2 : Comparing times: [2022-08-23 15:04:23.282175] and [2022-08-23 15:04:23.567227]
2022-08-23 15:04:43,567 DEBUG root Thread-2 : response
2022-08-23 15:04:43,567 DEBUG root Thread-2 : {'active': True, 'cameras': {'egress-north': {'status': 'running'}, 'ingress-north': {'status': 'offline'}}}
2022-08-23 15:04:44,410 DEBUG root Thread-1 : Captures: 11
2022-08-23 15:04:44,564 DEBUG root Thread-1 : docker_log: INFO:egress-north:2022-08-23T15:04:43+00:00: Health Score: 70%
2022-08-23 15:04:44,565 DEBUG root Thread-1 : log_line: ['INFO', 'egress-north', '2022-08-23T15:04:43+00']
127.0.0.1 - - [23/Aug/2022 15:04:44] "GET / HTTP/1.1" 200 -
2022-08-23 15:04:44,619 DEBUG root Thread-2 : state:
2022-08-23 15:04:44,619 DEBUG root Thread-2 : {'container_active': False, 'last_log_times': {'egress-north': datetime.datetime(2022, 8, 23, 15, 4, 44, 565161), 'ingress-north': datetime.datetime(2022, 8, 23, 15, 4, 23, 282175)}, 'container_running': True}
2022-08-23 15:04:44,619 DEBUG root Thread-2 : Comparing times: [2022-08-23 15:04:44.565161] and [2022-08-23 15:04:24.619875]
2022-08-23 15:04:44,619 DEBUG root Thread-2 : Comparing times: [2022-08-23 15:04:23.282175] and [2022-08-23 15:04:24.619875]
2022-08-23 15:04:44,619 DEBUG root Thread-2 : response
2022-08-23 15:04:44,620 DEBUG root Thread-2 : {'active': True, 'cameras': {'egress-north': {'status': 'running'}, 'ingress-north': {'status': 'offline'}}}
127.0.0.1 - - [23/Aug/2022 15:04:45] "GET / HTTP/1.1" 200 -
2022-08-23 15:04:45,701 DEBUG root Thread-2 : state:
2022-08-23 15:04:45,701 DEBUG root Thread-2 : {'container_active': False, 'last_log_times': {'egress-north': datetime.datetime(2022, 8, 23, 15, 4, 44, 565161), 'ingress-north': datetime.datetime(2022, 8, 23, 15, 4, 23, 282175)}, 'container_running': True}
2022-08-23 15:04:45,701 DEBUG root Thread-2 : Comparing times: [2022-08-23 15:04:44.565161] and [2022-08-23 15:04:25.701526]
2022-08-23 15:04:45,701 DEBUG root Thread-2 : Comparing times: [2022-08-23 15:04:23.282175] and [2022-08-23 15:04:25.701526]
2022-08-23 15:04:45,701 DEBUG root Thread-2 : response
2022-08-23 15:04:45,701 DEBUG root Thread-2 : {'active': True, 'cameras': {'egress-north': {'status': 'running'}, 'ingress-north': {'status': 'offline'}}}
2022-08-23 15:04:46,565 DEBUG root Thread-1 : Captures: 12
2022-08-23 15:04:46,674 DEBUG root Thread-1 : docker_log: INFO:egress-north:2022-08-23T15:04:43+00:00: Health Score: 70%
Docker logs for stream over that time period:
INFO:ingress-north:2022-08-23T15:03:48+00:00: Health Score: 90%
INFO:egress-north:2022-08-23T15:03:48+00:00: Health Score: 75%
INFO:ingress-north:2022-08-23T15:03:53+00:00: Health Score: 90%
INFO:egress-north:2022-08-23T15:03:53+00:00: Health Score: 75%
INFO:ingress-north:2022-08-23T15:03:58+00:00: Health Score: 90%
INFO:egress-north:2022-08-23T15:03:58+00:00: Health Score: 75%
INFO:ingress-north:2022-08-23T15:04:03+00:00: Health Score: 90%
INFO:egress-north:2022-08-23T15:04:03+00:00: Health Score: 75%
INFO:ingress-north:2022-08-23T15:04:08+00:00: Health Score: 90%
INFO:egress-north:2022-08-23T15:04:08+00:00: Health Score: 70%
INFO:ingress-north:2022-08-23T15:04:13+00:00: Health Score: 90%
INFO:egress-north:2022-08-23T15:04:13+00:00: Health Score: 70%
INFO:ingress-north:2022-08-23T15:04:18+00:00: Health Score: 90%
INFO:egress-north:2022-08-23T15:04:18+00:00: Health Score: 70%
INFO:ingress-north:2022-08-23T15:04:23+00:00: Health Score: 90%
INFO:egress-north:2022-08-23T15:04:23+00:00: Health Score: 70%
INFO:ingress-north:2022-08-23T15:04:28+00:00: Health Score: 90%
INFO:egress-north:2022-08-23T15:04:28+00:00: Health Score: 70%
INFO:ingress-north:2022-08-23T15:04:33+00:00: Health Score: 90%
INFO:egress-north:2022-08-23T15:04:33+00:00: Health Score: 70%
INFO:ingress-north:2022-08-23T15:04:38+00:00: Health Score: 90%
INFO:egress-north:2022-08-23T15:04:38+00:00: Health Score: 70%
INFO:ingress-north:2022-08-23T15:04:43+00:00: Health Score: 90%
INFO:egress-north:2022-08-23T15:04:43+00:00: Health Score: 70%
INFO:ingress-north:2022-08-23T15:04:48+00:00: Health Score: 90%
INFO:egress-north:2022-08-23T15:04:48+00:00: Health Score: 70%
INFO:ingress-north:2022-08-23T15:04:53+00:00: Health Score: 90%
INFO:egress-north:2022-08-23T15:04:53+00:00: Health Score: 70%
INFO:ingress-north:2022-08-23T15:04:58+00:00: Health Score: 90%
INFO:egress-north:2022-08-23T15:04:58+00:00: Health Score: 70%
Describe the bug
A clear and concise description of what the bug is.
