femioladeji

from skimage.io import imread
from skimage.filters import threshold_otsu
import matplotlib.pyplot as plt

car_image = imread("car.jpg", as_grey=True)
# it should be a 2 dimensional array
print(car_image.shape)

# the next line is not compulsory however, a grey scale pixel
# in skimage ranges between 0 & 1. multiplying it with 255

femioladeji

from skimage import measure
from skimage.measure import regionprops
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import localization

# this gets all the connected regions and groups them together
label_image = measure.label(localization.binary_car_image)
fig, (ax1) = plt.subplots(1)
ax1.imshow(localization.gray_car_image, cmap="gray");

femioladeji

from skimage import measure
from skimage.measure import regionprops
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import localization

# this gets all the connected regions and groups them together
label_image = measure.label(localization.binary_car_image)

# getting the maximum width, height and minimum width and height that a license plate can be

femioladeji

import numpy as np
from skimage.transform import resize
from skimage import measure
from skimage.measure import regionprops
import matplotlib.patches as patches
import matplotlib.pyplot as plt
import cca2

# on the image I'm using, the headlamps were categorized as a license plate
# because their shapes were similar

femioladeji

import os
import numpy as np
from sklearn.svm import SVC
from sklearn.model_selection import cross_val_score
from sklearn.externals import joblib
from skimage.io import imread
from skimage.filters import threshold_otsu

letters = [
            '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D',

femioladeji

import os
import segmentation
from sklearn.externals import joblib

# load the model
current_dir = os.path.dirname(os.path.realpath(__file__))
model_dir = os.path.join(current_dir, 'models/svc/svc.pkl')
model = joblib.load(model_dir)

classification_result = []

femioladeji

from skimage.io import imread
from skimage.filters import threshold_otsu
import matplotlib.pyplot as plt

car_image = imread("car.jpg", as_gray=True)
# it should be a 2 dimensional array
print(car_image.shape)

# the next line is not compulsory however, a grey scale pixel
# in skimage ranges between 0 & 1. multiplying it with 255

femioladeji

{
    "manifest_version": 2,
    "name": "Social Media Screentime",
    "description": "Control how much time you invest in social media",
    "short_name": "social media screentime",
    "version": "1.0",
    "browser_action": {
      "default_popup": "index.html"
    },
    "background": {

femioladeji

const urls = [
  '*://*.facebook.com/',
  '*://*.twitter.com/',
  '*://*.youtube.com/',
  '*://*.instagram.com/'
]

let active = {};

const end = () => {

femioladeji

const urls = [
  '*://*.facebook.com/',
  '*://*.twitter.com/',
  '*://*.youtube.com/',
  '*://*.instagram.com/'
]

const STORAGE = chrome.storage.local;

let active = {};