def perspective_transform(image, points, ratio = None):
# print( "print(ing points in finalize " + str(points)
height, width = image.shape[0], image.shape[1]
if ratio:
points = points.reshape(4,2) * ratio
else:
points = points.reshape(4,2)
# points = imutils.boundary_coords(points, width, height)
warped = imutils.four_point_transform(image = image.copy(), pts = points) # for use with minAreaRect resul
return warped
def finalize(image, points, ratio):
print "printing points in finalize " + str(points)
warped = imutils.four_point_transform(
image=image.copy(),
pts=points.reshape(4, 2) * ratio) # for use with minAreaRect result
gray = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (5, 5), 0)
gaussian_threshold = cv2.adaptiveThreshold(src = blur, maxValue = 25, adaptiveMethod = cv2.ADAPTIVE_THRESH_GAUSSIAN_C,\
thresholdType = cv2.THRESH_BINARY, blockSize = 11, C = 2)
return gaussian_threshold
def perspective_transform(image, points, ratio=None):
# print( "print(ing points in finalize " + str(points)
points = np.array(
list(points)
) # added this 4/17/18 to keep the idea of points, corners as tuples consistent.
height, width = image.shape[0], image.shape[1]
if ratio:
points = points.reshape(4, 2) * ratio
else:
points = points.reshape(4, 2)
# points = imutils.boundary_coords(points, width, height)
warped = imutils.four_point_transform(
image=image.copy(), pts=points) # for use with minAreaRect resul
return warped
contours = sorted(
contours, key=cv2.contourArea,
reverse=True)[:5] # added the [:5] per the kickass scanner example
rect = cv2.minAreaRect(contours[0])
# print rect
box = cv2.boxPoints(rect)
print box
box = np.int0(box)
# img = np.zeros((3500,3500,3), np.uint8)
# detected = cv2.drawContours(image.copy(),[box],0,(0,255,0),1)
# <----- WARPING AND THRESHOLDING -------> #
warped = imutils.four_point_transform(
image=orig, pts=target.reshape(4, 2) *
ratio) # this is the original. Works on sample4.jpg
# warped = imutils.four_point_transform(image = orig, pts = box.reshape(4, 2) * ratio) # for use with minAreaRect result
# warped = imutils.four_point_transform(image = orig.copy(), pts = box.reshape(4, 2) * ratio) # for use with minAreaRect result
# convert the warped image to grayscale, then threshold it
warped = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)
warped = threshold_adaptive(warped, 251, offset=10)
warped = warped.astype("uint8") * 255
images = [detected, orig.copy(), warped]
functions.plot_images(images)
src = []
N = (0, 0)
tempL = getVertices(contours, top, slope, tempL)
tempM = getVertices(contours, right, slope, tempM)
tempO = getVertices(contours, bottom, slope, tempO)
L = updateCornerOr(orientation, tempL)
M = updateCornerOr(orientation, tempM)
O = updateCornerOr(orientation, tempO)
iflag, N = getIntersection(M[1], M[2], O[3], O[2], N)
src.append(L[0])
src.append(M[1])
src.append(N)
src.append(O[3])
src = np.asarray(src, np.float32)
warped = four_point_transform(img, src)
#cv2.imshow("warped",warped)
cv2.circle(img, N, 1, (0, 0, 255), 2)
cv2.drawContours(img, contours, top, (255, 0, 0), 2)
cv2.drawContours(img, contours, right, (0, 255, 0), 2)
cv2.drawContours(img, contours, bottom, (0, 0, 255), 2)
warped = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)
cv2.imwrite('userTest.png', warped)
file_path = 'userTest.png'
try:
with open(file_path, 'rb') as image_file:
image = Image.open(image_file)
image.load()
codes = zbarlight.scan_codes('qrcode', image)
#print('QR codes: %s' % codes)