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Scanner.py
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Scanner.py
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# import the necessary packages
from transform import four_point_transform
from skimage.filters import threshold_local
import numpy as np
import argparse
import cv2
import imutils
def scan(path,side):
# load the image and compute the ratio of the old height
# to the new height, clone it, and resize it
image = cv2.imread(path)
ratio = image.shape[0] / 500.0
orig = image.copy()
image = imutils.resize(image, height = 500)
# convert the image to grayscale, blur it, and find edges
# in the image
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5, 5), 0)
#edged = cv2.Canny(gray, 75, 200)
edged = cv2.Canny(gray, 150, 200)
# find the contours in the edged image, keeping only the
# largest ones, and initialize the screen contour
cnts = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
cnts = sorted(cnts, key = cv2.contourArea, reverse = True)[:5]
# loop over the contours
for c in cnts:
# approximate the contour
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.02 * peri, True)
# if our approximated contour has four points, then we
# can assume that we have found our screen
if len(approx) == 4:
screenCnt = approx
break
# apply the four point transform to obtain a top-down
# view of the original image
warped = four_point_transform(orig, screenCnt.reshape(4, 2) * ratio)
# show the original and scanned images
newimg = cv2.resize(warped,(1000,630))
if(side=="front"):
cv2.imwrite("temp_front.jpg",newimg)
elif(side=="back"):
cv2.imwrite("temp_back.jpg",newimg)
return newimg