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Real-time license plate recognition with 'openalpr' using a video file as input
# 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
@ZIKO94ZIKO

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@ZIKO94ZIKO ZIKO94ZIKO commented Sep 6, 2019

could you please tell me how can i run it in windows

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