def __init__(self):
#Inquire Credentials
self.buUn = urllib.quote_plus(raw_input("BU TA Username: "******"BU TA Password: "******"Camera", img)
# OCR
tesseract.SetCvImage(img, self.api)
match = re.search(r"(U\d{8})", self.api.GetUTF8Text())
if match:
BUID = match.groups()[0]
print
# Lookup BUID
profile = self.lookupBUID(BUID)
# Prompt for approval
if profile != False and raw_input("Approve (y/n): ") == "y":
self.approveBUID(BUID)
# If Esc is pressed, quit (Also the fps)
if cv.WaitKey(100) == 27: break
def adaptiveThresholding(inputImage, neighborhoodWidth=71, offsetFromMean=15):
"""
Apply adaptive thresholding to a given image. Uses a
neighborhoodWidth x neighborhoodWidth kernel. Threshold is set at
mean intensity within kernel + offsetFromMean.
"""
outputImage = cv.CreateImage(cv.GetSize(inputImage), cv.IPL_DEPTH_8U, 1)
cv.AdaptiveThreshold(inputImage, outputImage, 255, cv.CV_THRESH_BINARY,
cv.CV_ADAPTIVE_THRESH_MEAN_C, neighborhoodWidth,
offsetFromMean)
return outputImage
def adaptive_threshold(image):
new_image = cv.CreateImage((image.width, image.height), image.depth, 1)
cv.AdaptiveThreshold(image, new_image, 125, cv.CV_ADAPTIVE_THRESH_MEAN_C,
cv.CV_THRESH_BINARY, 7, 10)
return new_image
def preprocessing(im):
out = cv.CreateImage(cv.GetSize(im), cv.IPL_DEPTH_8U, 1)
cv.CvtColor(im, out, cv.CV_BGR2GRAY)
cv.AdaptiveThreshold(out, out, 255.0, cv.CV_THRESH_BINARY,
cv.CV_ADAPTIVE_THRESH_MEAN_C, 11)
return out
def adaptivethreshold():
display(src, "Source Image")
cv.AdaptiveThreshold(src, dst, 255, cv.CV_ADAPTIVE_THRESH_MEAN_C,
cv.CV_THRESH_BINARY_INV, 3, 5)
display(dst, "Destination Image")
cv.WaitKey(0)
image = cv.LoadImageM(path_root + f)
for plate in anpr.detect_plates(image):
#quick_show(image)
#quick_show(plate)
#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)
print 'baz'
quick_show(baz)
print 'bar'
quick_show(bar)
print 'segmented'
quick_show(segmented)
image_path = 'plate.png'
image_path2 = 'plate2.png'