def batch_func(data_name):
C_8U = loadData(data_name, loader_func=loadRGBA)
A_8U = alpha(C_8U)
I_32F = luminance(C_8U)
N_32F, D_32F = estimateNormal(I_32F)
result_dir = resultDir(batch_name)
N_file = resultFile(result_dir, data_name)
saveNormal(N_file, N_32F, A_8U)
def batch_func(data_name):
C_8U = loadData(data_name, loader_func=loadRGBA)
A_8U = alpha(C_8U)
I_32F = luminance(C_8U)
N_32F, D_32F = estimateNormal(I_32F)
result_dir = resultDir(batch_name)
N_file = resultFile(result_dir, data_name)
saveNormal(N_file, N_32F, A_8U)
def main(input_file, output_file, quiet):
C_8U = loadRGBA(input_file)
A_8U = alpha(C_8U)
I_32F = luminance(C_8U)
N_32F, D_32F = estimateNormal(I_32F)
if output_file:
saveResult(input_file, A_8U, N_32F)
if quiet:
return
showResult(C_8U, D_32F, N_32F, A_8U)
def main(input_file, output_file, quiet):
C_8U = loadRGBA(input_file)
A_8U = alpha(C_8U)
I_32F = luminance(C_8U)
N_32F, D_32F = estimateNormal(I_32F)
if output_file:
saveResult(input_file, A_8U, N_32F)
if quiet:
return
showResult(C_8U, D_32F, N_32F, A_8U)
def colorToNormal(C_8U, fill_background=True):
rgb_8U = rgb(C_8U)
A_8U = alpha(C_8U)
C_32F = to32F(rgb_8U)
N_32F = 2.0 * C_32F - 1.0
if fill_background:
N_32F[A_8U < 10, :] = np.array([0.0, 0.0, 0.0])
N_32F_normalized = normalizeImage(N_32F)
return N_32F_normalized
