def detectCarplate(cv_image=None, pil_image=None, image_path=None, threshold_method='otsu'):
'''
wrapped method to get the result of a car plate
pseudo:
convert into cv_image
then find contours
process the image into threshold image (only binary color)
foreach contour image:
put into the network
save in an array
sort the array (optional)
convert to string and return the string
:param cv_image: image in cv2 form (numpy)
:param pil_image: image in pil form (Image)
:param image_path: image path (str)
:param threshold_method: (best threshold so far)
:return: a string of car plate number
'''
if cv_image is None and pil_image is None and image_path is None:
raise Exception('one of them must not be None')
number = 1
if threshold_method == 'normal':
number = 2
if threshold_method == 'normal-otsu':
number = 3
if cv_image is not None:
result = ''
img = resize(cv_image, 320)
result_character = findContours(img, number)
if number == 1:
currentImg = ip2.preprocessOtsuThreshold(img)
elif number == 2:
currentImg = ip2.preprocessWithThreshold(img)
else:
currentImg = ip2.preprocessWithThreshold(ip2.preprocessWithThreshold(img))
for character in result_character: #type: cu.Character
x, y, w, h = character.boundingRect
blurredImage = ip2.gaussianBlur(
ip2.resizeWithSize(ip2.addBorder(ip2.addPadding(currentImg[y:y+h, x:x+w]), 8), (32, 32)), 3)
caffe_image = singleimage.cv2caffe(blurredImage)
character.index, character.score = singleimage.getProb(net, caffe_image)
character.score = character.score[0][character.index]
result_character = sortresult.sortCarplateResult(result_character)
for character in result_character:
result += singleimage.convertIndex(character.index)
cv2.imwrite('test/'+str(character.index)+'-'+str(character.identity)+'.png', character.getImage(img))
if len(result) <= 4 and number == 1:
return detectCarplate(cv_image, threshold_method='normal-otsu')
elif len(result) <= 4 and number == 3:
return detectCarplate(cv_image, threshold_method='normal')
elif len(result) <= 4 and number == 2:
return result
return result
elif pil_image is not None:
return detectCarplate(cv_image=ip2.PIL2CVImage(pil_image), threshold_method=threshold_method)
else:
return detectCarplate(cv_image=cv2.imread(image_path), threshold_method=threshold_method)
def eliminateNonMaybe(ori_img, maybeCharacter, total, min_thresh, number, display=None): if number is 1: thresh_img = ip2.preprocessOtsuThreshold(ori_img) elif number is 2: thresh_img = ip2.preprocessWithThreshold(ori_img, min_thresh) else: thresh_img = ip2.preprocessOtsuThreshold(ip2.preprocessWithThreshold(ori_img)) cu.normalChecking(thresh_img, ori_img, total, 150, display) for t in total.getArray(): if display is not None: ip2.drawRect(display, t.boundingRect, MAGENTA) maybeCharacter.append(t)
def compute(self, thresh=150, selection=1):
ori_path = self.ori_path
if ori_path == '':
return
save_path = 'temp.png'
compare1_img = None
compare2_img = None
compare3_img = None
if selection == 1:
compare1_img = ip2.preprocessWithThreshold(
resize(cv2.imread(ori_path), 320), thresh)
compare3_img = ip2.preprocessWithSpecialThresh(
resize(cv2.imread(ori_path), 320), thresh)
if selection == 1 or selection == 2:
compare2_img = ip2.erodePreprocessThreshold(
resize(cv2.imread(ori_path), 320), thresh, self.sliderValue)
print 'enter here'
if compare1_img is not None:
cv2.imwrite(save_path, compare1_img)
self.compare1.setPixmap(QtGui.QPixmap(QtCore.QString(save_path)))
cv2.imwrite(save_path, compare2_img)
self.compare2.setPixmap(QtGui.QPixmap(QtCore.QString(save_path)))
if compare3_img is not None:
cv2.imwrite(save_path, compare3_img)
self.compare3.setPixmap(QtGui.QPixmap(QtCore.QString(save_path)))
def recursiveChecking(thresh_img, ori_img, min_thresh, total, offset_x, offset_y): if min_thresh <= 180: contours, hierarchy = ip2.findContours(thresh_img) for cnt in contours: char = Character(cnt) if char.isMaybeSize(): if char.aspectRatio >= MAX_ASPECTRATIO: crop_img = ori_img[char.y:char.y+char.h, char.x:char.x+char.w] crop_img = cc.inRangeOfWhite(crop_img) new_thresh_img = ip2.preprocessWithThreshold(crop_img, min_thresh+10) recursiveChecking(new_thresh_img, crop_img, min_thresh+10, total, offset_x+char.x, offset_y+char.y) else: char.setXY(offset_x+char.x, offset_y+char.y) # char.increaseSize() total.__add__(char)
def guiRun(file, number):
'''
used by gui
:param file: image path
:param number: threshold method, 1-'otsu' 2-'threshold' 3-'normal-otsu'
:return:
'''
img = cv2.imread(file)
if img is not None:
img = resize(img, 320)
cv2.imwrite('gui/resize.png', img)
# display = img.copy()
characters = findContours(img, number)
greyscale = ip2.greyscale(img)
white = cc.newInRangeOfWhite(img)
threshold = ip2.preprocessWithThreshold(img)
threshold_otsu = ip2.preprocessOtsuThreshold(img)
otsu_normal = ip2.preprocessOtsuThreshold(threshold)
if number == 1:
contoursImage = threshold_otsu.copy()
currentImage = threshold_otsu.copy()
elif number == 2:
contoursImage = threshold.copy()
currentImage = threshold.copy()
else:
contoursImage = otsu_normal.copy()
currentImage = otsu_normal.copy()
contoursImage = ip2.colour(contoursImage)
indices = []
scores = []
for cnt in characters: #type: cu.Character
x, y, w, h = cnt.boundingRect
ip2.drawRect(contoursImage, (x, y, w, h), ip2.RED)
cv_image = ip2.resizeWithSize(ip2.addBorder(ip2.addPadding(currentImage[y:y+h, x:x+w]), 8), (32, 32))
caffe_image = singleimage.cv2caffe(cv_image)
prob, score = singleimage.getProb(net, caffe_image)
cnt.index = int(prob)
cnt.score = score[0][prob]
indices.append(int(prob))
scores.append(score[0][prob])
# indices, scores = sortResultv2(characters, indices, scores)
characters = sortresult.sortCarplateResult(characters)
carplate_str = ''
for cnt in characters:
carplate_str += singleimage.convertIndex(cnt.index)
if len(carplate_str) <= 4 and number == 1:
return guiRun(file, 3)
elif len(carplate_str) <= 4 and number == 3:
return guiRun(file, 2)
cv2.imwrite('gui/greyscale.png', greyscale)
cv2.imwrite('gui/white.png', white)
cv2.imwrite('gui/threshold.png', threshold)
cv2.imwrite('gui/threshold_otsu.png', threshold_otsu)
cv2.imwrite('gui/otsu_normal.png', otsu_normal)
cv2.imwrite('gui/contours.png', contoursImage)
cv2.imwrite('gui/current.png', currentImage)
return carplate_str, characters, scores
else:
return '', None