import cv2
import numpy as np

img = cv2.imread('box.png')

img = cv2.blur(img,(5,5))

hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
h,s,v = cv2.split(hsv)


cv2.imwrite('box_h.png', cv2.applyColorMap(h, cv2.COLORMAP_JET))
cv2.imwrite('box_s.png', cv2.applyColorMap(s, cv2.COLORMAP_JET))
cv2.imwrite('box_v.png', cv2.applyColorMap(v, cv2.COLORMAP_JET))


thresh0 = cv2.adaptiveThreshold(s,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY_INV,11,2)
thresh1 = cv2.adaptiveThreshold(v,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY_INV,11,2)
thresh2 = cv2.adaptiveThreshold(v,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY_INV,11,2)
thresh = cv2.bitwise_or(thresh0, thresh1)

cv2.imwrite('box_ht.png', thresh2)
cv2.imwrite('box_st.png', thresh0)
cv2.imwrite('box_vt.png', thresh1)

kernel = np.ones((3, 3), np.uint8)
thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
thresh = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)

mask = np.ones_like(v) * 255

contours,heirarchy = cv2.findContours(thresh,cv2.RETR_LIST,cv2.CHAIN_APPROX_NONE)
for contour in contours:
  hull = cv2.convexHull(contour)
  area = cv2.contourArea(contour)
  if area > 10.0:
    cv2.drawContours(mask,[hull],-1,0,-1)


mask = cv2.morphologyEx(255 - mask, cv2.MORPH_ERODE, kernel, iterations=3)
result = cv2.bitwise_and(img, cv2.merge([mask]*3))

cv2.imwrite('box_masked.png', result)