def multiPipeline(qr):
auxList = []
listResults = []
### Read Images
images = readImage.readImage(qr, array = True)
if (not isinstance(images,list)):
auxList.append(images)
images = auxList
for i,outputRI in enumerate(images):
if(isinstance(outputRI, str)):
continue
## Return 1 if it is blurry
blurry = pP.isBlurry(outputRI)
## Find Biggest Square
## Find Counturs Biggest Square
## Sort points of every contour
## Perspective Transform for Biggest Square
inputBS = outputRI
outputBS = biggestSquare(inputBS)
## Find X-Markers Group (a.k.a. Test Square)
## Set perspective X-Markers Group
## Adjust Offset X-Markers Group
## Binarize X-Markers Group
## Find Contours using Binarized X-Markers Group
## Keep copy of RGB image for later use
inputXG = outputBS
outputXG = findXMGroup(inputXG)
### Find Individual X-Markers Boxes (a.k.a. sitios de prueba)
inputXB = outputXG
outputXB = findXMBox(inputXB)
## X-Marker Analisis (a.k.a. sitios de prueba)
## Apply Mask
## Erosion Dilation
## Blob Detection
## Assses Blob Areas and Assign Results
inputXA = outputXB
inputXA = analizeXMBlobs(inputXA)
### Image Quality Analysis
if (not isinstance(inputXA,str)):
inputXA['blurry'] = blurry
inputXA['count'] = i
listResults.append(inputXA)
return listResults
def completePipeline(qr):
### Read Image
outputRI = readImage.readImage(qr)
if(isinstance(outputRI, str)):
return(outputRI)
## Return 1 if it is blurry
blurry = pP.isBlurry(outputRI)
## Find Biggest Square
## Find Counturs Biggest Square
## Sort points of every contour
## Perspective Transform for Biggest Square
inputBS = outputRI
outputBS = biggestSquare(inputBS)
## Find X-Markers Group (a.k.a. Test Square)
## Set perspective X-Markers Group
## Adjust Offset X-Markers Group
## Binarize X-Markers Group
## Find Contours using Binarized X-Markers Group
## Keep copy of RGB image for later use
inputXG = outputBS
outputXG = findXMGroup(inputXG)
### Find Individual X-Markers Boxes (a.k.a. sitios de prueba)
inputXB = outputXG
outputXB = findXMBox(inputXB)
## X-Marker Analisis (a.k.a. sitios de prueba)
## Apply Mask
## Erosion Dilation
## Blob Detection
## Assses Blob Areas and Assign Results
inputXA = outputXB
inputXA = analizeXMBlobs(inputXA)
### Image Quality Analysis
if (not isinstance(inputXA,str)):
inputXA['blurry'] = blurry
return inputXA
import perspective as pPe
import indAnalysis as inA
# %% [markdown]
# ## Golden Master Image Algorithm (AS IS)
0
# %%
batch = False
qr = '601170500100404'
# %% [markdown]
# ### Read Image
# %%
# Read from local file local = True, from db local = False, count = Test repeated
imgBGR = readImage.readImage(qr, local=False, ext='png', count=0)
if (isinstance(imgBGR, str)):
print(imgBGR)
else:
# show the original image
if (not batch):
plt.subplot(111), plt.imshow(cv2.cvtColor(imgBGR, cv2.COLOR_BGR2RGB))
plt.show()
# %% [markdown]
# ### Find Border of Biggest Square
# %%
if (isinstance(imgBGR, str)):
print(imgBGR)
else:
imgResized = pP.resizeImg(imgBGR, 728)
#allInit.initConversationWeight()
#allInit.initCoreWeight()
#allInit.initMlpWeight()
setOfImages = imageArray() # создаем массив объектов
#counter = howMuchFaces(setOfImages)
setOfImages = sortImagesArray(
howMuchFaces(setOfImages),
setOfImages) # сделали ортировку массива. Каждый второй это не лицо
for i in range(setOfImages.__len__()):
name = setOfImages[i].path + setOfImages[i].name
key = True
print(name)
contollerOfError(setOfImages[i], 1)
readImage.readImage(name)
while (key == True):
#Это все работает
valueList = [] # Здесь будет список объектов
i, j = 0, 0 # НА всякий случай сброс
for j in range(0, 2):
for i in range(0, 6): # Создаем список объектов
value = classSystem.Image(
i, j
) # передаем индекс для создания объекта с нужным индексом
valueList.append(value) # создаем объект с нужным индексом
"""
for i in range(len(valueList)):
valueList[i].convCalculate()
valueList[i].subCalculate()
mlps.readDataForInputMlp()
def main(): # 主函数
unzip() # 解压缩
docuFaceInfo() # 记录信息
x_train, y_train, x_test, y_test = readImage() # 测试集与训练集的分类整理（路径与对应人名编号）
x_train_img, x_test_img = returnImage(x_train, x_test) # 图片读取
trainModel(x_train_img, y_train, x_test_img, y_test) # CNN训练
def main(pathOdom_,pathImage_,pathWeight_):
# file path
pathOdom = pathOdom_
pathImage = pathImage_
pathWeight = pathWeight_
# initilize the gtsam solver
iSam = PoseNetiSam()
# initilize posenet
poseNet = trainer(pathWeight,pathImage,100,False,True,True) # comment for debug
# initialize the plot tool
fig = plt.figure(1)
# parameter
resultsId = 1
iSamUpdateRate = 1 # times of updates in one iteration
timestamp = 0
startId = 0
# read data
images = readImage(pathImage)
