def produce_15_hist():
# These modules are imported locally as Matplotlib is a headache and takes time to import
import os
import matplotlib.pyplot as plt
all_paths = [os.path.join("data/sift/train",i) for i in os.listdir("data/sift/train")]
for pu in all_paths:
train_imagepaths, train_labels = sub_sample_images(pu, n_sample=50)
print(pu)
label_pu = os.path.basename(pu)
train_image_feats = get_bags_of_sifts(train_imagepaths, kmeans)
sm = np.sum(train_image_feats,axis=0)
plt.hist(sm,50)
plt.tick_params(
axis='x', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
labelbottom=False) # labels along the bottom edge are off
plt.title(label_pu)
plt.savefig(os.path.join(r"C:\Users\User\Desktop\figs",label_pu))
plt.clf()
if a[2] not in categories: categories.append(a[2])
print("Categories: ", categories)
''' Step 1: Represent each image with the appropriate feature
Each function to construct features should return an N x d matrix, where
N is the number of paths passed to the function and d is the
dimensionality of each image representation. See the starter code for
each function for more details. '''
print('Extracting SIFT features\n')
#TODO: You code build_vocabulary function in util.py
voc_size = 50
kmeans = build_vocabulary(train_image_paths, vocab_size=voc_size)
#TODO: You code get_bags_of_sifts function in util.py
train_image_feats = get_bags_of_sifts(train_image_paths, kmeans)
test_image_feats = get_bags_of_sifts(test_image_paths, kmeans)
#If you want to avoid recomputing the features while debugging the
#classifiers, you can either 'save' and 'load' the extracted features
#to/from a file.
#np.save('train_image_feats', train_image_feats)
#np.save('test_image_feats', test_image_feats)
#train_image_feats = np.load('train_image_feats.npy')
#test_image_feats = np.load('test_image_feats.npy')
make_avg_histogram(train_labels, train_image_feats, categories)
''' Step 2: Classify each test image by training and using the appropriate classifier
Each function to classify test features will return an N x l cell array,