def dewarp_book(image):
"""Fix and image warp (dewarp an image).
Parameters
----------
image : numpy ndarray
The input image.
Returns
-------
numpy ndarray
The dewarped image.
"""
# get input image ration to keep best output resolution quality
ratio = image.shape[0] / 500.0
# copy source image for filter operations
orig = image.copy()
# resize the input image
image = imutils.resize(image, height=500)
# convert rgb input image to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5, 5), 0)
# sigma parameter is used for automatic Canny edge detection
sigma = 0.33
# compute the median of the single channel pixel intensities
v = np.median(image)
# apply automatic Canny edge detection using the computed median
lower = int(max(0, (1.0 - sigma) * v))
upper = int(min(255, (1.0 + sigma) * v))
edged = cv2.Canny(image, lower, upper)
# perform dilate morphological filter to connect teh image pixel points
'''kernel = np.ones((5,5),np.uint8)
edged = cv2.dilate(edged,kernel,iterations = 1)'''
# find contours
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:
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.02 * peri, True)
if len(approx) == 4:
screenCnt = approx
break
# apply the four point transform for book dewarping
warped = four_point_transform(orig, screenCnt.reshape(4, 2) * ratio)
return warped
cv2.line(image, pts[0], pts[2], (0, 255, 0), thickness=2)
cv2.line(image, pts[1], pts[3], (0, 255, 0), thickness=2)
cv2.line(image, pts[2], pts[3], (0, 255, 0), thickness=2)
pointIndex = pointIndex + 1
def show_window():
while True:
# print(pts,pointIndex-1)
cv2.imshow("img", image)
if pointIndex == 4:
break
if cv2.waitKey(20) & 0xFF == 27:
break
cv2.namedWindow("img")
cv2.setMouseCallback("img", draw_circle)
show_window()
warped, temp, transformation_matrix = four_point_transform(
image, np.array(pts[:4], dtype="float32"), pts[4])
np.save("transormation_matrix.npy", transformation_matrix)
cv2.circle(warped, tuple(temp[0][0]), 1, (0, 255, 0), -1)
# show the original and warped images
cv2.imshow("Original", image)
# cv2.imshow("Warped", warped) #Uncommment if you want to see the transformed image
cv2.waitKey(0)
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")
# cv2.drawContours(image, [screenCnt], -1, (0, 255, 0), 2)
# cv2.imshow("Outline", image)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
# 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)
# 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)
T = threshold_local(warped, 11, offset=10, method="gaussian")
warped = (warped > T).astype("uint8") * 255
# show the original and scanned images
print("STEP 3: Apply perspective transform")
# cv2.imshow("Original", imutils.resize(orig, height = 650))
cv2.imshow("Scanned", imutils.resize(warped, height=650))
cv2.waitKey(0)