proin · proin
diff --git a/openalpr_camera.py b/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()
diff --git a/openalpr_video.py b/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()
diff --git a/tw.conf b/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
diff --git a/tx2.conf b/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
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