396 lines
15 KiB
Python
396 lines
15 KiB
Python
from __future__ import annotations
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from pathlib import Path
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import numpy as np
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from PIL import Image, ImageDraw, ImageFont
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ROOT = Path(__file__).resolve().parents[1]
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WORK_DIR = ROOT / "tmp" / "liu-bei-handpaint-sample"
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UNIT_DIR = ROOT / "src" / "assets" / "images" / "units"
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DOCS_DIR = ROOT / "docs"
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FRAME = 313
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DIRECTIONS = ("south", "east", "north", "west")
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BASE_FRAMES_PER_DIRECTION = 16
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ACTION_COUNTS = {
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"attack": 10,
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"strategy": 8,
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"item": 8,
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"hurt": 4,
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"celebrate": 6,
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}
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ACTION_OFFSETS = {
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"attack": 0,
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"strategy": 10,
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"item": 18,
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"hurt": 26,
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"celebrate": 30,
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}
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ACTION_FRAMES_PER_DIRECTION = sum(ACTION_COUNTS.values())
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def keyed_rgba(image: Image.Image) -> Image.Image:
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rgba = image.convert("RGBA")
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arr = np.array(rgba)
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rgb = arr[:, :, :3].astype(np.int16)
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r, g, b = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
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key = (r > 185) & (b > 185) & (g < 125)
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near_key = (r > 145) & (b > 135) & (g < 150) & ((r + b - g * 2) > 180)
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alpha = np.where(key | near_key, 0, 255).astype(np.uint8)
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body = alpha > 0
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magenta_fringe = body & (r > 155) & (b > 135) & (g < 130)
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arr[:, :, 0] = np.where(magenta_fringe, np.minimum(arr[:, :, 0], 70), arr[:, :, 0])
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arr[:, :, 2] = np.where(magenta_fringe, np.minimum(arr[:, :, 2], 70), arr[:, :, 2])
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arr[:, :, 3] = alpha
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return Image.fromarray(arr, "RGBA")
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def crop_subject(image: Image.Image) -> Image.Image:
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rgba = keyed_rgba(image)
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rgba = keep_subject_components(rgba)
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alpha = np.array(rgba.getchannel("A"))
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ys, xs = np.where(alpha > 0)
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if len(xs) == 0:
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return Image.new("RGBA", (FRAME, FRAME), (0, 0, 0, 0))
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pad = 8
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left = max(int(xs.min()) - pad, 0)
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top = max(int(ys.min()) - pad, 0)
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right = min(int(xs.max()) + pad + 1, rgba.width)
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bottom = min(int(ys.max()) + pad + 1, rgba.height)
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return rgba.crop((left, top, right, bottom))
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def keep_subject_components(image: Image.Image) -> Image.Image:
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arr = np.array(image.convert("RGBA"))
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alpha = arr[:, :, 3] > 0
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height, width = alpha.shape
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visited = np.zeros_like(alpha, dtype=bool)
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components: list[tuple[int, int, int, int, int, float, float]] = []
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for start_y in range(height):
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for start_x in range(width):
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if visited[start_y, start_x] or not alpha[start_y, start_x]:
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continue
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stack = [(start_x, start_y)]
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visited[start_y, start_x] = True
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xs: list[int] = []
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ys: list[int] = []
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while stack:
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x, y = stack.pop()
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xs.append(x)
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ys.append(y)
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for ny in (y - 1, y, y + 1):
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if ny < 0 or ny >= height:
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continue
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for nx in (x - 1, x, x + 1):
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if nx < 0 or nx >= width or visited[ny, nx] or not alpha[ny, nx]:
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continue
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visited[ny, nx] = True
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stack.append((nx, ny))
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area = len(xs)
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if area < 24:
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continue
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left, right = min(xs), max(xs)
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top, bottom = min(ys), max(ys)
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components.append((area, left, top, right, bottom, (left + right) / 2, (top + bottom) / 2))
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if not components:
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return image
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image_center_x = width / 2
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image_center_y = height * 0.58
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anchor = max(
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components,
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key=lambda component: component[0]
