| from email.mime import image |
| import torch |
| import base64 |
| import gradio as gr |
| import numpy as np |
| from PIL import Image,ImageOps,ImageDraw, ImageFont |
| from io import BytesIO |
| import random |
| MAX_COLORS = 12 |
| def get_random_bool(): |
| return random.choice([True, False]) |
|
|
| def add_white_border(input_image, border_width=10): |
| """ |
| 为PIL图像添加指定宽度的白色边框。 |
| |
| :param input_image: PIL图像对象 |
| :param border_width: 边框宽度(单位:像素) |
| :return: 带有白色边框的PIL图像对象 |
| """ |
| border_color = 'white' |
| |
| img_with_border = ImageOps.expand(input_image, border=border_width, fill=border_color) |
| return img_with_border |
|
|
| def process_mulline_text(draw, text, font, max_width): |
| """ |
| Draw the text on an image with word wrapping. |
| """ |
| lines = [] |
| words = text.split() |
|
|
| |
| current_line = "" |
| for word in words: |
| test_line = f"{current_line} {word}".strip() |
| |
| bbox = draw.textbbox((0, 0), test_line, font=font) |
| text_left, text_top, text_right, text_bottom = bbox |
|
|
| width, _ = (text_right - text_left, text_bottom - text_top) |
|
|
| if width <= max_width: |
| |
| current_line = test_line |
| else: |
| |
| lines.append(current_line) |
| current_line = word |
| |
| lines.append(current_line) |
| return lines |
|
|
|
|
|
|
| def add_caption(image, text, position = "bottom-mid", font = None, text_color= 'black', bg_color = (255, 255, 255) , bg_opacity = 200): |
| if text == "": |
| return image |
| image = image.convert("RGBA") |
| draw = ImageDraw.Draw(image) |
| width, height = image.size |
| lines = process_mulline_text(draw,text,font,width) |
| text_positions = [] |
| maxwidth = 0 |
| for ind, line in enumerate(lines[::-1]): |
| bbox = draw.textbbox((0, 0), line, font=font) |
| text_left, text_top, text_right, text_bottom = bbox |
| text_width, text_height = (text_right - text_left, text_bottom - text_top) |
| if position == 'bottom-right': |
| text_position = (width - text_width - 10, height - (text_height + 20)) |
| elif position == 'bottom-left': |
| text_position = (10, height - (text_height + 20)) |
| elif position == 'bottom-mid': |
| text_position = ((width - text_width) // 2, height - (text_height + 20) ) |
| height = text_position[1] |
| maxwidth = max(maxwidth,text_width) |
| text_positions.append(text_position) |
| rectpos = (width - maxwidth) // 2 |
| rectangle_position = [rectpos - 5, text_positions[-1][1] - 5, rectpos + maxwidth + 5, text_positions[0][1] + text_height + 5] |
| image_with_transparency = Image.new('RGBA', image.size) |
| draw_with_transparency = ImageDraw.Draw(image_with_transparency) |
| draw_with_transparency.rectangle(rectangle_position, fill=bg_color + (bg_opacity,)) |
| |
| image.paste(Image.alpha_composite(image.convert('RGBA'), image_with_transparency)) |
| print(ind,text_position) |
| draw = ImageDraw.Draw(image) |
| for ind, line in enumerate(lines[::-1]): |
| text_position = text_positions[ind] |
| draw.text(text_position, line, fill=text_color, font=font) |
| |
| return image.convert('RGB') |
|
|
| def get_comic(images,types = "4panel",captions = [],font = None,pad_image = None): |
| if pad_image == None: |
| pad_image = Image.open("./images/pad_images.png") |
|
|
| if types == "No typesetting (default)": |
| return images |
| elif types == "Four Pannel": |
| return get_comic_4panel(images,captions,font,pad_image) |
| else: |
| return get_comic_classical(images,captions,font,pad_image) |
|
|
| def get_caption_group(images_groups,captions = []): |
| caption_groups = [] |
| for i in range(len(images_groups)): |
| length = len(images_groups[i]) |
| caption_groups.append(captions[:length]) |
| captions = captions[length:] |
| if len(caption_groups[-1]) < len(images_groups[-1]): |
| caption_groups[-1] = caption_groups[-1] + [""] * (len(images_groups[-1]) - len(caption_groups[-1])) |
| return caption_groups |
|
|
| def get_comic_classical(images,captions = None,font = None,pad_image = None): |
| if pad_image == None: |
| raise ValueError("pad_image is None") |
| images = [add_white_border(image) for image in images] |
| pad_image = pad_image.resize(images[0].size, Image.LANCZOS) |
| images_groups = distribute_images2(images,pad_image) |
| print(images_groups) |
| if captions != None: |
| captions_groups = get_caption_group(images_groups,captions) |
| |
| row_images = [] |
| for ind, img_group in enumerate(images_groups): |
