# C.4 Concatenate histograms from R, G, B one below the other into a single histogram
histo = np.concatenate([histo_r, histo_g, histo_b])
matplotlib.pyplot.bar(range(0, len(histo)), histo, 0.8, None, None, color='red')
######
# C.5 PUT YOUR CODE INTO THE FUNCTION extractColorHistogram( im ) IN week1.py
######
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Step D. Compute distances between vectors [You should implement]
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# D.1 Open images and extract their RGB histograms
im1 = imread('../../data/objects/flower/1.jpg')
histo1 = week1.extractColorHistogram(im1)
im2 = imread('../../data/objects/flower/3.jpg')
histo2 = week1.extractColorHistogram(im2)
# D.2 Compute euclidean distance: d=Σ(x-y)^2
# Note: the ***smaller*** the value, the more similar the histograms
dist_euc = np.linalg.norm(histo2-histo1)
print dist_euc
# D.3 Compute histogram intersection distance: d=Σmin(x, y)
# Note: the ***larger*** the value, the more similar the histograms
dist_inter = np.sum(np.minimum(histo1,histo2))
print dist_inter
# D.4 Compute chi-2 similarity: d= Σ(x-y)^2 / (x+y)
# Note: the ***larger*** the value, the more similar the histograms
# C.2 Compute histogram from channel R using the bincount command, as indicated in the handout
histo_r = np.bincount(im_r, None, 256)
# C.3 Compute now the histograms from the other channels, that is G and B
im_g = im[:, :, 1].flatten()
histo_g = np.bincount(im_g, None, 256)
im_b = im[:, :, 2].flatten()
histo_b = np.bincount(im_b, None, 256)
# C.4 Concatenate histograms from R, G, B one below the other into a single histogram
histo = np.concatenate([histo_r, histo_g, histo_b])
print histo
histo_opdr = week1.extractColorHistogram(im)
print histo_opdr
matplotlib.pyplot.bar(range(0, len(histo_opdr)), histo_opdr, 0.8, None, None, edgecolor='red')
######
# C.5 PUT YOUR CODE INTO THE FUNCTION extractColorHistogram( im ) IN week1.py
######
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Step D. Compute distances between vectors [You should implement]
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
reload(week1)
# D.1 Open images and extract their RGB histograms
im1 = imread('../../data/objects/flower/1.jpg')
histo1 = week1.extractColorHistogram(im1)
ax.set_xticks(labels)
ax.set_ylabel("Relative frequency")
ax.set_xlabel("Bin label")
plt.bar(np.arange(len(histo)), histo, 1.0, color='gray', edgecolor='none')
######
# C.5 PUT YOUR CODE INTO THE FUNCTION extractColorHistogram( im ) IN week1.py
######
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Step D. Compute distances between vectors [You should implement]
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# D.1 Open images and extract their RGB histograms
im1 = plt.imread('../../data/objects/flower/1.jpg')
histo1 = week1.extractColorHistogram(im1)
im2 = plt.imread('../../data/objects/flower/3.jpg')
histo2 = week1.extractColorHistogram(im2)
# D.2 Compute euclidean distance: d=Σ(x-y)^2
# Note: the ***smaller*** the value, the more similar the histograms
dist_euc = sum(map(lambda x, y: (x - y) ** 2,
histo1 / math.sqrt(sum(map(lambda x: x * x, histo1))),
histo2 / math.sqrt(sum(map(lambda x: x * x, histo2)))))
# D.3 Compute histogram intersection distance: d=Σmin(x, y)
# Note: the ***larger*** the value, the more similar the histograms
dist_hi = sum(map(min,
histo1 / float(sum(histo1)),
histo2 / float(sum(histo2))))
enumeration = range(len(values))
plt.plot(enumeration, values,
QUERY_PLOT_SYMBOLS[AVERAGE_PRECISION], label='average')
plt.plot(enumeration, values,
QUERY_PLOT_SYMBOLS[AVERAGE_PRECISION][0] + '--', label=None)
enumeration = [enumeration[0] - 0.5]\
+ enumeration\
+ [enumeration[-1] + 0.5]
ax.set_xticks(range(len(values)))
ax.set_xticklabels(map(str, BOW_SIZES))
# PART 5. STEP 6. COMPUTE THE PRECISION@{5,10} FOR HISTOGRAM BASED RETRIEVAL
# Generate histogram for each image in the dataset
print time.strftime("[%H:%M:%S] Computing part 5, step 6")
getHistogram = lambda x: week1.extractColorHistogram(np.array(plt.imread(x)))
histograms = {d['norm']: {filename: getHistogram(IMG_FOLDER + filename)
for filename in os.listdir(IMG_FOLDER)}
for d in DISTANCE_MEASURES}
color_dist_matrices = week3.distance_matrices(histograms, DISTANCE_MEASURES)
color_ranking = {}
for q in QUERIES:
for d in color_dist_matrices:
r = week3.rank_images(color_dist_matrices[d], q)
for n in PRECISION_LIMITS:
p = week3.custom_precision_at_N(q, indexed_labels, r, n)
print "Precision@%d with %s measure for %s's color histograms: %f"\
% (n, d, q, p)
print time.strftime("[%H:%M:%S] Done.")