import os import json import pandas as pd import cv2 import numpy as np from PIL import Image, ImageOps from .rich import console from .generate import generate from .noise import add_noise from .animation import sample_from_cv2, sample_to_cv2, anim_frame_warp from .animation_key_frames import DeformAnimKeys, LooperAnimKeys from .video_audio_utilities import get_frame_name, get_next_frame from .depth import DepthModel from .colors import maintain_colors from .parseq_adapter import ParseqAnimKeys from .seed import next_seed from .blank_frame_reroll import blank_frame_reroll from .image_sharpening import unsharp_mask from .load_images import get_mask, load_img, get_mask_from_file from .hybrid_video import hybrid_generation, hybrid_composite from .hybrid_video import get_matrix_for_hybrid_motion, get_matrix_for_hybrid_motion_prev, get_flow_for_hybrid_motion, get_flow_for_hybrid_motion_prev, image_transform_ransac, image_transform_optical_flow from .save_images import save_image from .composable_masks import compose_mask_with_check from .settings import get_keys_to_exclude from .deforum_controlnet import unpack_controlnet_vids, is_controlnet_enabled # Webui from modules.shared import opts, cmd_opts, state, sd_model from modules import lowvram, devices, sd_hijack def render_animation(args, anim_args, video_args, parseq_args, loop_args, controlnet_args, animation_prompts, root): # handle hybrid video generation if anim_args.animation_mode in ['2D','3D']: if anim_args.hybrid_composite or anim_args.hybrid_motion in ['Affine', 'Perspective', 'Optical Flow']: args, anim_args, inputfiles = hybrid_generation(args, anim_args, root) # path required by hybrid functions, even if hybrid_comp_save_extra_frames is False hybrid_frame_path = os.path.join(args.outdir, 'hybridframes') # handle controlnet video input frames generation if is_controlnet_enabled(controlnet_args): unpack_controlnet_vids(args, anim_args, video_args, parseq_args, loop_args, controlnet_args, animation_prompts, root) # use parseq if manifest is provided use_parseq = parseq_args.parseq_manifest != None and parseq_args.parseq_manifest.strip() # expand key frame strings to values keys = DeformAnimKeys(anim_args) if not use_parseq else ParseqAnimKeys(parseq_args, anim_args) loopSchedulesAndData = LooperAnimKeys(loop_args, anim_args) # resume animation start_frame = 0 if anim_args.resume_from_timestring: for tmp in os.listdir(args.outdir): if ".txt" in tmp : pass else: filename = tmp.split("_") # don't use saved depth maps to count number of frames if anim_args.resume_timestring in filename and "depth" not in filename: start_frame += 1 #start_frame = start_frame - 1 # create output folder for the batch os.makedirs(args.outdir, exist_ok=True) print(f"Saving animation frames to:\n{args.outdir}") # save settings for the batch exclude_keys = get_keys_to_exclude('general') settings_filename = os.path.join(args.outdir, f"{args.timestring}_settings.txt") with open(settings_filename, "w+", encoding="utf-8") as f: args.__dict__["prompts"] = animation_prompts s = {} for d in [dict(args.__dict__), dict(anim_args.__dict__), dict(parseq_args.__dict__), dict(loop_args.__dict__)]: for key, value in d.items(): if key not in exclude_keys: s[key] = value json.dump(s, f, ensure_ascii=False, indent=4) # resume from timestring if anim_args.resume_from_timestring: args.timestring = anim_args.resume_timestring # Always enable pseudo-3d with parseq. No need for an extra toggle: # Whether it's used or not in practice is defined by the schedules if use_parseq: anim_args.flip_2d_perspective = True # expand prompts out to per-frame if use_parseq: prompt_series = keys.prompts else: prompt_series = pd.Series([np.nan for a in range(anim_args.max_frames)]) for i, prompt in animation_prompts.items(): prompt_series[int(i)] = prompt prompt_series = prompt_series.ffill().bfill() # check for video inits