Mascot Processor

Mascot Processor

Convert animated character MP4s into transparent-background assets (GIF, APNG, WebM) for use in infographics, videos, and overlays.

When to Use

• The user provides mascot or character MP4 files to convert
• The user asks to remove a background from pixel-art or 2D animation
• The user wants a transparent sticker or mascot asset pack

Output Location

Write into an output directory the user names. If they do not name one, default to ./mascot-pack/ in the current working directory and say so before writing.

mascot-pack/
  source/     # Original MP4s preserved
  gif/        # 160px wide, 24fps (lightweight overlays)
  apng/       # 160px wide, 24fps, full 8-bit alpha
  webm/       # Original resolution, 24fps, VP9 alpha (video compositing)
  gif-hd/     # Original resolution, 24fps (high-fidelity)
  apng-hd/    # Original resolution, 24fps, full 8-bit alpha (high-fidelity)

Naming

Name files by the action being performed, one word: walking.gif, loading.gif, waving.gif.

Dependencies

pip install numpy scipy pillow
# ffmpeg must be on PATH: brew install ffmpeg (macOS) or apt install ffmpeg (Debian/Ubuntu)

import numpy as np
from scipy import ndimage
from PIL import Image

The Process

Step 0: Auto-Detect Background Type

Sample the corner pixels of frame 0. If the green channel dominates (G > R + 0.15 AND G > B + 0.15 AND G > 0.4), use Green Screen Mode. Otherwise use White Background Mode.

Green screen preserves every non-green character color (blue, yellow, brown, and so on). White background requires color isolation, which has edge cases with multi-colored characters. Avoid green props in green screen scenes, since they will be removed along with the backdrop.

---

GREEN SCREEN MODE (Default Method)

Kill ALL green pixels everywhere. No flood-fill, no exceptions. 2px dilation to eat fringe.

def remove_green(frame_path):
    """Kill all green pixels aggressively. 2px dilation for fringe."""
    img = Image.open(frame_path).convert("RGBA")
    a = np.array(img).astype(float)
    r, g, b = a[:,:,0]/255, a[:,:,1]/255, a[:,:,2]/255
    maxc = np.maximum(r, np.maximum(g, b))
    minc = np.minimum(r, np.minimum(g, b))
    diff = maxc - minc
    sat = np.where(maxc == 0, 0, diff / maxc)
    hue = np.zeros_like(r)
    mask = diff > 0
    idx = mask & (maxc == g)
    hue[idx] = 60 * ((b[idx] - r[idx]) / diff[idx]) + 120
    idx = mask & (maxc == r)
    hue[idx] = (60 * ((g[idx] - b[idx]) / diff[idx]) + 360) % 360
    idx = mask & (maxc == b)
    hue[idx] = 60 * ((r[idx] - g[idx]) / diff[idx]) + 240

    # Broad green detection: catch everything
    green_dominant = (g > r + 0.10) & (g > b + 0.10) & (g > 0.35)
    green_hue = (hue > 60) & (hue  0.15)
    is_green = green_dominant | green_hue

    # Dilate 2px to eat all green fringe
    is_green = ndimage.binary_dilation(is_green, iterations=2)

    result = np.array(img)
    result[is_green, 3] = 0
    return Image.fromarray(result)

---

WHITE BACKGROUND MODE

Single-Hue Isolation (default for white bg)

For characters built from one saturated hue plus black outlines and details, on a white or off-white background.

def remove_bg_single_hue(frame_path):
    """Color isolation: keep saturated color and black, kill everything else."""
    img = Image.open(frame_path).convert("RGBA")
    a = np.array(img).astype(float)
    r, g, b = a[:,:,0]/255, a[:,:,1]/255, a[:,:,2]/255
    maxc = np.maximum(r, np.maximum(g, b))
    minc = np.minimum(r, np.minimum(g, b))
    diff = maxc - minc
    sat = np.where(maxc == 0, 0, diff / maxc)
    val = maxc

    # Keep only saturated (the character's color) or dark (black outlines, eyes)
    keep = (sat > 0.25) | (val  0.30)
    keep = keep & ~grayish

    result = np.array(img)
    result[~keep, 3] = 0
    return Image.fromarray(result)

Multicolor (for characters with yellow/gold effects on white bg)

Auto-detect: sample frame 30. If hue range 20 to 70 holds a significant number of saturated pixels, use this mode.

def remove_bg_multicolor(frame_path):
    """Color isolation with hue awareness for characters mixing cool and warm tones."""
    img = Image.open(frame_path).convert("RGBA")
    a = np.array(img).astype(float)
    r, g, b = a[:,:,0]/255, a[:,:,1]/255, a[:,:,2]/255
    maxc = np.maximum(r, np.maximum(g, b))
    minc = np.minimum(r, np.minimum(g, b))
    diff = maxc - minc
    sat = np.where(maxc == 0, 0, diff / maxc)
    val = maxc
    hue = np.zeros_like(r)
    mask = diff > 0
    idx = mask & (maxc == r)
    hue[idx] = (60 * ((g[idx] - b[idx]) / diff[idx]) + 360) % 360
    idx = mask & (maxc == g)
    hue[idx] = 60 * ((b[idx] - r[idx]) / diff[idx]) + 120
    idx = mask & (maxc == b)
    hue[idx] = 60 * ((r[idx] - g[idx]) / diff[idx]) + 240

    # Keep saturated (any color) OR dark
    keep = (sat > 0.25) | (val  20) & (hue  0.15)
    keep = keep | yellow_range

    keep = ndimage.binary_erosion(keep, iterations=2)
    grayish = (sat  0.35)
    keep = keep & ~grayish

    result = np.array(img)
    result[~keep, 3] = 0
    return Image.fromarray(result)

---

Frame-by-Frame Audit (mandatory for white bg mode)

After processing, audit EVERY frame:

opaque = result[:,:,3] > 0
suspect = opaque & (sat = 0.35)
# suspect.sum() MUST be 0 for every frame

If ANY frame fails, tighten thresholds and reprocess. Do NOT output until all frames pass.

