import argparse import math import os import cv2 import subprocess from datetime import timedelta from urllib.parse import urlparse import re import numpy as np import PIL from PIL import Image, ImageDraw import datetime import torch import torchvision import torch.distributed as dist from torch.utils.data.distributed import DistributedSampler from torch.nn.parallel import DistributedDataParallel as DDP import torchvision.transforms as transforms import torch.nn.functional as F import torch.utils.checkpoint from einops import rearrange import random from skimage.metrics import structural_similarity as compare_ssim from diffusers.utils import load_image def export_to_video(video_frames, output_video_path, fps): fourcc = cv2.VideoWriter_fourcc(*"mp4v") h, w, _ = video_frames[0].shape video_writer = cv2.VideoWriter( output_video_path, fourcc, fps=fps, frameSize=(w, h)) for i in range(len(video_frames)): img = cv2.cvtColor(video_frames[i], cv2.COLOR_RGB2BGR) video_writer.write(img) def export_to_gif(frames, output_gif_path, fps): """ Export a list of frames to a GIF. Args: - frames (list): List of frames (as numpy arrays or PIL Image objects). - output_gif_path (str): Path to save the output GIF. - duration_ms (int): Duration of each frame in milliseconds. """ # Convert numpy arrays to PIL Images if needed pil_frames = [Image.fromarray(frame) if isinstance( frame, np.ndarray) else frame for frame in frames] pil_frames[0].save(output_gif_path.replace('.mp4', '.gif'), format='GIF', append_images=pil_frames[1:], save_all=True, duration=100, loop=0) from PIL import Image import numpy as np def export_gif_with_ref(start_image, frames, end_image, reference_image, output_gif_path, fps): """ Export a list of frames into a GIF with columns and an additional version with only frames. Args: - start_image (PIL.Image): The starting image. - frames (list): List of frames (as numpy arrays or PIL Image objects). - end_image (PIL.Image): The ending image. - reference_image (PIL.Image): The reference image. - output_gif_path (str): Path to save the output GIF. - fps (int): Frames per second for the GIF. """ # Convert numpy frames to PIL Images if needed pil_frames = [Image.fromarray(frame) if isinstance(frame, np.ndarray) else frame for frame in frames] # Get dimensions of images width, height = start_image.size # Resize the reference image and frames to match the height of start and end images if needed reference_image = reference_image.resize((reference_image.width, height)) resized_frames = [frame.resize((frame.width, height)) for frame in pil_frames] # Create a new image for each frame with the three columns column_frames = [] for frame in resized_frames: # Create an empty image with the total width for all three columns new_width = start_image.width + reference_image.width + end_image.width+frame.width combined_frame = Image.new('RGB', (new_width, height)) # Paste the start image, reference image, and frame into the new image combined_frame.paste(start_image, (0, 0)) combined_frame.paste(reference_image, (start_image.width, 0)) combined_frame.paste(end_image, (start_image.width + reference_image.width, 0)) combined_frame.paste(frame, (start_image.width + reference_image.width+end_image.width, 0)) column_frames.append(combined_frame) # Calculate frame duration in milliseconds based on fps frame_duration = 150 # Save the GIF with columns column_frames[0].save(output_gif_path, format='GIF', append_images=column_frames[1:], save_all=True, duration=frame_duration, loop=0) def tensor_to_vae_latent(t, vae): video_length = t.shape[1] t = rearrange(t, "b f c h w -> (b f) c h w") latents = vae.encode(t).latent_dist.sample() latents = rearrange(latents, "(b f) c h w -> b f c h w", f=video_length) latents = latents * vae.config.scaling_factor return latents def download_image(url): original_image = ( lambda image_url_or_path: load_image(image_url_or_path) if urlparse(image_url_or_path).scheme else PIL.Image.open(image_url_or_path).convert("RGB") )(url) return original_image def map_ssim_distance(dis): if dis > 0.95: return 1 elif dis > 0.9: return 2 elif dis > 0.85: return 3 elif dis > 0.80: return 4 elif dis > 0.75: return 5 elif dis > 0.70: return 6 elif dis > 0.65: return 7 elif dis > 0.60: return 8 elif dis > 0.55: return 9 else: return 10 def calculate_ssim(frame1, frame2): # convert