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*.01), h*(resize_output*.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} """)