datasource = gen_data_cam.get_data()
startTimestamp = images.getStartId() # start timestamp
endTimestamp = images.getEndId() # end timestamp
odom = odometry(pathOdom,startTimestamp,endTimestamp)
# sensor info
poseNetCov = [4.2,7.1,0.4]
# records of path
records = list()
groundTruth = list()
evalTime = list()
# prior info
priorMu = [-56,-79,-1.56]
priorCov = [10,10,10]
iSam.initialize(priorMu,priorCov) # adding prior
_,imgTimestamp,currGT = images.getImage(startId)
image = datasource.images[startId]
feed={tf.get_default_graph().get_tensor_by_name('Placeholder:0'): np.expand_dims(image, axis=0) }
tic = time.time()
measurement = poseNet.sess.run([tf.get_default_graph().get_tensor_by_name('fc9/fc9:0')], feed) # comment for debug
measurement_x = np.squeeze(measurement)[0]
measurement_y = np.squeeze(measurement)[1]
measurement_theta = np.squeeze(measurement)[-1]
measurement = np.array([measurement_x,measurement_y,measurement_theta])
startId += 1
iSam.addObs(measurement,poseNetCov) # adding first measurement # comment for debug
# iSam.addObs(currGT,poseNetCov) # comment for release
# iSam.printGraph()
currEstimate = iSam.update() # update the graph
evalTime.append(time.time()-tic)
# currEstimate = priorMu # comment for release
records.append(currEstimate)
groundTruth.append(currGT)
iterations = images.length()-1
# iterations = 50 # comment for release
# localization begins here
print("\nBegin localization:\n")
for i in tqdm(range(iterations)):
_,imgTimestamp,currGT = images.getImage(startId)
image = datasource.images[startId]
feed={tf.get_default_graph().get_tensor_by_name('Placeholder:0'): np.expand_dims(image, axis=0) }
tic = time.time()
# adding odometry
# BETA: matching the frequency with the sensor measurement, cumsum the motion to reduce factor. Gaussian Assumption of the odometry and independence are made
motionCum = [0,0,0]
motionCovCum = [0,0,0]
while timestamp<=imgTimestamp:
motion,motionCov,timestamp = odom.getOdometry()
motionCum = [motion[0]+motionCum[0],
motion[1]+motionCum[1],
motion[2]+motionCum[2]]
motionCovCum = [motionCov[0]+motionCovCum[0],
motionCov[1]+motionCovCum[1],
motionCov[2]+motionCovCum[2]]
# iSam.printGraph() # comment for release
# print("ready to step.\n")
iSam.step(motionCum,motionCovCum)
# getting measurement and update image timestamp
# measurement = poseNet.test(image,0,1) # comment for debug
# measurement = [measurement[0],measurement[1],measurement[-1]]
measurement = poseNet.sess.run([tf.get_default_graph().get_tensor_by_name('fc9/fc9:0')], feed) # comment for debug
measurement_x = np.squeeze(measurement)[0]
measurement_y = np.squeeze(measurement)[1]
measurement_theta = np.squeeze(measurement)[-1]
measurement = np.array([measurement_x,measurement_y,measurement_theta])
groundTruth.append(currGT)
# print("ready to add measurement.\n")
# adding measurement factor
iSam.addObs(measurement,poseNetCov) # comment for debug
# iSam.addObs(currGT,poseNetCov) # comment for release
# optimize factor graph
# print("ready to update.\n")
currentEst = iSam.update(iSamUpdateRate) # comment for debug
evalTime.append(time.time()-tic)
# currentEst = currGT # comment for release
records.append(currentEst)
# increment the Id
startId += 1
# plot tool for the calculated trajetory
x = records[-1][1]
y = records[-1][0]
plt.scatter(x,y,c='g') # plot line from x_t-1 to x_t
x = groundTruth[-1][1]
y = groundTruth[-1][0]
plt.scatter(x,y,c='r') # plot line of ground truth from x_t-1 to x_t
plt.scatter(measurement[1],measurement[0],c='b') # plot points of posenet prediction
if(i == 0):
plt.legend(['prediction',' ground truth','posenet prediction'])
# plt.draw() # comment if not dynamic plotting
# plt.pause(0.01) # whether need to pause?
# print("Done {} iterations, {} iterations in total".format(i+1,iterations))
# print mean and stddev error
error = np.zeros([images.length(),3])
for i in range(images.length()):
error[i,:] = abs(iSam.getEstimate(i+1)-np.array(groundTruth[i]))
meanErr = np.sum(error,axis=0)/len(error)
stdErr = np.std(error,axis=0)
print("The mean error is {} and standard deviation is {}.".format(meanErr,stdErr))
# store estimation
pickleOut = open('dict.{0}_estimation'.format(resultsId),'wb')
pickle.dump(np.asarray(records),pickleOut)
pickleOut.close()
# store ground truth
pickleOut = open('dict.{0}_groundTruth'.format(resultsId),'wb')
pickle.dump(np.asarray(groundTruth),pickleOut)
pickleOut.close()
# store evalTime
pickleOut = open('dict.{}_evalTime'.format(resultsId),'wb')
pickle.dump(np.asarray(evalTime),pickleOut)
pickleOut.close()
# save fig
fig.savefig("Trajectory{0}".format(resultsId))