def AnprLocalised(im, bbox, preProcessedModel):
if len(preProcessedModel) == 0:
print "Warning: recognition model is empty"
#Deskew
expandedBbox, bestInd, bestAngle = deskew.Deskew(im, bbox)
#print "expandedBbox", expandedBbox
deskewedIm = deskew.RotateAndCrop(im, expandedBbox, bestAngle)
#Split into characters
scoreIm = deskewMarkedPlates.RgbToPlateBackgroundScore(deskewedIm)
#misc.imshow(scoreIm)
charBboxes, charCofGs = detectblobs.DetectCharacters(scoreIm)
if charBboxes == None or charCofGs == None:
return None
#Recognise individual characters
details = []
bestGuess = ""
bestGuessConfidence = []
for bbx, cofg in zip(charBboxes, charCofGs):
charScores, candidateImgs = recognisechars.ProcessPatch(
deskewedIm, bbx, cofg, preProcessedModel)
#for ch in charScores[:5]:
# print ch[0], ch[1]
details.append(charScores)
bestChar = charScores[0][1]
bestGuess += bestChar
#Look at the ranked results and see the proportion that
#are in agreement in the top 10
countAgree = 0
countTested = 0
for sc, ch, im, srcId in charScores[1:11]:
if bestChar == ch:
countAgree += 1
countTested += 1
bestGuessConfidence.append(float(countAgree) / countTested)
return bestGuess, bestGuessConfidence, details
for photoNum, photo in enumerate(plates):
fina = photo[0]['file']
reg = photo[1]['reg']
foundObjId = photo[1]['object']
if foundObjId == objId:
break
bboxes = plateCharBboxes[objId]
plateStr = plateString[objId]
charCofG = plateCharCofGs[objId]
bbox, angle = plateCharBboxAndAngle[objId]
plateStrStrip = managetrainingchars.StripInternalSpaces(plateStr)
#ViewPlate(fina, bbox, angle, bboxes, charCofG)
im = misc.imread(fina)
rotIm = deskew.RotateAndCrop(im, bbox, angle)
print "Plate", plateCount, "of", len(testObjIds)
for i, (bbx, cCofG) in enumerate(zip(bboxes, charCofG)):
expectedChar = None
if plateStrStrip is not None and len(plateStrStrip) == len(bboxes):
expectedChar = plateStrStrip[i]
print reg, expectedChar, cCofG, bbx
charScores, candidateImgs = ProcessPatch(rotIm, bbx, cCofG,
preProcessedModel)
expectedCharFiltered = expectedChar
bestCharFiltered = charScores[0][1]
if expectedCharFiltered == "I": expectedCharFiltered = "1"
im = misc.imread(actualFina)
finaImSplitPath = os.path.split(fina)
finaImSplitExt = os.path.splitext(finaImSplitPath[1])
outRootFina = "train" + "/" + finaImSplitExt[0]
for plate in photo[1:]:
bbox = plate['bbox']
reg = plate['reg']
xran = (bbox[0], bbox[0]+bbox[2])
yran = (bbox[1], bbox[1]+bbox[3])
print count, actualFina, xran, yran
bbox, bestInd, bestAngle = deskew.Deskew(im, (xran, yran))
rotIm = deskew.RotateAndCrop(im, (xran, yran), bestAngle)
#misc.imsave("rotIm{0}.png".format(count), rotIm)
imScore = RgbToPlateBackgroundScore(rotIm)
#normContrast = exposure.equalize_hist(imScore)
normContrast = exposure.rescale_intensity(imScore)
#normContrast = exposure.equalize_adapthist(imScore)
thresh = 0.6 * (normContrast.min() + normContrast.max())
#normContrast = (normContrast > 0.5)
#print normContrast.min(), normContrast.max()
misc.imsave("{0}.png".format(outRootFina), normContrast)
pickle.dump((bbox, bestAngle), open("{0}.deskew".format(outRootFina), "wb"), protocol=-1)