def capture(self):
'''
Function to capture the images and give them the names
according to their captured time and date.
'''
camera = self.ids['camera']
timestr = time.strftime("%Y%m%d_%H%M%S")
camera.export_to_png("Doc{}.png".format(timestr))
print("Captured")
image = cv2.imread("Doc{}.png".format(timestr)) # read in the image
image = cv2.resize(
image, (1300, 800)
) # resizing because opencv does not work well with bigger images
orig = image.copy()
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # RGB To Gray Scale
cv2.imshow("Title", gray)
blurred = cv2.GaussianBlur(
gray, (5, 5), 0
) # (5,5) is the kernel size and 0 is sigma that determines the amount of blur
cv2.imshow("Blur", blurred)
edged = cv2.Canny(blurred, 30,
50) # 30 MinThreshold and 50 is the MaxThreshold
cv2.imshow("Canny", edged)
contours, hierarchy = cv2.findContours(
edged, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE
) # retrieve the contours as a list, with simple apprximation model
contours = sorted(contours, key=cv2.contourArea, reverse=True)
# the loop extracts the boundary contours of the page
for c in contours:
p = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.02 * p, True)
if len(approx) == 4:
target = approx
break
approx = mapper.mapp(target) # find endpoints of the sheet
pts = np.float32([[0, 0], [800, 0], [800, 800],
[0, 800]]) # map to 800*800 target window
op = cv2.getPerspectiveTransform(
approx, pts) # get the top or bird eye view effect
dst = cv2.warpPerspective(orig, op, (800, 800))
cv2.imshow("Scanned", dst)
cv2.waitKey(0)
cv2.destroyAllWindows()
def Scanner(path):
img_path = os.path.expanduser(path)
img = cv2.imread(img_path, 1)
img = cv2.resize(img, (600, 600))
# cv2.imshow('original',img)
orig = img.copy()
gray_img = cv2.cvtColor(img,
cv2.COLOR_BGR2GRAY) #convertig into gray image
#cv2.imshow('gray',gray_img)
blur_img = cv2.GaussianBlur(gray_img, (5, 5),
0) #converting into blurred image
#cv2.imshow('blur',blur_img)
edged_img = cv2.Canny(blur_img, 30, 50) #detecting edges
#cv2.imshow('edged',edged_img)
contours, hierarchy = cv2.findContours(edged_img, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(img, contours, -1, (0, 255, 0), 1)
contours = sorted(contours, key=cv2.contourArea, reverse=True)
for c in contours:
p = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.02 * p, True)
if len(approx) == 4:
target = approx
break
approx = mapper.mapp(target)
pts = np.float32([[0, 0], [600, 0], [600, 600], [0, 600]])
op = cv2.getPerspectiveTransform(approx, pts)
dst = cv2.warpPerspective(orig, op, (600, 600))
# cv2.imshow('final',dst)
#b&w
imgg = cv2.cvtColor(dst, cv2.COLOR_BGR2GRAY)
scanned_image = cv2.adaptiveThreshold(imgg, 255, 1, 1, 7, 2)
scanned_image = cv2.bitwise_not(scanned_image)
scanned_image = cv2.medianBlur(scanned_image, 3)
# cv2.imshow("scanned output",scanned_image)
return scanned_image
cv2.imshow('Title', gray)
kernel = np.ones((1, 1), np.uint8)
gray = cv2.dilate(gray, kernel, iterations=1)
gray = cv2.erode(gray, kernel, iterations=1)
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
#cv2.imshow('Blurred',blurred)
edged = cv2.Canny(blurred, 30, 50)
#cv2.imshow('Canny',edged)
contours, heirarchy = cv2.findContours(edged, cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
contours = sorted(contours, key=cv2.contourArea, reverse=True)
for c in contours:
p = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.04 * p, True)
if len(approx) == 4:
target = approx
break
approx = mapper.mapp(target)
pts = np.float32([[0, 0], [800, 0], [800, 800], [0, 800]])
op = cv2.getPerspectiveTransform(approx, pts)
dst = cv2.warpPerspective(ori, op, (800, 800))
cv2.imshow('Scanned Copy', dst)
