You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
50 lines
2.1 KiB
Python
50 lines
2.1 KiB
Python
import cv2
|
|
|
|
def find_tables(image):
|
|
BLUR_KERNEL_SIZE = (17, 17)
|
|
STD_DEV_X_DIRECTION = 0
|
|
STD_DEV_Y_DIRECTION = 0
|
|
blurred = cv2.GaussianBlur(image, BLUR_KERNEL_SIZE, STD_DEV_X_DIRECTION, STD_DEV_Y_DIRECTION)
|
|
MAX_COLOR_VAL = 255
|
|
BLOCK_SIZE = 15
|
|
SUBTRACT_FROM_MEAN = -2
|
|
|
|
img_bin = cv2.adaptiveThreshold(
|
|
~blurred,
|
|
MAX_COLOR_VAL,
|
|
cv2.ADAPTIVE_THRESH_MEAN_C,
|
|
cv2.THRESH_BINARY,
|
|
BLOCK_SIZE,
|
|
SUBTRACT_FROM_MEAN,
|
|
)
|
|
vertical = horizontal = img_bin.copy()
|
|
SCALE = 5
|
|
image_width, image_height = horizontal.shape
|
|
horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (int(image_width / SCALE), 1))
|
|
horizontally_opened = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, horizontal_kernel)
|
|
vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1, int(image_height / SCALE)))
|
|
vertically_opened = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, vertical_kernel)
|
|
|
|
horizontally_dilated = cv2.dilate(horizontally_opened, cv2.getStructuringElement(cv2.MORPH_RECT, (40, 1)))
|
|
vertically_dilated = cv2.dilate(vertically_opened, cv2.getStructuringElement(cv2.MORPH_RECT, (1, 60)))
|
|
|
|
mask = horizontally_dilated + vertically_dilated
|
|
contours, heirarchy = cv2.findContours(
|
|
mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE,
|
|
)
|
|
|
|
MIN_TABLE_AREA = 1e5
|
|
contours = [c for c in contours if cv2.contourArea(c) > MIN_TABLE_AREA]
|
|
perimeter_lengths = [cv2.arcLength(c, True) for c in contours]
|
|
epsilons = [0.1 * p for p in perimeter_lengths]
|
|
approx_polys = [cv2.approxPolyDP(c, e, True) for c, e in zip(contours, epsilons)]
|
|
bounding_rects = [cv2.boundingRect(a) for a in approx_polys]
|
|
|
|
# The link where a lot of this code was borrowed from recommends an
|
|
# additional step to check the number of "joints" inside this bounding rectangle.
|
|
# A table should have a lot of intersections. We might have a rectangular image
|
|
# here though which would only have 4 intersections, 1 at each corner.
|
|
# Leaving that step as a future TODO if it is ever necessary.
|
|
images = [image[y:y+h, x:x+w] for x, y, w, h in bounding_rects]
|
|
return images
|