# plt.savefig('../results/wrong_kitchen.png')
# plt.show()
# util.save_wordmap(wordmap, filename)
# visual_recog.build_recognition_system(num_workers=6)
# conf, accuracy = visual_recog.evaluate_recognition_system(num_workers=6)
# print(conf)
# print(accuracy)
# wrong = np.load("../results/wrong_prediction.npy")
# for item in wrong:
# file_path, actual, predict = item
# print(file_path, actual, predict)
# if actual == 1 and predict == 7:
# print(file_path)
# vgg16 = torchvision.models.vgg16(pretrained=True).double()
# vgg16.eval()
# print(list(vgg16.children()))
vgg16_weights = util.get_VGG16_weights()
# deep_recog.build_recognition_system(vgg16_weights, num_workers=6)
# deep = np.load("../results/trained_system_deep.npz", allow_pickle=True)
# print(deep['features'].shape)
# print(deep['labels'].shape)
conf, accuracy = deep_recog.evaluate_recognition_system(vgg16_weights,
num_workers=4)
# conf = np.load("../results/conf_deep.npy")
print(conf)
print(accuracy)
# print(np.trace(conf)/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')
img = visual_words.get_visual_words(image, dictionary)
#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())
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())