import os import imageio.v3 as iio from PIL import Image, ImageDraw import numpy as np import torch from torchvision import transforms def draw_overall_gradient_polyline_on_image(image, line_width, points, start_color): """ - image (Image): target image to draw on. - line_width (int): initial line width. - points (list of tuples): list of points forming the polyline, each point is (x, y). - start_color (tuple): starting color of the line (R, G, B). Return: - Image: original image with the gradient polyline drawn. """ def get_distance(p1, p2): return ((p2[0] - p1[0]) ** 2 + (p2[1] - p1[1]) ** 2) ** 0.5 # Create a new image with the same size as the original new_image = Image.new('RGBA', image.size) draw = ImageDraw.Draw(new_image, 'RGBA') points = points[::-1] # Compute total length total_length = sum(get_distance(points[i], points[i+1]) for i in range(len(points)-1)) # Accumulated length accumulated_length = 0 # Draw the gradient polyline for start_point, end_point in zip(points[:-1], points[1:]): segment_length = get_distance(start_point, end_point) steps = int(segment_length) for i in range(steps): # Current accumulated length current_length = accumulated_length + (i / steps) * segment_length # Alpha from fully opaque to fully transparent alpha = int(255 * (1 - current_length / total_length)) color = (*start_color, alpha) # Interpolated coordinates x = int(start_point[0] + (end_point[0] - start_point[0]) * i / steps) y = int(start_point[1] + (end_point[1] - start_point[1]) * i / steps) # Dynamic line width, decreasing from initial width to 1 dynamic_line_width = int(line_width * (1 - (current_length / total_length))) dynamic_line_width = max(dynamic_line_width, 1) # minimum width is 1 to avoid 0 draw.line([(x, y), (x + 1, y)], fill=color, width=dynamic_line_width) accumulated_length += segment_length return new_image def add_weighted(rgb, track): rgb = np.array(rgb) # [H, W, C] "RGB" track = np.array(track) # [H, W, C] "RGBA" # Compute weights from the alpha channel alpha = track[:, :, 3] / 255.0 # Expand alpha to 3 channels to match RGB alpha = np.stack([alpha] * 3, axis=-1) # Blend the two images blend_img = track[:, :, :3] * alpha + rgb * (1 - alpha) return Image.fromarray(blend_img.astype(np.uint8)) def draw_tracks_on_video(video, tracks, visibility=None, track_frame=24): color_map = [ (102, 153, 255), (0, 255, 255), (255, 255, 0), (255, 102, 204), (0, 255, 0) ] circle_size = 12 line_width = 16 video = video[0].permute(0, 2, 3, 1).byte().detach().cpu().numpy() # (81, 480, 832, 3), uint8 tracks = tracks[0].long().detach().cpu().numpy() if visibility is not None: visibility = visibility[0].detach().cpu().numpy() output_frames = [] # Process the video for t in range(video.shape[0]): # Extract current frame frame = video[t] frame = Image.fromarray(frame).convert("RGB") # Draw tracks for n in range(tracks.shape[1]): if visibility is not None and visibility[t, n] == 0: continue # Track coordinate at current frame track_coord = tracks[t, n] tracks_coord = tracks[max(t-track_frame, 0):t+1, n] # Draw a circle draw = ImageDraw.Draw(frame) draw.ellipse((track_coord[0] - circle_size, track_coord[1] - circle_size, track_coord[0] + circle_size, track_coord[1] + circle_size), fill=color_map[n % len(color_map)]) # Draw the polyline track_image = draw_overall_gradient_polyline_on_image(frame, line_width, tracks_coord, color_map[n % len(color_map)]) frame = add_weighted(frame, track_image) # Save current frame output_frames.append(frame.convert("RGB")) return output_frames def draw_mouse_track(track_video, tracks): mouse_icon_path = "assets/mouse_icon.png" # replace with your icon path mouse_icon = Image.open(mouse_icon_path).convert("RGBA") icon_size = (64, 64) # adjust icon size if needed icon_trans = (24, 16) mouse_icon = mouse_icon.resize(icon_size, Image.Resampling.LANCZOS) # Store processed frames output_frames = [] for t in range(len(track_video)): # Convert to PIL image pil_frame = track_video[t].convert("RGBA") # Get the track coordinate at the current frame (assume using the first track) track_coord = tracks[0, t, 0].numpy() width, height = pil_frame.size # note: size is (width, height) # Convert to pixel coordinates x = int(track_coord[0]) y = int(track_coord[1]) # Compute paste position (using the icon offset as reference) icon_w, icon_h = mouse_icon.size icon_trans_w, icon_trans_h = icon_trans paste_x = max(0, min(x-icon_trans_w, width - icon_trans_w)) paste_y = max(0, min(y-icon_trans_h, height - icon_trans_h)) # Paste the icon pil_frame.paste(mouse_icon, (paste_x, paste_y), mouse_icon) # Convert back to RGB and store final_frame = np.array(pil_frame.convert("RGB")) output_frames.append(final_frame) return output_frames if __name__ == "__main__": save_dir = "saved_visuals" os.makedirs(save_dir, exist_ok=True) video_type = "image" # "video" or "image" fps = 16 video_name = "Pexels_3C_product_0" video_path = f"MoveBench/en/video/{video_name}.mp4" track_path = f"MoveBench/en/track/single/{video_name}_tracks.npy" visibility_path = f"MoveBench/en/track/single/{video_name}_visibility.npy" frames = iio.imread(video_path, plugin="FFMPEG") # plugin="pyav" if video_type == "video": video = torch.tensor(frames).permute(0, 3, 1, 2)[None].float() # [B, T, C, H, W] else: t = len(frames) video = torch.tensor(frames).permute(0, 3, 1, 2)[0:1].repeat(t, 1, 1, 1)[None].float() tracks = torch.tensor(np.load(track_path)).float() visibility = torch.tensor(np.load(visibility_path)).float() track_video = draw_tracks_on_video(video, tracks, visibility) track_video_with_mouse = draw_mouse_track(track_video, tracks) iio.imwrite(f"{save_dir}/{video_name}.mp4", track_video_with_mouse, fps=fps, plugin="FFMPEG")