![]() Plate_dimensions = (0.03*label_image.shape, 0.08*label_image.shape, 0.15*label_image.shape, 0.3*label_image.shape) # getting the maximum width, height and minimum width and height that a license plate can be # print(label_image.shape) #width of car img Label_image = measure.label(binary_car_image) # this gets all the connected regions and groups them together # CCA (finding connected regions) of binary image # ax2.imshow(gray_car_image, cmap="gray") Threshold_value = threshold_otsu(gray_car_image)īinary_car_image = gray_car_image > threshold_valueĪx2.imshow(binary_car_image, cmap="gray") # will make it range between 0 & 255 (something we can relate better with # the next line is not compulsory however, a grey scale pixel # car_image = imread("car.png", as_gray=True) # car image -> grayscale image -> binary imageĬar_image = imread("./output/frame%d.jpg"%(count-1), as_gray=True)Ĭar_image = imutils.rotate(car_image, 270) Installed Libraries from skimage.io import imreadįrom skimage.filters import threshold_otsuĬomplete Source Code from skimage.io import imreadĬv2.imwrite("./output/frame%d.jpg" % count, frame) Last, run the project with the command “ py main.py” Next, import the source code you’ve download to your P圜harm IDE. Step 2: Import the project to your P圜harm IDE.Step 1: Download the given source code below.įirst, download the given source code below and unzip the source code. #License plate recognition source code c language how toThese are the steps on how to run Real-Time Plate Number Detection OpenCV Python With Source Code ![]()
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