def main():
opts = get_opts()
## Q1.1
img_path = join(opts.data_dir, 'kitchen/sun_aasmevtpkslccptd.jpg')
img = Image.open(img_path)
img = np.array(img).astype(np.float32)/255
filter_responses = visual_words.extract_filter_responses(opts, img)
util.display_filter_responses(opts, filter_responses)
# ## Q1.2
n_cpu = util.get_num_CPU()
visual_words.compute_dictionary(opts, n_worker=n_cpu)
## Q1.3
img_path = join(opts.data_dir, 'windmill/sun_bsngeuxxmgmcsesp.jpg')
img = Image.open(img_path)
img = np.array(img).astype(np.float32)/255
dictionary = np.load(join(opts.out_dir, 'dictionary.npy'))
wordmap = visual_words.get_visual_words(opts, img, dictionary)
util.visualize_wordmap(wordmap)
## Q2.1-2.4
n_cpu = util.get_num_CPU()
visual_recog.build_recognition_system(opts, n_worker=n_cpu)
## Q2.5
n_cpu = util.get_num_CPU()
conf, accuracy = visual_recog.evaluate_recognition_system(opts, n_worker=n_cpu)
print(conf)
print(accuracy)
np.savetxt(join(opts.out_dir, 'confmat.csv'), conf, fmt='%d', delimiter=',')
np.savetxt(join(opts.out_dir, 'accuracy.txt'), [accuracy], fmt='%g')
def main():
opts = get_opts()
## Q1.1
img_path = join(opts.data_dir, 'kitchen/sun_aasmevtpkslccptd.jpg')
img = Image.open(img_path)
img = np.array(img).astype(np.float32) / 255
filter_responses = visual_words.extract_filter_responses(opts, img)
util.display_filter_responses(opts, filter_responses)
def main():
opts = get_opts()
# Q1.1
img_path = join(opts.data_dir, 'aquarium/sun_aztvjgubyrgvirup.jpg')
img = Image.open(img_path)
img = np.array(img).astype(np.float32) / 255
filter_responses = visual_words.extract_filter_responses(opts, img)
util.display_filter_responses(opts, filter_responses)
# Q1.2
n_cpu = util.get_num_CPU()
visual_words.compute_dictionary(opts, n_worker=n_cpu)
# Q1.3
### Uncomment for picture 1 ###
img_path = join(opts.data_dir, 'kitchen/sun_aasmevtpkslccptd.jpg')
### Uncomment for picture 2 ###
# img_path = join(opts.data_dir, 'aquarium/sun_acrxheaggpuqwdwm.jpg')
### Uncomment for picture 3 ###
# img_path = join(opts.data_dir, 'desert/sun_banypouestzeimab.jpg')
####################################################################
img = Image.open(img_path)
img = np.array(img).astype(np.float32) / 255
dictionary = np.load(join(opts.out_dir, 'dictionary.npy'))
wordmap = visual_words.get_visual_words(opts, img, dictionary)
util.visualize_wordmap(wordmap)
# Q2.1-2.4
n_cpu = util.get_num_CPU()
visual_recog.build_recognition_system(opts, n_worker=n_cpu)
# Q2.5
n_cpu = util.get_num_CPU()
conf, accuracy = visual_recog.evaluate_recognition_system(opts,
n_worker=n_cpu)
# Q3.2
# n_cpu = util.get_num_CPU()
# custom.build_recognition_system(opts, n_worker=n_cpu)
# n_cpu = util.get_num_CPU()
# conf, accuracy = custom.evaluate_recognition_system(opts, n_worker=n_cpu)
print(conf)
print(accuracy)
np.savetxt(join(opts.out_dir, 'confmat.csv'),
conf,
fmt='%d',
delimiter=',')
np.savetxt(join(opts.out_dir, 'accuracy.txt'), [accuracy], fmt='%g')
import util
import matplotlib.pyplot as plt
import visual_words
import visual_recog
import deep_recog
