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import cv2
import numpy as np
canny = rho = threshold = minLen = maxGap = None
def draw():
lines = cv2.HoughLinesP(canny, rho, np.pi / 180,
threshold, None, minLen, maxGap)
dst = cv2.cvtColor(canny, cv2.COLOR_GRAY2BGR)
for l in lines[0]:
x1, y1, x2, y2 = l
cv2.line(dst, (x1, y1), (x2, y2), (0, 0, 255), 1)
cv2.imshow('demo', dst)
def onRho(v):
global rho
rho = v
draw()
def onThreshold(v):
global threshold
threshold = v
draw()
def onMinLen(v):
global minLen
minLen = v
draw()
def onMaxGap(v):
global maxGap
maxGap = v
draw()
def main():
global canny, rho, threshold, minLen, maxGap
im = cv2.imread('doc1.jpg')
h, w, _ = im.shape
min_w = 200
scale = w * 1. / min_w
h_proc = int(h * 1. / scale)
w_proc = int(w * 1. / scale)
im_dis = cv2.resize(im, (w_proc, h_proc))
gray = cv2.cvtColor(im_dis, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (3, 3), 0)
high_thres = cv2.threshold(
gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[0]
low_thres = high_thres * 0.5
canny = cv2.Canny(gray, low_thres, high_thres)
rho, threshold, minLen, maxGap = 1, w_proc / 3, w_proc / 3, 20
cv2.namedWindow('demo', cv2.WINDOW_NORMAL)
cv2.createTrackbar('rho', 'demo', rho, 5, onRho)
cv2.createTrackbar('threshold', 'demo', threshold, w_proc, onThreshold)
cv2.createTrackbar('minLen', 'demo', minLen, w_proc, onMinLen)
cv2.createTrackbar('maxGap', 'demo', maxGap, 50, onMaxGap)
draw()
cv2.waitKey(0)
cv2.destroyAllWindows()
if __name__ == '__main__':
main()
import sys
import cv2
import numpy as np
from matplotlib import pyplot as plt
class Line:
def __init__(self, l):
self.point = l
x1, y1, x2, y2 = l
self.c_x = (x1 + x2) / 2
self.c_y = (y1 + y2) / 2
def show(im):
msg = 'press any key to continue'
cv2.namedWindow(msg, cv2.WINDOW_NORMAL)
cv2.imshow(msg, im)
cv2.waitKey(0)
cv2.destroyAllWindows()
def intersection(l1, l2):
x1, y1, x2, y2 = l1.point
x3, y3, x4, y4 = l2.point
a1, b1 = y2 - y1, x1 - x2
c1 = a1 * x1 + b1 * y1
a2, b2 = y4 - y3, x3 - x4
c2 = a2 * x3 + b2 * y3
det = a1 * b2 - a2 * b1
assert det, "lines are parallel"
return (1. * (b2 * c1 - b1 * c2) / det, 1. * (a1 * c2 - a2 * c1) / det)
def scannerLite(im, debug=False):
# resize
h, w, _ = im.shape
min_w = 200
scale = min(10., w * 1. / min_w)
h_proc = int(h * 1. / scale)
w_proc = int(w * 1. / scale)
im_dis = cv2.resize(im, (w_proc, h_proc))
# gray
gray = cv2.cvtColor(im_dis, cv2.COLOR_BGR2GRAY)
# blur
#gray = cv2.blur(gray, (3, 3))
#gray = cv2.GaussianBlur(gray, (3,3), 0)
# canny edges detection
high_thres = cv2.threshold(
gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[0]
low_thres = high_thres * 0.5
canny = cv2.Canny(gray, low_thres, high_thres)
if debug:
show(canny)
# lines detection
lines = cv2.HoughLinesP(
canny, 1, np.pi / 180, w_proc / 3, None, w_proc / 3, 20)
if debug:
t = cv2.cvtColor(canny, cv2.COLOR_GRAY2BGR)
# classify lines
hori, vert = [], []
for l in lines[0]:
x1, y1, x2, y2 = l
if abs(x1 - x2) > abs(y1 - y2):
hori.append(Line(l))
else:
vert.append(Line(l))
if debug:
cv2.line(t, (x1, y1), (x2, y2), (0, 0, 255), 1)
if debug:
show(t)
# not enough
if len(hori) < 2:
if not hori or hori[0].c_y > h_proc / 2:
hori.append(Line((0, 0, w_proc - 1, 0)))
if not hori or hori[0].c_y <= h_proc / 2:
hori.append(Line((0, h_proc - 1, w_proc - 1, h_proc - 1)))
if len(vert) < 2:
if not vert or vert[0].c_x > w_proc / 2:
vert.append(Line((0, 0, 0, h_proc - 1)))
if not vert or vert[0].c_x <= w_proc / 2:
vert.append(Line((w_proc - 1, 0, w_proc - 1, h_proc - 1)))
hori.sort(key=lambda l: l.c_y)
vert.sort(key=lambda l: l.c_x)
# corners
if debug:
for l in [hori[0], vert[0], hori[-1], vert[-1]]:
x1, y1, x2, y2 = l.point
cv2.line(t, (x1, y1), (x2, y2), (0, 255, 255), 1)
img_pts = [intersection(hori[0], vert[0]), intersection(hori[0], vert[-1]),
intersection(hori[-1], vert[0]), intersection(hori[-1], vert[-1])]
for i, p in enumerate(img_pts):
x, y = p
img_pts[i] = (x * scale, y * scale)
if debug:
cv2.circle(t, (int(x), int(y)), 1, (255, 255, 0), 3)
if debug:
show(t)
# perspective transform
w_a4, h_a4 = 1654, 2339
#w_a4, h_a4 = 600, 800
dst_pts = np.array(
((0, 0), (w_a4 - 1, 0), (0, h_a4 - 1), (w_a4 - 1, h_a4 - 1)),
np.float32)
img_pts = np.array(img_pts, np.float32)
transmtx = cv2.getPerspectiveTransform(img_pts, dst_pts)
return cv2.warpPerspective(im, transmtx, (w_a4, h_a4))
if __name__ == '__main__':
im = cv2.imread('doc1.jpg')
show(im)
dst = scannerLite(im, debug=True)
show(dst)
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