cv-stroke-temporal-color-rendering

name	cv-stroke-temporal-color-rendering
description	Render stroke sequences to grayscale images with temporal intensity encoding where earlier strokes are brighter and later strokes fade to encode drawing order

Stroke Temporal Color Rendering

Overview

Hand-drawn sketches are stored as ordered stroke sequences. When rendering to an image, encoding the drawing order as pixel intensity — earlier strokes brighter, later strokes darker — gives CNNs an additional temporal signal beyond pure geometry. This simple trick improves doodle classification accuracy by 1-3% over flat-color rendering.

Quick Start

import cv2
import numpy as np
import json

def draw_strokes(raw_strokes, size=64, line_width=6):
    img = np.zeros((256, 256), np.uint8)
    for t, stroke in enumerate(raw_strokes):
        color = max(255 - t * 13, 30)  # fade from 255 to ~30
        for i in range(len(stroke[0]) - 1):
            cv2.line(img,
                     (stroke[0][i], stroke[1][i]),
                     (stroke[0][i + 1], stroke[1][i + 1]),
                     color, line_width)
    if size != 256:
        img = cv2.resize(img, (size, size))
    return img

strokes = json.loads(drawing_string)
img = draw_strokes(strokes, size=64)

Workflow

1. Parse stroke JSON: list of [[x0, x1, ...], [y0, y1, ...]] per stroke
2. Create a blank canvas (256x256 grayscale)
3. Draw each stroke with intensity 255 - t * step, clamped to a minimum
4. Resize to target CNN input size (64x64 or 128x128)
5. Normalize to [0, 1] or apply model-specific preprocessing

Key Decisions

• Fade rate: 13 per stroke works for ~20-stroke drawings; adjust for longer sequences
• Minimum intensity: clamp at 30 to keep late strokes visible
• Line width: 6px at 256x256 base; scale proportionally for smaller canvases
• vs. flat color: temporal encoding adds ~1-3% top-3 accuracy at zero computational cost
• vs. RGB channels: encode first/middle/last strokes in R/G/B for richer signal with pretrained models

References

• Greyscale MobileNet [LB=0.892]