import skimage
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()
def main():
opts = get_opts()
print('L is', opts.L)
print('K is', opts.K)
print('alpha is', opts.alpha)
print()
# Q1.1
img_path = join(opts.data_dir, 'kitchen/sun_aasmevtpkslccptd.jpg')
img = Image.open(img_path)
img = np.array(img).astype(np.float32) / 255
filter_responses = visual_words.extract_filter_responses(opts, img)
# imageio.imsave('../results/filter_responses.jpg',filter_responses)
util.visualize_wordmap(img)
util.display_filter_responses(opts, filter_responses)
## # Q1.2
n_cpu = util.get_num_CPU()
visual_words.compute_dictionary(opts, n_worker=n_cpu)
dictionary = np.load(join(opts.out_dir, 'dictionary.npy'))
###
# ## Q1.3
img_path = join(opts.data_dir, 'kitchen/sun_aasmevtpkslccptd.jpg')
img = Image.open(img_path)
img = np.array(img).astype(np.float32) / 255
wordmap = visual_words.get_visual_words(opts, img, dictionary)
util.visualize_wordmap(img)
util.visualize_wordmap(wordmap)
#
img_path = join(opts.data_dir, 'waterfall/sun_bbeqjdnienanmmif.jpg')
img = Image.open(img_path)
img = np.array(img).astype(np.float32) / 255
wordmap = visual_words.get_visual_words(opts, img, dictionary)
util.visualize_wordmap(img)
util.visualize_wordmap(wordmap)
#
img_path = join(opts.data_dir, 'windmill/sun_bratfupeyvlazpba.jpg')
img = Image.open(img_path)
img = np.array(img).astype(np.float32) / 255
wordmap = visual_words.get_visual_words(opts, img, dictionary)
util.visualize_wordmap(img)
util.visualize_wordmap(wordmap)
img_path = join(opts.data_dir, 'desert/sun_adjlepvuitklskrz.jpg')
img = Image.open(img_path)
img = np.array(img).astype(np.float32) / 255
wordmap = visual_words.get_visual_words(opts, img, dictionary)
util.visualize_wordmap(img)
util.visualize_wordmap(wordmap)
#
# Q2.1-2.4
n_cpu = util.get_num_CPU()
visual_recog.build_recognition_system(opts, n_worker=n_cpu)
## Q2.5
n_cpu = util.get_num_CPU()
conf, accuracy = visual_recog.evaluate_recognition_system(opts,
n_worker=n_cpu)
print(conf)
print(accuracy)
np.savetxt(join(opts.out_dir, 'confmat.csv'),
conf,
fmt='%d',
delimiter=',')
np.savetxt(join(opts.out_dir, 'accuracy.txt'), [accuracy], fmt='%g')
for i in range(len(temp_data_list)):
filter_responses.append(
np.load(temp_data_path + '/' + temp_data_list[i]))
all_filter_response = np.concatenate(filter_responses)
kmeans = sklearn.cluster.KMeans(n_clusters=K).fit(all_filter_response)
dictionary = kmeans.cluster_centers_
np.save('dictionary.npy', dictionary)
def removeTmpFiles(path):
if path.split("/")[-1] == '.DS_Store':
os.remove(path)
elif os.path.isdir(path):
for filename in os.listdir(path):
removeTmpFiles(path + "/" + filename)
if __name__ == "__main__":
image_path = '../data/aquarium/sun_aztvjgubyrgvirup.jpg'
# image=imageio.imread(image_path)
image = cv2.imread(image_path)
print(image.shape)
response_map = extract_filter_responses(image)
# dictionary = np.load("dictionary.npy")
# wordmap=get_visual_words(image,dictionary)
util.display_filter_responses(response_map)
# util.save_wordmap(wordmap,'labelme_aacpgupgzvdjapw_wordmap.jpeg')