| row_images.append(get_row_image2(img_group ,captions= captions_groups[ind] if captions != None else None,font = font)) |
|
|
| return [combine_images_vertically_with_resize(row_images)] |
|
|
|
|
|
|
| def get_comic_4panel(images,captions = [],font = None,pad_image = None): |
| if pad_image == None: |
| raise ValueError("pad_image is None") |
| pad_image = pad_image.resize(images[0].size, Image.LANCZOS) |
| images = [add_white_border(image) for image in images] |
| assert len(captions) == len(images) |
| for i,caption in enumerate(captions): |
| images[i] = add_caption(images[i],caption,font = font) |
| images_nums = len(images) |
| pad_nums = int((4 - images_nums % 4) % 4) |
| images = images + [pad_image for _ in range(pad_nums)] |
| comics = [] |
| assert len(images)%4 == 0 |
| for i in range(len(images)//4): |
| comics.append(combine_images_vertically_with_resize([combine_images_horizontally(images[i*4:i*4+2]), combine_images_horizontally(images[i*4+2:i*4+4])])) |
| |
| return comics |
|
|
| def get_row_image(images): |
| row_image_arr = [] |
| if len(images)>3: |
| stack_img_nums = (len(images) - 2)//2 |
| else: |
| stack_img_nums = 0 |
| while(len(images)>0): |
| if stack_img_nums <=0: |
| row_image_arr.append(images[0]) |
| images = images[1:] |
| elif len(images)>stack_img_nums*2: |
| if get_random_bool(): |
| row_image_arr.append(concat_images_vertically_and_scale(images[:2])) |
| images = images[2:] |
| stack_img_nums -=1 |
| else: |
| row_image_arr.append(images[0]) |
| images = images[1:] |
| else: |
| row_image_arr.append(concat_images_vertically_and_scale(images[:2])) |
| images = images[2:] |
| stack_img_nums-=1 |
| return combine_images_horizontally(row_image_arr) |
|
|
| def get_row_image2(images,captions = None, font = None): |
| row_image_arr = [] |
| if len(images)== 6: |
| sequence_list = [1,1,2,2] |
| elif len(images)== 4: |
| sequence_list = [1,1,2] |
| else: |
| raise ValueError("images nums is not 4 or 6 found",len(images)) |
| random.shuffle(sequence_list) |
| index = 0 |
| for length in sequence_list: |
| if length == 1: |
| if captions != None: |
| images_tmp = add_caption(images[0],text = captions[index],font= font) |
| else: |
| images_tmp = images[0] |
| row_image_arr.append( images_tmp) |
| images = images[1:] |
| index +=1 |
| elif length == 2: |
| row_image_arr.append(concat_images_vertically_and_scale(images[:2])) |
| images = images[2:] |
| index +=2 |
|
|
| return combine_images_horizontally(row_image_arr) |
|
|
|
|
|
|
| def concat_images_vertically_and_scale(images,scale_factor=2): |
| |
| |
| widths = [img.width for img in images] |
| if not all(width == widths[0] for width in widths): |
| raise ValueError('All images must have the same width.') |
| |
| |
| total_height = sum(img.height for img in images) |
| |
| |
| max_width = max(widths) |
| concatenated_image = Image.new('RGB', (max_width, total_height)) |
|
|
| |
| current_height = 0 |
| for img in images: |
| concatenated_image.paste(img, (0, current_height)) |
| current_height += img.height |
|
|
| |
| new_height = concatenated_image.height // scale_factor |
| new_width = concatenated_image.width // scale_factor |
| resized_image = concatenated_image.resize((new_width, new_height), Image.LANCZOS) |
| |
| return resized_image |
|
|
|
|
| def combine_images_horizontally(images): |
| |
|
|
| |
| widths, heights = zip(*(i.size for i in images)) |
|
|
| |
| total_width = sum(widths) |
| max_height = max(heights) |
|
|
| |
| new_im = Image.new('RGB', (total_width, max_height)) |
|
|
| |
| x_offset = 0 |
| for im in images: |
| new_im.paste(im, (x_offset, 0)) |
| x_offset += im.width |
|
|
| return new_im |
|
|
| def combine_images_vertically_with_resize(images): |
| |
| |
| widths, heights = zip(*(i.size for i in images)) |
| |
| |
| min_width = min(widths) |
| |
| |
| resized_images = [] |
| for img in images: |
| |
| new_height = int(min_width * img.height / img.width) |
| |
| resized_img = img.resize((min_width, new_height), Image.LANCZOS) |
| resized_images.append(resized_img) |
| |
| |
| total_height = sum(img.height for img in resized_images) |
| |
| |
| new_im = Image.new('RGB', (min_width, total_height)) |
| |
| |
| y_offset = 0 |
| for im in resized_images: |
| new_im.paste(im, (0, y_offset)) |
| y_offset += im.height |
|
|
| return new_im |
|
|
| def distribute_images2(images, pad_image): |
| groups = [] |
| remaining = len(images) |
