def handle_client_traffic(self, client_socket, client_addr):
"""
This method will handle incoming traffic from a given client.
:param client_socket:
:param client_addr:
:return:
"""
lot_id = client_socket.recv(4)
lot_id = unpack('!I', lot_id)
apiclient = openalpr_api.DefaultApi()
file_name = str(getpid())
img_file = open(file_name, 'wr+')
payload = client_socket.recv(1024)
while (payload):
print "Receiving..."
img_file.write(payload)
payload = client_socket.recv(1024)
img_file.close()
response = apiclient.recognize_post('sk_6cb172cdd75b54826a2089b0',
"plate,color,make,makemodel",
'Users/mohammadsial/Desktop/test',
country="us")
plate = response.plate.results[0].plate
confidence = response.plate.results[0].confidence
self.write_to_log('Plate : ' + plate)
self.write_to_log('Confidence : ' + str(confidence))
self.put_entry_in_db(lot_id, plate)
def __init__(self, region, confidence, api_key):
"""Init cloud api processing."""
import openalpr_api
super().__init__(region=region, confidence=confidence)
self._api = openalpr_api.DefaultApi()
self._api_key = api_key
def callAPI(image_url):
target_path = _j('tmp', image_url.split('/')[-1])
urllib.urlretrieve(image_url, target_path)
with open(target_path, "rb") as image_file:
image_bytes = base64.b64encode(image_file.read())
api_instance = openalpr_api.DefaultApi()
secret_key = 'sk_e014dacb3b70f1a4a902b301'
country = "us"
recognize_vehicle = 0
state = ''
return_image = 0
topn = 10
prewarp = ''
try:
api_response = api_instance.recognize_bytes(image_bytes,
secret_key,
country,
recognize_vehicle=0,
state='',
return_image=0,
topn=10,
prewarp='')
return api_response
except ApiException as e:
print "Exception when calling DefaultApi->recognize_bytes: %s\n" % e
def process_car_image(image_path):
# create an instance of the API class
api_instance = openalpr_api.DefaultApi()
secret_key = 'sk_4fb6feea66fb6b5524b2d984' # str | The secret key used to authenticate your account. You can view your secret key by visiting https://cloud.openalpr.com/
country = 'br' # str | Defines the training data used by OpenALPR. \"us\" analyzes North-American style plates. \"eu\" analyzes European-style plates. This field is required if using the \"plate\" task You may use multiple datasets by using commas between the country codes. For example, 'au,auwide' would analyze using both the Australian plate styles. A full list of supported country codes can be found here https://github.com/openalpr/openalpr/tree/master/runtime_data/config
recognize_vehicle = 0 # int | If set to 1, the vehicle will also be recognized in the image This requires an additional credit per request (optional) (default to 0)
state = '' # str | Corresponds to a US state or EU country code used by OpenALPR pattern recognition. For example, using \"md\" matches US plates against the Maryland plate patterns. Using \"fr\" matches European plates against the French plate patterns. (optional) (default to )
return_image = 0 # int | If set to 1, the image you uploaded will be encoded in base64 and sent back along with the response (optional) (default to 0)
topn = 10 # int | The number of results you would like to be returned for plate candidates and vehicle classifications (optional) (default to 10)
prewarp = '' # str | Prewarp configuration is used to calibrate the analyses for the angle of a particular camera. More information is available here http://doc.openalpr.com/accuracy_improvements.html#calibration (optional) (default to )
try:
# call api
api_response = api_instance.recognize_file(
image_path,
secret_key,
country,
recognize_vehicle=recognize_vehicle,
state=state,
return_image=return_image,
topn=topn,
prewarp=prewarp)
# print(api_response)
print('Start processing for ' + api_response.results[0].plate)
try:
# sucessful plate recognition
plate = api_response.results[0].plate
file_name = now.strftime("%Y-%m-%d") + '_logs.csv'
# SINESP informations
time = now.strftime("%Y-%m-%d %H:%M")
columns = [time, plate] + get_car_info(plate)
# writing log
write_log(plate, file_name, columns)
except:
# fail plate recognition
print('-> Fail Plate Recognition')
return 'Fail'
except ApiException as e:
print("Exception when calling DefaultApi> recognize_file: %s\n" % e)
def Number_plate(path,count):
api_instance = openalpr_api.DefaultApi()
with open(path, "rb") as image_file:
encoded_string = base64.b64encode(image_file.read())
image_bytes = encoded_string # str | The image file that you wish to analyze encoded in base64
secret_key = 'sk_DEMODEMODEMODEMODEMODEMO'
country = 'in' # A full list of supported country codes can be found here https://github.com/openalpr/openalpr/tree/master/runtime_data/config
recognize_vehicle = 0 #If set to 1, the vehicle will also be recognized in the image This requires an additional credit per request (optional) (default to 0)
state = '' # str | Corresponds to a US state or EU country code used by OpenALPR pattern recognition. For example, using \"md\" matches US plates against the Maryland plate patterns. Using \"fr\" matches European plates against the French plate patterns. (optional) (default to )
return_image = 0 # int | If set to 1, the image you uploaded will be encoded in base64 and sent back along with the response (optional) (default to 0)
topn = 3 # int | The number of results you would like to be returned for plate candidates and vehicle classifications (optional) (default to 10)
