def testRollerBlock():
import matplotlib.pyplot as plt
import platform
import cv2
import os
if "Windows" in platform.system():
filename = r"D:\chengxu\python\project\digitRecognise\com\huitong\gasMeterv1\data\img\style1\2.jpg"
elif "Linux" in platform.system():
filename = r"/home/allen/work/digitRecognise/com/huitong/gasMeterv1/data/img/style1/000.jpg"
style1 = GasmeterStyle1(desImageDepth=1)
image = cv2.imread(filename)
ImageTool.showImagePIL(image)
style1.setImage(image)
rollerBlackImage = style1.getRollerBlackArea()
# rollerBlackImage = cv2.cvtColor(rollerBlackImage,cv2.COLOR_BGR2RGB)
title = str(rollerBlackImage.shape) + os.path.basename(filename)
# rollerBlackImage = ImageTool.convertImgBGR2Gray(rollerBlackImage)
# ret,rollerBlackImage = ImageTool.getOTSUGrayImage(rollerBlackImage)
plt.figure()
plt.imshow(rollerBlackImage)
plt.title(title)
plt.show()
def getPredict(hps, mode, gasmeter_filename, save_file_name):
xp = tf.placeholder(
tf.float32,
[None, captchaBoxHeight * captchaBoxWidth * gen.ImageDepth])
yp = tf.placeholder(tf.float32,
[None, captchaCharacterLength * CHAR_SET_LEN])
model = ResNetModel.ResNetModel(hps, xp, yp, mode, captchaBoxHeight,
captchaBoxWidth, gen.ImageDepth)
model.create_graph(captchaCharacterLength)
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
saver = tf.train.Saver()
saver.restore(sess, save_file_name)
# image = Image.open(gasmeter_filename)
# image = image.resize((captchaBoxWidth,captchaBoxHeight), Image.BICUBIC)
image = cv2.imread(gasmeter_filename)
image = ImageTool.imageResize(image, captchaBoxWidth, captchaBoxHeight)
if gen.ImageDepth == 1:
image = ImageTool.convertImgBGR2Gray(image)
# image = ImageTool.convertImgRGB2Gray(image)
ImageTool.showImagePIL(image)
images = ImageTool.repeatImage2Tensor(image, hps.batch_nums)
feed_dict = {xp: images, model.is_training_ph: False}
outputs = sess.run([model.outputs], feed_dict=feed_dict)
text = get_predict_text(outputs)
return text
def getLCDAreaData(image):
"""
获取 LCD 区域图片数据
:param image: cv2 读进来的图片对象
"""
image = ImageTool.preProcessImage(image)
blackMask = MaskTool.getBlackMaskBGR()
image = blackMask.getInterestImageAreaData(image)
shape = image.shape
grayImage = ImageTool.convertImgBGR2Gray(image)
mid = int(shape[0] / 2)
splitImageBox = (0, mid, shape[1], shape[0])
splitImageGray = ImageTool.getCropImageByBox(grayImage, splitImageBox)
splitImage = ImageTool.getCropImageByBox(image, splitImageBox)
# 显示剪切的 lcd 屏所在的下半屏灰度图
ImageTool.showImagePIL(splitImageGray, "splitImageGray")
retval, otsuImage = ImageTool.getOTSUGrayImage(splitImageGray)
otsuImage = ImageTool.convertImgGray2BGR(otsuImage)
lower = (250, 250, 250)
upper = (255, 255, 255)
lcdBoxCorner = ImageTool.getInterestBoxCornerPointByColor(
otsuImage, lower, upper)
lcdBox = LCDLightDetect.getMinBox(lcdBoxCorner)
# ImageTool.showBoxInImageByBox(splitImage, lcdBox)
lcdImage = ImageTool.getCropImageByBox(splitImage, lcdBox)
return lcdImage
def getPredict(hps, mode, gasmeter_filename, save_file_name):
xp = tf.placeholder(
tf.float32,
[None, captchaBoxHeight * captchaBoxWidth * gen.ImageDepth])
yp = tf.placeholder(tf.float32,
[None, captchaCharacterLength * CHAR_SET_LEN])
model = ResNetModel.ResNetModel(hps, xp, yp, mode, captchaBoxHeight,
captchaBoxWidth, gen.ImageDepth)
model.create_graph(captchaCharacterLength)
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
saver = tf.train.Saver()
saver.restore(sess, save_file_name)
# images = gen.get_batch_gasmeter_digit_area_from_filename(gasmeter_filename, hps.batch_nums)
image = cv2.imread(gasmeter_filename)
# gasmeter = GasmeterStyle0(captchaBoxWidth,captchaBoxHeight,desImageDepth=gen.ImageDepth)
gasmeter = GasmeterStyle1(captchaBoxWidth,
captchaBoxHeight,
desImageDepth=gen.ImageDepth)
gasmeter.setImage(image)
image = gasmeter.getRollerBlackArea()
ImageTool.showImagePIL(image)
images = ImageTool.repeatImage2Tensor(image, hps.batch_nums)
feed_dict = {xp: images, model.is_training_ph: False}