To Reproduce
Steps to reproduce the behavior:
API_KEY also the the Skip. It keeps on decreasing my API_KEY, it looks like API Call/s, I just want to call the API_KEY If the camera sees or detects the plate number from the camera stream.Expected behavior
I just want to run the alpr_video.py from a camera stream. Then every plate number that would be detected by the camera, I just want to call the API then suddenly it will recognize it.
Image of vehicle
If applicable, add the image you are trying to recognize.
Desktop (please complete the following information):
Additional context
#!/usr/bin/env python
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from PIL import Image
import argparse
import cv2
import json
import requests
import tempfile
import time
def parse_arguments():
parser = argparse.ArgumentParser(
description='Read license plates from a video and output the result as JSON.',
epilog='For example: python alpr_video.py --api MY_API_KEY --start 900 --end 2000 --skip 3 "/path/to/cars.mp4"')
parser.add_argument('--api', help='Your API key.', required=True)
#parser.add_argument('--skip', help='Read 1 out of N frames.', type=int)
return parser.parse_args()
def main():
args = parse_arguments()
result = []
cap = cv2.VideoCapture(0)
frame_id = 0
while(cap.isOpened()):
ret, frame = cap.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
cv2.imshow('frame',gray)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
#frame_id += 1
#if args.skip and frame_id % args.skip != 0:
#continue
#if args.start and frame_id < args.start:
#continue
#if args.end and frame_id > args.end:
#break
#print('Reading frame %s' % frame_id)
fp = tempfile.NamedTemporaryFile()
im = Image.fromarray(frame)
im.save(fp, 'JPEG')
fp.seek(0)
response = requests.post(
'https://platerecognizer.com/plate-reader/',
files=dict(upload=fp),
headers={'Authorization': 'Token ' + args.api})
result.append(response.json())
time.sleep(1)
print(json.dumps(result, indent=2))
cap.release()
cv2.destroyAllWindows()
if __name__ == '__main__':
main()
Describe the bug
I wanted to report a bug that I encountered while using the program. Here are the details:
We have an active license for PR (Plate Recognizer). I have copied the stream_monitor.py file to monitor the heartbeat from my project. In our development environment, we successfully set up the entire process, and everything works as expected.
However, we recently set up a new environment with a different license for 3 cameras. I have already configured 2 cameras in this environment. When I initiate the stream monitor function or file, I receive the following result:
{"active": True, "cameras": {}}
To Reproduce
Here is my config file
[DEFAULT]
timezone = UTC
[cameras]
webhook_target = http://127.0.0.1:8000/en-us/platerecognizer/webhook
webhook_image = yes
webhook_image_type = original, plate
webhook_caching = yes
mmc = true
[[3d92981d-5344-4e0c-8734-181b7a8cd7c0]]
active = yes
memory_decay = 200
url = rtsp://admin:[email protected]:8554/CH001.sdp
[[3cc21d76-3a34-4794-8851-7c1c50bb29b5]]
active = yes
memory_decay = 200
url = rtsp://admin:[email protected]:8554/CH001.sdp
Here is the command I used to monitor:
python strream_monitor.py
Additionally, I have included the script to pull the content response from the exposed URL endpoint:
curl = "http://localhost:8002" logger_detection.info("Requesting Cameras status from %s ... " % curl) response = requests.get(curl)
Expected behavior
Expected Behavior:
I expected the following response:
{"active": true, "cameras": {"3cc21d76-3a34-4794-8851-7c1c50bb29b5": {"status": "running"}}}
Actual Behavior:
However, the actual response I received was:
{"active": True, "cameras": {}}
I would appreciate it if you could look into this issue and provide a resolution. Please let me know if you require any further information or if there's anything else I can assist you with.
