def generate_new_round(dframeList, currentBand, this_round, handicap = False):
dframe = dframeList[currentBand]
sortedFrame = MMS.sort_by_mms(dframeList, currentBand, False, handicap)
oppset = {}
for i,item in sortedFrame['history'].iteritems():
os=set()
if (item!=""):
for opp in item.split(";"):
if (opp.lower() != 'bye'):
os.add(int(opp[1:-1]))
oppset[sortedFrame['id'][i]]=os
finalPairing = {}
currentIndex = []
currentSet = set()
finalList = []
if (this_round == 0): #special treatment for the very first round for performance consideration
first_pairing = get_foldpairing(len(sortedFrame))
finalList.append((0, first_pairing))
else:
totalCount = [0]
get_mutation(sortedFrame, 0, finalPairing, currentIndex, currentSet, oppset, totalCount)
topPairings = sorted(finalPairing.iteritems())[0]
random.shuffle(topPairings[1])
for item in topPairings[1]:
finalList.append((topPairings[0], item))
return finalList, sortedFrame
def azureml_main(dataframe1 = None, results = None):
# Execution logic goes here
if (dataframe1 is None):
return pandas.DataFrame(columns=['id','name','rank','history'])
rawframe = dataframe1.fillna("")
df, handicap = read_round(rawframe)
for i in range(0, len(df)-1):
df[i] = update_round(df[i], results)
df[i] = MMS.sort_by_mms(df, i, dropAux=False, handicap=handicap[i])
#allStanding.append(dfRound[i].copy())
#newframe = update_round(rawframe, results)
#newframe = pandas.concat(df, ignore_index=True)
newframe = df[0]
for i in range(1, len(df)-1):
if handicap[i]:
newframe.loc[len(newframe)] = ['&','&','&','&','&','&','&']
else:
newframe.loc[len(newframe)] = ['=','=','=','=','=','=','=']
newframe = newframe.append(df[i])
# If a zip file is connected to the third input port is connected,
# it is unzipped under ".\Script Bundle". This directory is added
# to sys.path. Therefore, if your zip file contains a Python file
# mymodule.py you can import it using:
# import mymodule
# Return value must be of a sequence of pandas.DataFrame
return newframe
def addNewContainer(self, containername):
if containername == Names.Containers["SMS"]:
self.myContainers[containername] = SMS.SMS(containername, self)
elif containername == Names.Containers["RPDU"]:
self.myContainers[containername] = RPDU.RPDU(containername, self)
elif containername == Names.Containers["UD"]:
self.myContainers[containername] = UD.UD(containername, self)
elif containername == Names.Containers["WSP"]:
self.myContainers[containername] = WSP.WSP(containername, self)
elif containername == Names.Containers["WBXML"]:
self.myContainers[containername] = WBXML.WBXML(containername, self)
elif containername == Names.Containers["SIMCMD"]:
self.myContainers[containername] = SIMCmd.SIMCmd(
containername, self)
elif containername == Names.Containers["MMS"]:
self.myContainers[containername] = MMS.MMS(containername, self)
elif containername == Names.Containers["ENVELOPE"]:
self.myContainers[containername] = Envelope.Envelope(
containername, self)
def FaceDetection(image):
try:
FaceFound = 'false'
#Detection Models
Prototxt = "ModelFaceRec.prototxt.txt" #ConfigValues.ReturnRDODProtoTXT()
Model = "ModelFaceRec.caffemodel" #ConfigValues.ReturnRTODModel()
SystemConfidence = ConfigValues.ReturnRTODSystemConfidence()
net = cv2.dnn.readNetFromCaffe(Prototxt, Model)
# load the input image and construct an input blob for the image
# by resizing to a fixed 300x300 pixels and then normalizing it
(h, w) = image.shape[:2]
blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 1.0,
(300, 300), (104.0, 177.0, 123.0))
# pass the blob through the network and obtain the detections and
# predictions
print("[INFO] computing object detections...")
net.setInput(blob)
detections = net.forward()
# loop over the detections
DetectedFaces = []
for i in range(0, detections.shape[2]):
# extract the confidence (i.e., probability) associated with the
# prediction
confidence = detections[0, 0, i, 2]
# filter out weak detections by ensuring the `confidence` is
# greater than the minimum confidence
if confidence > SystemConfidence:
# compute the (x, y)-coordinates of the bounding box for the
# object
box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
(startX, startY, endX, endY) = box.astype("int")
#Grab ROI
#[startY:endY, startX:endX]
roi = image[startY:endY, startX:endX]
DetectedFaces.append(roi)
for df in DetectedFaces:
FaceFound = 'true'
#upload to DropBox
#Path to Temp Save Alert Images
Image_Path = "FaceDetection.jpeg"
#Save Image to Disk
cv2.imwrite(Image_Path, df)
URL = DropBox.UploadToDropBox(Image_Path)
print("DropBox Image Public Share URL: " + str(URL))
#Send MMS
if (ConfigValues.ReturnTwilioMMSEnabled() == 'true'):
PhoneDestination = ConfigValues.ReturnAlertPhoneDestination()
for PD in PhoneDestination:
MMS.Send_MMS(
"FaceDetection " +
str(datetime.datetime.today().strftime(
'%d-%m-%Y-%H-%M-%S')), PD, URL)
print("MMS Alerts Sent")
return FaceFound
except:
print("Oops!, Face Detection Error: ", sys.exc_info()[0], "occurred.")