outputs = sess.run([model.outputs], feed_dict=feed_dict)
text = get_predict_text(outputs)
return text
def fun8():
from com.huitong.gasMeterv1.framework.tool.GenDigitsImage import GenDigitsPicture
characterLength = 1
width = 15
height = 30
bkgColor = (20,20,20)
fontColor = (200,200,200)
fontSizes = (29,)
gen = GenDigitsPicture(characterLength, width, height)
while True:
text,image = gen.get_text_and_image(backgroundColor=bkgColor,fontColor=fontColor,fontSizes=fontSizes)
ImageTool.showImagePIL(image,text)
def getPredict(hps, mode, save_file_name, gen):
xp = tf.placeholder(
tf.float32,
[None, captchaBoxHeight * captchaBoxWidth * gen.ImageDepth])
yp = tf.placeholder(tf.float32,
[None, captchaCharacterLength * CHAR_SET_LEN])
model = ResNetModel.ResNetModel(hps, xp, yp, mode, captchaBoxHeight,
captchaBoxWidth, gen.ImageDepth)
model.create_graph(captchaCharacterLength)
gen1 = GenImageGasMeterStyle1m1(captchaCharacterLength,
captchaBoxWidth,
captchaBoxHeight,
imageDepth=1)
gen2 = GenImageGasMeterStyle1m2(captchaCharacterLength,
captchaBoxWidth,
captchaBoxHeight,
imageDepth=1)
gen3 = GenImageGasMeterStyle1m3(captchaCharacterLength,
captchaBoxWidth,
captchaBoxHeight,
imageDepth=1)
gens = [gen1, gen2, gen3]
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
saver = tf.train.Saver()
saver.restore(sess, save_file_name)
while True:
gen = random.choice(gens)
oriText, image = gen.get_text_and_image()
images = ImageTool.repeatImage2Tensor(image, hps.batch_nums)
feed_dict = {xp: images, model.is_training_ph: False}
outputs = sess.run([model.outputs], feed_dict=feed_dict)
predictText = get_predict_text(outputs)
title = "text:%s, predict:%s" % (oriText, predictText)
ImageTool.showImagePIL(image, title)
print(title)
def test():
import cv2
import platform
import os
style = GasmeterStyle0(desImageDepth=1)
if "Linux" in platform.system():
filename = r'/home/allen/work/digitRecognise/com/huitong/gasMeterv1/data/img/style0/10.jpg'
elif "Windows" in platform.system():
filename = r"D:\chengxu\python\project\digitRecognise\com\huitong\gasMeterv1\data\img\style0\10.jpg"
image = cv2.imread(filename)
title = os.path.basename(filename)
style.setImage(image)
image = style.getRollerBlackArea()
# ret, image = ImageTool.getOTSUGrayImage(image)
# image = cv2.cvtColor(image,cv2.COLOR_BGR2RGB)
ImageTool.showImagePIL(image, str(image.shape) + title)
def getLCDBox(self):
"""
"""
shape = self._image.shape
mid = int(shape[0] / 2)
splitImageBox = (0, mid, shape[1], shape[0])
splitImageGray = ImageTool.getCropImageByBox(self._grayImage,
splitImageBox)
ImageTool.showImagePIL(splitImageGray)
splitImage = ImageTool.getCropImageByBox(self._image, splitImageBox)
retval, otsuImage = ImageTool.getOTSUGrayImage(splitImageGray)
otsuImage = ImageTool.convertImgGray2BGR(otsuImage)
lower = (250, 250, 250)
upper = (255, 255, 255)
lcdBoxCorner = ImageTool.getInterestBoxCornerPointByColor(
otsuImage, lower, upper)
lcdBox = ImageTool.getBoxFromBoxCorner(lcdBoxCorner)
self._lcdBox = lcdBox
ImageTool.showBoxInImageByBoxCornerPoint(splitImage, lcdBoxCorner,
"lcd")
def lcdLighted(lcdImage):
"""
检测lcd 区域图片是否点亮，如果亮返回True，如果不亮返回False
:param lcdImage: cv2 读进来的图片对象
"""
lower = (0, 0, 0)
upper = (50, 50, 50)
blackBoxCorner = ImageTool.getInterestBoxCornerPointByColor(
lcdImage, lower, upper)
# 如果获得的 lcd 区域有黑色区域，说明没有点亮。
if blackBoxCorner is not None:
return False
lcdImageGray = ImageTool.convertImgBGR2Gray(lcdImage)
retval, otsuImage = ImageTool.getOTSUGrayImage(lcdImageGray)
otsuImage = ImageTool.convertImgGray2BGR(otsuImage)
notLcdBoxCorner = ImageTool.getInterestBoxCornerPointByColor(
otsuImage, lower, upper)
ImageTool.showImagePIL(lcdImage, "lcdimage")
return notLcdBoxCorner is None
def fun9():
filename = r"D:\chengxu\python\project\digitRecognise\com\huitong\gasMeterv1\data\img\gasmeterRoller\000041.jpg"
image = Image.open(filename)
image = image.resize((128,64),Image.CUBIC)
ImageTool.showImagePIL(image,"des")