prewarp = '' # str | Prewarp configuration is used to calibrate the analyses for the angle of a particular camera. More information is available here http://doc.openalpr.com/accuracy_improvements.html#calibration (optional) (default to )
try:
a=[]
api_response = api_instance.recognize_bytes(image_bytes, secret_key, country, recognize_vehicle=recognize_vehicle, state=state, return_image=return_image, topn=topn, prewarp=prewarp)
# pprint(api_response)
i =0
if(len(api_response.results)!=0) :
for i in api_response.results[0].candidates :
a.append(i.plate)
if len(a)==1 or len(a)==2:
a.append(a[0])
a.append(a[0])
return a
else :
return ["NO_PLATE",'','']
except ApiException as e:
print ("Exception when calling DefaultApi->recognize_bytes: %s\n" % e)
# for i in range(1,41):
# a=Number_plate("Vehicles\\1 ("+str(i)+").jpg",5)
# print(a)
print(a[0])
def detectNumberPlate(fileName):
import time
import openalpr_api
from openalpr_api.rest import ApiException
from pprint import pprint
import json
# create an instance of the API class
api_instance = openalpr_api.DefaultApi()
import base64
encoded = base64.b64encode(open(fileName, "rb").read())
image_bytes = encoded # str | The image file that you wish to analyze encoded in base64
secret_key = 'sk_97a11cda807cbf257bffa0fd' # str | The secret key used to authenticate your account. You can view your secret key by visiting https://cloud.openalpr.com/
country = 'in' # str | Defines the training data used by OpenALPR. \"us\" analyzes North-American style plates. \"eu\" analyzes European-style plates. This field is required if using the \"plate\" task You may use multiple datasets by using commas between the country codes. For example, 'au,auwide' would analyze using both the Australian plate styles. A full list of supported country codes can be found here https://github.com/openalpr/openalpr/tree/master/runtime_data/config
recognize_vehicle = 0 # int | If set to 1, the vehicle will also be recognized in the image This requires an additional credit per request (optional) (default to 0)
state = '' # str | Corresponds to a US state or EU country code used by OpenALPR pattern recognition. For example, using \"md\" matches US plates against the Maryland plate patterns. Using \"fr\" matches European plates against the French plate patterns. (optional) (default to )
return_image = 1 # int | If set to 1, the image you uploaded will be encoded in base64 and sent back along with the response (optional) (default to 0)
topn = 10 # int | The number of results you would like to be returned for plate candidates and vehicle classifications (optional) (default to 10)
prewarp = '' # str | Prewarp configuration is used to calibrate the analyses for the angle of a particular camera. More information is available here http://doc.openalpr.com/accuracy_improvements.html#calibration (optional) (default to )
try:
api_response = api_instance.recognize_bytes(image_bytes, secret_key, country, recognize_vehicle=recognize_vehicle, state=state, return_image=return_image, topn=topn, prewarp=prewarp)
pprint(api_response)
jsonFormat =str(api_response)
jsonFormat = jsonFormat.replace('\'','"')
jsonFormat = jsonFormat.replace('None','"None"')
myDict = json.loads(jsonFormat)
#print(some)
#print('dic type is : '+ str(type(dic)))
try:
nplate = myDict["results"][0]["candidates"][0]['plate']
import data
return data.doQuery(nplate)
except:
print("No number plate detected")
except ApiException as e:
print "Exception when calling DefaultApi->recognize_bytes: %s\n" % e
from utils import label_map_util
from utils import visualization_utils as vis_util
# What model to download.
MODEL_NAME = 'Cars'
# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_CKPT = MODEL_NAME + '/output_inference_graph.pb'
# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = os.path.join('Cars', 'car_label_map.pbtxt')
NUM_CLASSES = 1
api = openalpr_api.DefaultApi()
secret_key = ''
country = 'us'
recognize_vehicle = 0
state = ''
return_image = 0
topn = 1
prewarp = ''
def getLicensePlateNumber(filer):
try:
js = api.recognize_file(filer,
secret_key,
country,
recognize_vehicle=recognize_vehicle,
import time
import openalpr_api
import os
from os import walk
from openalpr_api.rest import ApiException
from pprint import pprint
# create an instance of the API class
api_instance = openalpr_api.DefaultApi()
#with open("7.png", "rb") as f:
#data = f.read()
#image_bytes = data.encode("base64")
import base64
import glob
SIZE = []
txtfiles = []
cls_id = 0
for file1 in glob.glob1(os.getcwd() + '/Images/', "*.png"):
print(file1)
with open('Images/' + file1, "rb") as image_file:
image_bytes = base64.b64encode(image_file.read())
#image_bytes = '7.png' # str | The image file that you wish to analyze encoded in base64
secret_key = 'sk_DEMODEMODEMODEMODEMODEMO'
country = 'in'
recognize_vehicle = 0
state = ''
return_image = 1
topn = 1
prewarp = ''
try:
api_response = api_instance.recognize_bytes(
image_bytes,
import openalpr_api
apiclient = openalpr_api.DefaultApi()
response = apiclient.recognize_post("sk_DEMODEMODEMODEMODEMODEMO", "plate,color,make,makemodel", image="/storage/projects/alpr/samples/testing/car1.jpg", country="us")
for plate_obj in response.plate.results:
print "Plate: %s - %f percent" % (plate_obj.plate, plate_obj.confidence)
print "Color: %s - %f percent" % (response.color[0].value, response.color[0].confidence)
print "Make: %s - %f percent" % (response.make[0].value, response.make[0].confidence)
print "Make-model: %s - %f percent" % (response.makemodel[0].value, response.makemodel[0].confidence)