Real-time license plate recognition with 'openalpr' using a video file as input. Please check out my "Building and Testing 'openalpr' on Jetson TX2" post for more information: https://jkjung-avt.github.io/openalpr-on-tx2/

openalpr_camera.py

# test_camera.py
#
# Open an RTSP stream and feed image frames to 'openalpr'
# for real-time license plate recognition.

import numpy as np
import cv2
from openalpr import Alpr


RTSP_SOURCE  = 'rtsp://face:[email protected]:554/live.sdp'
WINDOW_NAME  = 'openalpr'
FRAME_SKIP   = 15


def open_cam_rtsp(uri, width=1280, height=720, latency=2000):
    gst_str = ('rtspsrc location={} latency={} ! '
               'rtph264depay ! h264parse ! omxh264dec ! nvvidconv ! '
               'video/x-raw, width=(int){}, height=(int){}, format=(string)BGRx ! '
               'videoconvert ! appsink').format(uri, latency, width, height)
    return cv2.VideoCapture(gst_str, cv2.CAP_GSTREAMER)


def main():
    alpr = Alpr('tw', 'tx2.conf', '/usr/local/share/openalpr/runtime_data')
    if not alpr.is_loaded():
        print('Error loading OpenALPR')
        sys.exit(1)
    alpr.set_top_n(3)
    #alpr.set_default_region('new')

    cap = open_cam_rtsp(RTSP_SOURCE)
    if not cap.isOpened():
        alpr.unload()
        sys.exit('Failed to open video file!')
    cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_AUTOSIZE)
    cv2.setWindowTitle(WINDOW_NAME, 'OpenALPR video test')

    _frame_number = 0
    while True:
        ret_val, frame = cap.read()
        if not ret_val:
            print('VidepCapture.read() failed. Exiting...')
            break

        _frame_number += 1
        if _frame_number % FRAME_SKIP != 0:
            continue
        cv2.imshow(WINDOW_NAME, frame)

        results = alpr.recognize_ndarray(frame)
        for i, plate in enumerate(results['results']):
            best_candidate = plate['candidates'][0]
            print('Plate #{}: {:7s} ({:.2f}%)'.format(i, best_candidate['plate'].upper(), best_candidate['confidence']))

        if cv2.waitKey(1) == 27:
            break

    cv2.destroyAllWindows()
    cap.release()
    alpr.unload()


if __name__ == "__main__":
    main()

openalpr_video.py

# test_video.py
#
# Open a video input file and feed each image frame to 'openalpr'
# for license plate recognition.

import numpy as np
import cv2
from openalpr import Alpr


VIDEO_SOURCE = '/home/nvidia/Videos/alpr/2018-03-09-0850.mp4'
WINDOW_NAME  = 'openalpr'
FRAME_SKIP   = 15

def main():
    alpr = Alpr('tw', 'tx2.conf', '/usr/local/share/openalpr/runtime_data')
    if not alpr.is_loaded():
        print('Error loading OpenALPR')
        sys.exit(1)
    alpr.set_top_n(3)
    #alpr.set_default_region('new')

    cap = cv2.VideoCapture(VIDEO_SOURCE)
    if not cap.isOpened():
        alpr.unload()
        sys.exit('Failed to open video file!')
    cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_AUTOSIZE)
    cv2.setWindowTitle(WINDOW_NAME, 'OpenALPR video test')

    _frame_number = 0
    while True:
        ret_val, frame = cap.read()
        if not ret_val:
            print('VidepCapture.read() failed. Exiting...')
            break