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- abs(component[5] - image_center_x) * 3
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- abs(component[6] - image_center_y) * 1.4,
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)
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anchor_area, left, top, right, bottom, anchor_cx, anchor_cy = anchor
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expanded = (left - 70, top - 80, right + 70, bottom + 65)
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keep = np.zeros_like(alpha, dtype=bool)
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for component in components:
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area, c_left, c_top, c_right, c_bottom, cx, cy = component
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touches_cell_edge = c_left <= 2 or c_right >= width - 3 or c_top <= 2 or c_bottom >= height - 3
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if component != anchor and touches_cell_edge and area < anchor_area * 0.55:
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continue
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near_anchor = (
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c_right >= expanded[0]
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and c_left <= expanded[2]
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and c_bottom >= expanded[1]
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and c_top <= expanded[3]
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)
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central = 0.04 * width <= cx <= 0.96 * width
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substantial = area >= max(55, anchor_area * 0.025)
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if component == anchor or (near_anchor and central and substantial):
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keep[c_top : c_bottom + 1, c_left : c_right + 1] |= alpha[c_top : c_bottom + 1, c_left : c_right + 1]
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arr[:, :, 3] = np.where(keep, arr[:, :, 3], 0).astype(np.uint8)
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return Image.fromarray(arr, "RGBA")
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def quantize_alpha(image: Image.Image) -> Image.Image:
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rgba = image.convert("RGBA")
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arr = np.array(rgba)
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arr[:, :, 3] = np.where(arr[:, :, 3] > 24, 255, 0).astype(np.uint8)
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return Image.fromarray(arr, "RGBA")
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def fit_subject(
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source: Image.Image,
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max_width: int = 286,
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max_height: int = 300,
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bottom: int = 306,
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stretch_x: float = 1.18,
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) -> Image.Image:
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subject = crop_subject(source)
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if subject.width <= 1 or subject.height <= 1:
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return Image.new("RGBA", (FRAME, FRAME), (0, 0, 0, 0))
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scale = min(max_width / subject.width, max_height / subject.height)
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size = (max(1, round(subject.width * scale)), max(1, round(subject.height * scale)))
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subject = subject.resize(size, Image.Resampling.LANCZOS)
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stretched_width = min(max_width, max(1, round(subject.width * stretch_x)))
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if stretched_width != subject.width:
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subject = subject.resize((stretched_width, subject.height), Image.Resampling.LANCZOS)
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subject = quantize_alpha(subject)
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frame = Image.new("RGBA", (FRAME, FRAME), (0, 0, 0, 0))
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x = (FRAME - subject.width) // 2
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y = bottom - subject.height
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frame.alpha_composite(subject, (x, y))
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frame = quantize_alpha(frame)
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return quantize_alpha(keep_subject_components(frame))
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def crop_grid(image: Image.Image, row: int, col: int, rows: int, cols: int, x_pad: int = 0, y_pad: int = 0) -> Image.Image:
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cell_w = image.width / cols
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cell_h = image.height / rows
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left = max(0, int(round(col * cell_w)) - x_pad)
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top = max(0, int(round(row * cell_h)) - y_pad)
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right = min(image.width, int(round((col + 1) * cell_w)) + x_pad)
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bottom = min(image.height, int(round((row + 1) * cell_h)) + y_pad)
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return image.crop((left, top, right, bottom))
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def crop_center(image: Image.Image, center_x: int, center_y: int, width: int, height: int) -> Image.Image:
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left = max(0, center_x - width // 2)
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top = max(0, center_y - height // 2)
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right = min(image.width, center_x + width // 2)
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bottom = min(image.height, center_y + height // 2)
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return image.crop((left, top, right, bottom))
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def build_base_sheet(base_source: Image.Image) -> Image.Image:
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rows: list[list[Image.Image]] = []
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for row in range(4):
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frames = [fit_subject(crop_grid(base_source, row, col, 4, 8, 0, 2)) for col in range(8)]
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idle = [frames[0].copy() for _ in range(8)]
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walk = frames
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rows.append(idle + walk)
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sheet = Image.new("RGBA", (FRAME * BASE_FRAMES_PER_DIRECTION, FRAME * len(DIRECTIONS)), (0, 0, 0, 0))
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for row, frames in enumerate(rows):
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for col, frame in enumerate(frames):
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sheet.alpha_composite(frame, (col * FRAME, row * FRAME))
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return sheet
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def build_action_source_frames(action_source: Image.Image) -> dict[str, list[Image.Image]]:
