コード例 #1
0
def run_tailornet():
    gender = 'female'
    garment_class = 'pant'
    thetas, betas, gammas = get_single_frame_inputs(garment_class, gender)
    # # uncomment the line below to run inference on sequence data
    # thetas, betas, gammas = get_sequence_inputs(garment_class, gender)

    # load model
    tn_runner = get_tn_runner(gender=gender, garment_class=garment_class)
    smpl = SMPL4Garment(gender=gender)

    # make out directory if doesn't exist
    if not os.path.isdir(OUT_PATH):
        os.mkdir(OUT_PATH)

    # run inference
    for i, (theta, beta, gamma) in enumerate(zip(thetas, betas, gammas)):
        print(i, len(thetas))
        # normalize y-rotation to make it front facing
        theta_normalized = normalize_y_rotation(theta)
        with torch.no_grad():
            pred_verts_d = tn_runner.forward(
                thetas=torch.from_numpy(theta_normalized[None, :].astype(np.float32)).cuda(),
                betas=torch.from_numpy(beta[None, :].astype(np.float32)).cuda(),
                gammas=torch.from_numpy(gamma[None, :].astype(np.float32)).cuda(),
            )[0].cpu().numpy()

        # get garment from predicted displacements
        body, pred_gar = smpl.run(beta=beta, theta=theta, garment_class=garment_class, garment_d=pred_verts_d)
        pred_gar = remove_interpenetration_fast(pred_gar, body)

        # save body and predicted garment
        body.write_ply(os.path.join(OUT_PATH, "body_{:04d}.ply".format(i)))
        pred_gar.write_ply(os.path.join(OUT_PATH, "pred_gar_{:04d}.ply".format(i)))
コード例 #2
0
def run_tailornet():
    gender = 'male'
    garment_class = 'short-pant'
    garment_class_pairs = {
        'shirt': [],
        't-shirt': [],
        'pant': ['shirt', 't-shirt'],
        'short-pant': ['shirt', 't-shirt'],
        'skirt': ['shirt', 't-shirt']
    }
    thetas, betas, gammas = get_single_frame_inputs(garment_class, gender)
    # # uncomment the line below to run inference on sequence data
    # thetas, betas, gammas = get_sequence_inputs(garment_class, gender)

    # load model
    tn_runner = get_tn_runner(gender=gender, garment_class=garment_class)
    # from trainer.base_trainer import get_best_runner
    # tn_runner = get_best_runner("/BS/cpatel/work/data/learn_anim/tn_baseline/{}_{}/".format(garment_class, gender))
    smpl = SMPL4Garment(gender=gender)

    # make out directory if doesn't exist
    if not os.path.isdir(OUT_PATH):
        os.mkdir(OUT_PATH)

    # run inference
    for i, (theta, beta, gamma) in enumerate(zip(thetas, betas, gammas)):
        print(i, len(thetas))
        # normalize y-rotation to make it front facing
        theta_normalized = normalize_y_rotation(theta)
        with torch.no_grad():
            pred_verts_d = tn_runner.forward(
                thetas=torch.from_numpy(theta_normalized[None, :].astype(
                    np.float32)).cuda(),
                betas=torch.from_numpy(beta[None, :].astype(
                    np.float32)).cuda(),
                gammas=torch.from_numpy(gamma[None, :].astype(
                    np.float32)).cuda(),
            )[0].cpu().numpy()

        # get garment from predicted displacements
        body, pred_gar = smpl.run(beta=beta,
                                  theta=theta,
                                  garment_class=garment_class,
                                  garment_d=pred_verts_d)
        pred_gar = remove_interpenetration_fast(pred_gar, body)

        hv, hf, mapping = get_hres(pred_gar.v, pred_gar.f)
        pred_gar_hres = Mesh(hv, hf)

        for garment_class_pair in garment_class_pairs[garment_class]:
            gar_pair_hres = Mesh(filename=os.path.join(
                OUT_PATH, "bcnet_model_{}_gar_hres_{}_{:04d}.obj".format(
                    BCNET_MODEL_NUM, garment_class_pair, i)))
            # print(len(body.v), body.f.min(), body.f.max())
            # print(len(pred_gar_hres.v), pred_gar_hres.f.min(), pred_gar_hres.f.max())
            # print(np.vstack((body.v, pred_gar_hres.v)).shape, np.min(pred_gar_hres.f + len(body.v)), np.max(pred_gar_hres.f + len(body.v)))
            # gar_pair_hres = remove_interpenetration_fast(gar_pair_hres, pred_gar_hres)
            for _ in range(3):
                gar_pair_hres = remove_interpenetration_fast(
                    gar_pair_hres,
                    Mesh(np.vstack((body.v, pred_gar_hres.v)),
                         np.vstack((body.f, pred_gar_hres.f + len(body.v)))))
            gar_pair_hres.write_obj(
                os.path.join(
                    OUT_PATH,
                    "bcnet_model_{}_gar_hres_{}_inter_{}_{:04d}.obj".format(
                        BCNET_MODEL_NUM, garment_class_pair, garment_class,
                        i)))

        # save body and predicted garment
        body.write_obj(
            os.path.join(OUT_PATH,
                         "bcnet_model_{}.obj".format(BCNET_MODEL_NUM)))
        pred_gar.write_obj(
            os.path.join(
                OUT_PATH, "bcnet_model_{}_gar_{}_{:04d}.obj".format(
                    BCNET_MODEL_NUM, garment_class, i)))
        pred_gar_hres.write_obj(
            os.path.join(
                OUT_PATH, "bcnet_model_{}_gar_hres_{}_{:04d}.obj".format(
                    BCNET_MODEL_NUM, garment_class, i)))