def exec_oneshot(self, *args):
keyframes = self.global_extractor.get_n_keyframes(self.n_globals)
n_keyframes = len(keyframes)
interps = []
for name in self.curves.keys():
for dim in self.dimensions[name].keys():
if not self.dimensions[name][dim]:
continue
index = -1
if dim == "rx": index = 1
if dim == "ry": index = 2
if dim == "rz": index = 3
curve = []
for i in range(len(Motion.mocap["single"][name])):
curve.append([Motion.mocap["single"][name].item(i, 0), Motion.mocap["single"][name].item(i, index)])
curve = np.array(curve)
for i in range(1, n_keyframes):
k1 = keyframes[i-1]
k2 = keyframes[i]
seg_curve = curve[k1:k2+1]
interp = interp_f.unconstrained(seg_curve, 100)
interps.append(interp)
for i in range(len(interps)):
seg = interps[i]
if len(seg.curve) <= 3:
interps[i] = interp_f.line(seg.curve, 100)
self.interps = interps
self.app.update()
return interps
def tighten_fitting_with_breaking(self, interps):
for i in range(len(interps)):
interp = interps[i]
refit = interp_f.unconstrained( interp.curve
, Algorithm.interp_res )
cost_org = Algorithm.eval_method_refit(interp)[0]
cost_new = Algorithm.eval_method_refit(refit)[0]
if cost_org == 0:
continue
# print "%s %s %d improved = before:%2.3f after:%2.3f" % i, cost_org, cost_new)
if 1 - (cost_new / float(cost_org)) > Algorithm.refit_cost:
interps[i] = refit
return interps
def eval_errors(self):
def keyframe_of_worst(cases):
max_cost = 0
keyframe = -1
for (cost, index) in cases:
if cost > max_cost:
max_cost = cost
keyframe = index
return keyframe
def combine_costs(cases):
return max([case[0] for c in cases])
worst_cases = []
for i in range(1, n_keyframes):
k1 = keyframes[i-1]
k2 = keyframes[i]
seg = make_seg.unconstrained(self.curve[k1:k2+1], self.res)
cost, index = self.cost_fn(seg)
worst_cases.append([cost, k1 + index])
return combine_costs(cases)
