def main():
for ind in range(len(K)):
start = time.time()
print('Started at: ',start)
opts = get_opts()
opts.L = L[ind]
opts.K = K[ind]
opts.alpha = alpha[ind]
print('filter_scales',opts.filter_scales)
print('L is', opts.L)
print('K is', opts.K)
print('alpha is', opts.alpha)
n_cpu = util.get_num_CPU()
visual_words.compute_dictionary(opts, n_worker=n_cpu)
dictionary = np.load(join(opts.out_dir, 'dictionary.npy'))
# 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')
print('Finished at: ',time.time())
print('It took', ((time.time()-start)/60), 'minutes to execute the iteration.')
def main():
opts = get_opts()
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)
n_cpu = util.get_num_CPU()
visual_words.compute_dictionary(opts, n_worker=1)
img_path = join(opts.data_dir, 'kitchen/sun_aaqhazmhbhefhakh.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)
n_cpu = util.get_num_CPU()
visual_recog.build_recognition_system(opts, n_worker=n_cpu)
n_cpu = util.get_num_CPU()
conf, accuracy, incorrect = 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)
# ## 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, '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')
def build_recognition_system(vgg16,num_workers=2):
'''
Creates a trained recognition system by generating training features from all training images.
[input]
* vgg16: prebuilt VGG-16 network.
* num_workers: number of workers to process in parallel
[saved]
* features: numpy.ndarray of shape (N,K)
* labels: numpy.ndarray of shape (N)
'''
# ----- TODO -----
num_workers=util.get_num_CPU()
vgg16_weights=util.get_VGG16_weights()
train_data = np.load("../data/train_data.npz")
train_name,labels = train_data['image_names'],train_data['labels']
lenth=len(train_name)
vgg16=torchvision.models.vgg16(pretrained=True)
features=[]
for i in range(0,lenth):
image_path = os.path.join('../data/',train_name[i][0])
args=[i,image_path,vgg16]
single_feature=get_image_feature(args)
features.append(single_feature)
features=np.asarray(features)
#np.savez('../code/trained_system_deep.npz', features=features, labels=labels)
print('saved train_system_deep.npz')
return features,labels
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)/255.0
#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, 'desert/sun_acrqldhmwdraspza.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.get_feature_from_wordmap(opts, wordmap)
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_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
#
# img_path = join(opts.data_dir, 'aquarium/sun_acusadxqppxaqouk.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)
# 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')
def main():
opts = get_opts()
## Q1.1
# img_path = join(opts.data_dir, 'kitchen/sun_aasmevtpkslccptd.jpg')
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_one_image(opts, img)
# visual_words.compute_dictionary(opts, n_worker=n_cpu)
## Q1.3
# img_path = join(opts.data_dir, 'desert/sun_aaqyzvrweabdxjzo.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)
import numpy as np
import torchvision
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())
for n in range(0,N):
dist[n]= distance.euclidean(feature,train_features[n])
return dist
pass
## to call my won extract feature function
def process(train_files,label,vgg16_weights,i):
feature=np.zeros((1001))
path = os.path.join('../data/',train_files[0])
image = imageio.imread(path).astype('double')
image=skimage.transform.resize(image,(224,224,3))
feature[:1000]=network_layers.extract_deep_feature(image,vgg16_weights)
feature[1000]=label
#add label to the end of the feature
feature_label=np.asarray(feature)
np.save('../deep_features/{}.npy'.format(i), feature_label)
print('get deep features:',i,'shape:',feature_label.shape)
return feature
pass
if __name__ == '__main__':
vgg16 = torchvision.models.vgg16(pretrained=True).double()
vgg16.eval()
# build_recognition_system(vgg16,8)
num_workers=util.get_num_CPU()
evaluate_recognition_system(vgg16,num_workers)
print('all finished')
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')
# In-built modules
import os
import multiprocessing
# External modules
import imageio
import numpy as np
# Local python modules
import util
import visual_words
# Globals
PROGRESS = 0
PROGRESS_LOCK = multiprocessing.Lock()
NPROC = util.get_num_CPU()
label_to_string = {
0: 'auditorium',
1: 'baseball_field',
2: 'desert',
3: 'highway',
4: 'kitchen',
5: 'laundromat',
6: 'waterfall',
7: 'windmill'
}
def build_recognition_system(num_workers=2):
'''
def tune(alpha, filter_scales, K, L, D):
"""
Tunes the recognition system
[input]
* alpha: list of number of sampling pixels