the frames to grayscale images since the compare_ssim function accepts grayscale images gray_frame1 = cv2.cvtColor(frame1, cv2.COLOR_RGB2GRAY) gray_frame2 = cv2.cvtColor(frame2, cv2.COLOR_RGB2GRAY) # compute SSIM ssim = compare_ssim(gray_frame1, gray_frame2) return ssim def mse(image1, image2): err = np.sum((image1.astype("float") - image2.astype("float")) ** 2) err /= float(image1.shape[0] * image1.shape[1]) return err def calculate_video_motion_distance(frames_data): # obtain the number of frames in the video frame_count, _, _, _ = frames_data.shape # init similarities = [] # calculate the similarity between each two frames for frame_index in range(1, frame_count): prev_frame = frames_data[frame_index - 1, :, :, :] current_frame = frames_data[frame_index, :, :, :] # calculate the similarity, you can choose to use SSIM or MSE, etc. similarity = calculate_ssim(prev_frame, current_frame) similarities.append(similarity) # calculate the mean similarity as the motion distance of the video motion_distance = np.mean(similarities) return similarities, motion_distance def load_images_from_folder_to_pil(folder, target_size=(512, 512)): images = [] valid_extensions = {".jpg", ".jpeg", ".png", ".bmp", ".gif", ".tiff"} # Add or remove extensions as needed def frame_number(filename): # Try the pattern 'frame_x_7fps' new_pattern_match = re.search(r'frame_(\d+)_7fps', filename) if new_pattern_match: return int(new_pattern_match.group(1)) # If the new pattern is not found, use the original digit extraction method matches = re.findall(r'\d+', filename) if matches: if matches[-1] == '0000' and len(matches) > 1: return int(matches[-2]) # Return the second-to-last sequence if the last is '0000' return int(matches[-1]) # Otherwise, return the last sequence return float('inf') # Return 'inf' # Sorting files based on frame number # sorted_files = sorted(os.listdir(folder), key=frame_number) sorted_files = sorted(os.listdir(folder)) # Load, resize, and convert images for filename in sorted_files: ext = os.path.splitext(filename)[1].lower() if ext in valid_extensions: img_path = os.path.join(folder, filename) img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED) # Read image with original channels if img is not None: # Resize image img = cv2.resize(img, target_size, interpolation=cv2.INTER_AREA) # Convert to uint8 if necessary if img.dtype == np.uint16: img = (img / 256).astype(np.uint8) # Ensure all images are in RGB format if len(img.shape) == 2: # Grayscale image img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB) elif len(img.shape) == 3 and img.shape[2] == 3: # Color image in BGR format img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # Convert the numpy array to a PIL image pil_img = Image.fromarray(img) images.append(pil_img) return images def extract_frames_from_video(video_path): video_capture = cv2.VideoCapture(video_path) frames = [] if not video_capture.isOpened(): return frames while True: ret, frame = video_capture.read() if not ret: break frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) pil_image = Image.fromarray(frame_rgb) frames.append(pil_image) video_capture.release() return frames def export_gif_side_by_side(ref_frame,sketches, frames, output_gif_path, fps): """ Export a list of frames into a GIF with columns and an additional version with only frames. Args: - start_image (PIL.Image): The starting image. - frames (list): List of frames (as numpy arrays or PIL Image objects). - end_image (PIL.Image): The ending image. - reference_image (PIL.Image): The reference image. - output_gif_path (str): Path to save the output GIF. - fps (int): Frames per second for the GIF. """ # Convert numpy frames to PIL Images if needed pil_frames = [Image.fromarray(frame) if isinstance(frame, np.ndarray) else frame for frame in frames] # Get dimensions of images width, height = pil_frames[0].size resized_frames = [frame.resize((width, height)) for frame in pil_frames] resized_sketches = [sketch.resize((width, height)) for sketch in sketches] ref_frame=ref_frame.resize((width, height)) # Create a new image for each frame with the three columns column_frames = [] for i, frame in enumerate(resized_frames): # Create an empty image with the total width for all three columns new_width = resized_sketches[0].width + frame.width+frame.width