Compute Items Features

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import json
from utils import load_items

with open('parameters.json', 'r') as infile:
    params = json.load(infile)

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

items = load_items(ITEM_FOLDER)


    

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import cv2, glob
from utils import imread_rgb, imread_gray, compute_colors

def worker(item):
    folder = ITEM_FOLDER + '/' + item + '/'
    files = glob.glob(folder + '*.png')
    for filename in files:
        image_RGB = imread_rgb(filename)
        if not image_RGB is None:
            image_RGB = cv2.resize(image_RGB,(RESIZE_X,RESIZE_Y))
            file_mask = filename[:-4] + '_mask.pgm'
            mask = imread_gray(file_mask)
            hist, cc = compute_colors(image_RGB, mask)
            dominant_colors = {'hist': hist,
                               'cluster_centers': map(lambda t: list(t), cc)}
            with open(filename[:-4] + '_dc.json', 'w') as outfile:
                json.dump(dominant_colors, outfile)


    

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%%time
from multiprocessing import Pool

print('Computing Dominant colors of images')
print('* resized to %d x %d' % (RESIZE_X,RESIZE_Y))
print('* LAB space')
print('* KMeans')
#print('* MiniBatchKMeans')
pool_size = 6
pool = Pool(pool_size)
result = []
for item in items:
    result.append( pool.apply_async(worker, (item,)) )
pool.close()
pool.join()
for r in result:
     r.get()


    

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import glob
from utils import ...

item_view = []
VVV = []
for item in items:
    folder = ITEM_FOLDER + '/' + item + '/'
    files = glob.glob(folder + '*_XXX.YYY')
    for filename in files:
        ...
        item_view.append(filename)
        VVV.append(len(des))


    

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from matplotlib import pyplot as plt
%matplotlib inline
plt.hist(VVV,bins=60);


    

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[(ns, str(iv.split('/')[-1][:-9])) for ns, iv in sorted(zip(VVV,item_view), reverse=True) if ns>2000]


    

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[(ns, str(iv.split('/')[-1][:-9])) for ns, iv in sorted(zip(VVV,item_view), reverse=True) if ns<50]


    

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from matplotlib import pyplot as plt
%matplotlib inline


    

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import cv2, numpy as np
from ipywidgets import interact
from utils import imread_rgb, plot_colors

def load_and_plot(item,view):
    try:
        prefix = ITEM_FOLDER + '/' + item + '/' + item + '_' + view
        filename = prefix + '.png'
        image_RGB = imread_rgb(filename)
        if not image_RGB is None:
            with open(filename[:-4] + '_dc.json', 'r') as infile:
                dc = json.load(infile)
            hist = dc['hist']
            cc = dc['cluster_centers']
            bar = plot_colors(hist, cc)
            plt.subplot(121); plt.imshow(image_RGB); plt.axis('off');
            plt.subplot(122); plt.imshow(bar); plt.axis('off');
    except (IOError, OSError):
        print('File not found')


    

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views = ['top_01','top-side_01','top-side_02','bottom_01','bottom-side_01','bottom-side_02']
interact(load_and_plot,item=items,view=views);


    

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for item in items:
    for view in views:
        print(item + '_' + view)
        load_and_plot(item,view)
        plt.show()


    

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from matplotlib import pyplot as plt
%matplotlib inline


    

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import cv2, numpy as np
from ipywidgets import interact
from utils import imread_rgb, imread_gray, compute_colors, plot_colors

def compute_and_plot(item,view):
    prefix = ITEM_FOLDER + '/' + item + '/' + item + '_' + view
    filename = prefix + '.png'
    image_RGB = imread_rgb(filename)
    if not image_RGB is None:
        image_RGB = cv2.resize(image_RGB,(RESIZE_X,RESIZE_Y))
        filename = prefix + '_mask.pgm'
        mask = imread_gray(filename)
        plt.subplot(121); plt.imshow(image_RGB); plt.axis('off');
        hist, cc = compute_colors(image_RGB, mask)
        bar = plot_colors(hist, cc)
        plt.subplot(122); plt.imshow(bar); plt.axis('off'); plt.title(item + '_' + view);


    

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views = ['top_01','top-side_01','top-side_02','bottom_01','bottom-side_01','bottom-side_02']
interact(compute_and_plot,item=items,view=views);


    

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for item in items:
    for view in views:
        print(item + '_' + view)
        plot(item,view)
        plt.show()