import os
import cv
import anpr
from quick_show import quick_show
files = os.listdir('data/examples')
counter = 0
for f in files:
image = cv.LoadImage('data/examples/'+f)
for plate in anpr.detect_plates(image):
zzz = cv.CreateImage(cv.GetSize(plate), cv.IPL_DEPTH_8U, 3)
cv.Smooth(plate, zzz)
#
cv.PyrMeanShiftFiltering(plate, zzz, 40, 15)
foo = anpr.greyscale(plate)
segmented = cv.CreateImage(cv.GetSize(plate), cv.IPL_DEPTH_8U, 1)
bar = cv.CreateImage(cv.GetSize(plate), cv.IPL_DEPTH_8U, 1)
cv.EqualizeHist(foo, segmented)
cv.AdaptiveThreshold(segmented, bar, 255,
cv.CV_ADAPTIVE_THRESH_GAUSSIAN_C, cv.CV_THRESH_BINARY_INV,
plate.height%2 == 0 and (plate.height+1) or plate.height,
plate.height/2)
baz = cv.CreateImage(cv.GetSize(plate), cv.IPL_DEPTH_8U, 1)
el = cv.CreateStructuringElementEx(1, 2, 0, 0, cv.CV_SHAPE_RECT)
cv.Erode(bar, baz, el)
#quick_show(plate)
#quick_show(segmented)
#quick_show(bar)
quick_show(baz)
for char in anpr.find_characters(foo, baz):
Created by Oliver Smith on 2009-09-26.
Copyright (c) 2009 Oliver Smith. All rights reserved.
"""
import os
import cv
import anpr
from quick_show import quick_show
files = os.listdir('data/examples')
counter = 0
for f in files:
image = cv.LoadImage('data/examples/' + f)
for plate in anpr.detect_plates(image):
plate_grey = anpr.greyscale(plate)
hist = cv.CreateHist([256], cv.CV_HIST_ARRAY, [[0, 255]])
cv.CalcHist([plate_grey], hist)
total_pixels = plate_grey.width * plate_grey.height
accum = 0
threshold = 0
for i in range(0, 256):
accum += cv.QueryHistValue_1D(hist, i)
if accum > total_pixels * .45:
threshold = i
break
print threshold
plate_mono = cv.CreateImage((plate.width, plate.height),
cv.IPL_DEPTH_8U, 1)
cv.Threshold(plate_grey, plate_mono, threshold, 255.0,
cv.CV_THRESH_BINARY)
import os
import cv
import anpr
from quick_show import quick_show
files = os.listdir('data/examples')
counter = 0
for f in files:
image = cv.LoadImage('data/examples/' + f)
for plate in anpr.detect_plates(image):
zzz = cv.CreateImage(cv.GetSize(plate), cv.IPL_DEPTH_8U, 3)
cv.Smooth(plate, zzz)
#
cv.PyrMeanShiftFiltering(plate, zzz, 40, 15)
foo = anpr.greyscale(plate)
segmented = cv.CreateImage(cv.GetSize(plate), cv.IPL_DEPTH_8U, 1)
bar = cv.CreateImage(cv.GetSize(plate), cv.IPL_DEPTH_8U, 1)
cv.EqualizeHist(foo, segmented)
cv.AdaptiveThreshold(
segmented, bar, 255, cv.CV_ADAPTIVE_THRESH_GAUSSIAN_C,
cv.CV_THRESH_BINARY_INV,
plate.height % 2 == 0 and (plate.height + 1) or plate.height,
plate.height / 2)
baz = cv.CreateImage(cv.GetSize(plate), cv.IPL_DEPTH_8U, 1)
el = cv.CreateStructuringElementEx(1, 2, 0, 0, cv.CV_SHAPE_RECT)
cv.Erode(bar, baz, el)
#quick_show(plate)
#quick_show(segmented)
#quick_show(bar)
quick_show(baz)
Created by Oliver Smith on 2009-09-26.
Copyright (c) 2009 Oliver Smith. All rights reserved.
"""
import os
import cv
import anpr
from quick_show import quick_show
files = os.listdir('data/examples')
counter = 0
for f in files:
image = cv.LoadImage('data/examples/'+f)
for plate in anpr.detect_plates(image):
plate_grey = anpr.greyscale(plate)
hist = cv.CreateHist([256], cv.CV_HIST_ARRAY, [[0,255]])
cv.CalcHist([plate_grey], hist)
total_pixels = plate_grey.width * plate_grey.height
accum = 0
threshold = 0
for i in range(0,256):
accum += cv.QueryHistValue_1D(hist, i)
if accum > total_pixels*.45:
threshold = i
break
print threshold
plate_mono = cv.CreateImage((plate.width,plate.height),
cv.IPL_DEPTH_8U,
1)
cv.Threshold(plate_grey,