def main():
# Read dataset
datasets = [
Dataset('highway', 1050, 1350),
Dataset('fall', 1461, 1560),
Dataset('traffic', 951, 1050)
]
metricses = []
for dataset in range(3):
print "Computing dataset " + str(dataset + 1)
imgs = datasets[dataset].readInput()
imgs_GT = datasets[dataset].readGT()
# Split dataset
train = imgs[:len(imgs) / 2]
test = imgs[len(imgs) / 2:]
test_GT = imgs_GT[len(imgs) / 2:]
metrics_array = f1score_alpha(train, test, test_GT)
metricses.append(metrics_array)
# TASK 2.2 - Plot F1-score vs Alpha
alpha_range = np.around(np.arange(0, 6, 0.1), decimals=2)
metricses = np.array(metricses)
x = [alpha_range, alpha_range, alpha_range]
y = [metricses[0, :], metricses[1, :], metricses[2, :]]
axis = ["Alpha", "F1-score"]
labels = ["Highway", "Fall", "Traffic"]
ev.plotGraphics(x, y, axis, labels)
def f1_p(train, test, test_GT, alpha, ro, conn):
prints = False
# F1-score vs #Pixels
p_range = np.around(np.arange(0,100,10))
metrics_array = []
results = background_substraction(train, test, alpha, ro, prints)
results = hole_filling(results, conn, prints)
for p in p_range:
sys.stdout.write("(p=" + str(p)+")\n")
results_filtered = area_filtering(results, conn, p, prints)
metrics = results_evaluation(results_filtered, test_GT, prints)
metrics_array.append(metrics);
# TASK 2.2 - Plot F1-score vs Alpha
x = p_range
metrics_array = np.array(metrics_array)
y = metrics_array[:, 2]
f1_max = np.max(metrics_array[:, 2])
f1_max_idx = np.argmax(metrics_array[:, 2])
best_p = p_range[f1_max_idx]
print "F1: " + str(np.around(f1_max,decimals=4)) + " (p="+str(best_p)+")"
axis = ["#Pixels", "F1-score"]
labels = []
ev.plotGraphics(x, y, axis, labels)
def f1score_alpha(train, test, test_GT, conn, ro, p, prints=False):
# Task 1.2 - F1-score vs Alpha
alpha_range = np.around(np.arange(1,5,0.2),decimals=2)
metrics_array = []
for alpha in alpha_range:
sys.stdout.write("(alpha=" + str(np.around(alpha,decimals=2))+") ")
t= time.time()
results = background_substraction(train, test, alpha, ro, prints)
results = hole_filling(results, conn, prints)
results = area_filtering(results, p, prints)
metrics = results_evaluation(results, test_GT, prints)
metrics_array.append(metrics);
elapsed = time.time() - t
sys.stdout.write(str(elapsed) + ' sec \n')
# TASK 2.2 - Plot F1-score vs Alpha
x = [alpha_range, alpha_range, alpha_range]
metrics_array = np.array(metrics_array)
y = [metrics_array[:, 0], metrics_array[:, 1],metrics_array[:, 2]]
f1_max = np.max(metrics_array[:, 2])
f1_max_idx = np.argmax(metrics_array[:, 2])
best_alpha = alpha_range[f1_max_idx]
print "F1: " + str(np.around(f1_max,decimals=4)) + " (alpha="+str(best_alpha)+")"
axis = ["Alpha", "F1-score"]
labels = ["Precision", "Recall", "F1"]
ev.plotGraphics(x, y, axis, labels)
def auc_vs_p(train, test, test_GT, ro, conn):
# auc vs #Pixels
p_range = np.around(np.arange(0, 1000, 10))
auc_array = []
for p in p_range:
sys.stdout.write("(p=" + str(p)+") ")
auc = precision_recall_curve(train, test, test_GT, ro, conn, p, False)
auc_array.append(auc);
# TASK 2.2 - Plot F1-score vs Alpha
x = p_range
y = np.array(auc_array)
auc_max = np.max(y)
auc_max_idx = np.argmax(y)
best_p = p_range[auc_max_idx]
print "AUC: " + str(np.around(auc_max,decimals=4)) + " (p="+str(best_p)+")"
axis = ["#Pixels", "AUC"]
labels = []
ev.plotGraphics(x, y, axis, labels)
print y
def auc_all():
# p_range = np.concatenate([np.array([0]),np.around(np.arange(0, 1000, 10))])
p_range = np.around(np.arange(0, 1000, 10))
# p_range2 = np.concatenate([np.array([0]),np.around(np.arange(0, 2000, 50))])
p_range2 = np.around(np.arange(0, 2000, 50))
x = [p_range, p_range,p_range2]
y_traffic = [0.54865382, 0.5592443, 0.56580872, 0.571033, 0.57604746, 0.58004003,
0.5837208, 0.58664876, 0.58901124, 0.59100597, 0.59249389, 0.59403019,
0.59510371, 0.59685786, 0.59798599, 0.5998437, 0.60081657, 0.60195222,