combined_frame = Image.new('RGB', (new_width, height)) # Paste the start image, reference image, and frame into the new image combined_frame.paste(ref_frame, (0, 0)) combined_frame.paste(resized_sketches[i], (resized_sketches[0].width, 0)) combined_frame.paste(frame, (resized_sketches[0].width+resized_sketches[0].width, 0)) column_frames.append(combined_frame) # Calculate frame duration in milliseconds based on fps frame_duration = 150 # Save the GIF with columns column_frames[0].save(output_gif_path, format='GIF', append_images=column_frames[1:], save_all=True, duration=frame_duration, loop=0) #shuffle operation def safe_round(coords, size): height, width = size[1], size[2] rounded_coords = np.round(coords).astype(int) rounded_coords[:, 0] = np.clip(rounded_coords[:, 0], 0, width - 1) rounded_coords[:, 1] = np.clip(rounded_coords[:, 1], 0, height - 1) return rounded_coords def random_number(num_points,size,coords0,coords1): shuffle_indices = np.random.permutation(np.arange(coords0.shape[0])) shuffled_coords0 = coords0[shuffle_indices] shuffled_coords1 = coords1[shuffle_indices] indices = np.random.choice(np.arange(shuffled_coords0.shape[0]), size=num_points, replace=False) # selected_coords0 = coords0[indices] # selected_coords1 = coords1[indices] selected_coords0 = shuffled_coords0[indices] selected_coords1 = shuffled_coords1[indices] h, w = size[1], size[2] mask0 = np.zeros((h, w), dtype=np.uint8) mask1 = np.zeros((h, w), dtype=np.uint8) for i, (coord0, coord1) in enumerate(zip(selected_coords0, selected_coords1)): x0, y0 = coord0 x1, y1 = coord1 # import ipdb;ipdb.set_trace() mask0[y0, x0] = i + 1 mask1[y1, x1] = i + 1 return mask0,mask1 def split_and_shuffle(image, coordinates): assert image.shape[1] % 2 == 0 and image.shape[2] % 2 == 0, "Height and width must be even." H, W = image.shape[1], image.shape[2] patches_img = [ image[:, :H//2, :W//2], image[:, :H//2, W//2:], image[:, H//2:, :W//2], image[:, H//2:, W//2:] ] patch_coords = [ (0, H//2, 0, W//2), (0, H//2, W//2, W), (H//2, H, 0, W//2), (H//2, H, W//2, W) ] indices = list(range(4)) random.shuffle(indices) new_patch_coords = [ (0, 0), (0, W//2), (H//2, 0), (H//2, W//2) ] new_coordinates = np.zeros_like(coordinates) for i, (r, c) in enumerate(coordinates): for idx, (r1, r2, c1, c2) in enumerate(patch_coords): if r1 <= r < r2 and c1 <= c < c2: new_r = r - r1 + new_patch_coords[indices.index(idx)][0] new_c = c - c1 + new_patch_coords[indices.index(idx)][1] new_coordinates[i] = [new_r, new_c] break shuffled_img = torch.cat([ torch.cat([patches_img[indices[0]], patches_img[indices[1]]], dim=2), torch.cat([patches_img[indices[2]], patches_img[indices[3]]], dim=2) ], dim=1) return shuffled_img, new_coordinates import os import cv2 def extract_frames_from_videos(video_folder): for filename in os.listdir(video_folder): if filename.endswith('.mp4'): video_path = os.path.join(video_folder, filename) frames_folder = os.path.join("processed_video", os.path.splitext(filename)[0]) os.makedirs(frames_folder, exist_ok=True) cap = cv2.VideoCapture(video_path) frame_count = 0 while True: ret, frame = cap.read() if not ret: break frame_filename = os.path.join(frames_folder, f'frame_{frame_count:04d}.jpg') cv2.imwrite(frame_filename, frame) frame_count += 1 cap.release() print(f'Extracted {frame_count} frames from {filename} and saved to {frames_folder}') def create_videos_from_frames(base_folder, output_folder, frame_rate=30): for root, dirs, files in os.walk(base_folder): frames = [] for file in sorted(files): if file.endswith(('.jpg', '.png')): frame_path = os.path.join(root, file) frames.append(frame_path) if len(frames) == 14: video_name = os.path.basename(root) + '.mp4' video_path = os.path.join(output_folder, video_name) fourcc = cv2.VideoWriter_fourcc(*'mp4v') first_frame = cv2.imread(frames[0]) height, width, layers = first_frame.shape video_writer = cv2.VideoWriter(video_path, fourcc, frame_rate, (width, height)) for frame in frames: img = cv2.imread(frame) video_writer.write(img) video_writer.release() print(f'Created video: {video_path}') def random_rotate(image, angle_range=(-60, 60)): angle = random.uniform(*angle_range) return image.rotate(angle, fillcolor=(255, 255, 255)) def random_crop(image,ratio=0.9): width, height = image.size ratio = random.uniform(0.6, 1.0) # print('ratio',ratio) top = random.randint(0, height - int(height*ratio)) left = random.randint(0, width - int(width*ratio)) image=image.crop((left, top, left + int( width*ratio), top + int(height*ratio))) image=image.resize((width,height)) return image def random_flip(image): if random.random() < 0.5: image = image.transpose(Image.FLIP_LEFT_RIGHT) if random.random() < 0.5: image = image.transpose(Image.FLIP_TOP_BOTTOM) return image def patch_shuffle(image, num_patches): C, H, W = image.shape assert H % num_patches == 0 and W % num_patches == 0, "Image dimensions must be divisible by num_patches" patch_size_h = H // num_patches patch_size_w = W // num_patches patches = image.unfold(1, patch_size_h, patch_size_h).unfold(2, patch_size_w, patch_size_w) patches = patches.contiguous().view(C, num_patches * num_patches, patch_size_h, patch_size_w) shuffle_idx = torch.randperm(num_patches * num_patches) shuffled_patches = patches[:, shuffle_idx, :, :] shuffled_patches = shuffled_patches.view(C, num_patches, num_patches, patch_size_h, patch_size_w) shuffled_image = shuffled_patches.permute(0, 1, 3, 2, 4).contiguous() shuffled_image = shuffled_image.view(C, H, W) return shuffled_image def augment_image(image,k): image = random_rotate(image) image = random_crop(image) image = random_flip(image) # torch_image = torchvision.transforms.ToTensor()(image) # patch_shuffled_image = patch_shuffle(torch_image, k) # to_pil = transforms.ToPILImage() # image = to_pil(patch_shuffled_image) return image def load_images_from_folder(folder): image_list = [] for filename in os.listdir(folder): if filename.endswith(".png") or filename.endswith(".jpg") or filename.endswith(".jpeg"): img_path = os.path.join(folder, filename) try: img = Image.open(img_path) image_list.append(img) except Exception as e: print(f"Error loading image {filename}: {e}") return image_list def get_mask(model, input_img, s=640): input_img = (input_img / 255).astype(np.float32) h, w = h0, w0 = input_img.shape[:-1] h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s) ph, pw = s - h, s - w img_input = np.zeros([s, s, 3], dtype=np.float32) img_input[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] = cv2.resize(input_img, (w, h)) img_input = np.transpose(img_input, (2, 0, 1)) img_input = img_input[np.newaxis, :] tmpImg = torch.from_numpy(img_input).type(torch.FloatTensor).to(model.device) with torch.no_grad(): pred = model(tmpImg) pred = pred.cpu().numpy()[0] pred = np.transpose(pred, (1, 2, 0)) pred = pred[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] pred = cv2.resize(pred, (w0, h0))[:, :, np.newaxis] return pred # code from def safe_round(coords, size): height, width = size[1], size[2] rounded_coords = np.round(coords).astype(int) rounded_coords[:, 0] = np.clip(rounded_coords[:, 0], 0, width - 1) rounded_coords[:, 1] = np.clip(rounded_coords[:, 1], 0, height - 1) return rounded_coords def random_number(num_points,size,coords0,coords1): shuffle_indices = np.random.permutation(np.arange(coords0.shape[0])) shuffled_coords0 = coords0[shuffle_indices] shuffled_coords1 = coords1[shuffle_indices] indices = np.random.choice(np.arange(shuffled_coords0.shape[0]), size=num_points, replace=False) # selected_coords0 = coords0[indices] # selected_coords1 = coords1[indices] selected_coords0 = shuffled_coords0[indices] selected_coords1 = shuffled_coords1[indices] h, w = size[1], size[2] mask0 = np.zeros((h, w), dtype=np.uint8) mask1 = np.zeros((h, w), dtype=np.uint8) for i, (coord0, coord1) in enumerate(zip(selected_coords0, selected_coords1)): x0, y0 = coord0 x1, y1 = coord1 # import ipdb;ipdb.set_trace() mask0[y0, x0] = i + 1 mask1[y1, x1] = i + 1 return mask0,mask1 import torch def split_and_shuffle(image, keypoints, num_rows, num_cols): """ Split the image into tiles, shuffle them, and update the keypoints accordingly. Parameters: - image: Tensor of shape (3, H, W) - keypoints: Tensor of shape (num_k, 2) - num_rows: int, number of rows to split - num_cols: int, number of columns to split Returns: - shuffled_image: Tensor of shape (3, H, W) - new_keypoints: Tensor of shape (num_k, 2) """ C, H, W = image.shape # Calculate padding to make H and W divisible by num_rows and num_cols pad_h = (num_rows - H % num_rows) % num_rows pad_w = (num_cols - W % num_cols) % num_cols # Pad the image H_padded = H + pad_h W_padded = W + pad_w padded_image = torch.zeros((C, H_padded, W_padded), dtype=image.dtype).to(image.device) padded_image[:, :H, :W] = image # Compute tile size tile_height = H_padded // num_rows tile_width = W_padded // num_cols # Reshape and permute to get tiles tiles = padded_image.reshape(C, num_rows, tile_height, num_cols, tile_width) tiles = tiles.permute(1, 3, 0, 2, 4).contiguous() num_tiles = num_rows * num_cols tiles = tiles.view(num_tiles, C, tile_height, tile_width) # Shuffle the tiles idx_shuffle = torch.randperm(num_tiles).to(image.device) tiles_shuffled = tiles[idx_shuffle] # Reshape back to image tiles_shuffled = tiles_shuffled.view(num_rows, num_cols, C, tile_height, tile_width) shuffled_image = tiles_shuffled.permute(2, 0, 3, 1, 4).contiguous() shuffled_image = shuffled_image.view(C, H_padded, W_padded) shuffled_image = shuffled_image[:, :H, :W] # Crop back to original size # Update keypoints x = keypoints[:, 0] y = keypoints[:, 1] # Compute the tile indices where the keypoints are located tile_rows = (y / tile_height).long() tile_cols = (x / tile_width).long() tile_indices = tile_rows * num_cols + tile_cols # Shape: (num_k,) # Create inverse mapping from old tile indices to new tile positions idx_unshuffle = torch.argsort(idx_shuffle) # idx_unshuffle[old_index] = new_index # Get new tile indices for each keypoint new_tile_indices = idx_unshuffle[tile_indices] new_tile_rows = new_tile_indices // num_cols new_tile_cols = new_tile_indices % num_cols # Compute offsets within the tile offset_x = x % tile_width offset_y = y % tile_height # Compute new keypoints coordinates new_x = new_tile_cols * tile_width + offset_x new_y = new_tile_rows * tile_height + offset_y # Ensure keypoints are within image boundaries new_x = new_x.clamp(0, W - 1) new_y = new_y.clamp(0, H - 1) new_keypoints = torch.stack([new_x, new_y], dim=1) return shuffled_image, new_keypoints def generate_point_map(size, coords0, coords1): h, w = size[1], size[2] mask0 = np.zeros((h, w), dtype=np.uint8) mask1 = np.zeros((h, w), dtype=np.uint8) for i, (coord0, coord1) in enumerate(zip(coords0, coords1)): x0, y0 = coord0 x1, y1 = coord1 x0, y0 = int(round(x0)), int(round(y0)) x1, y1 = int(round(x1)), int(round(y1)) if 0 <= x0 < w and 0 <= y0 < h: mask0[y0, x0] = i + 1 if 0 <= x1 < w and 0 <= y1 < h: mask1[y1, x1] = i + 1 return mask0, mask1 def select_multiple_points(points0, points1, num_points): N = len(points0) num_points = min(num_points, N) indices = np.random.choice(N, size=num_points, replace=False) selected_points0 = points0[indices] selected_points1 = points1[indices] return selected_points0, selected_points1 def generate_point_map_frames(size, coords0, coords1,visibility): h, w = size[1], size[2] mask0 = np.zeros((h, w), dtype=np.uint8) num_frames = coords1.shape[0] mask1 = np.zeros((num_frames, h, w), dtype=np.uint8) for i, coord0 in enumerate(coords0): x0, y0 = coord0 x0, y0 = int(round(x0)), int(round(y0)) if 0 <= x0 < w and 0 <= y0 < h: mask0[y0, x0] = i + 1 for frame_idx in range(num_frames): coords_frame = coords1[frame_idx] for i, coord1 in enumerate(coords_frame): x1, y1 = coord1 x1, y1 = int(round(x1)), int(round(y1)) if 0 <= x1 < w and 0 <= y1 < h and visibility[frame_idx,i]==True: mask1[frame_idx, y1, x1] = i + 1 return mask0, mask1 import numpy as np def extract_patches(image, coords, patch_size): N = coords.shape[0] channels, H, W = image.shape patches = np.zeros((N, channels, patch_size, patch_size), dtype=image.dtype) half_size = patch_size // 2 for i in range(N): x0, y0 = coords[i] x0 = int(round(x0)) y0 = int(round(y0)) # Define the patch region in the image x_start_img = x0 - half_size x_end_img = x0 + half_size + 1 y_start_img = y0 - half_size y_end_img = y0 + half_size + 1 # Define the region in the patch to fill x_start_patch = 0 y_start_patch = 0 x_end_patch = patch_size y_end_patch = patch_size # Adjust for boundaries if x_start_img < 0: x_start_patch = -x_start_img x_start_img = 0 if y_start_img < 0: y_start_patch = -y_start_img y_start_img = 0 if x_end_img > W: x_end_patch -= (x_end_img - W) x_end_img = W if y_end_img > H: y_end_patch -= (y_end_img - H) y_end_img = H # Calculate the actual sizes patch_height = y_end_patch - y_start_patch patch_width = x_end_patch - x_start_patch img_height = y_end_img - y_start_img img_width = x_end_img - x_start_img # Ensure the sizes match if patch_height != img_height or patch_width != img_width: min_height = min(patch_height, img_height) min_width = min(patch_width, img_width) y_end_patch = y_start_patch + min_height y_end_img = y_start_img + min_height x_end_patch = x_start_patch + min_width x_end_img = x_start_img + min_width # Assign the image patch to the patches array patches[i, :, y_start_patch:y_end_patch, x_start_patch:x_end_patch] = \ image[:, y_start_img:y_end_img, x_start_img:x_end_img] return patches def generate_point_feature_map_frames_naive(image, size, coords0, coords1, visibility, patch_size): channels, H, W = size num_frames = coords1.shape[0] N = coords0.shape[0] # Extract patches from the reference image at coords0 patches = extract_patches(image, coords0, patch_size) half_size = patch_size // 2 # Initialize the feature maps feature_maps = np.zeros((num_frames, channels, H, W), dtype=image.dtype) for frame_idx in range(num_frames): feature_map = np.zeros((channels, H, W), dtype=image.dtype) coords_frame = coords1[frame_idx] for i in range(N): if visibility[frame_idx, i]: x1, y1 = coords_frame[i] x1 = int(round(x1)) y1 = int(round(y1)) # Define the patch region in the feature map x_start_map = x1 - half_size x_end_map = x1 + half_size + 1 y_start_map = y1 - half_size y_end_map = y1 + half_size + 1 # Define the region in the patch to use x_start_patch = 0 y_start_patch = 0 x_end_patch = patch_size y_end_patch = patch_size # Adjust for boundaries if x_start_map < 0: x_start_patch = -x_start_map x_start_map = 0 if y_start_map < 0: y_start_patch = -y_start_map y_start_map = 0 if x_end_map > W: x_end_patch -= (x_end_map - W) x_end_map = W if y_end_map > H: y_end_patch -= (y_end_map - H) y_end_map = H # Calculate the actual sizes patch_height = y_end_patch - y_start_patch patch_width = x_end_patch - x_start_patch map_height = y_end_map - y_start_map map_width = x_end_map - x_start_map # Ensure the sizes match if patch_height != map_height or patch_width != map_width: min_height = min(patch_height, map_height) min_width = min(patch_width, map_width) y_end_patch = y_start_patch + min_height y_end_map = y_start_map + min_height x_end_patch = x_start_patch + min_width x_end_map = x_start_map + min_width # Place the patch into the feature map feature_map[:, y_start_map:y_end_map, x_start_map:x_end_map] = \ patches[i, :, y_start_patch:y_end_patch, x_start_patch:x_end_patch] feature_maps[frame_idx] = feature_map return feature_maps import os from PIL import Image import numpy as np from moviepy.editor import ImageSequenceClip def export_gif_side_by_side_complete(ref_frame, sketches, frames, output_gif_path, supp_dir,fps): """ Export frames into a GIF and an MP4 video with columns, and save individual frames and sketches. Args: - ref_frame (PIL.Image or np.ndarray): The reference image. - sketches (list): List of sketch images (as numpy arrays or PIL Image objects). - frames (list): List of frames (as numpy arrays or PIL Image objects). - output_gif_path (str): Path to save the output GIF. - fps (int): Frames per second for the GIF and MP4. """ # Ensure the output directory exists output_dir = os.path.dirname(output_gif_path) if not os.path.exists(output_dir): os.makedirs(output_dir) # Get the base name of the output file (without extension) base_name = os.path.splitext(os.path.basename(output_gif_path))[0] # Create subdirectories for sketches and frames sketch_dir = os.path.join(supp_dir,"sketches") frame_dir = os.path.join(supp_dir,"frames") os.makedirs(sketch_dir, exist_ok=True) os.makedirs(frame_dir, exist_ok=True) # Convert numpy arrays to PIL Images if needed pil_frames = [Image.fromarray(frame) if isinstance(frame, np.ndarray) else frame for frame in frames] pil_sketches = [Image.fromarray(sketch) if isinstance(sketch, np.ndarray) else sketch for sketch in sketches] ref_frame = Image.fromarray(ref_frame) if isinstance(ref_frame, np.ndarray) else ref_frame # Get dimensions of images width, height = pil_frames[0].size # Resize images resized_frames = [frame.resize((width, height)) for frame in pil_frames] resized_sketches = [sketch.resize((width, height)) for sketch in pil_sketches] ref_frame = ref_frame.resize((width, height)) # Save each sketch frame for i, sketch in enumerate(resized_sketches): sketch_filename = os.path.join(sketch_dir, f"{base_name}_sketch_{i:04d}.png") sketch.save(sketch_filename) # Save each frame for i, frame in enumerate(resized_frames): frame_filename = os.path.join(frame_dir, f"{base_name}_frame_{i:04d}.png") frame.save(frame_filename) # Save reference frame ref_filename = os.path.join(supp_dir, f"{base_name}_reference.png") ref_frame.save(ref_filename) # Create a new image for each frame with the three columns column_frames = [] for i, frame in enumerate(resized_frames): # Create an empty image with the total width for all three columns new_width = ref_frame.width + resized_sketches[i].width + frame.width combined_frame = Image.new('RGB', (new_width, height)) # Paste the reference image, sketch, and frame into the new image combined_frame.paste(ref_frame, (0, 0)) combined_frame.paste(resized_sketches[i], (ref_frame.width, 0)) combined_frame.paste(frame, (ref_frame.width + resized_sketches[i].width, 0)) column_frames.append(combined_frame) # Calculate frame duration in milliseconds based on fps frame_duration = int(1000 / fps) # Save the GIF with columns column_frames[0].save(output_gif_path, format='GIF', append_images=column_frames[1:], save_all=True, duration=frame_duration, loop=0) # Save the MP4 video with the same content output_mp4_path = os.path.join(supp_dir , 'result.mp4') # Convert PIL Images to numpy arrays for moviepy video_frames = [np.array(frame) for frame in column_frames] clip = ImageSequenceClip(video_frames, fps=fps) clip.write_videofile(output_mp4_path, codec='libx264') def export_gif_with_ref_complete(start_image, frames, end_image, reference_image, output_gif_path, supp_dir, fps): """ Export a list of frames into a GIF with columns, save individual images and frames, and create an MP4 video, following the storage method of 'export_gif_side_by_side_complete'. Args: - start_image (PIL.Image or np.ndarray): The starting image. - frames (list): List of frames (as numpy arrays or PIL Image objects). - end_image (PIL.Image or np.ndarray): The ending image. - reference_image (PIL.Image or np.ndarray): The reference image. - output_gif_path (str): Path to save the output GIF. - supp_dir (str): Directory to save supplementary files. - fps (int): Frames per second for the GIF and MP4. """ # Ensure the output directory exists output_dir = os.path.dirname(output_gif_path) if not os.path.exists(output_dir): os.makedirs(output_dir) # Get the base name of the output file (without extension) base_name = os.path.splitext(os.path.basename(output_gif_path))[0] # Create subdirectories for images and frames start_end_dir = os.path.join(supp_dir, "start_end_images") frame_dir = os.path.join(supp_dir, "frames") reference_dir = os.path.join(supp_dir, "reference") os.makedirs(start_end_dir, exist_ok=True) os.makedirs(frame_dir, exist_ok=True) os.makedirs(reference_dir, exist_ok=True) # Convert numpy arrays to PIL Images if needed pil_frames = [Image.fromarray(frame) if isinstance(frame, np.ndarray) else frame for frame in frames] start_image = Image.fromarray(start_image) if isinstance(start_image, np.ndarray) else start_image end_image = Image.fromarray(end_image) if isinstance(end_image, np.ndarray) else end_image reference_image = Image.fromarray(reference_image) if isinstance(reference_image, np.ndarray) else reference_image # Get dimensions of images width, height = start_image.size # Resize images to match the height reference_image = reference_image.resize((reference_image.width, height)) resized_frames = [frame.resize((frame.width, height)) for frame in pil_frames] # Save start_image, end_image, and reference_image start_image_filename = os.path.join(start_end_dir, f"{base_name}_start.png") start_image.save(start_image_filename) end_image_filename = os.path.join(start_end_dir, f"{base_name}_end.png") end_image.save(end_image_filename) reference_image_filename = os.path.join(reference_dir, f"{base_name}_reference.png") reference_image.save(reference_image_filename) # Save each frame for i, frame in enumerate(resized_frames): frame_filename = os.path.join(frame_dir, f"{base_name}_frame_{i:04d}.png") frame.save(frame_filename) # Create a new image for each frame with the columns column_frames = [] for i, frame in enumerate(resized_frames): # Calculate the total width for all columns new_width = start_image.width + reference_image.width + end_image.width + frame.width combined_frame = Image.new('RGB', (new_width, height)) # Paste the images into the combined frame combined_frame.paste(start_image, (0, 0)) combined_frame.paste(reference_image, (start_image.width, 0)) combined_frame.paste(end_image, (start_image.width + reference_image.width, 0)) combined_frame.paste(frame, (start_image.width + reference_image.width + end_image.width, 0)) column_frames.append(combined_frame) # Calculate frame duration in milliseconds based on fps frame_duration = int(1000 / fps) # Save the GIF with columns column_frames[0].save(output_gif_path, format='GIF', append_images=column_frames[1:], save_all=True, duration=frame_duration, loop=0) # Save the MP4 video with the same content output_mp4_path = os.path.join(supp_dir, 'result.mp4') # Convert PIL Images to numpy arrays for moviepy video_frames = [np.array(frame) for frame in column_frames] clip = ImageSequenceClip(video_frames, fps=fps) clip.write_videofile(output_mp4_path, codec='libx264') def export_gif_side_by_side_complete_ablation(ref_frame, sketches, frames, output_gif_path, supp_dir,fps): """ Export frames into a GIF and an MP4 video with columns, and save individual frames and sketches. Args: - ref_frame (PIL.Image or np.ndarray): The reference image. - sketches (list): List of sketch images (as numpy arrays or PIL Image objects). - frames (list): List of frames (as numpy arrays or PIL Image objects). - output_gif_path (str): Path to save the output GIF. - fps (int): Frames per second for the GIF and MP4. """ # Ensure the output directory exists output_dir = os.path.dirname(output_gif_path) if not os.path.exists(output_dir): os.makedirs(output_dir) # Get the base name of the output file (without extension) base_name = os.path.splitext(os.path.basename(output_gif_path))[0] # Create subdirectories for sketches and frames sketch_dir = os.path.join(supp_dir,"sketches") frame_dir = os.path.join(supp_dir,"frames") os.makedirs(sketch_dir, exist_ok=True) os.makedirs(frame_dir, exist_ok=True) # Convert numpy arrays to PIL Images if needed pil_frames = [Image.fromarray(frame) if isinstance(frame, np.ndarray) else frame for frame in frames] pil_sketches = [Image.fromarray(sketch) if isinstance(sketch, np.ndarray) else sketch for sketch in sketches] ref_frame = Image.fromarray(ref_frame) if isinstance(ref_frame, np.ndarray) else ref_frame # Get dimensions of images width, height = pil_frames[0].size # Resize images resized_frames = [frame.resize((width, height)) for frame in pil_frames] resized_sketches = [sketch.resize((width, height)) for sketch in pil_sketches] ref_frame = ref_frame.resize((width, height)) # Save each sketch frame for i, sketch in enumerate(resized_sketches): sketch_filename = os.path.join(sketch_dir, f"{base_name}_sketch_{i:04d}.png") sketch.save(sketch_filename) # Save each frame for i, frame in enumerate(resized_frames): frame_filename = os.path.join(frame_dir, f"{base_name}_frame_{i:04d}.png") frame.save(frame_filename) # Save reference frame ref_filename = os.path.join(supp_dir, f"{base_name}_reference.png") ref_frame.save(ref_filename) # Create a new image for each frame with the three columns column_frames = [] rgb_frames = [] for i, frame in enumerate(resized_frames): # Create an empty image with the total width for all three columns new_width = ref_frame.width + resized_sketches[i].width + frame.width combined_frame = Image.new('RGB', (new_width, height)) # Paste the reference image, sketch, and frame into the new image combined_frame.paste(ref_frame, (0, 0)) combined_frame.paste(resized_sketches[i], (ref_frame.width, 0)) combined_frame.paste(frame, (ref_frame.width + resized_sketches[i].width, 0)) column_frames.append(combined_frame) rgb_frames.append(frame) # Calculate frame duration in milliseconds based on fps frame_duration = int(1000 / fps) # Save the GIF with columns column_frames[0].save(output_gif_path, format='GIF', append_images=column_frames[1:], save_all=True, duration=frame_duration, loop=0) # Save the MP4 video with the same content output_mp4_path = supp_dir+'.mp4' # Convert PIL Images to numpy arrays for moviepy video_frames = [np.array(frame) for frame in column_frames] rgb_frames = [np.array(frame) for frame in rgb_frames] clip = ImageSequenceClip(rgb_frames, fps=fps) clip.write_videofile(output_mp4_path, codec='libx264')