def get_frames(query_path, video_base_path):
query_array = []
video = cv2.VideoCapture(query_path)
fps = video.get(cv2.CAP_PROP_FPS)
got_frame = True
while got_frame:
got_frame, frame = video.read()
if frame is None:
break
width = len(frame[0])
height = len(frame)
if height == 0 or width == 0:
break
if width < height:
frame = np.rot90(frame, 1)
query_array.append(frame)
query_array_localized = Localization.find_screen(query_array, fps)
Recognize.recognize(query_array_localized, video_base_path, fps)
def PlateRecognize(img, model):
try:
#Tìm biển số xe
img = PlateDetect.MainProcess(img)
list_Of_Char_Img,All_Char_Img = CharacterSegment.MainProcess(img)
cv2.imshow(" ",All_Char_Img)
cv2.waitKey(0)
#Labeling.Labeling(list_Of_Char_Img)
list_Of_Char = Recognize.char_predict(list_Of_Char_Img, model)
return list_Of_Char
except:
return None
def Recognizer(Function):
A = Recognize.vocabulary(Function).pop()
#A = Recognize.vocabulary(Function)
A = A[2:len(A) - 2]
table = []
table = A.split('], [')
TrTable = []
row = []
for i in table:
row = i.split(', ')
TrTable.append(row)
return TrTable
def Recognizer(
Function, task
): #возвращает таблицу истинности введённой функции, парсит в список
OPNames = Recognize.vocabulary(Function, task)
if (OPNames[0] == -1):
return OPNames
A = OPNames.pop()
A = A[2:len(A) - 2]
table = []
table = A.split('], [')
TrTable = []
row = []
for i in table:
row = i.split(', ')
TrTable.append(row)
return TrTable
def read():
"""Save lats image and recognize the image"""
global other # top 5 perdiction
path = 'test.jpg'
image.save(path)
# recognize the character in the image
top1, top5 = Recognize.recognize(path, model)
other = []
mapping = '0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabdefghnqrt'
text_1.insert(END, mapping[top1])
for val in top5[1:]:
other.append(mapping[val])
button_3['text'] = mapping[top5[1]]
button_4['text'] = mapping[top5[2]]
button_5['text'] = mapping[top5[3]]
button_6['text'] = mapping[top5[4]]
w.delete('all') # clear canvas
draw.rectangle((0, 0, 400, 400), fill='black') # clear the image
import Recognize as R
import closure_1978 as Main
if __name__ == '__main__':
R.main('./lang/js/grammar/javascript.fbjson',
'./lang/js/bugs/closure.1978.js', Main.my_predicate)
#Populate 'database' folder with the dataset of images to be recognize from
import Recognize
recognizer = Recognize.Recognize()
#recognize takes path to the image to be recognized
recognizer.recognize('011.jpg')
import Recognize as R
import find_93623752 as Main
if __name__ == '__main__':
R.main('./lang/find/grammar/grammar.json',
'./lang/find/bugs/find.93623752', Main.my_predicate)
import os # accessing the os functions import check_camera import Capture_Image import Train_Image import Recognize Recognize.recognize_attendence()
import Recognize as R
import grep_9c45c193 as Main
if __name__ == '__main__':
R.main('./lang/grep/grammar/grammar.json',
'./lang/grep/bugs/grep.9c45c193', Main.my_predicate)
def RecognizeFaces():
res = Recognize.recognize_attendence()
message2.configure(text=res)
def RecognizeFaces():
Recognize.recognize_attendence()
key = input("Nhap phim bat ky de tro lai main menu")
mainMenu()
import Recognize as R
import grep_54d55bba as Main
if __name__ == '__main__':
R.main('./lang/grep/grammar/grammar.json',
'./lang/grep/bugs/grep.54d55bba', Main.my_predicate)
def markattend():
#os.system("py Recognize.py")
Recognize.recognize_attendence()
def student_page():
Recognize.recognize_attendence()
return render_template('student_page.html')
import Recognize as R
import grep_3c3bdace as Main
if __name__ == '__main__':
R.main('./lang/grep/grammar/grammar.json',
'./lang/grep/bugs/grep.3c3bdace', Main.my_predicate)
def rfaces_call():
tkStatus.set("Recognizing Faces...")
status_label.update()
Recognize.recognize_attendence()
tkStatus.set("Faces Recognized.")
status_label.update()
import Recognize as R
import rhino_386 as Main
import sys
if __name__ == '__main__':
R.main('./lang/js/grammar/javascript.fbjson',
'./lang/js/bugs/rhino.386.js', Main.my_predicate)
import Recognize as R
import find_c8491c11 as Main
if __name__ == '__main__':
R.main('./lang/find/grammar/grammar.json',
'./lang/find/bugs/find.c8491c11', Main.my_predicate)
def recognize(self):
import Recognize
Recognize.recognize_face(self)
import Recognize as R
import find_dbcb10e9 as Main
if __name__ == '__main__':
R.main('./lang/find/grammar/grammar.json',
'./lang/find/bugs/find.dbcb10e9', Main.my_predicate)
import Recognize as R
import lua_5_3_5__4 as Main
import sys
if __name__ == '__main__':
R.main('./lang/lua/grammar/lua.fbjson', './lang/lua/bugs/4.lua',
Main.my_predicate)
import Recognize as R
import find_07b941b1 as Main
if __name__ == '__main__':
R.main('./lang/find/grammar/grammar.json',
'./lang/find/bugs/find.07b941b1', Main.my_predicate)
import Recognize as R
import clojure_2518 as Main
if __name__ == '__main__':
R.main('./lang/clojure/grammar/clojure.fbjson',
'./lang/clojure/bugs/clj-2518.clj', Main.my_predicate)
img = cv2.resize(img,(600,h))
return img
except:
return None
def PlateRecognize(img, model):
try:
#Tìm biển số xe
img = PlateDetect.MainProcess(img)
list_Of_Char_Img,All_Char_Img = CharacterSegment.MainProcess(img)
cv2.imshow(" ",All_Char_Img)
cv2.waitKey(0)
#Labeling.Labeling(list_Of_Char_Img)
list_Of_Char = Recognize.char_predict(list_Of_Char_Img, model)
return list_Of_Char
except:
return None
Constant.image_file = 'Test1.jpg'
#Load hình ảnh từ file lên
#31,33 loi
#Test28 1430
model = Recognize.getModelStruct()
img = ReadAndResize(Constant.image_file)
list_Of_Char = PlateRecognize(img, model)
print(list_Of_Char)
cv2.waitKey(0)
def RecognizeFaces():
Recognize.recognize_attendence()
input("Enter any key to return main menu")
mainMenu()
def recognizefaces():
Recognize.recognize_attendence()