In [1]:

    

import cv2, numpy as np
from matplotlib import pyplot as plt
%matplotlib inline


    

In [2]:

    

from utils import read_json

params = read_json('parameters.json')

RESIZE_X = params['resize']['x']
RESIZE_Y = params['resize']['y']
ITEM_FOLDER = params['item_folder']


    

In [19]:

    

bin_stamp = '170405145336'
contents = ["Colgate_Toothbrush_4PK","Epsom_Salts","Duct_Tape",
            "Bath_Sponge","Crayons","Burts_Bees_Baby_Wipes"]


    

In [37]:

    

bin_stamp = '170405145538'
contents = ["glue_sticks","tissue_box","laugh_out_loud_jokes",
            "toilet_brush","expo_eraser","table_cloth"]


    

In [38]:

    

contents = [s.lower() for s in contents]


    

In [39]:

    

from utils import imread_rgb, compute_sift

filename_bin = 'bin/' + bin_stamp + '.png'
image_bin = imread_rgb(filename_bin)
(kp_bin, des_bin) = compute_sift(image_bin)


    

    




1067 features detected






    

In [40]:

    

from utils import match_items

items = list(contents)
item_d, recognised_items, mask_items = match_items(image_bin, kp_bin, des_bin, items)


    

    




Item: "Training_items/glue_sticks/glue_sticks_top_01_sift.npy" Good features: 5
Item: "Training_items/tissue_box/tissue_box_top_01_sift.npy" Good features: 3
Item: "Training_items/laugh_out_loud_jokes/laugh_out_loud_jokes_top_01_sift.npy" Good features: 12
Item: "Training_items/toilet_brush/toilet_brush_top_01_sift.npy" Good features: 2
Item: "Training_items/expo_eraser/expo_eraser_top_01_sift.npy" Good features: 26
Item: "Training_items/table_cloth/table_cloth_top_01_sift.npy" Good features: 3






    

In [41]:

    

items = [s for s in contents if s not in recognised_items]
items


    

    
Out[41]:





['glue_sticks', 'tissue_box', 'toilet_brush', 'table_cloth']

In [42]:

    

kernel = np.ones((3,3),np.uint8)
mask_items = cv2.dilate(mask_items,kernel,iterations = 5)
plt.imshow(mask_items,cmap='gray'), plt.axis('off');


    

In [43]:

    

from utils import imread_gray
filename_bin = 'bin/' + bin_stamp + '.pgm'
image_depth = imread_gray(filename_bin)
plt.imshow(image_depth,cmap='gray'); plt.axis('off');


    

In [44]:

    

from utils import fill_holes

image_depth = fill_holes(image_depth)
plt.imshow(image_depth,cmap='gray');


    

In [45]:

    

image_depth = cv2.bitwise_and(image_depth,image_depth,mask=255-mask_items)
pix_depth = image_depth.flatten()
pix_depth = pix_depth[pix_depth>0]
dmin, dmax = (np.min(pix_depth),np.max(pix_depth))
h,b = np.histogram(pix_depth,bins=dmax-dmin)
sh = np.cumsum(h)
b_high = [b_val for b_val,sh_val in zip(b,sh) if sh_val>sh[-1]/2]
dcut = int(b_high[0])
plt.hist(pix_depth,bins=dmax-dmin+1);
hmax = np.around(max(h)+1000,-3)
plt.plot([dcut,dcut],[0,hmax],'r-')
plt.axis([dmin,dmax,0,hmax]);


    

In [46]:

    

upper_bin = cv2.inRange(image_depth,int(dmin),dcut-1)
#kernel = np.ones((3,3),np.uint8)
#upper_bin = cv2.dilate(upper_bin,kernel,iterations = 3)
plt.imshow(cv2.bitwise_and(image_bin,image_bin,mask=upper_bin));


    

In [47]:

    

lower_bin = cv2.inRange(image_depth,dcut,int(dmax))
#kernel = np.ones((3,3),np.uint8)
#lower_bin = cv2.dilate(lower_bin,kernel,iterations = 3)
plt.imshow(cv2.bitwise_and(image_bin,image_bin,mask=lower_bin));


    

In [30]:

    

from utils import cluster_colors

positions_up, weights_up = cluster_colors(image_bin, upper_bin, items, n_cc=15)


    

In [31]:

    

pos_ok = [(p,w[0][1]) for p,w in zip(positions_up, weights_up) if len(w)==1]
pos_unkw = [(p,w) for p,w in zip(positions_up, weights_up) if len(w)>1]


    

In [32]:

    

positions_low, weights_low = cluster_colors(image_bin, lower_bin, items, n_cc=15)


    

In [33]:

    

pos_ok += [(p,w[0][1]) for p,w in zip(positions_low, weights_low) if len(w)==1]
pos_unkw += [(p,w) for p,w in zip(positions_low, weights_low) if len(w)>1]


    

In [34]:

    

it_col = {'table_cloth':(255,0,255),'tissue_box':(0,255,255),'glue_sticks':(255,0,0),'toilet_brush':(255,255,0),
          'burts_bees_baby_wipes':(255,0,255),'bath_sponge':(0,255,255),'duct_tape':(255,0,0)}
image_disp = image_bin.copy()
mask_disp = upper_bin.copy()
for item in items:
    it_pos = [p for p,it in pos_ok if it==item]
    for cnt in it_pos:
        cv2.drawContours(image_disp,cnt,-1,it_col[item],2)
        cv2.drawContours(mask_disp,cnt,-1,(255,),-1)
for pos,w in pos_unkw:
    for cnt in pos:
        cv2.drawContours(image_disp,cnt,-1,(0,0,0),1)
        #cv2.drawContours(mask_disp,cnt,-1,(255,),-1)
plt.imshow(image_disp); plt.axis('off');


    

In [35]:

    

weights_up


    

    
Out[35]:





[[(0.3673108002099183, 'burts_bees_baby_wipes')],
 [(0.20315875717707649, 'bath_sponge'),
  (0.18146955855392888, 'burts_bees_baby_wipes')],
 [(0.34096318218506177, 'duct_tape'),
  (0.7565547364229781, 'bath_sponge'),
  (0.46790119562131299, 'burts_bees_baby_wipes')],
 [],
 [(0.62626451465486799, 'burts_bees_baby_wipes')],
 [(0.31688939452454423, 'duct_tape'),
  (0.63127314879963325, 'bath_sponge'),
  (0.39041915523986714, 'burts_bees_baby_wipes')],
 [],
 []]

In [36]:

    

weights_low


    

    
Out[36]:





[[(0.13966181745367628, 'duct_tape'),
  (0.30346627837839418, 'bath_sponge'),
  (0.18007448811111415, 'burts_bees_baby_wipes')],
 [(0.10279278080405843, 'burts_bees_baby_wipes')],
 [],
 []]

In [ ]: