def TSNE_orb(name):
conn = sqlite3.connect(dirm.sqlite_file)
c = conn.cursor()
dist = orb_cb_handler.get_distributions()
X_Ids = []
X_data = []
for d in dist:
x_id = d[0]
x_data = d[1:]
X_Ids.append(x_id)
X_data.append(x_data)
X_data = np.array(X_data)
model = TSNE(n_components=2)
tsne_x = model.fit_transform(X_data)
tsneHandler.storeTsneValsWIds(name, tsne_x, X_Ids)
return tsne_x, X_Ids
def TSNE_orb(name):
conn = sqlite3.connect(dirm.sqlite_file)
c = conn.cursor()
dist = orb_cb_handler.get_distributions()
X_Ids = []
X_data = []
for d in dist:
x_id = d[0]
x_data = d[1:]
X_Ids.append(x_id)
X_data.append(x_data)
X_data = np.array(X_data)
model = TSNE(n_components=2)
tsne_x = model.fit_transform(X_data)
tsneHandler.storeTsneValsWIds(name, tsne_x, X_Ids)
return tsne_x, X_Ids
def perform():
#imagesHandler.load_images()
#colourHandler.extract_colour_distribution_from_all_images("RGB")
RGB_data = colourHandler.getColourDistForAllImages("RGB")
RGB_data = np.array(RGB_data,dtype=None)
RGB_data= np.delete(RGB_data,0,1)
LAB_data = colourHandler.getColourDistForAllImages("LAB")
LAB_data = np.array(RGB_data,dtype=None)
LAB_data= np.delete(RGB_data,0,1)
gistVals = util.loadCSV("gistvals")
gist_data = np.array(gistVals)
#hogHandler.extract_hog_from_all_images()
hog_data = hogHandler.getHogValsforAllImages()
hog_data = np.array(hog_data,dtype=None)
hog_data= np.delete(hog_data,0,1)
hog_data = np.array(hog_data)
#surfCodebook.run_codebook(n_clusters,400, 0.3, cv2.INTER_CUBIC, 0)
surf_data = surf_cb_handler.get_distributions()
surf_data = np.array(surf_data,dtype=None)
surf_data= np.delete(surf_data,0,1)
sift_data = sift_cb_handler.get_distributions()
sift_data = np.array(sift_data,dtype=None)
sift_data= np.delete(sift_data,0,1)
orb_data = orb_cb_handler.get_distributions()
orb_data = np.array(surf_data,dtype=None)
orb_data= np.delete(surf_data,0,1)
est = KMeans(n_clusters=30)
print(79 * '_')
print('% 9s' % 'init'
' time inertia h**o compl v-meas ARI AMI silhouette')
bench_k_means(est, "colourPerfomanceVmeta", colour_data)
bench_k_means(est, "hogPerfomanceVmeta", hog_data)
#bench_k_means(est, "surfPerfomanceVmeta", surf_data)
def perform():
#imagesHandler.load_images()
#colourHandler.extract_colour_distribution_from_all_images("RGB")
RGB_data = colourHandler.getColourDistForAllImages("RGB")
RGB_data = np.array(RGB_data, dtype=None)
RGB_data = np.delete(RGB_data, 0, 1)
LAB_data = colourHandler.getColourDistForAllImages("LAB")
LAB_data = np.array(RGB_data, dtype=None)
LAB_data = np.delete(RGB_data, 0, 1)
gistVals = util.loadCSV("gistvals")
gist_data = np.array(gistVals)
#hogHandler.extract_hog_from_all_images()
hog_data = hogHandler.getHogValsforAllImages()
hog_data = np.array(hog_data, dtype=None)
hog_data = np.delete(hog_data, 0, 1)
hog_data = np.array(hog_data)
#surfCodebook.run_codebook(n_clusters,400, 0.3, cv2.INTER_CUBIC, 0)
surf_data = surf_cb_handler.get_distributions()
surf_data = np.array(surf_data, dtype=None)
surf_data = np.delete(surf_data, 0, 1)
sift_data = sift_cb_handler.get_distributions()
sift_data = np.array(sift_data, dtype=None)
sift_data = np.delete(sift_data, 0, 1)
orb_data = orb_cb_handler.get_distributions()
orb_data = np.array(surf_data, dtype=None)
orb_data = np.delete(surf_data, 0, 1)
est = KMeans(n_clusters=30)
print(79 * '_')
print(
'% 9s' % 'init'
' time inertia h**o compl v-meas ARI AMI silhouette')
bench_k_means(est, "colourPerfomanceVmeta", colour_data)
bench_k_means(est, "hogPerfomanceVmeta", hog_data)
#hogHandler.extract_hog_from_all_images()
hog_data = hogHandler.getHogValsforAllImages()
hog_data = np.array(hog_data, dtype=None)
hog_data = np.delete(hog_data, 0, 1)
hog_data = np.array(hog_data)
#surfCodebook.run_codebook(n_clusters,400, 0.3, cv2.INTER_CUBIC, 0)
surf_data = surf_cb_handler.get_distributions()
surf_data = np.array(surf_data, dtype=None)
surf_data = np.delete(surf_data, 0, 1)
sift_data = sift_cb_handler.get_distributions()
sift_data = np.array(sift_data, dtype=None)
sift_data = np.delete(sift_data, 0, 1)
orb_data = orb_cb_handler.get_distributions()
orb_data = np.array(orb_data, dtype=None)
orb_data = np.delete(orb_data, 0, 1)
def perLabel(label_name, labels, sample_size, n_clusters):
print(79 * '_')
print label_name
print(
'% 9s' % 'feature'
' time inertia h**o compl v-meas ARI AMI silhouette')
#print "number of distinct classes for true labels for ",label_name, len(Counter(labels))
estimator = KMeans(n_clusters=n_clusters)
bench_k_means(labels, sample_size, estimator, "RGB", rgb_data)
bench_k_means(labels, sample_size, estimator, "LAB", lab_data)
bench_k_means(labels, sample_size, estimator, "HOG", hog_data)
#hogHandler.extract_hog_from_all_images()
hog_data = hogHandler.getHogValsforAllImages()
hog_data = np.array(hog_data,dtype=None)
hog_data= np.delete(hog_data,0,1)
hog_data = np.array(hog_data)
#surfCodebook.run_codebook(n_clusters,400, 0.3, cv2.INTER_CUBIC, 0)
surf_data = surf_cb_handler.get_distributions()
surf_data = np.array(surf_data,dtype=None)
surf_data= np.delete(surf_data,0,1)
sift_data = sift_cb_handler.get_distributions()
sift_data = np.array(sift_data,dtype=None)
sift_data= np.delete(sift_data,0,1)
orb_data = orb_cb_handler.get_distributions()
orb_data = np.array(orb_data,dtype=None)
orb_data= np.delete(orb_data,0,1)
def perLabel(label_name,labels,sample_size,n_clusters):
print(79 * '_')
print label_name
print('% 9s' % 'feature'
' time inertia h**o compl v-meas ARI AMI silhouette')
#print "number of distinct classes for true labels for ",label_name, len(Counter(labels))
estimator = KMeans(n_clusters=n_clusters)
bench_k_means(labels,sample_size,estimator, "RGB", rgb_data)
bench_k_means(labels,sample_size,estimator, "LAB", lab_data)
bench_k_means(labels,sample_size,estimator, "HOG", hog_data)
bench_k_means(labels,sample_size,estimator, "GIST", gist_data)