* filter scales: list of filter scales to extract responses
* K: list of visual words
* L: list of layers in SPM
[saves]
* dictionary, trained model and results per test
"""
opts = get_opts()
n_cpu = util.get_num_CPU()
OUT_DIR = opts.out_dir
# Tests
test = 0
total_tests = len(L) * len(K) * len(filter_scales) \
* len(alpha) * len(D)
for d in D:
opts.D = d
for fs in filter_scales:
opts.filter_scales = fs
for a in alpha:
opts.alpha = a
for k in K:
opts.K = k
for l in L:
opts.L = l
test += 1
# Create test directory
opts.out_dir = OUT_DIR + f"/test_l-{l}_k-{k}_fs-{len(fs)}_a-{a}_d-{d}"
if not os.path.exists(opts.out_dir):
os.mkdir(opts.out_dir)
print(f"TEST [{test}/{total_tests}]: L-{l}_K-{k}_fs-{len(fs)}_a-{a}_d-{d}")
# Dictionary
if not os.path.exists(join(opts.out_dir, "dictionary.npy")):
print("\tBuilding Dictionary")
start = time()
visual_words.compute_dictionary(opts, n_worker=n_cpu)
print(f"Time {(time() - start) / 60.0}")
else:
print("\tDictionary exists")
# Train
if not os.path.exists(join(opts.out_dir, "trained_system.npz")):
print("\tBuilding Recognition System")
start = time()
visual_recog.build_recognition_system(opts, n_worker=n_cpu)
print(f"Time {(time() - start) / 60.0}")
else:
print("\tRecognition system exists")
# Test
if not os.path.exists(join(opts.out_dir, "accuracy.txt")) or \
not os.path.exists(join(opts.out_dir, "confmat.csv")) or \
not os.path.exists(join(opts.out_dir, "model.npz")):
# Test
print("\tEvaluation")
start = time()
conf, acc = visual_recog.evaluate_recognition_system(opts,
n_worker=n_cpu)
print(f"Time {(time() - start) / 60.0}")
# Results
print(f"Confusion Matrix\n{conf}\n Accuracy: {acc}")
np.savetxt(join(opts.out_dir, 'confmat.csv'), conf,
fmt='%d', delimiter=',')
np.savetxt(join(opts.out_dir, 'accuracy.txt'), [acc], fmt='%g')
np.savez_compressed(join(opts.out_dir, "model.npz"),
filter_scales=fs, K=k, L=l, alpha=a, acc=acc, conf_mat=conf)
else:
print("\tEvaluation exists")
def main():
opts = get_opts()
## Q1.1 - Filter responses
# img_path = join(opts.data_dir, 'aquarium/sun_aztvjgubyrgvirup.jpg')
# img_path = join(opts.data_dir, 'kitchen/sun_aasmevtpkslccptd.jpg')
# img_path = join(opts.data_dir, 'desert/sun_aaqyzvrweabdxjzo.jpg')
# print("Q1.1.2 - Extract filter responses")
# img = Image.open(img_path)
# img.show()
# img = np.array(img).astype(np.float32)/255
# start = time()
# filter_responses = visual_words.extract_filter_responses(opts, img)
# print(f"Time: {(time() - start) / 60.0}")
# util.display_filter_responses(opts, filter_responses)
## Q1.2 - Dictionary
print("Q1.2 - Building dictionary")
start = time()
n_cpu = util.get_num_CPU()
visual_words.compute_dictionary(opts, n_worker=n_cpu)
print(f"Time: {(time() - start) / 60.0}")
## Q1.3 - Wordmaps
# img_path = join(opts.data_dir, 'park/labelme_aumetbzppbkuwju.jpg')
# img_path = join(opts.data_dir, 'laundromat/sun_aaxufyiupegixznm.jpg')
# img_path = join(opts.data_dir, 'highway/sun_beakjawckqywuhzw.jpg')
# img_path = join(opts.data_dir, 'desert/sun_bfyksyxmxcrgvlqw.jpg')
# img_path = join(opts.data_dir, "desert/sun_bqljzuxtzgthsjqt.jpg")
# img_path = join(opts.data_dir, 'kitchen/sun_anmauekjnmmqhigr.jpg')
# print("Q1.3 - Visual words")
# start = time()
# img = Image.open(img_path)
# img.show()
# 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)
# print(f"Time: {(time() - start) / 60.0}")
# util.visualize_wordmap(wordmap)
## Q2.1-Q2.2 - Histograms
# img_path = join(opts.data_dir, 'kitchen/sun_aasmevtpkslccptd.jpg')
# dictionary = np.load(join(opts.out_dir, 'dictionary.npy'))
# img = Image.open(img_path)
# img = np.array(img).astype(np.float32)/255
# wordmap = visual_words.get_visual_words(opts, img, dictionary)
# print("Q2.1 - Feature from wordmap")
# start = time()
# hist = visual_recog.get_feature_from_wordmap(opts, wordmap)
# print(np.sum(hist))
# print(f"Time: {(time() - start) / 60.0}")
# print("Q2.2 - Feature from wordmap SPM")
# hist = visual_recog.get_feature_from_wordmap_SPM(opts, wordmap)
# print(f"Time: {(time() - start) / 60.0}")
# print(np.sum(hist))
## Q2.3 - 2.4 - Recognition System
print("Q2.3-2.4 - Building recognition system")
start = time()
n_cpu = util.get_num_CPU()
visual_recog.build_recognition_system(opts, n_worker=n_cpu)
print(f"Time {(time() - start) / 60.0}")
## Q2.5 - Evaluation
print("Q2.5 - Evaluating recognition system")
start = time()
n_cpu = util.get_num_CPU()
conf, acc = visual_recog.evaluate_recognition_system(opts, n_worker=n_cpu)
print(f"Time {(time() - start) / 60.0}")
print(f"Confusion Matrix\n{conf}\n Accuracy: {acc}")
np.savetxt(join(opts.out_dir, 'confmat.csv'),
conf,
fmt='%d',
delimiter=',')
np.savetxt(join(opts.out_dir, 'accuracy.txt'), [acc], fmt='%g')
