def main(image):
CnnClassifier = DetectChars.loadCNNClassifier() # attempt KNN training
#response = str(input('Do you want to see the Intermediate images: '))
"""
if response == 'Y' or response == 'y':
showSteps = True
else:
showSteps = False
"""
if CnnClassifier == False: # if KNN training was not successful
print(
"\nerror: CNN traning was not successful\n") # show error message
return # and exit program
imgOriginalScene = cv2.imread(image) # open image
h, w = imgOriginalScene.shape[:2]
# As the image may be blurr so we sharpen the image.
#kernel_shapening4 = np.array([[-1,-1,-1],[-1,9,-1],[-1,-1,-1]])
#imgOriginalScene = cv2.filter2D(imgOriginalScene,-1,kernel_shapening4)
#imgOriginalScene = cv2.resize(imgOriginalScene,(1000,600),interpolation = cv2.INTER_LINEAR)
imgOriginalScene = cv2.resize(imgOriginalScene, (0, 0),
fx=1.4,
fy=1.4,
interpolation=cv2.INTER_LINEAR)
#imgOriginalScene = cv2.fastNlMeansDenoisingColored(imgOriginalScene,None,10,10,7,21)
#imgOriginal = imgOriginalScene.copy()
if imgOriginalScene is None: # if image was not read successfully
print("\nerror: image not read from file \n\n"
) # print error message to std out
os.system("pause") # pause so user can see error message
return # and exit program
listOfPossiblePlates = DetectPlates.detectPlatesInScene(
imgOriginalScene) # detect plates. We get a list of
# combinations of contours that may be a plate.
listOfPossiblePlates = DetectChars.detectCharsInPlates(
listOfPossiblePlates) # detect chars in plates
if showSteps == True:
cv2.imshow("imgOriginalScene", imgOriginalScene) # show scene image
if len(listOfPossiblePlates) == 0: # if no plates were found
print("\nno license plates were detected\n"
) # inform user no plates were found
response = ' '
return response, imgOriginalScene
else: # else
# if we get in here list of possible plates has at leat one plate
# sort the list of possible plates in DESCENDING order (most number of chars to least number of chars)
listOfPossiblePlates.sort(
key=lambda possiblePlate: len(possiblePlate.strChars),
reverse=True)
# suppose the plate with the most recognized chars (the first plate in sorted by string length descending order) is the actual plate
licPlate = listOfPossiblePlates[0]
if showSteps == True:
cv2.imshow(
"imgPlate",
licPlate.imgPlate) # show crop of plate and threshold of plate
cv2.waitKey(0)
if len(licPlate.strChars) == 0: # if no chars were found in the plate
print("\nno characters were detected\n\n") # show message
return ' ', imgOriginalScene # and exit program
# end if
drawRedRectangleAroundPlate(
imgOriginalScene, licPlate) # draw red rectangle around plate
print("\nlicense plate read from ", image, " :", licPlate.strChars,
"\n")
print("----------------------------------------")
if showSteps == True:
writeLicensePlateCharsOnImage(
imgOriginalScene,
licPlate) # write license plate text on the image
cv2.imshow("imgOriginalScene",
imgOriginalScene) # re-show scene image
cv2.imwrite("imgOriginalScene.png",
imgOriginalScene) # write image out to file
cv2.waitKey(0) # hold windows open until user presses a key
return licPlate.strChars, licPlate.imgPlate
def main(image):
CnnClassifier = DetectChars.loadCNNClassifier() # load CNN
if CnnClassifier == False: # if CNN fails
print("\nerror: CNN traning was not successful\n") # error
return
imgOriginalScene = cv2.imread(image) # open image
#cv2.imshow(" Original image", imgOriginalScene)
cv2.imwrite(
os.path.join(
'C:/Users/Siddhant Rao/Desktop/ALPR-master/Main Program/output',
'original-img.jpg'), imgOriginalScene)
cv2.waitKey(0)
imgOriginalScene = cv2.resize(imgOriginalScene, (0, 0),
fx=1.4,
fy=1.4,
interpolation=cv2.INTER_LINEAR)
# cv2.imshow("Resized Original image", imgOriginalScene)
cv2.waitKey(0)
cv2.imwrite(
os.path.join(
'C:/Users/Siddhant Rao/Desktop/ALPR-master/Main Program/output',
'resize-img.jpg'), imgOriginalScene)
if imgOriginalScene is None:
print("\nerror: image not read from file \n\n")
os.system("pause")
return
# combinations of contours that may be a plate.
listOfPossiblePlates = DetectPlates.detectPlatesInScene(imgOriginalScene)
# detect chars in plates
listOfPossiblePlates = DetectChars.detectCharsInPlates(
listOfPossiblePlates)
if len(listOfPossiblePlates) == 0: # if no plates were found
print("\nno license plates were detected\n"
) # inform user no plates were found
response = ' '
return response, imgOriginalScene
else:
# sort from most number of chars to least number of chars)
listOfPossiblePlates.sort(
key=lambda possiblePlate: len(possiblePlate.strChars),
reverse=True)
# if 1st plate is the actual plate
licPlate = listOfPossiblePlates[0]
if len(licPlate.strChars) == 0:
print("\nno characters were detected\n\n")
return ' ', imgOriginalScene
drawRedRectangleAroundPlate(imgOriginalScene, licPlate)
print("\nlicense plate read from ", image, " :", licPlate.strChars,
"\n")
print("----------------------------------------")
#
writeLicensePlateCharsOnImage(imgOriginalScene, licPlate)
#
cv2.imwrite(
os.path.join(
'C:/Users/Siddhant Rao/Desktop/ALPR-master/Main Program/output',
'Detected-img.jpg'), imgOriginalScene)
#cv2.waitKey(0)
return licPlate.strChars, licPlate.imgPlate