For multicolor mode with glow effects, some fringe is inherent, so visual inspection is sufficient.

---

Output Generation

1. Extract frames: ffmpeg -i input.mp4 {tmpdir}/frame_%04d.png
2. Auto-detect background by sampling the corners for green
3. Process each frame through the appropriate function
4. Audit (white bg mode)
5. Generate all 5 outputs:

# GIF 160px
ffmpeg -framerate 24 -i clean_%04d.png \
  -vf "scale=160:-1:flags=lanczos,split[s0][s1];[s0]palettegen=reserve_transparent=on:transparency_color=000000:stats_mode=diff[p];[s1][p]paletteuse=alpha_threshold=128:dither=none" \
  -loop 0 output.gif

# GIF HD (original resolution)
ffmpeg -framerate 24 -i clean_%04d.png \
  -vf "split[s0][s1];[s0]palettegen=reserve_transparent=on:transparency_color=000000:stats_mode=diff[p];[s1][p]paletteuse=alpha_threshold=128:dither=none" \
  -loop 0 output.gif

# WebM (original resolution, VP9 alpha)
ffmpeg -framerate 24 -i clean_%04d.png \
  -c:v libvpx-vp9 -pix_fmt yuva420p -b:v 2M -auto-alt-ref 0 -an output.webm

# APNG (via Pillow), both 160px and HD
imgs = [Image.open(p) for p in clean_frames]
imgs[0].save("output.apng", save_all=True, append_images=imgs[1:],
             duration=42, loop=0, disposal=2)  # 42ms = 24fps

---

Alternative Methods (backup if defaults aren't working)

These were tested and work for specific situations. Switch to them if the default isn't producing clean results.

Alt 1: Green Screen, Flood-Fill from Edges

Only removes green connected to the image borders. Use when: the character has intentional green elements (plant stems, green accessories) that need to be preserved. Tradeoff: green between body parts (gaps in arms and legs) may survive.

# Same green detection as default, but add flood-fill:
labeled, _ = ndimage.label(is_green)
border_mask = np.zeros_like(is_green)
border_mask[0,:] = True; border_mask[-1,:] = True
border_mask[:,0] = True; border_mask[:,-1] = True
seed = border_mask & is_green
border_labels = set(labeled[seed].flatten()) - {0}
bg_green = np.isin(labeled, list(border_labels))
bg_green = ndimage.binary_dilation(bg_green, iterations=1)

Alt 2: Green Screen, Exact Hex Match

Targets only the specific green screen color with euclidean color distance. Use when: the character has green elements AND flood-fill leaves too much bleed. Tradeoff: may leave green fringe from MP4 compression blending.

def remove_exact_green(fpath, tolerance=60):
    img = Image.open(fpath).convert("RGBA")
    arr = np.array(img).astype(int)
    # Target: R=4, G=250, B=3 (sample the corners to confirm your own value)
    dr = arr[:,:,0] - 4
    dg = arr[:,:,1] - 250
    db = arr[:,:,2] - 3
    dist = np.sqrt(dr*dr + dg*dg + db*db)
    is_bg = dist < tolerance
    result = np.array(img)
    result[is_bg, 3] = 0
    return Image.fromarray(result)

Alt 3: White Background, Flood-Fill from Edges

Removes only white pixels connected to the borders. Use when: the character has intentional white elements inside (white accessories, highlights). Tradeoff: white between body gaps may survive, and compression fringe at the edges remains.

bg_color = np.mean([arr[0,0,:3], arr[0,-1,:3], arr[-1,0,:3], arr[-1,-1,:3]], axis=0).astype(int)
diff = np.abs(arr[:,:,:3].astype(int) - bg_color)
color_match = np.all(diff <= 30, axis=2)
labeled, _ = ndimage.label(color_match)
# ... flood-fill from borders, same pattern as green

Alt 4: Anti-Aliasing, Gaussian Blur Alpha

Softens jagged edges after removal. Use when: edges look too harsh or pixelated at larger sizes. Apply AFTER any removal method. Note: only effective in APNG and WebM, since GIF has 1-bit alpha. All 4 AA methods (hard, alpha blur, gradient, smooth) produced identical results at 160px, so this only matters for HD output.

from PIL import ImageFilter
alpha_img = Image.fromarray((keep * 255).astype(np.uint8))
alpha_smooth = alpha_img.filter(ImageFilter.GaussianBlur(radius=1.5))
result[:,:,3] = np.array(alpha_smooth)
# Then kill white pixels that got alpha from blur bleeding
result[white_mask, 3] = 0

---

What NOT to Use

| Method | Why It Fails | |--------|-------------| | FFmpeg colorkey | Misses blended edge pixels from MP4 compression | | rembg (AI) | Designed for photos, creates shading and halos on pixel art |

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: NulightJens
• Source: NulightJens/jensai-skills
• License: MIT

Install and usage instructions live in the source repository linked above.