def execute_ALPR(event):
"""
runs the full license plate recognition process.
function is called when user clicks on the execut button on the gui
"""
#time the function execution
start_time = time.time()
root_folder = os.path.dirname(os.path.realpath(__file__))
models_folder = os.path.join(root_folder, 'ml_models')
pre_process = PreProcess(imagepath)
plate_like_objects = pre_process.get_plate_like_objects()
# plotting.plot_cca(pre_process.full_car_image,
# pre_process.plate_objects_cordinates)
license_plate = license_plate_extract(plate_like_objects, pre_process)
if len(license_plate) == 0:
return False
ocr_instance = OCROnObjects(license_plate)
if ocr_instance.candidates == {}:
wx.MessageBox("No character was segmented",
"Character Segmentation" ,wx.OK|wx.ICON_ERROR)
return False
# plotting.plot_cca(license_plate, ocr_instance.candidates['coordinates'])
deep_learn = DeepMachineLearning()
text_result = deep_learn.learn(ocr_instance.candidates['fullscale'],
os.path.join(models_folder, 'SVC_model', 'SVC_model.pkl'),
(20, 20))
text_phase = TextClassification()
scattered_plate_text = text_phase.get_text(text_result)
plate_text = text_phase.text_reconstruction(scattered_plate_text,
ocr_instance.candidates['columnsVal'])
print 'ALPR process took '+ str(time.time() - start_time) + ' seconds'
listResult.InsertStringItem(listRow, plate_text)
plate_num = Mvrd(plate_text)
details = plate_num.get_data()
if details == False or details == {}:
wx.MessageBox("Vehicle Information could not be retrieved",
"Information Retrieval", wx.OK|wx.ICON_ERROR)
return False;
listResult.SetStringItem(listRow, 1, details['Owner Name'])
listResult.SetStringItem(listRow, 2, details['Isssue Date'])
listResult.SetStringItem(listRow, 3, details['Expiry Date'])
listResult.SetStringItem(listRow, 4, details['Chasis Number'])
listResult.SetStringItem(listRow, 5, details['Model'])
#db_aspect.save_alpr(plate_text, str(datetime.today()))
def execute_ALPR():
"""
runs the full license plate recognition process.
function is called when user clicks on the execute button on the gui
"""
# time the function execution
start_time = time.time()
root_folder = os.path.dirname(os.path.realpath(__file__))
models_folder = os.path.join(root_folder, 'ml_models')
# import requests
# r = requests.get(url, auth=('admin', 'admin'))
# file = open(imagepath, "w")
# file.write(r.content)
# file.close()
imagepath=url
pre_process = PreProcess(imagepath)
plate_like_objects = pre_process.get_plate_like_objects()
# plotting.plot_cca(pre_process.full_car_image,
# pre_process.plate_objects_cordinates)
license_plate = license_plate_extract(plate_like_objects, pre_process)
if len(license_plate) == 0:
return False
ocr_instance = OCROnObjects(license_plate)
if ocr_instance.candidates == {}:
# print("No Characters Was Segmented")
# wx.MessageBox("No character was segmented",
# "Character Segmentation" ,wx.OK|wx.ICON_ERROR)
return False
# plotting.plot_cca(license_plate, ocr_instance.candidates['coordinates'])
deep_learn = DeepMachineLearning()
text_result = deep_learn \
.learn(ocr_instance.candidates['fullscale'], os.path.join(models_folder, 'SVC_model', 'SVC_model.pkl'),
(20, 20))
text_phase = TextClassification()
scattered_plate_text = text_phase.get_text(text_result)
plate_text = text_phase.text_reconstruction(scattered_plate_text, ocr_instance.candidates['columnsVal'])
# print('ALPR process took ' + str(time.time() - start_time) + ' seconds')
print(plate_text)
class TestTextClassification():
@classmethod
def setup_class(self):
self.text_class_instance = TextClassification()
def test_get_text(self):
print 'Testing the get_text method'
print 'A string should be displayed from a 2D array'
# print "get_text([['A'], ['5'], ['D'], ['B']]) = A5DB"
text = self.text_class_instance.get_text([['A'], ['5'], ['D'], ['B']])
assert text == 'A5DB'
def test_text_reconstruction(self):
print 'Testing the text_reconstruction method'
print 'It should re arrange the string based on the 1D array of x-axis'
# print "text_reconstruction('DACB', [10, 1, 6, 4])"
new_text = self.text_class_instance.text_reconstruction(
'DACB', [10, 1, 6, 4])
assert new_text == 'ABCD'
def execute_ALPR(event):
"""
runs the full license plate recognition process.
function is called when user clicks on the execut button on the gui
"""
#time the function execution
start_time = time.time()
root_folder = os.path.dirname(os.path.realpath(__file__))
models_folder = os.path.join(root_folder, 'ml_models')
pre_process = PreProcess(imagepath)
plate_like_objects = pre_process.get_plate_like_objects()
# plotting.plot_cca(pre_process.full_car_image,
# pre_process.plate_objects_cordinates)
license_plate = license_plate_extract(plate_like_objects, pre_process)
if len(license_plate) == 0:
return False
ocr_instance = OCROnObjects(license_plate)
#plotting.plot_cca(license_plate, ocr_instance.candidates['coordinates'])
deep_learn = DeepMachineLearning()
text_result = deep_learn.learn(ocr_instance.candidates['fullscale'],
os.path.join(models_folder, 'SVC_model', 'SVC_model.pkl'),
(20, 20))
text_phase = TextClassification()
scattered_plate_text = text_phase.get_text(text_result)
plate_text = text_phase.text_reconstruction(scattered_plate_text,
ocr_instance.candidates['columnsVal'])
print 'ALPR process took '+ str(time.time() - start_time) + ' seconds'
listResult.InsertStringItem(listRow, plate_text)
listResult.SetStringItem(listRow, 1, str(datetime.today()))
db_aspect.save_alpr(plate_text, str(datetime.today()))
def setup_class(self):
self.text_class_instance = TextClassification()