def main():
# construct the argument parse and parse the arguments
# ap = argparse.ArgumentParser()
# ap.add_argument("-i", "--image", help = "path to the image file")
# ap.add_argument("-c", "--coords", help = "comma seperated list of source points")
# args = vars(ap.parse_args())
# load the image and grab the source coordinates (i.e. the list of
# of (x, y) points)
# NOTE: using the 'eval' function is bad form, but for this example
# let's just roll with it -- in future posts I'll show you how to
# automatically determine the coordinates without pre-supplying them
image = array(Image.open("./images/carImage.jpg"))
imshow(image)
pts = ginput(4)
# pts = np.array(eval(args["coords"]), dtype = "float32")
# apply the four point tranform to obtain a "birds eye view" of
# the image
warped = fourPointsTransform(image, pts)
# show the original and warped images
# cv2.imshow("Original", image)
cv2.imshow("Warped", warped)
cv2.waitKey(0)
def turboScan():
#edge detection
# load the image and compute the ratio of the old height
# to the new height, clone it, and resize it
image = np.array(Image.open('./images/example_01.png').convert('L'))
# convert the image to grayscale, blur it, and find edges
# in the image
gray = cv2.GaussianBlur(image, (5, 5), 0)
edged = cv2.Canny(gray, 75, 200)
# show the original image and the edge detected image
print "STEP 1: Edge Detection"
cv2.imshow("Image", image)
cv2.imshow("Edged", edged)
#use edge into an image to find the document
(cnts, _) = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
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
# show the contour (outline) of the piece of paper
print "STEP 2: Find contours of paper"
bck2RGB = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
cv2.drawContours(bck2RGB, [screenCnt], -1, (0, 255, 0), 2)
cv2.imshow("Contourned", bck2RGB)
#apply perspective to find the top-down document
# apply the four point transform to obtain a top-down
# view of the original image
warped = fourPointsTransform(bck2RGB, screenCnt.reshape(4,2))
# convert the warped image to grayscale, then threshold it
# to give it that 'black and white' paper effect
#warped = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)
#warped = threshold_adaptive(warped, 250, offset = 10)
#warped = warped.astype("uint8") * 255
# show the original and scanned images
print "STEP 3: Apply perspective transform"
cv2.imshow("Scanned", warped)
bwImage = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)
#bwImage = threshold_adaptive(warped, 250, offset=10)
cv2.imshow("ScannedWB", bwImage)
cv2.waitKey(0)
print 'finished'