Thank you for your attention to this matter.
@marcbelmont These two files seem to be out of context regarding the rest of the files: https://adolfo-platerecognizer.tinytake.com/msc/OTQ2Mzg1OV8yMzA1NjczMA
I was thinking about moving the files used for the same solution to a deeper folder as install_script, say
All of this is just in case the solutions are isolated from each other.
Folder: https://github.com/parkpow/deep-license-plate-recognition/tree/master/docker
Describe the bug
The windows install file, downloaded from https://app.platerecognizer.com/static/installer/PlateRecognizer-Installer.exe, was detected as malware by Windows Defender.
VirusTotal checking that URL also flagged it as malware:
To Reproduce
Steps to reproduce the behavior:
Check your file with VirusTotal to see yourself.
Describe the bug
The automated installer did not work for me on Ubuntu. It kept asking me to "start Docker", which didn't make sense. I had docker installed, and tried sudo systemctl start docker but nothing ever happened when I clicked "refresh" on the installer wizard website.
Fix
Check if the current user has enough permissions to run Docker and display additional instructions.
Hi, dear developer. I'm the author of ffmpegcv, I'm searching the usage cases of the ffmpegcv and appreciate your project for recognition software. As the project refer the ffmpegcv, and comment:
video-editor/video_editor.py: grayscale videos are not supported by ffmpegcv
The ffmpegcv supports 'gray scale' already, and it's faster than reading bgr/rgb.
cap = ffmpegcv.VideoCapture(video_path, pix_fmt='gray')
ret, frame = cap.read()
frame.shape #(H,W,1)
frame = np.squeeze(frame) #(H,W)ffmpegcv cap.fps is not reliable calculate FPS
The ffmpegcv and the opencv read the fps from the video properties. Loop the whole video file is not efficient. Would you be more specific in your case?
Describe the bug
When trying to run the current latest image, docker run fails with error
PlateRecognizer v1.27.0
Starting online registration
You have used 2/2 license(s).
License Key: <redacted>
Usage since 2021-12-30: <redacted> calls.
Loading...
Loading detection zones.
Low disk space. Make sure to have at least 100MB of free disk space.
To Reproduce
Steps to reproduce the behavior:
Execute Docker run command as per documentation
docker run --rm -t -p 8080:8080 -v license:/license -e TOKEN=<redacted> -e LICENSE_KEY=<redacted> platerecognizer/alpr
Expected behavior
Container should start rather than terminating immediately
Image of vehicle
n/a
Desktop (please complete the following information):
Additional context
We found the image pushed prior to this latest one was also failing for the same reason (now tagged as platerecognizer/alpr:22-01-25.
Currently the last working version for us running locally or in production is platerecognizer/alpr:21-12-21
Huge caveat here is that I'm assuming those containers are built from this repo 😆
So after I run a plate_recognition.py I got this error: {"detail":"Invalid token.","status_code":403}. What should I do with this? Thanks in advance.
I'm having trouble figuring out how to download snapshot images using your API. I've reviewed the documentation, but I'm still unclear on the exact steps or endpoints to use for this purpose.
Describe the bug
FFMPEGCV incorrectly reads video fps as 60 most of the time.
This alters the speed of the videos produced by video-editor.
To Reproduce
Steps to reproduce the behavior:
import ffmpegcvcap = ffmpegcv.VideoCapture(video_path)cap.fpsExpected behavior
Should read correct fps.
Additional context
I've replaced it with normal OpenCV fps_cap.get(cv2.CAP_PROP_FPS).
It works better, but also reports incorrect fps (like 2000) for some videos.
I've added FPS environment variable to override auto-detected one for such files, but we need some reliable solution.
Most of the params look similar
Describe the bug
The total_calls parameter in /statistics seems to be hardcoded
To Reproduce
Invoke 'https://api.platerecognizer.com/v1/statistics/
Expected behavior
total_calls should be the summation of all calls per month
Observed behavior
total_calls is hardcoded to 2500
Image of vehicle
N/A
Additional context
Example output (2 successive calls)
# call 1 to stats
Jul 5 11:48:04 homeserver detect:[2920][monitor_m8]: DEBUG [Plate Recognizer API usage stats: {"usage": {"month": 7, "calls": 100, "year": 2019}, "total_calls": 2500}]
# invoke ALPR api to detected
# call 2 to stats
Jul 5 11:49:34 homeserver detect:[2942][monitor_m8]: DEBUG [Plate Recognizer API usage stats: {"usage": {"month": 7, "calls": 101, "year": 2019}, "total_calls": 2500}]
Hello Marc!
I'm also working on the plate detection part of an ALPR system and would love to connect with you to discuss some things. I've created a pretty good model using Yolov3 and a custom dataset. You can reach me at [email protected]
Ps, sorry about creating an issue but I couldn't connect on LinkedIn without premium
Will it work for the philippine plate country?
Thanks in advance for your reply.
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