        _frame_number += 1
        if _frame_number % FRAME_SKIP != 0:
            continue
        cv2.imshow(WINDOW_NAME, frame)

        results = alpr.recognize_ndarray(frame)
        for i, plate in enumerate(results['results']):
            best_candidate = plate['candidates'][0]
            print('Plate #{}: {:7s} ({:.2f}%)'.format(i, best_candidate['plate'].upper(), best_candidate['confidence']))

        if cv2.waitKey(1) == 27:
            break

    cv2.destroyAllWindows()
    cap.release()
    alpr.unload()


if __name__ == "__main__":
    main()

tw.conf

; 25-45, 35-55, 45-65, 55-75, 65-85
char_analysis_min_pct = 0.25
char_analysis_height_range = 0.20
char_analysis_height_step_size = 0.10
char_analysis_height_num_steps = 5

segmentation_min_speckle_height_percent = 0.3
segmentation_min_box_width_px = 4
segmentation_min_charheight_percent = 0.5;
segmentation_max_segment_width_percent_vs_average = 1.35;

plate_width_mm = 380.0
plate_height_mm = 160.0

multiline = 0

char_height_mm = 94
char_width_mm = 47
char_whitespace_top_mm = 36
char_whitespace_bot_mm = 26

template_max_width_px = 152
template_max_height_px = 64

; Higher sensitivity means less lines
plateline_sensitivity_vertical = 25
plateline_sensitivity_horizontal = 45

; Regions smaller than this will be disqualified
min_plate_size_width_px = 80
min_plate_size_height_px = 35

; Results with fewer or more characters will be discarded
postprocess_min_characters = 4
postprocess_max_characters = 7

ocr_language = ltw

; Override for postprocess letters/numbers regex. 
postprocess_regex_letters = [A-Z]
postprocess_regex_numbers = [0-9]

; Whether the plate is always dark letters on light background, light letters on dark background, or both
; value can be either always, never, or auto
invert = auto

tx2.conf

; Specify the path to the runtime data directory
runtime_dir = /usr/local/share/openalpr/runtime_data


ocr_img_size_percent = 1.33333333
state_id_img_size_percent = 2.0

; Calibrating your camera improves detection accuracy in cases where vehicle plates are captured at a steep angle
; Use the openalpr-utils-calibrate utility to calibrate your fixed camera to adjust for an angle
; Once done, update the prewarp config with the values obtained from the tool
;prewarp =
;prewarp = planar,1280.000000,720.000000,0.000850,0.000750,0.080000,0.975000,0.815000,0.000000,0.000000
; This is for 0309 photos
; prewarp = planar,1280.000000,720.000000,0.000600,0.000900,0.060000,1.000000,1.000000,0.000000,0.000000
; This is for 0313 photos
prewarp = planar,1280.000000,720.000000,0.000550,0.000750,0.130000,1.000000,1.000000,0.000000,0.000000

; detection will ignore plates that are too large.  This is a good efficiency technique to use if the 
; plates are going to be a fixed distance away from the camera (e.g., you will never see plates that fill 
; up the entire image
max_plate_width_percent = 15
max_plate_height_percent = 15

; detection_iteration_increase is the percentage that the LBP frame increases each iteration.
; It must be greater than 1.0.  A value of 1.01 means increase by 1%, 1.10 increases it by 10% each time. 
; So a 1% increase would be ~10x slower than 10% to process, but it has a higher chance of landing 
; directly on the plate and getting a strong detection
detection_iteration_increase = 1.05

; The minimum detection strength determines how sure the detection algorithm must be before signaling that 
; a plate region exists.  Technically this corresponds to LBP nearest neighbors (e.g., how many detections 
; are clustered around the same area).  For example, 2 = very lenient, 9 = very strict.
detection_strictness = 3

; The detection doesn't necessarily need an extremely high resolution image in order to detect plates
; Using a smaller input image should still find the plates and will do it faster
; Tweaking the max_detection_input values will resize the input image if it is larger than these sizes 
; max_detection_input_width/height are specified in pixels
max_detection_input_width = 1920
max_detection_input_height = 1080

; detector is the technique used to find license plate regions in an image.  Value can be set to
; lbpcpu    - default LBP-based detector uses the system CPU  
; lbpgpu    - LBP-based detector that uses Nvidia GPU to increase recognition speed.
; lbpopencl - LBP-based detector that uses OpenCL GPU to increase recognition speed.  Requires OpenCV 3.0
; morphcpu  - Experimental detector that detects white rectangles in an image.  Does not require training.
;detector = lbpcpu
detector = lbpgpu

