def main():
# dataset = Dataset('highway',1050, 1350)
# dataset = Dataset('fall', 1461, 1560)
dataset = Dataset('traffic', 951, 1050)
imgs = dataset.readInput()
imgs_GT = dataset.readGT()
train = imgs[:len(imgs) / 2]
test = imgs[len(imgs) / 2:]
test_GT = imgs_GT[len(imgs) / 2:]
# Background substraction
g = GaussianModelling(alpha=3.5, adaptive_ratio=0.15)
# 0.6319(2.4, 0.15) --- 0.6896(3.2 / 0.05) --- 0.6376(3.5, / 0.15)
g.fit(train)
results = g.predict(test)
ims = []
fig = plt.figure()
for i in range(len(results)):
im = plt.imshow(results[i], cmap='gray', animated=True)
plt.axis("off")
ims.append([im])
anim = animation.ArtistAnimation(fig,
ims,
interval=len(results),
blit=True)
anim.save('animation.gif', writer='imagemagick', fps=10)
plt.show()
def background_substraction(train, test, alpha, ro, prints=True):
if prints:
t = time.time()
sys.stdout.write('Computing background substraction... ')
g = GaussianModelling(alpha=alpha,adaptive_ratio=ro)
g.fit(train)
results = g.predict(test)
if prints:
elapsed = time.time() - t
sys.stdout.write(str(elapsed) + ' sec \n')
return results
def single_execution(train, test, test_GT, alpha, ro):
sys.stdout.write('Computing background substraction... ')
# Background substraction
g = GaussianModelling(alpha=alpha, adaptive_ratio=ro)
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')
print "Recall: " + str(metrics[0] * 100)
print "Precision: " + str(metrics[1] * 100)
print "F1: " + str(metrics[2] * 100)
def grid_search(train, test, test_GT):
alpha_range = np.around(np.arange(2.5, 4.52, 0.1), decimals=2)
ro_range = np.around(np.arange(0, 0.4, 0.05), decimals=2)
f1_matrix = np.zeros([len(alpha_range), len(ro_range)])
for i in range(len(alpha_range)):
alpha = alpha_range[i]
for j in range(len(ro_range)):
ro = ro_range[j]
sys.stdout.write("(alpha=" + str(alpha) + ", ro=" + str(ro) + ") ")
# Background substraction
g = GaussianModelling(alpha=alpha, adaptive_ratio=ro)
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')
f1_matrix[i, j] = metrics[2]
# Plot grid search
X, Y = np.meshgrid(ro_range, alpha_range)
Z = f1_matrix
f1_max = np.max(f1_matrix)
f1_max_idx = np.argmax(f1_matrix)
best_alpha = Y.flatten()[f1_max_idx]
best_ro = X.flatten()[f1_max_idx]
print "F1: " + str(np.around(f1_max, decimals=5)) + " (alpha=" + str(
best_alpha) + ", ro=" + str(best_ro) + ")"
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.plot_surface(X, Y, Z, cmap='plasma')
axis = ["Ro", "Alpha", "F1-score"]
ax.set_xlabel(axis[0])
ax.set_ylabel(axis[1])
ax.set_zlabel(axis[2])
# plt.savefig('grid_search.png',dpi=300)
plt.show()
def precision_recall_curve(train, test, test_GT):
sys.stdout.write('Computing Precision-Recall curve... ')
# Background substraction
g = GaussianModelling(adaptive_ratio=0.15, grayscale_modelling=False)
g.fit(train)
scores = g.predict_probabilities(test)
t = time.time()
precision, recall, auc_val = ev.getPR_AUC(test_GT, scores)
elapsed = time.time() - t
sys.stdout.write(str(elapsed) + ' sec \n')
plt.step(recall, precision, color='g', alpha=0.2, where='post')
plt.fill_between(recall, precision, step='post', alpha=0.2, color='g')
print "AUC: " + str(auc_val)
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.ylim([0.0, 1.0])
plt.xlim([0.0, 1.0])
# plt.title("Precision-Recall curve (AUC=" + str(auc_val) + ")" )
plt.title("Precision-Recall curve - Fall")
plt.show()
def f1score_alpha(train, test, test_GT):
# Task 1.2 - F1-score vs Alpha
alpha_range = np.around(np.arange(0, 6, 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)
metrics_array = np.array(metrics_array)
return metrics_array[:, 2]
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)