import cv2
import numpy as np
class Helpers(object):
'''
Image manipulation helper functions
'''
def show(self, img, windowName='Image'):
screen_res = 1280.0, 720.0
scale_width = screen_res[0] / img.shape[1]
scale_height = screen_res[1] / img.shape[0]
scale = min(scale_width, scale_height)
window_width = int(img.shape[1] * scale)
window_height = int(img.shape[0] * scale)
cv2.namedWindow(windowName, cv2.WINDOW_NORMAL)
cv2.resizeWindow(windowName, window_width, window_height)
cv2.imshow(windowName, img)
cv2.waitKey(0)
cv2.destroyAllWindows()
def isCv2(self):
return cv2.__version__.startswith('2.')
def thresholdify(self, img):
img = cv2.adaptiveThreshold(img.astype(np.uint8), 255, cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY, 11, 3)
return 255 - img
def Canny(self, image):
edges = cv2.Canny(image, 100, 200)
self.show(edges)
return edges
def dilate(self, image, kernel):
cv2.dilate(image, kernel)
return image
def largestContour(self, image):
if self.isCv2():
contours, h = cv2.findContours(
image, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
else:
_, contours, h = cv2.findContours(
image, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
return max(contours, key=cv2.contourArea)
def largest4SideContour(self, image):
contours, h = cv2.findContours(
image, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours = sorted(contours, key=cv2.contourArea, reverse=True)
for cnt in contours[:min(5,len(contours))]:
#im = image.copy()
#cv2.drawContours(im, cnt, -1, (255,255,255), 5)
#self.show(im,'contour')
if len(self.approx(cnt)) == 4:
return cnt
return None
def make_it_square(self, image, side_length=306):
return cv2.resize(image, (side_length, side_length))
def area(self, image):
return float(image.shape[0] * image.shape[1])
def cut_out_sudoku_puzzle(self, image, contour):
x, y, w, h = cv2.boundingRect(contour)
image = image[y:y + h, x:x + w]
return self.make_it_square(image, min(image.shape))
def binarized(self, image):
for i in xrange(image.shape[0]):
for j in xrange(image.shape[1]):
image[i][j] = 255 * int(image[i][j] != 255)
return image
def approx(self, cnt):
peri = cv2.arcLength(cnt, True)
app = cv2.approxPolyDP(cnt, 0.01 * peri, True)
return app
def get_rectangle_corners(self, cnt):
pts = cnt.reshape(4, 2)
rect = np.zeros((4, 2), dtype="float32")
# the top-left point has the smallest sum whereas the
# bottom-right has the largest sum
s = pts.sum(axis=1)
rect[0] = pts[np.argmin(s)]
rect[2] = pts[np.argmax(s)]
# compute the difference between the points -- the top-right
# will have the minumum difference and the bottom-left will
# have the maximum difference
diff = np.diff(pts, axis=1)
rect[1] = pts[np.argmin(diff)]
rect[3] = pts[np.argmax(diff)]
return rect
def warp_perspective(self, rect, grid):
(tl, tr, br, bl) = rect
widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
# ...and now for the height of our new image
heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
# take the maximum of the width and height values to reach
# our final dimensions
maxWidth = max(int(widthA), int(widthB))
maxHeight = max(int(heightA), int(heightB))
# construct our destination points which will be used to
# map the screen to a top-down, "birds eye" view
dst = np.array([
[0, 0],
[maxWidth - 1, 0],
[maxWidth - 1, maxHeight - 1],
[0, maxHeight - 1]], dtype="float32")
# calculate the perspective transform matrix and warp
# the perspective to grab the screen
M = cv2.getPerspectiveTransform(rect, dst)
warp = cv2.warpPerspective(grid, M, (maxWidth, maxHeight))
return self.make_it_square(warp)
def getTopLine(self, image):
for i, row in enumerate(image):
if np.any(row):
return i
return None
def getBottomLine(self, image):
for i in xrange(image.shape[0] - 1, -1, -1):
if np.any(image[i]):
return i
return None
def getLeftLine(self, image):
for i in xrange(image.shape[1]):
if np.any(image[:, i]):
return i
return None
def getRightLine(self, image):
for i in xrange(image.shape[1] - 1, -1, -1):
if np.any(image[:, i]):
return i
return None
def rowShift(self, image, start, end, length):
shifted = np.zeros(image.shape)
if start + length < 0:
length = -start
elif end + length >= image.shape[0]:
length = image.shape[0] - 1 - end
for row in xrange(start, end + 1):
shifted[row + length] = image[row]
return shifted
def colShift(self, image, start, end, length):
shifted = np.zeros(image.shape)
if start + length < 0:
length = -start
elif end + length >= image.shape[1]:
length = image.shape[1] - 1 - end
for col in xrange(start, end + 1):
shifted[:, col + length] = image[:, col]
return shifted