using_vid_init = anim_args.animation_mode == 'Video Input' # load depth model for 3D predict_depths = (anim_args.animation_mode == '3D' and anim_args.use_depth_warping) or anim_args.save_depth_maps predict_depths = predict_depths or (anim_args.hybrid_composite and anim_args.hybrid_comp_mask_type in ['Depth','Video Depth']) if predict_depths: depth_model = DepthModel('cpu' if cmd_opts.lowvram or cmd_opts.medvram else root.device) depth_model.load_midas(root.models_path, root.half_precision) if anim_args.midas_weight < 1.0: depth_model.load_adabins(root.models_path) # depth-based hybrid composite mask requires saved depth maps if anim_args.hybrid_composite and anim_args.hybrid_comp_mask_type =='Depth': anim_args.save_depth_maps = True else: depth_model = None anim_args.save_depth_maps = False # state for interpolating between diffusion steps turbo_steps = 1 if using_vid_init else int(anim_args.diffusion_cadence) turbo_prev_image, turbo_prev_frame_idx = None, 0 turbo_next_image, turbo_next_frame_idx = None, 0 # resume animation prev_img = None color_match_sample = None if anim_args.resume_from_timestring: last_frame = start_frame-1 if turbo_steps > 1: last_frame -= last_frame%turbo_steps path = os.path.join(args.outdir,f"{args.timestring}_{last_frame:05}.png") img = cv2.imread(path) #img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # Changed the colors on resume prev_img = img if anim_args.color_coherence != 'None': color_match_sample = img if turbo_steps > 1: turbo_next_image, turbo_next_frame_idx = prev_img, last_frame turbo_prev_image, turbo_prev_frame_idx = turbo_next_image, turbo_next_frame_idx start_frame = last_frame+turbo_steps args.n_samples = 1 frame_idx = start_frame # reset the mask vals as they are overwritten in the compose_mask algorithm mask_vals = {} noise_mask_vals = {} mask_vals['everywhere'] = Image.new('1', (args.W, args.H), 1) noise_mask_vals['everywhere'] = Image.new('1', (args.W, args.H), 1) mask_image = None if args.use_init and args.init_image != None and args.init_image != '': _, mask_image = load_img(args.init_image, shape=(args.W, args.H), use_alpha_as_mask=args.use_alpha_as_mask) mask_vals['init_mask'] = mask_image noise_mask_vals['init_mask'] = mask_image # Grab the first frame masks since they wont be provided until next frame if mask_image is None and args.use_mask: mask_vals['init_mask'] = get_mask(args) noise_mask_vals['init_mask'] = get_mask(args) # TODO?: add a different default noise mask if anim_args.use_mask_video: mask_vals['video_mask'] = get_mask_from_file(get_next_frame(args.outdir, anim_args.video_mask_path, frame_idx, True), args) noise_mask_vals['video_mask'] = get_mask_from_file(get_next_frame(args.outdir, anim_args.video_mask_path, frame_idx, True), args) else: mask_vals['video_mask'] = None noise_mask_vals['video_mask'] = None #Webui state.job_count = anim_args.max_frames while frame_idx < anim_args.max_frames: #Webui state.job = f"frame {frame_idx + 1}/{anim_args.max_frames}" state.job_no = frame_idx + 1 if state.interrupted: break print(f"\033[36mAnimation frame: \033[0m{frame_idx}/{anim_args.max_frames} ") noise = keys.noise_schedule_series[frame_idx] strength = keys.strength_schedule_series[frame_idx] scale = keys.cfg_scale_schedule_series[frame_idx] contrast = keys.contrast_schedule_series[frame_idx] kernel = int(keys.kernel_schedule_series[frame_idx]) sigma = keys.sigma_schedule_series[frame_idx] amount = keys.amount_schedule_series[frame_idx] threshold = keys.threshold_schedule_series[frame_idx] hybrid_comp_schedules = { "alpha": keys.hybrid_comp_alpha_schedule_series[frame_idx], "mask_blend_alpha": keys.hybrid_comp_mask_blend_alpha_schedule_series[frame_idx], "mask_contrast": keys.hybrid_comp_mask_contrast_schedule_series[frame_idx], "mask_auto_contrast_cutoff_low": int(keys.hybrid_comp_mask_auto_contrast_cutoff_low_schedule_series[frame_idx]), "mask_auto_contrast_cutoff_high": int(keys.hybrid_comp_mask_auto_contrast_cutoff_high_schedule_series[frame_idx]), } scheduled_sampler_name = None scheduled_clipskip = None mask_seq = None noise_mask_seq = None if anim_args.enable_steps_scheduling and keys.steps_schedule_series[frame_idx] is not None: args.steps = int(keys.steps_schedule_series[frame_idx]) if anim_args.enable_sampler_scheduling and keys.sampler_schedule_series[frame_idx] is not None: scheduled_sampler_name = keys.sampler_schedule_series[frame_idx].casefold() if anim_args.enable_clipskip_scheduling and keys.clipskip_schedule_series[frame_idx] is not None: scheduled_clipskip = int(keys.clipskip_schedule_series[frame_idx]) if args.use_mask and keys.mask_schedule_series[frame_idx] is not None: mask_seq = keys.mask_schedule_series[frame_idx] if anim_args.use_noise_mask and keys.noise_mask_schedule_series[frame_idx] is not None: noise_mask_seq = keys.noise_mask_schedule_series[frame_idx] if args.use_mask and not anim_args.use_noise_mask: noise_mask_seq = mask_seq depth = None if anim_args.animation_mode == '3D' and (cmd_opts.lowvram or cmd_opts.medvram): # Unload the main checkpoint and load the depth model lowvram.send_everything_to_cpu() sd_hijack.model_hijack.undo_hijack(sd_model) devices.torch_gc() depth_model.to(root.device) # emit in-between frames if turbo_steps > 1: tween_frame_start_idx = max(0, frame_idx-turbo_steps) for tween_frame_idx in range(tween_frame_start_idx, frame_idx): tween = float(tween_frame_idx - tween_frame_start_idx + 1) / float(frame_idx - tween_frame_start_idx) print(f" Creating in-between frame: {tween_frame_idx}; tween:{tween:0.2f};") advance_prev = turbo_prev_image is not None and tween_frame_idx > turbo_prev_frame_idx advance_next = tween_frame_idx > turbo_next_frame_idx if depth_model is not None: assert(turbo_next_image is not None) depth = depth_model.predict(turbo_next_image, anim_args, root.half_precision) if advance_prev: turbo_prev_image, _ = anim_frame_warp(turbo_prev_image, args, anim_args, keys, tween_frame_idx, depth_model, depth=depth, device=root.device, half_precision=root.half_precision) if advance_next: turbo_next_image, _ = anim_frame_warp(turbo_next_image, args, anim_args, keys, tween_frame_idx, depth_model, depth=depth, device=root.device, half_precision=root.half_precision) # hybrid video motion - warps turbo_prev_image or turbo_next_image to match motion if tween_frame_idx > 0: if anim_args.hybrid_motion in ['Affine', 'Perspective']: if anim_args.hybrid_motion_use_prev_img: if advance_prev: matrix = get_matrix_for_hybrid_motion_prev(tween_frame_idx, (args.W, args.H), inputfiles, turbo_prev_image, anim_args.hybrid_motion) turbo_prev_image = image_transform_ransac(turbo_prev_image, matrix, anim_args.hybrid_motion, cv2.BORDER_WRAP if anim_args.border == 'wrap' else cv2.BORDER_REPLICATE) if advance_next: matrix = get_matrix_for_hybrid_motion_prev(tween_frame_idx, (args.W, args.H), inputfiles, turbo_next_image, anim_args.hybrid_motion) turbo_next_image = image_transform_ransac(turbo_next_image, matrix, anim_args.hybrid_motion, cv2.BORDER_WRAP if anim_args.border == 'wrap' else cv2.BORDER_REPLICATE) else: matrix = get_matrix_for_hybrid_motion(tween_frame_idx-1, (args.W, args.H), inputfiles, anim_args.hybrid_motion) if advance_prev: turbo_prev_image = image_transform_ransac(turbo_prev_image, matrix, anim_args.hybrid_motion, cv2.BORDER_WRAP if anim_args.border == 'wrap' else cv2.BORDER_REPLICATE) if advance_next: turbo_next_image = image_transform_ransac(turbo_next_image, matrix, anim_args.hybrid_motion, cv2.BORDER_WRAP if anim_args.border == 'wrap' else cv2.BORDER_REPLICATE) if anim_args.hybrid_motion in ['Optical Flow']: if anim_args.hybrid_motion_use_prev_img: if advance_prev: flow = get_flow_for_hybrid_motion_prev(tween_frame_idx-1, (args.W, args.H), inputfiles, hybrid_frame_path, turbo_prev_image, anim_args.hybrid_flow_method, anim_args.hybrid_comp_save_extra_frames) turbo_prev_image = image_transform_optical_flow(turbo_prev_image, flow, cv2.BORDER_WRAP if anim_args.border == 'wrap' else cv2.BORDER_REPLICATE) if advance_next: flow = get_flow_for_hybrid_motion_prev(tween_frame_idx-1, (args.W, args.H), inputfiles, hybrid_frame_path, turbo_next_image, anim_args.hybrid_flow_method, anim_args.hybrid_comp_save_extra_frames) turbo_next_image = image_transform_optical_flow(turbo_next_image, flow, cv2.BORDER_WRAP if anim_args.border == 'wrap' else cv2.BORDER_REPLICATE) else: flow = get_flow_for_hybrid_motion(tween_frame_idx-1, (args.W, args.H), inputfiles, hybrid_frame_path, anim_args.hybrid_flow_method, anim_args.hybrid_comp_save_extra_frames) if advance_prev: turbo_prev_image = image_transform_optical_flow(turbo_prev_image, flow, cv2.BORDER_WRAP if anim_args.border == 'wrap' else cv2.BORDER_REPLICATE) if advance_next: turbo_next_image = image_transform_optical_flow(turbo_next_image, flow, cv2.BORDER_WRAP if anim_args.border == 'wrap' else cv2.BORDER_REPLICATE) turbo_prev_frame_idx = turbo_next_frame_idx = tween_frame_idx if turbo_prev_image is not None and tween < 1.0: img = turbo_prev_image*(1.0-tween) + turbo_next_image*tween else: img = turbo_next_image # intercept and override to grayscale if anim_args.color_force_grayscale: img = cv2.cvtColor(img.astype(np.uint8), cv2.COLOR_BGR2GRAY) img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) filename = f"{args.timestring}_{tween_frame_idx:05}.png" cv2.imwrite(os.path.join(args.outdir, filename), img) if anim_args.save_depth_maps: depth_model.save(os.path.join(args.outdir, f"{args.timestring}_depth_{tween_frame_idx:05}.png"), depth) if turbo_next_image is not None: prev_img = turbo_next_image # apply transforms to previous frame if prev_img is not None: prev_img, depth = anim_frame_warp(prev_img, args, anim_args, keys, frame_idx, depth_model, depth=None, device=root.device, half_precision=root.half_precision) # hybrid video motion - warps prev_img to match motion, usually to prepare for compositing if frame_idx > 0: if anim_args.hybrid_motion in ['Affine', 'Perspective']: if anim_args.hybrid_motion_use_prev_img: matrix = get_matrix_for_hybrid_motion_prev(frame_idx, (args.W, args.H), inputfiles, prev_img, anim_args.hybrid_motion) else: matrix = get_matrix_for_hybrid_motion(frame_idx-1, (args.W, args.H), inputfiles, anim_args.hybrid_motion) prev_img = image_transform_ransac(prev_img, matrix, anim_args.hybrid_motion, cv2.BORDER_WRAP if anim_args.border == 'wrap' else cv2.BORDER_REPLICATE) if anim_args.hybrid_motion in ['Optical Flow']: if anim_args.hybrid_motion_use_prev_img: flow = get_flow_for_hybrid_motion_prev(frame_idx-1, (args.W, args.H), inputfiles, hybrid_frame_path, prev_img, anim_args.hybrid_flow_method, anim_args.hybrid_comp_save_extra_frames) else: flow = get_flow_for_hybrid_motion(frame_idx-1, (args.W, args.H), inputfiles, hybrid_frame_path, anim_args.hybrid_flow_method, anim_args.hybrid_comp_save_extra_frames) prev_img = image_transform_optical_flow(prev_img, flow, cv2.BORDER_WRAP if anim_args.border == 'wrap' else cv2.BORDER_REPLICATE) # do hybrid video - composites video frame into prev_img (now warped if using motion) if anim_args.hybrid_composite: args, prev_img = hybrid_composite(args, anim_args, frame_idx, prev_img, depth_model, hybrid_comp_schedules, root) # apply color matching if anim_args.color_coherence != 'None': # video color matching hybrid_available = anim_args.hybrid_composite or anim_args.hybrid_motion in ['Optical Flow', 'Affine', 'Perspective'] if anim_args.color_coherence == 'Video Input' and hybrid_available: video_color_coherence_frame = int(frame_idx) % int(anim_args.color_coherence_video_every_N_frames) == 0 if video_color_coherence_frame: prev_vid_img = Image.open(os.path.join(args.outdir, 'inputframes', get_frame_name(anim_args.video_init_path) + f"{frame_idx:05}.jpg")) prev_vid_img = prev_vid_img.resize((args.W, args.H), Image.Resampling.LANCZOS) color_match_sample = np.asarray(prev_vid_img) color_match_sample = cv2.cvtColor(color_match_sample, cv2.COLOR_RGB2BGR) if color_match_sample is None: color_match_sample = prev_img.copy() else: prev_img = maintain_colors(prev_img, color_match_sample, anim_args.color_coherence) # intercept and override to grayscale if anim_args.color_force_grayscale: prev_img = cv2.cvtColor(prev_img, cv2.COLOR_BGR2GRAY) prev_img = cv2.cvtColor(prev_img, cv2.COLOR_GRAY2BGR) # apply scaling contrast_image = (prev_img * contrast).round().astype(np.uint8) # anti-blur if amount > 0: contrast_image = unsharp_mask(contrast_image, (kernel, kernel), sigma, amount, threshold, mask_image if args.use_mask else None) # apply frame noising if args.use_mask or anim_args.use_noise_mask: args.noise_mask = compose_mask_with_check(root, args, noise_mask_seq, noise_mask_vals, Image.fromarray(cv2.cvtColor(contrast_image, cv2.COLOR_BGR2RGB))) noised_image = add_noise(contrast_image, noise, args.seed, anim_args.noise_type, (anim_args.perlin_w, anim_args.perlin_h, anim_args.perlin_octaves, anim_args.perlin_persistence), args.noise_mask, args.invert_mask) # use transformed previous frame as init for current args.use_init = True args.init_sample = Image.fromarray(cv2.cvtColor(noised_image, cv2.COLOR_BGR2RGB)) args.strength = max(0.0, min(1.0, strength)) args.scale = scale # Pix2Pix Image CFG Scale - does *nothing* with non pix2pix checkpoints args.pix2pix_img_cfg_scale = float(keys.pix2pix_img_cfg_scale_series[frame_idx]) # grab prompt for current frame args.prompt = prompt_series[frame_idx] if args.seed_behavior == 'schedule' or use_parseq: args.seed = int(keys.seed_schedule_series[frame_idx]) if anim_args.enable_checkpoint_scheduling: args.checkpoint = keys.checkpoint_schedule_series[frame_idx] else: args.checkpoint = None #SubSeed scheduling if anim_args.enable_subseed_scheduling: args.subseed = int(keys.subseed_schedule_series[frame_idx]) args.subseed_strength = float(keys.subseed_strength_schedule_series[frame_idx]) if use_parseq: args.seed_enable_extras = True args.subseed = int(keys.subseed_series[frame_idx]) args.subseed_strength = keys.subseed_strength_series[frame_idx] prompt_to_print, *after_neg = args.prompt.strip().split("--neg") prompt_to_print = prompt_to_print.strip() after_neg = "".join(after_neg).strip() print(f"\033[32mSeed: \033[0m{args.seed}") print(f"\033[35mPrompt: \033[0m{prompt_to_print}") if after_neg and after_neg.strip(): print(f"\033[91mNeg Prompt: \033[0m{after_neg}") if not using_vid_init: # print motion table to cli if anim mode = 2D or 3D if anim_args.animation_mode in ['2D','3D']: print_render_table(anim_args, keys, frame_idx) # grab init image for current frame elif using_vid_init: init_frame = get_next_frame(args.outdir, anim_args.video_init_path, frame_idx, False) print(f"Using video init frame {init_frame}") args.init_image = init_frame if anim_args.use_mask_video: mask_vals['video_mask'] = get_mask_from_file(get_next_frame(args.outdir, anim_args.video_mask_path, frame_idx, True), args) if args.use_mask: args.mask_image = compose_mask_with_check(root, args, mask_seq, mask_vals, args.init_sample) if args.init_sample is not None else None # we need it only after the first frame anyway # setting up some arguments for the looper loop_args.imageStrength = loopSchedulesAndData.image_strength_schedule_series[frame_idx] loop_args.blendFactorMax = loopSchedulesAndData.blendFactorMax_series[frame_idx] loop_args.blendFactorSlope = loopSchedulesAndData.blendFactorSlope_series[frame_idx] loop_args.tweeningFrameSchedule = loopSchedulesAndData.tweening_frames_schedule_series[frame_idx] loop_args.colorCorrectionFactor = loopSchedulesAndData.color_correction_factor_series[frame_idx] loop_args.use_looper = loopSchedulesAndData.use_looper