def solve_maze(image):
try:
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresholded_image = utils.adaptive_threshold(gray_image,
cv2.THRESH_BINARY_INV)
print('Finding Contours')
contours, _ = cv2.findContours(thresholded_image, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
solution_image = np.zeros(gray_image.shape, dtype=np.uint8)
cv2.drawContours(solution_image, contours, 0, (255, 255, 255), 5)
kernel = np.ones((15, 15), dtype=np.uint8)
solution_image = cv2.dilate(solution_image, kernel)
eroded_image = cv2.erode(solution_image, kernel)
solution_image = cv2.absdiff(solution_image, eroded_image)
b, g, r = cv2.split(image)
b &= ~solution_image
g |= solution_image
r &= ~solution_image
solution_image = cv2.merge([b, g, r]).astype(np.uint8)
return solution_image
except Exception:
return None
os.path.abspath(os.path.join(os.path.split(inspect.getfile(inspect.currentframe()))[0], "..", "Image_Lib")))
if cmd_subfolder not in sys.path:
sys.path.insert(0, cmd_subfolder)
import image_utils as utils
ap = argparse.ArgumentParser(description="Solve orthogonal mazes")
ap.add_argument("-i", "--image", required = True, help = "Path to image file")
args = vars(ap.parse_args())
image = cv2.imread(args["image"])
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# cnts = utils.get_contours(gray_image, 200)
# cnt = sorted(cnts, key=cv2.contourArea, reverse=True)[0]
thresholded_image = utils.adaptive_threshold(gray_image, cv2.THRESH_BINARY_INV)
cv2.imshow("Output", utils.image_resize(thresholded_image, height=600))
cv2.waitKey()
_, cnts, _ = cv2.findContours(thresholded_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if len(cnts) != 2:
print len(cnts)
raise ValueError("Unable to solve maze - Failed at Contour finding!")
solution_image = np.zeros(gray_image.shape, dtype=np.uint8)
cv2.drawContours(solution_image, cnts, 0, (255,255,255),cv2.FILLED)
cv2.imshow("Output", utils.image_resize(solution_image, height=600))
cv2.waitKey()
kernel = np.ones((15, 15), dtype=np.uint8)
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)
warped = utils.adaptive_threshold(warped)
warped = warped.astype("uint8")
# show the original and scanned images
print "STEP 3: Apply perspective transform"
# cv2.imshow("Original", utils.image_resize(orig, height = 650))
cv2.imshow("Scanned", utils.image_resize(warped, height=650))
cv2.waitKey(0)
"Image_Lib")))
if cmd_subfolder not in sys.path:
sys.path.insert(0, cmd_subfolder)
import image_utils as utils
ap = argparse.ArgumentParser(description="Solve orthogonal mazes")
ap.add_argument("-i", "--image", required=True, help="Path to image file")
args = vars(ap.parse_args())
image = cv2.imread(args["image"])
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# cnts = utils.get_contours(gray_image, 200)
# cnt = sorted(cnts, key=cv2.contourArea, reverse=True)[0]
thresholded_image = utils.adaptive_threshold(gray_image, cv2.THRESH_BINARY_INV)
cv2.imshow("Output", utils.image_resize(thresholded_image, height=600))
cv2.waitKey()
_, cnts, _ = cv2.findContours(thresholded_image, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
if len(cnts) != 2:
print len(cnts)
raise ValueError("Unable to solve maze - Failed at Contour finding!")
solution_image = np.zeros(gray_image.shape, dtype=np.uint8)
cv2.drawContours(solution_image, cnts, 0, (255, 255, 255), cv2.FILLED)
cv2.imshow("Output", utils.image_resize(solution_image, height=600))
cv2.waitKey()
# 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)
warped = utils.adaptive_threshold(warped)
warped = warped.astype("uint8")
# show the original and scanned images
print "STEP 3: Apply perspective transform"
# cv2.imshow("Original", utils.image_resize(orig, height = 650))
cv2.imshow("Scanned", utils.image_resize(warped, height=650))
cv2.waitKey(0)
