import numpy as np
import util
import matplotlib
matplotlib.use('TkAgg')
from matplotlib import pyplot as plt
import visual_words
import visual_recog
import skimage.io
if __name__ == '__main__':
num_cores = util.get_num_CPU()
path_img = "../data/laundromat/sun_aiyluzcowlbwxmdb.jpg"
image = skimage.io.imread(path_img)
image = image.astype('float') / 255
#filter_responses = visual_words.extract_filter_responses(image)
#util.display_filter_responses(filter_responses)
#visual_words.compute_dictionary(num_workers=num_cores)
dictionary = np.load('dictionary.npy')
wordmap = visual_words.get_visual_words(image, dictionary)
filename = 'figure3'
util.save_wordmap(wordmap, filename)
#visual_recog.build_recognition_system(num_workers=num_cores)
#conf, accuracy = visual_recog.evaluate_recognition_system(num_workers=num_cores)
#print(conf)
#print(np.diag(conf).sum()/conf.sum())
import skimage.io
import time
if __name__ == '__main__':
start = time.time() #ctl
num_cores = util.get_num_CPU()
# path_img = "../data/kitchen/sun_avuzlcqxzrzteyvc.jpg"
path_img = "../data/aquarium/sun_aztvjgubyrgvirup.jpg"
image = skimage.io.imread(path_img)
image = image.astype('float') / 255
filter_responses = visual_words.extract_filter_responses(image)
# util.display_filter_responses(filter_responses)
# visual_words.compute_dictionary(num_workers=num_cores)
dictionary = np.load('dictionary.npy')
wordmap = visual_words.get_visual_words(image, dictionary)
util.save_wordmap(wordmap, "wordmap_n")
# visual_recog.get_feature_from_wordmap(wordmap,dictionary.shape[0]) #ctl
# visual_recog.get_feature_from_wordmap_SPM(wordmap,3,dictionary.shape[0]) #ctl
visual_recog.build_recognition_system(
num_workers=num_cores
) # approx 12 min for 100 files, about 2 hr for 1000 files
conf, accuracy = visual_recog.evaluate_recognition_system(
num_workers=num_cores) # approx 1 hr for 577 files
# print(conf)
# print(np.diag(conf).sum()/conf.sum())
end = time.time() #ctl
print("Time: ", end - start) #ctl
import numpy as np
import util
import matplotlib
from matplotlib import pyplot as plt
import visual_words
import visual_recog
import skimage.io
if __name__ == '__main__':
num_cores = util.get_num_CPU()
path_img = "../data/waterfall/sun_bolfhwtizbvyjmem.jpg"
image = skimage.io.imread(path_img)
image = image.astype('float') / 255
filter_responses = visual_words.extract_filter_responses(image)
util.display_filter_responses(filter_responses)
visual_words.compute_dictionary(num_workers=num_cores)
dictionary = np.load('dictionary.npy')
wordmap = visual_words.get_visual_words(image, dictionary)
util.save_wordmap(wordmap, "waterfall_3.jpg")
visual_recog.build_recognition_system(num_workers=num_cores)
conf, accurSacy = visual_recog.evaluate_recognition_system(
num_workers=num_cores)
print(conf)
print(np.diag(conf).sum() / conf.sum())
import deep_recog
import skimage
if __name__ == '__main__':
num_cores = util.get_num_CPU()
path_img = "../data/park/labelme_vtvrcfujsukawzl.jpg"
image = skimage.io.imread(path_img)
image = np.array(image) / 255
filter_responses = visual_words.extract_filter_responses(image)
util.display_filter_responses(filter_responses)
visual_words.compute_dictionary(num_workers=num_cores)
dictionary = np.load('dictionary.npy')
img = visual_words.get_visual_words(image, dictionary)
util.save_wordmap(img, '../results/wordmap_3.png')
visual_recog.build_recognition_system(num_workers=num_cores)
conf, accuracy = visual_recog.evaluate_recognition_system(
num_workers=num_cores)
print(conf)
print(np.diag(conf).sum() / conf.sum())
vgg16 = torchvision.models.vgg16(pretrained=True).double()
vgg16.eval()
deep_recog.build_recognition_system(vgg16, num_workers=num_cores // 2)
conf = deep_recog.evaluate_recognition_system(vgg16,
num_workers=num_cores // 2)
print(conf)
print(np.diag(conf).sum() / conf.sum())
if __name__ == '__main__':
num_cores = util.get_num_CPU()
path_img = "../data/kitchen/sun_aasmevtpkslccptd.jpg"
image = skimage.io.imread(path_img)
image = image.astype('float') / 255
filter_responses = visual_words.extract_filter_responses(image)
util.display_filter_responses(filter_responses)
visual_words.compute_dictionary(num_workers=num_cores)
dictionary = np.load('dictionary.npy')
filename = "test.jpg"
img = visual_words.get_visual_words(image, dictionary)
util.save_wordmap(img, filename)
visual_recog.build_recognition_system(num_workers=num_cores)
conf, accuracy = visual_recog.evaluate_recognition_system(
num_workers=num_cores)
print(conf)
print(np.diag(conf).sum() / conf.sum())
vgg16 = torchvision.models.vgg16(pretrained=True).double()
vgg16.eval()
deep_recog.build_recognition_system(vgg16, num_workers=num_cores)
conf, accuracy = deep_recog.evaluate_recognition_system(
vgg16, num_workers=num_cores)
print(conf)
print(np.diag(conf).sum() / conf.sum())
#path_img = "../data/auditorium/sun_aflgfyywvxbpeyxl.jpg"
#path_img = "../data/baseball_field/sun_aalztykafqwxrspj.jpg"
path_img = "../data/kitchen/sun_aasmevtpkslccptd.jpg"
#path_img = "../data/highway/sun_acpvugnkzrliaqir.jpg"
image = skimage.io.imread(path_img)
image = image.astype('float') / 255
filter_responses = visual_words.extract_filter_responses(image)
util.display_filter_responses(filter_responses)
visual_words.compute_dictionary(num_workers=num_cores)
dictionary = np.load('dictionary.npy')
wordmap = visual_words.get_visual_words(image, dictionary)
util.save_wordmap(wordmap, 'word_map.jpg')
visual_recog.build_recognition_system(num_workers=num_cores)
conf, accuracy = visual_recog.evaluate_recognition_system(
num_workers=num_cores)
print(conf)
print(accuracy)
#vgg16 = torchvision.models.vgg16(pretrained=True).double()
#vgg16.eval()
vgg16 = util.vgg16_fc7()
#deep_recog.build_recognition_system(vgg16,num_workers=num_cores//2)
conf, accuracy = deep_recog.evaluate_recognition_system(
vgg16, num_workers=num_cores // 2)