jkjung-avt/openalpr_camera.py

## 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:Face12345@10.15.19.201:554/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
	# 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:Face12345@10.15.19.201:554/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()
	# 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()
	; 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

	; 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