asset_library/action/concat_preview.py
2026-01-07 16:05:47 +01:00

164 lines
4.6 KiB
Python

import bpy
import math
import numpy as np
from pathlib import Path
def alpha_to_color(pixels_data, color):
"""Convert Alpha to WhiteBG"""
new_pixels_data = []
for i in pixels_data:
height, width, array_d = i.shape
mask = i[:, :, 3:]
background = np.array([color[0], color[1], color[2], 1], dtype=np.float32)
background = np.tile(background, (height * width))
background = np.reshape(background, (height, width, 4))
new_pixels_data.append(i * mask + background * (1 - mask))
# print(new_pixels_data)#Dbg
return new_pixels_data
def create_array(height, width):
return np.zeros((height * width * 4), dtype=np.float32)
def read_pixels_data(img, source_height, source_width):
img_w, img_h = img.size
if img_w != source_width:
scale = abs(img_w / source_width)
img.scale(int(img_w / scale), int(img_h / scale))
img_w, img_h = img.size
array = create_array(img_h, img_w)
img.pixels.foreach_get(array)
array = array.reshape(img_h, img_w, 4)
if array.shape[0] != source_height:
# print('ARRAY SHAPE', array.shape[:], source_height)
missing_height = int(abs(source_height - img_h) / 2)
empty_array = create_array(missing_height, source_width)
empty_array = empty_array.reshape(missing_height, source_width, 4)
array = np.vstack((empty_array, array, empty_array))
return array.reshape(source_height, source_width, 4)
def create_final(output_name, pixels_data, final_height, final_width):
# print('output_name: ', output_name)
new_img = bpy.data.images.get(output_name)
if new_img:
bpy.data.images.remove(new_img)
new_img = bpy.data.images.new(output_name, final_width, final_height)
new_img.generated_color = (0, 0, 0, 0)
# print('pixels_data: ', pixels_data)
new_img.pixels.foreach_set(pixels_data)
return new_img
def guess_input_format(img_list):
for i in img_list:
if i.size[0] == i.size[1]:
return i.size
def format_files(files, catalog_data):
img_dict = {}
for k, v in catalog_data.items():
if "/" not in k:
continue
img_dict[v["name"]] = [f for f in files if v["name"] in f]
return img_dict
def mosaic_export(
files,
catalog_data,
row=2,
columns=2,
auto_calculate=True,
bg_color=(
0.18,
0.18,
0.18,
),
resize_output=100,
):
img_dict = format_files(files, catalog_data)
for cat, files_list in img_dict.items():
if not files_list:
continue
for i in bpy.data.images:
bpy.data.images.remove(i)
img_list = []
chars = Path(files_list[0]).parts[-4]
output_dir = str(Path(files_list[0]).parent.parent)
ext = "jpg"
output_name = f"{chars}_{cat}.{ext}"
for img in files_list:
img_list.append(bpy.data.images.load(img, check_existing=True))
for i in img_list:
i.colorspace_settings.name = "Raw"
if auto_calculate:
rows = int(math.sqrt(len(img_list)))
columns = math.ceil(len(img_list) / rows)
if rows * columns < len(img_list):
raise AttributeError("Grid too small for number of images")
src_w, src_h = img_list[0].size
final_w = src_w * columns
final_h = src_h * rows
img_pixels = [read_pixels_data(img, src_h, src_w) for img in img_list]
# Check if there is enough "data" to create an horizontal stack
##It not, create empty array
h_stack = []
total_len = rows * columns
if len(img_pixels) < total_len:
for i in range(total_len - len(img_pixels)):
img_pixels.append(create_array(src_h, src_w).reshape(src_h, src_w, 4))
img_pixels = alpha_to_color(img_pixels, bg_color)
for i in range(0, len(img_pixels), columns):
h_stack.append(np.hstack(img_pixels[i : i + columns]))
if rows > 1:
combined_stack = np.vstack(h_stack[::-1])
else:
combined_stack = np.hstack((h_stack[:]))
combined_img = create_final(
output_name, combined_stack.flatten(), final_h, final_w
)
if resize_output != 100:
w, h = combined_img.size
combined_img.scale(w * (resize_output * 0.01), h * (resize_output * 0.01))
combined_img.filepath_raw = "/".join([output_dir, output_name])
combined_img.file_format = "JPEG"
combined_img.save()
print(
f"""
Image saved: {combined_img.filepath_raw}
"""
)