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attack_frames = [
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fit_subject(crop_grid(action_source, 0, col, 3, 10, 10, 28), 300, 300, 306)
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for col in range(10)
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]
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mid = [fit_subject(crop_grid(action_source, 1, col, 3, 5, 18, 26), 286, 300, 306) for col in range(5)]
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strategy_seed = mid[:3]
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item_seed = mid[3:]
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lower_raw = [
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crop_center(action_source, 470, 805, 330, 310),
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crop_center(action_source, 790, 805, 330, 310),
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crop_center(action_source, 1100, 805, 330, 310),
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]
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lower = [fit_subject(frame, 286, 300, 306) for frame in lower_raw]
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return {
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"attack": attack_frames,
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"strategy": [strategy_seed[index % len(strategy_seed)] for index in (0, 1, 2, 1, 0, 1, 2, 1)],
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"item": [item_seed[index % len(item_seed)] for index in range(8)],
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"hurt": [lower[0] for _ in range(4)],
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"celebrate": [lower[1], lower[2], lower[1], lower[2], lower[1], lower[2]],
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}
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def direction_frame(frame: Image.Image, direction: str) -> Image.Image:
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if direction == "west":
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return frame.transpose(Image.Transpose.FLIP_LEFT_RIGHT)
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return frame
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def build_action_sheet(action_source: Image.Image) -> Image.Image:
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source_frames = build_action_source_frames(action_source)
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sheet = Image.new("RGBA", (FRAME * ACTION_FRAMES_PER_DIRECTION, FRAME * len(DIRECTIONS)), (0, 0, 0, 0))
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for row, direction in enumerate(DIRECTIONS):
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for action, frames in source_frames.items():
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offset = ACTION_OFFSETS[action]
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for index, frame in enumerate(frames):
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sheet.alpha_composite(direction_frame(frame, direction), ((offset + index) * FRAME, row * FRAME))
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return sheet
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def representative_base_frames(base_sheet: Image.Image) -> list[Image.Image]:
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picks = [
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("south idle", 0, 0),
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("south walk", 0, 10),
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("east walk", 1, 10),
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("north walk", 2, 10),
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("west walk", 3, 10),
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]
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return [base_sheet.crop((col * FRAME, row * FRAME, (col + 1) * FRAME, (row + 1) * FRAME)) for _, row, col in picks]
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def representative_action_frames(action_sheet: Image.Image) -> list[Image.Image]:
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picks = [
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("attack 1", 1, 0),
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("attack 4", 1, 3),
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("attack 7", 1, 6),
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("strategy", 0, ACTION_OFFSETS["strategy"] + 2),
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("item", 0, ACTION_OFFSETS["item"] + 1),
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("hurt", 0, ACTION_OFFSETS["hurt"]),
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("celebrate", 0, ACTION_OFFSETS["celebrate"] + 1),
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]
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return [action_sheet.crop((col * FRAME, row * FRAME, (col + 1) * FRAME, (row + 1) * FRAME)) for _, row, col in picks]
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def checker_background(size: tuple[int, int], tile: int = 16) -> Image.Image:
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image = Image.new("RGBA", size, (67, 88, 54, 255))
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draw = ImageDraw.Draw(image)
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for y in range(0, size[1], tile):
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for x in range(0, size[0], tile):
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color = (77, 99, 59, 255) if ((x // tile) + (y // tile)) % 2 == 0 else (58, 76, 48, 255)
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draw.rectangle((x, y, x + tile - 1, y + tile - 1), fill=color)
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return image
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def draw_frame_on_bg(frame: Image.Image, size: int) -> Image.Image:
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bg = checker_background((size, size), 8)
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scaled = frame.resize((size, size), Image.Resampling.LANCZOS)
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bg.alpha_composite(scaled, (0, 0))
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return bg
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def save_contact_sheet(base_sheet: Image.Image, action_sheet: Image.Image) -> None:
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thumbs = representative_base_frames(base_sheet) + representative_action_frames(action_sheet)
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labels = [
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"idle",
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"walk S",
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"walk E",
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"walk N",
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"walk W",
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"atk 1",
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"atk 4",
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"atk 7",
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"cmd",
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"item",
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"hurt",
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"win",
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]
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thumb_size = 132
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pad = 18
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label_h = 24
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cols = 6
