import cv2
import numpy as np

# def image_resize(image, width = None, height = None, inter = cv2.INTER_AREA):
#     # initialize the dimensions of the image to be resized and
#     # grab the image size
#     dim = None
#     (h, w) = image.shape[:2]

#     # if both the width and height are None, then return the
#     # original image
#     if width is None and height is None:
#         return image

#     # check to see if the width is None
#     if width is None:
#         # calculate the ratio of the height and construct the
#         # dimensions
#         r = height / float(h)
#         dim = (int(w * r), height)

#     # otherwise, the height is None
#     else:
#         # calculate the ratio of the width and construct the
#         # dimensions
#         r = width / float(w)
#         dim = (width, int(h * r))

#     # resize the image
#     resized = cv2.resize(image, dim, interpolation = inter)

#     # return the resized image
#     return resized


img = cv2.imread(
    "/home/arizona/projects/svs_tech/demo-int-table/admin_front/assets/img/plan.png"
)
(h, w) = img.shape[:2]
t = 1600
img = cv2.resize(img, (t, int((h/w) * t)))

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gray = 255 - gray
gray = cv2.blur(gray, (8, 1))
gray = cv2.threshold(gray, 1, 255, cv2.THRESH_BINARY)[1]
contours, hierarchy = cv2.findContours(gray, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)

for cnt in contours:
    area = cv2.contourArea(cnt)
    if area > 150000 and area < 500000:
        cv2.drawContours(img, [cnt], 0, (255, 0, 0), 2)

cv2.imshow("img", img)
cv2.waitKey()