def get_random_sample(smpl):
pose = 0.65 * (np.random.rand(*smpl.pose_shape) - 0.5)
beta = 0.06 * (np.random.rand(*smpl.beta_shape) - 0.5)
trans = np.zeros(smpl.trans_shape)
parameters = np.concatenate([pose.ravel(), beta, trans])
# Create the body mesh
pointcloud = smpl.set_params(beta=beta, pose=pose, trans=trans)
# Render the silhouette
mesh = Mesh(pointcloud=pointcloud)
mesh.faces = smpl.faces
silhouette = mesh.render_silhouette(dim=(256, 256), show=False)
return silhouette, mesh, parameters
# Load model
# encoder = Encoder()
#encoder.train_step(random_sample.reshape((1, *random_sample.shape, 1)), parameters.reshape(1, *parameters.shape))
# encoder.load_weights(args.model)
encoder_inputs, encoder_outputs = SimpleEncoderArchitecture((256, 256, 1))
encoder = Model(inputs=encoder_inputs, outputs=encoder_outputs)
encoder.summary()
encoder.load_weights(einfo['model_weights_path'])
# Predict the parameters from the silhouette and generate a mesh
prediction = encoder.predict(silhouette.reshape(1, 256, 256, 1))
prediction = tf.cast(prediction, tf.float64)
print("Shape of predictions:'" + str(prediction.shape))
print(prediction[0, 82:85])
pred_pc, faces = smpl_model("../model.pkl", prediction[0, 72:82] * 0 + 1,
prediction[0, :72], prediction[0, 82:85] * 0)
# Render the mesh
pred_mesh = Mesh(pointcloud=pred_pc.numpy())
pred_mesh.faces = faces
if mesh is not None:
mesh.render3D()
print("Rendering prediction")
pred_mesh.render3D()
# Now render their silhouettes
# cv2.imshow("True silhouette", silhouette)
# pred_mesh.render_silhouette(title="Predicted silhouette")
