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test_bottle.py
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test_bottle.py
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import Image
import shutil
import dlib
import random
import os
import glob
from skimage import io
# print "\nTest1 accuracy: ", dlib.test_simple_object_detector('/home/jyotiska/Dropbox/Computer Vision/cupdataset_2_test.xml',"cupdetector_2.svm")
# print "\nTraining accuracy: ", dlib.test_simple_object_detector('/home/jyotiska/Dropbox/Computer Vision/cupdataset_3.xml',"cupdetector_3.svm")
detector = dlib.simple_object_detector("bottledetector.svm")
win_det = dlib.image_window()
win_det.set_image(detector)
win = dlib.image_window()
test_dir = '/home/jyotiska/Dropbox/Computer Vision/Bottle_test_convert'
assorted_dir = '/home/jyotiska/Dropbox/Computer Vision/Item bucket'
items =os.listdir(assorted_dir)
convert_i = 0
for f in glob.glob(test_dir+"/*.*"):
print "processing file:", f
img = io.imread(f)
extension = f.split(".")[1]
convert_file = "convert_bottle_"+str(convert_i)+"."+extension
shutil.copy(f,convert_file)
print "convert file:",convert_file
background = Image.open(convert_file)
dets = detector(img)
print "number of cups detected:", len(dets)
for d in dets:
x = d.left()
y = d.top()
width = d.right() - x
height = d.bottom() - y
print " detection position left,top,right,bottom:", d.left(), d.top(), d.right(), d.bottom()
r = random.randint(0,len(items)-1)
print r,items[r]
random_item = Image.open(assorted_dir+"/"+items[r])
# scale it a bit more, and adjust position
# Apply blur?
resized = random_item.resize( (int(1.2*width),int(1.2*height)) )
background.paste(resized, (d.left()-12,d.top()-10), resized)
background.show()
background.save(convert_file)
win.clear_overlay()
win.set_image(img)
win.add_overlay(dets)
convert_i += 1
raw_input("Hit enter to continue")