cv2.waitKey(0)
50) # 30 MinThreshold and 50 is the MaxThreshold
cv2.imshow("Canny", edged)
contours, hierarchy = cv2.findContours(
edged, cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE) # retrieve the contours as a list, with simple
contours = sorted(contours, key=cv2.contourArea, reverse=True)
# the loop extracts the boundary contours of the page
for c in contours:
p = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.02 * p, True)
if len(approx) == 4:
target = approx
break
approx = mapper.mapp(target) # find endpoints of the sheet
pts = np.float32([[0, 0], [800, 0], [800, 800],
[0, 800]]) # map to 800*800 target window
op = cv2.getPerspectiveTransform(approx,
pts) # get the top or bird eye view effect
dst = cv2.warpPerspective(orig, op, (800, 800))
cv2.imshow("Scanned", dst)
cv2.waitKey(0)
cv2.destroyAllWindows()
input = cv2.resize(input, (1300, 800))
copy = input.copy()
#converting to Gray-Scale
grayscale = cv2.cvtColor(input, cv2.COLOR_BGR2GRAY)
#applying GaussianBlur
gaussian = cv2.GaussianBlur(grayscale, (5, 5), 0)
#applying Canny-Edge-Detection Technique
cannyedge = cv2.Canny(gaussian, 30, 50)
contours, hierarchy = cv2.findContours(cannyedge, cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
contours = sorted(contours, key=cv2.contourArea, reverse=True)
for i in contours:
x = cv2.arcLength(i, True)
approx = cv2.approxPolyDP(i, 0.02 * x, True)
if len(approx) == 4:
a = approx
break
approx = mapper.mapp(a)
window = np.float32([[0, 0], [800, 0], [800, 800], [0, 800]])
top_view = cv2.getPerspectiveTransform(approx, window)
wrap = cv2.warpPerspective(copy, top_view, (800, 800))
cv2.imshow("Scanned Image", wrap)
def scanPicture(nameOfPicture):
image = cv2.imread(nameOfPicture)
image = cv2.resize(image, (1500, 880))
# creating copy of original image
orig = image.copy()
# convert to grayscale and blur to smooth
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
#blurred = cv2.medianBlur(gray, 5)
# apply Canny Edge Detection
edged = cv2.Canny(blurred, 0, 50)
orig_edged = edged.copy()
# find the contours in the edged image, keeping only the
# largest ones, and initialize the screen contour
(contours, _) = cv2.findContours(edged, cv2.RETR_LIST,
cv2.CHAIN_APPROX_NONE)
contours = sorted(contours, key=cv2.contourArea, reverse=True)
#x,y,w,h = cv2.boundingRect(contours[0])
#cv2.rectangle(image,(x,y),(x+w,y+h),(0,0,255),0)
# get approximate contour
for c in contours:
p = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.02 * p, True)
if len(approx) == 4:
target = approx
break
# mapping target points to 800x800 quadrilateral
approx = mapper.mapp(target)
pts2 = np.float32([[0, 0], [800, 0], [800, 800], [0, 800]])
M = cv2.getPerspectiveTransform(approx, pts2)
dst = cv2.warpPerspective(orig, M, (800, 800))
cv2.drawContours(image, [target], -1, (0, 255, 0), 2)
dst = cv2.cvtColor(dst, cv2.COLOR_BGR2GRAY)
# using thresholding on warped image to get scanned effect (If Required)
ret, th1 = cv2.threshold(dst, 127, 255, cv2.THRESH_BINARY)
th2 = cv2.adaptiveThreshold(dst,255,cv2.ADAPTIVE_THRESH_MEAN_C,\
cv2.THRESH_BINARY,11,2)
th3 = cv2.adaptiveThreshold(dst,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,\
cv2.THRESH_BINARY,11,2)
ret2, th4 = cv2.threshold(dst, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
# cv2.imshow("Original.jpg", orig)
# cv2.imshow("Original Gray.jpg", gray)
# cv2.imshow("Original Blurred.jpg", blurred)
# cv2.imshow("Original Edged.jpg", orig_edged)
# cv2.imshow("Outline.jpg", image)
# cv2.imshow("Thresh Binary.jpg", th1)
# cv2.imshow("Thresh mean.jpg", th2)
# cv2.imshow("Thresh gauss.jpg", th3)
# cv2.imshow("Otsu's.jpg", th4)
# cv2.imshow("dst.jpg", dst)
# cv2.waitKey(0)
return dst
