Hi,
Since a long time, I've a couple of errors when starting OpenAlpr.
Now, I suspect those errors to hinder a smooth running of the application
I checked the daemon.log file, I can't spot any error.
I've googled and seen a couple of posts, but they are about programming.
Any advice about the right approach to troubleshoot this problem ?
Config
======
brand new Raspberry Pi integrated cam
Console display
==============
pi@alpr:~ $ alprd -l 1 --config /etc/openalpr -f
INFO - Running OpenALPR daemon in the foreground.
INFO - Using: /etc/openalpr/alprd.conf for daemon configuration
INFO - Using: /home/pi/a-plate-capt/ for storing valid plate images
INFO - country: eu -- config file: /etc/openalpr/openalpr.conf
INFO - Stream 1: webcam
INFO - Starting camera 1
INFO - Video stream connecting...
/dev/video0 does not support memory mapping
munmap: Invalid argument
.
.
Unable to stop the stream.: Bad file descriptor
munmap: Invalid argument
.
.
INFO - Video stream connected
DEBUG - Writing plate 5721MJ9 (beausoleil-cam1-1459796237076) to queue.
WARN - Error connecting to Beanstalk. Result has not been saved.
/etc/openalpr/alprd.conf
========================
[daemon]
; country determines the training dataset used for recognizing plates. Valid values are: us, eu
country = eu
; text name identifier for this location
site_id = beausoleil
; Declare each stream on a separate line
; each unique stream should be defined as stream = [url]
; stream = webcam
stream = webcam
; topn is the number of possible plate character variations to report
topn = 1
; Determines whether images that contain plates should be stored to disk
store_plates = 1
; store_plates_location = /var/lib/openalpr/plateimages/
store_plates_location = /home/pi/a-plate-capt/
; upload address is the destination to POST to
upload_data = 0
; 10.0.1.184 = RPI, 10.0.1.246 = NAS (webserver etc)
/etc/openalpr/openalpr.conf
===========================
; Specify the path to the runtime data directory
; runtime_dir = /usr/local/share/openalpr/runtime_data
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 =
; 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 = 100
max_plate_height_percent = 100
; 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 = 1.1
; 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 = 640
max_detection_input_height = 480
; 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
; 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 = 0
; Bypasses plate detection. If this is set to 1, the library assumes that each region provided is a likely plate area.
skip_detection = 0
max_plate_angle_degrees = 15
ocr_min_font_point = 6
; Minimum OCR confidence percent to consider.
postprocess_min_confidence = 75
; 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_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