0.60377896, 0.60619143, 0.6073376, 0.60800021, 0.60910401, 0.60971082,
0.61069558, 0.61044473, 0.61120665, 0.61254456, 0.61291559, 0.61321382,
0.6133638, 0.61385636, 0.61263136, 0.61559219, 0.61488785, 0.61297316,
0.61356487, 0.61378428, 0.61202322, 0.61133323, 0.60994398, 0.61029301,
0.61085772, 0.61006678, 0.60799569, 0.60644023, 0.60678493, 0.60822458,
0.6077443, 0.60867033, 0.60805301, 0.60819197, 0.6092698, 0.60974066,
0.60882509, 0.60669797, 0.60818745, 0.60718756, 0.60773519, 0.60776267,
0.6078122, 0.60824679, 0.6095641, 0.60933804, 0.60776864, 0.60615064,
0.60669904, 0.60632972, 0.60869301, 0.60922683, 0.60929263, 0.61018956,
0.60990093, 0.61049604, 0.61015858, 0.61081188, 0.61023095, 0.60949348,
0.60957899, 0.60861016, 0.60771793, 0.60772563, 0.60574544, 0.60728,
0.60641079, 0.60923875, 0.60942574, 0.60876463, 0.60908811, 0.60917016,
0.60714994, 0.60661957, 0.60623696, 0.60641611, 0.60608787, 0.60609832,
0.60649841, 0.60685467, 0.60777793, 0.60863877]
y_highway = [0.47646952, 0.51264552, 0.51712399, 0.52016654, 0.52226093, 0.52292387,
0.52324357, 0.52385192, 0.524463, 0.52590177, 0.52727928, 0.52689404,
0.52689589, 0.52699987, 0.52692316, 0.52722481, 0.52747004, 0.52697665,
0.52713471, 0.52716288, 0.52698196, 0.52691934, 0.52779482, 0.52722408,
0.52739301, 0.52662726, 0.52663605, 0.52684491, 0.52615045, 0.52613233,
0.52673217, 0.52655387, 0.52701599, 0.52699287, 0.52696298, 0.52633702,
0.52651062, 0.52572597, 0.5247644, 0.52461462, 0.52392415, 0.52282114,
0.52152874, 0.52096475, 0.52080267, 0.52094456, 0.52068838, 0.51971823,
0.51961159, 0.51939336, 0.52018412, 0.52096747, 0.52012205, 0.51897652,
0.5194545, 0.51901482, 0.51966423, 0.5194988, 0.51878067, 0.51862623,
0.51815943, 0.51723116, 0.51618216, 0.51642436, 0.51654179, 0.51581763,
0.51608789, 0.51520603, 0.5162197, 0.51618521, 0.51574108, 0.51559478,
0.51431996, 0.51502, 0.51515404, 0.5150161, 0.51474541, 0.51451247,
0.51444362, 0.51442594, 0.51382708, 0.51312868, 0.51342386, 0.51360329,
0.51245204, 0.5115633, 0.51205942, 0.5110433, 0.51141787, 0.51271048,
0.5133746, 0.51237951, 0.51229149, 0.51209891, 0.51304629, 0.51176097,
0.51267674, 0.51321841, 0.51307787, 0.5136463 ]
y_fall = [0.60677031, 0.70924287, 0.73032806, 0.7406121, 0.7490444, 0.75348709,
0.76354178, 0.77049525, 0.7728623, 0.77931308, 0.78337424, 0.78399852,
0.78696076, 0.79017504, 0.79253606, 0.79500976, 0.79990754, 0.80621057,
0.81470682, 0.81729479, 0.81780651, 0.81886046, 0.82048388, 0.82309066,
0.82532147, 0.82611948, 0.8287993, 0.82972555, 0.83358859, 0.83771235,
0.83976428, 0.84408887, 0.84612412, 0.84775577, 0.84893319, 0.84928296,
0.85109461, 0.851044, 0.85095705, 0.85093486]
# y = [y_traffic, y_highway, y_fall]
x = p_range
y = y_traffic
axis = ["#Pixels", "AUC"]
# labels = ["Traffic", "Highway", "Fall"]
labels = []
ev.plotGraphics(x, y, axis, labels)
def f1score_alpha(train, test, test_GT):
# Task 1.2 - F1-score vs Alpha
alpha_range = np.around(np.arange(1.5, 2.5, 0.1), decimals=2)
metrics_array = []
for alpha in alpha_range:
sys.stdout.write("(alpha=" + str(np.around(alpha, decimals=2)) + ") ")
# Background substraction
g = GaussianModelling(alpha=alpha)
g.fit(train)
results = g.predict(test)
# Evaluation sklearn
t = time.time()
metrics = ev.getMetrics(test_GT, results)
elapsed = time.time() - t
sys.stdout.write(str(elapsed) + ' sec \n')
metrics_array.append(metrics)
# TASK 2.2 - Plot F1-score vs Alpha
x = [alpha_range, alpha_range, alpha_range]
metrics_array = np.array(metrics_array)
y = [metrics_array[:, 0], metrics_array[:, 1], metrics_array[:, 2]]
f1_max = np.max(metrics_array[:, 2])
f1_max_idx = np.argmax(metrics_array[:, 2])
best_alpha = alpha_range[f1_max_idx]
print "F1: " + str(np.around(
f1_max, decimals=4)) + " (alpha=" + str(best_alpha) + ")"
axis = ["Alpha", "F1-score"]
labels = ["Precision", "Recall", "F1"]
ev.plotGraphics(x, y, axis, labels)