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rows = 2
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out = Image.new("RGBA", (pad + cols * (thumb_size + pad), pad + rows * (thumb_size + label_h + pad)), (20, 24, 22, 255))
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draw = ImageDraw.Draw(out)
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font = ImageFont.load_default()
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for index, frame in enumerate(thumbs):
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x = pad + (index % cols) * (thumb_size + pad)
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y = pad + (index // cols) * (thumb_size + label_h + pad)
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tile = draw_frame_on_bg(frame, thumb_size)
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out.alpha_composite(tile, (x, y))
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draw.text((x, y + thumb_size + 5), labels[index], fill=(238, 224, 178, 255), font=font)
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out.convert("RGB").save(DOCS_DIR / "liu-bei-handpaint-sample-contact.png", optimize=True)
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def save_before_after(base_sheet: Image.Image) -> None:
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before = Image.open(WORK_DIR / "before-unit-liu-bei.png").convert("RGBA").crop((0, 0, FRAME, FRAME))
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after = base_sheet.crop((0, 0, FRAME, FRAME))
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out = Image.new("RGBA", (820, 360), (18, 21, 20, 255))
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draw = ImageDraw.Draw(out)
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font = ImageFont.load_default()
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draw.text((30, 18), "Before / After at 313px", fill=(238, 224, 178, 255), font=font)
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out.alpha_composite(draw_frame_on_bg(before, 160), (30, 48))
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out.alpha_composite(draw_frame_on_bg(after, 160), (220, 48))
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draw.text((30, 218), "Before / After at battle scale", fill=(238, 224, 178, 255), font=font)
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out.alpha_composite(draw_frame_on_bg(before, 68), (30, 252))
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out.alpha_composite(draw_frame_on_bg(after, 68), (128, 252))
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draw.text((30, 330), "left: previous checked-in Liu Bei, right: new hand-painted sample", fill=(172, 185, 166, 255), font=font)
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out.convert("RGB").save(DOCS_DIR / "liu-bei-handpaint-sample-before-after.png", optimize=True)
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def save_animation_gif(base_sheet: Image.Image, action_sheet: Image.Image) -> None:
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frames: list[Image.Image] = []
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for index in range(10):
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canvas = checker_background((540, 190), 10)
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idle = base_sheet.crop(((index % 8) * FRAME, 0, ((index % 8) + 1) * FRAME, FRAME))
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walk = base_sheet.crop(((8 + index % 8) * FRAME, FRAME, (9 + index % 8) * FRAME, 2 * FRAME))
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attack = action_sheet.crop((index * FRAME, FRAME, (index + 1) * FRAME, 2 * FRAME))
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for x, frame in ((22, idle), (190, walk), (358, attack)):
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scaled = frame.resize((150, 150), Image.Resampling.LANCZOS)
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canvas.alpha_composite(scaled, (x, 25))
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frames.append(canvas.convert("P", palette=Image.Palette.ADAPTIVE, colors=128))
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frames[0].save(
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DOCS_DIR / "liu-bei-handpaint-sample-animation.gif",
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save_all=True,
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append_images=frames[1:],
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optimize=True,
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duration=110,
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loop=0,
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)
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def validate_sheet(path: Path, expected_size: tuple[int, int]) -> None:
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image = Image.open(path).convert("RGBA")
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if image.size != expected_size:
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raise ValueError(f"{path.name}: expected {expected_size}, got {image.size}")
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alpha = np.array(image.getchannel("A"))
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partial = np.count_nonzero((alpha > 0) & (alpha < 255))
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if partial:
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raise ValueError(f"{path.name}: found {partial} partially transparent pixels")
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def main() -> None:
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base_source = Image.open(WORK_DIR / "source-base-motion.png").convert("RGBA")
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action_source = Image.open(WORK_DIR / "source-action.png").convert("RGBA")
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base_sheet = build_base_sheet(base_source)
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action_sheet = build_action_sheet(action_source)
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base_path = UNIT_DIR / "unit-liu-bei.png"
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action_path = UNIT_DIR / "unit-liu-bei-actions.png"
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base_sheet.save(base_path, optimize=True)
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action_sheet.save(action_path, optimize=True)
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save_contact_sheet(base_sheet, action_sheet)
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save_before_after(base_sheet)
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save_animation_gif(base_sheet, action_sheet)
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validate_sheet(base_path, (FRAME * BASE_FRAMES_PER_DIRECTION, FRAME * len(DIRECTIONS)))
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validate_sheet(action_path, (FRAME * ACTION_FRAMES_PER_DIRECTION, FRAME * len(DIRECTIONS)))
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print(f"Wrote {base_path}")
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print(f"Wrote {action_path}")
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print(f"Wrote {DOCS_DIR / 'liu-bei-handpaint-sample-contact.png'}")
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print(f"Wrote {DOCS_DIR / 'liu-bei-handpaint-sample-before-after.png'}")
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print(f"Wrote {DOCS_DIR / 'liu-bei-handpaint-sample-animation.gif'}")
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if __name__ == "__main__":
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main()
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