loop_args.imagesToKeyframe = loopSchedulesAndData.imagesToKeyframe if scheduled_clipskip is not None: opts.data["CLIP_stop_at_last_layers"] = scheduled_clipskip if anim_args.animation_mode == '3D' and (cmd_opts.lowvram or cmd_opts.medvram): depth_model.to('cpu') devices.torch_gc() lowvram.setup_for_low_vram(sd_model, cmd_opts.medvram) sd_hijack.model_hijack.hijack(sd_model) # sample the diffusion model image = generate(args, anim_args, loop_args, controlnet_args, root, frame_idx, sampler_name=scheduled_sampler_name) patience = 10 # intercept and override to grayscale if anim_args.color_force_grayscale: image = ImageOps.grayscale(image) image = ImageOps.colorize(image, black ="black", white ="white") # reroll blank frame if not image.getbbox(): print("Blank frame detected! If you don't have the NSFW filter enabled, this may be due to a glitch!") if args.reroll_blank_frames == 'reroll': while not image.getbbox(): print("Rerolling with +1 seed...") args.seed += 1 image = generate(args, anim_args, loop_args, controlnet_args, root, frame_idx, sampler_name=scheduled_sampler_name) patience -= 1 if patience == 0: print("Rerolling with +1 seed failed for 10 iterations! Try setting webui's precision to 'full' and if it fails, please report this to the devs! Interrupting...") state.interrupted = True state.current_image = image return elif args.reroll_blank_frames == 'interrupt': print("Interrupting to save your eyes...") state.interrupted = True state.current_image = image image = blank_frame_reroll(image, args, root, frame_idx) if image == None: return opencv_image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR) if not using_vid_init: prev_img = opencv_image if turbo_steps > 1: turbo_prev_image, turbo_prev_frame_idx = turbo_next_image, turbo_next_frame_idx turbo_next_image, turbo_next_frame_idx = opencv_image, frame_idx frame_idx += turbo_steps else: filename = f"{args.timestring}_{frame_idx:05}.png" save_image(image, 'PIL', filename, args, video_args, root) if anim_args.save_depth_maps: if cmd_opts.lowvram or cmd_opts.medvram: lowvram.send_everything_to_cpu() sd_hijack.model_hijack.undo_hijack(sd_model) devices.torch_gc() depth_model.to(root.device) depth = depth_model.predict(opencv_image, anim_args, root.half_precision) depth_model.save(os.path.join(args.outdir, f"{args.timestring}_depth_{frame_idx:05}.png"), depth) if cmd_opts.lowvram or cmd_opts.medvram: depth_model.to('cpu') devices.torch_gc() lowvram.setup_for_low_vram(sd_model, cmd_opts.medvram) sd_hijack.model_hijack.hijack(sd_model) frame_idx += 1 state.current_image = image args.seed = next_seed(args) def print_render_table(anim_args, keys, frame_idx): from rich.table import Table from rich import box table = Table(padding=0, box=box.ROUNDED) field_names = [] if anim_args.animation_mode == '2D': short_zoom = round(keys.zoom_series[frame_idx], 6) field_names += ["Angle", "Zoom"] field_names += ["Tr X", "Tr Y"] if anim_args.animation_mode == '3D': field_names += ["Tr Z", "Ro X", "Ro Y", "Ro Z"] if anim_args.enable_perspective_flip: field_names += ["Pf T", "Pf P", "Pf G", "Pf F"] for field_name in field_names: table.add_column(field_name, justify="center") rows = [] if anim_args.animation_mode == '2D': rows += [str(keys.angle_series[frame_idx]),str(short_zoom)] rows += [str(keys.translation_x_series[frame_idx]),str(keys.translation_y_series[frame_idx])] if anim_args.animation_mode == '3D': rows += [str(keys.translation_z_series[frame_idx]),str(keys.rotation_3d_x_series[frame_idx]),str(keys.rotation_3d_y_series[frame_idx]),str(keys.rotation_3d_z_series[frame_idx])] if anim_args.enable_perspective_flip: rows +=[str(keys.perspective_flip_theta_series[frame_idx]), str(keys.perspective_flip_phi_series[frame_idx]), str(keys.perspective_flip_gamma_series[frame_idx]), str(keys.perspective_flip_fv_series[frame_idx])] table.add_row(*rows) console.print(table)