def main():
gt_model = model.rigid_model(100)
W = gt_model.W
inf_model = factor(W)
inf_model.register(gt_model)
model.compare(inf_model, gt_model)
def advancedSearch():
if request.method == "GET":
return render_template('advancedSearch.html')
else:
form = request.form
select_choices = form["majors"]
select_size = form["sizes"]
select_location = form["locations"]
# def choose():
# if
forms = {
"m": select_choices,
"s": select_size,
"l": select_location
#"colleges": model.compare(select_choices,select_size,select_location)
}
colleges = model.compare(forms)
# select_majors_list.append("m")
# select_size_list.append("s")
# select_location_list.append("l")
# print("forms: "+str(forms))
# print(select_choices)
# print(select_size)
# print(select_location)
# print(select_majors_list)
#model.compare(forms)
return render_template('Compare.html', forms=forms, colleges=colleges)
# Factor to C and B matrices.
C, B = factor_S_sharp_to_C_and_B(S_sharp, n_basis)
# Build linear model.
scene = model.BasisShapeModel(Rs,
Bs=B.reshape(n_basis, 3, B.shape[1]),
C=C,
Ts=Ts)
return scene
if __name__ == '__main__':
# Set the seed.
np.random.seed(0)
# Generate some synthetic data.
n_frames = 200
gt_model = model.simple_model(n_frames)
W = gt_model.W
# Use the Dai algorithm.
inf_model = factor(W, use_method_1=0)
# Register to ground truth
inf_model.register(gt_model)
model.compare(inf_model, gt_model, visualize=False)
else:
# Recover S_sharp
S_sharp = S_sharp_from_Rs(W_cent, Rs)
# Factor to C and B matrices.
C, B = factor_S_sharp_to_C_and_B(S_sharp, n_basis)
# Build linear model.
scene = model.BasisShapeModel(Rs, Bs = B.reshape(n_basis, 3, B.shape[1]), C=C, Ts=Ts)
return scene
if __name__ == '__main__':
# Set the seed.
np.random.seed(0)
# Generate some synthetic data.
n_frames = 200
gt_model = model.simple_model(n_frames)
W = gt_model.W
# Use the Dai algorithm.
inf_model = factor(W, use_method_1 = 0)
# Register to ground truth
inf_model.register(gt_model)
model.compare(inf_model, gt_model, visualize=False)
