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
def on_epoch_end(self, epoch, logs=None): """ Store the model loss and accuracy at the end of every epoch, and store a model prediction on data """ self.epoch_log.write( json.dumps({ 'epoch': epoch, 'loss': logs['loss'] }) + '\n') if (epoch + 1) % self.period == 0 or epoch == 0: # Predict on all of the given silhouettes for data_type, data in self.pred_data.items(): if data is not None: if not isinstance(data, list) or type(data) == np.array: data = np.array(data) data = data.reshape( (1, data.shape[0], data.shape[1], data.shape[2])) #for i, silhouette in enumerate(data): # # Save silhouettes # silhouette *= 255 # cv2.imwrite(os.path.join(self.pred_path, "{}_epoch.{:03d}.gt_silh_{:03d}.png".format(data_type, epoch + 1, i)), silhouette.astype("uint8")) preds = self.model.predict(data) #print("Predictions: " + str(preds)) for i, pred in enumerate(preds[1], 1): #self.smpl.set_params(pred[:72].reshape((24, 3)), pred[72:82], pred[82:]) #self.smpl.save_to_obj(os.path.join(self.pred_path, "{}_pred_{:03d}.obj".format(data_type, i))) #print_mesh(os.path.join(self.pred_path, "epoch.{:03d}.{}_gt_{:03d}.obj".format(epoch, data_type, i)), gt[i-1], smpl.faces) print_mesh( os.path.join( self.pred_path, "{}_epoch.{:03d}.pred_{:03d}.obj".format( data_type, epoch + 1, i)), pred, self.smpl.faces) # Store predicted silhouette and the difference between it and the GT silhouette gt_silhouette = (data[i - 1] * 255).astype("uint8").reshape( data.shape[1], data.shape[2]) #cv2.imwrite(os.path.join(self.pred_path, "{}_epoch.{:03d}.gt_silh_{:03d}.png".format(data_type, epoch + 1, i)), gt_silhouette) pred_silhouette = Mesh( pointcloud=pred).render_silhouette(show=False) #cv2.imwrite(os.path.join(self.pred_path, "{}_epoch.{:03d}.pred_silh_{:03d}.png".format(data_type, epoch + 1, i)), pred_silhouette) diff_silh = abs(gt_silhouette - pred_silhouette) #print(diff_silh.shape) #cv2.imshow("Diff silh", diff_silh) #cv2.imwrite(os.path.join(self.pred_path, "{}_epoch.{:03d}.diff_silh_{:03d}.png".format(data_type, epoch + 1, i)), diff_silh.astype("uint8")) silh_comp = np.concatenate( [gt_silhouette, pred_silhouette, diff_silh]) cv2.imwrite( os.path.join( self.pred_path, "{}_epoch.{:03d}.silh_comp_{:03d}.png".format( data_type, epoch + 1, i)), silh_comp.astype("uint8")) if self.gt_pc[data_type] is not None: print_mesh( os.path.join( self.pred_path, "{}_epoch.{:03d}.gt_pc_{:03d}.obj".format( data_type, epoch + 1, i)), self.gt_pc[data_type], self.smpl.faces) print_point_clouds( os.path.join( self.pred_path, "{}_epoch.{:03d}.comparison_{:03d}.obj". format(data_type, epoch + 1, i)), [pred, self.gt_pc[data_type]], [(255, 0, 0), (0, 255, 0)]) if self.visualise: # Show a random sample rand_index = np.random.randint(low=0, high=len(data)) + 1 mesh = Mesh(filepath=os.path.join( self.pred_path, "{}_epoch.{:03d}.pred_{:03d}.obj". format(data_type, epoch + 1, rand_index))) # Show the true silhouette true_silh = data[rand_index - 1] true_silh = true_silh.reshape(true_silh.shape[:-1]) plt.imshow(true_silh, cmap='gray') plt.title("True {} silhouette {:03d}".format( data_type, rand_index)) plt.show() # Show the predicted silhouette and mesh mesh.render_silhouette( title="Predicted {} silhouette {:03d}".format( data_type, rand_index)) diff_silh = cv2.imread( "{}_epoch.{:03d}.diff_silh_{:03d}.png".format( data_type, epoch + 1, rand_index)) cv2.imshow( "Predicted {} silhouette {:03d}".format( data_type, rand_index), diff_silh) try: mesh.render3D() except Exception: pass
dilate = 1 if dilate == 1: morph_mask = np.array([[0.34, 0.34, 0.34], [0.34, 1.00, 0.34], [0.34, 0.34, 0.34]]) new_img = binary_closing(shifted_img != 0, structure=morph_mask, iterations=1).astype(np.uint8) new_img *= 255 else: new_img = shifted_img return new_img if __name__ == "__main__": mesh_dir = "/data/cvfs/hjhb2/projects/deep_optimiser/example_meshes/" obj_paths = os.listdir(mesh_dir) for obj_path in obj_paths: mesh = Mesh(os.path.join(mesh_dir, obj_path)) silh = mesh.render_silhouette(dim=[256, 256], show=True) normalised_silh = normalise_img(silh, dim=(128, 128)) #plt.imshow(silh_cropped, cmap="gray") plt.imshow(normalised_silh, cmap="gray") plt.show() augmented_silh = augment_image(normalised_silh) plt.imshow(augmented_silh, cmap="gray") plt.show()