; If set to true, all results must match a postprocess text pattern if a pattern is available.  
; If not, the result is disqualified. 
must_match_pattern = 1

; Bypasses plate detection.  If this is set to 1, the library assumes that each region provided is a likely plate area.
skip_detection = 0

; Specifies the full path to an image file that constrains the detection area.  Only the plate regions allowed through the mask 
; will be analyzed.  The mask image must match the resolution of your image to be analyzed.  The mask is black and white.  
; Black areas will be ignored, white areas will be searched.  An empty value means no mask (scan the entire image)
detection_mask_image = 

; OpenALPR can scan the same image multiple times with different randomization.  Setting this to a value larger than
; 1 may increase accuracy, but will increase processing time linearly (e.g., analysis_count = 3 is 3x slower)
analysis_count = 1

; OpenALPR detects high-contrast plate crops and uses an alternative edge detection technique.  Setting this to 0.0 
; would classify  ALL images as high-contrast, setting it to 1.0 would classify no images as high-contrast. 
contrast_detection_threshold = 0.9

max_plate_angle_degrees = 15

ocr_min_font_point = 6

; Minimum OCR confidence percent to consider.
postprocess_min_confidence = 80

; Any OCR character lower than this will also add an equally likely
; chance that the character is incorrect and will be skipped.  Value is a confidence percent
postprocess_confidence_skip_level = 80


debug_general         = 0
debug_timing          = 0
debug_detector        = 0
debug_prewarp         = 0
debug_state_id        = 0
debug_plate_lines     = 0
debug_plate_corners   = 0
debug_char_segment    = 0
debug_char_analysis   = 0
debug_color_filter    = 0
debug_ocr             = 0
debug_postprocess     = 0
debug_show_images     = 0
debug_pause_on_frame  = 0

you search the path right .? tape this command find / -path *runtime_data* , and copy the result in line 16 of file openalpr_video.py.

you search the path right .? tape this command find / -path *runtime_data* , and copy the result in line 16 of file openalpr_video.py.

When I try to tape the above mentioned command,It just thrown Invalid Switch.
Will you please mention the command to find the path that need to mention in line 16 of file"openalpr_video.py" ?

did you follow this steps in this link : https://jkjung-avt.github.io/openalpr-on-tx2/ , .?
in step 3: you will find openalpr directory , after taping cd openalpr write command ls you should find : runtime_data

HI Mr, @jkjung-avt i want to cropping each vehicle and save it in path how can be do that.

@zongoalbert, sounds like you need to use an object detector to detect vehicles (as bounding boxes) and then crop images with the bounding boxes.

yes i try to detect vehicles (as bounding boxes) the result : is just blury images like this photos :

NB : the resolution of video : 4K , how can be cropped images with high resolution and without blury?

you can find the original video here : https://drive.google.com/drive/u/0/folders/10tqL9tIB5AmNJrGXIBPhkOp8-F_paASs?fbclid=IwAR1Ht7Bw5hyva5OaThRtoyqFHgXr02erJFYA9D-GAD2wAHbiXonl4bjWdaY

It's not guaranteed that 4K camera would produce clear pictures. You could use some photo viewer/editor application to check the original 4K image. Try zoom in the vehicle of interest and check whether the license plate is clear and readable first.

Yes SIR i do zoom and screen shout with tool windows os , AND i lance command line : alpr -c eu /path/imagescreened.png, its work , t think the problem is about cropping not resolution of video , could you please add cropping rear of vehicle or croping ALPR in your code?

please Sir could you take a look of the code ? .

Cropping images is very simple. Please reference this post on StackOverflow.

https://stackoverflow.com/questions/15589517/how-to-crop-an-image-in-opencv-using-python

i try you installation and guide . i execute all fix but when i try show a error

Traceback (most recent call last):
File "test.py", line 66, in
main()
File "test.py", line 52, in main
results = alpr.recognize_ndarray(frame)
AttributeError: Alpr instance has no attribute 'recognize_ndarray'

can help me

Which version of Python are you using?