| if len(images) <= 8: |
| group_sizes = [4] |
| else: |
| group_sizes = [4, 6] |
|
|
| size_index = 0 |
| while remaining > 0: |
| size = group_sizes[size_index%len(group_sizes)] |
| if remaining < size and remaining < min(group_sizes): |
| size = min(group_sizes) |
| if remaining > size: |
| new_group = images[-remaining: -remaining + size] |
| else: |
| new_group = images[-remaining:] |
| groups.append(new_group) |
| size_index += 1 |
| remaining -= size |
| print(remaining,groups) |
| groups[-1] = groups[-1] + [pad_image for _ in range(-remaining)] |
|
|
| return groups |
| |
|
|
| def distribute_images(images, group_sizes=(4, 3, 2)): |
| groups = [] |
| remaining = len(images) |
| |
| while remaining > 0: |
| |
| for size in sorted(group_sizes, reverse=True): |
| |
| |
| if remaining >= size or remaining == len(images): |
| if remaining > size: |
| new_group = images[-remaining: -remaining + size] |
| else: |
| new_group = images[-remaining:] |
| groups.append(new_group) |
| remaining -= size |
| break |
| |
| elif remaining < min(group_sizes) and groups: |
| groups[-1].extend(images[-remaining:]) |
| remaining = 0 |
| |
| return groups |
|
|
| def create_binary_matrix(img_arr, target_color): |
| mask = np.all(img_arr == target_color, axis=-1) |
| binary_matrix = mask.astype(int) |
| return binary_matrix |
|
|
| def preprocess_mask(mask_, h, w, device): |
| mask = np.array(mask_) |
| mask = mask.astype(np.float32) |
| mask = mask[None, None] |
| mask[mask < 0.5] = 0 |
| mask[mask >= 0.5] = 1 |
| mask = torch.from_numpy(mask).to(device) |
| mask = torch.nn.functional.interpolate(mask, size=(h, w), mode='nearest') |
| return mask |
|
|
| def process_sketch(canvas_data): |
| binary_matrixes = [] |
| base64_img = canvas_data['image'] |
| image_data = base64.b64decode(base64_img.split(',')[1]) |
| image = Image.open(BytesIO(image_data)).convert("RGB") |
| im2arr = np.array(image) |
| colors = [tuple(map(int, rgb[4:-1].split(','))) for rgb in canvas_data['colors']] |
| colors_fixed = [] |
|
|
| r, g, b = 255, 255, 255 |
| binary_matrix = create_binary_matrix(im2arr, (r,g,b)) |
| binary_matrixes.append(binary_matrix) |
| binary_matrix_ = np.repeat(np.expand_dims(binary_matrix, axis=(-1)), 3, axis=(-1)) |
| colored_map = binary_matrix_*(r,g,b) + (1-binary_matrix_)*(50,50,50) |
| colors_fixed.append(gr.update(value=colored_map.astype(np.uint8))) |
|
|
| for color in colors: |
| r, g, b = color |
| if any(c != 255 for c in (r, g, b)): |
| binary_matrix = create_binary_matrix(im2arr, (r,g,b)) |
| binary_matrixes.append(binary_matrix) |
| binary_matrix_ = np.repeat(np.expand_dims(binary_matrix, axis=(-1)), 3, axis=(-1)) |
| colored_map = binary_matrix_*(r,g,b) + (1-binary_matrix_)*(50,50,50) |
| colors_fixed.append(gr.update(value=colored_map.astype(np.uint8))) |
|
|
| visibilities = [] |
| colors = [] |
| for n in range(MAX_COLORS): |
| visibilities.append(gr.update(visible=False)) |
| colors.append(gr.update()) |
| for n in range(len(colors_fixed)): |
| visibilities[n] = gr.update(visible=True) |
| colors[n] = colors_fixed[n] |
|
|
| return [gr.update(visible=True), binary_matrixes, *visibilities, *colors] |
|
|
| def process_prompts(binary_matrixes, *seg_prompts): |
| return [gr.update(visible=True), gr.update(value=' , '.join(seg_prompts[:len(binary_matrixes)]))] |
|
|
| def process_example(layout_path, all_prompts, seed_): |
|
|
| all_prompts = all_prompts.split('***') |
|
|
| binary_matrixes = [] |
| colors_fixed = [] |
|
|
| im2arr = np.array(Image.open(layout_path))[:,:,:3] |
| unique, counts = np.unique(np.reshape(im2arr,(-1,3)), axis=0, return_counts=True) |
| sorted_idx = np.argsort(-counts) |
|
|
| binary_matrix = create_binary_matrix(im2arr, (0,0,0)) |
| binary_matrixes.append(binary_matrix) |
| binary_matrix_ = np.repeat(np.expand_dims(binary_matrix, axis=(-1)), 3, axis=(-1)) |
| colored_map = binary_matrix_*(255,255,255) + (1-binary_matrix_)*(50,50,50) |
| colors_fixed.append(gr.update(value=colored_map.astype(np.uint8))) |
|
|
| for i in range(len(all_prompts)-1): |
| r, g, b = unique[sorted_idx[i]] |
| if any(c != 255 for c in (r, g, b)) and any(c != 0 for c in (r, g, b)): |
| binary_matrix = create_binary_matrix(im2arr, (r,g,b)) |
| binary_matrixes.append(binary_matrix) |
| binary_matrix_ = np.repeat(np.expand_dims(binary_matrix, axis=(-1)), 3, axis=(-1)) |
| colored_map = binary_matrix_*(r,g,b) + (1-binary_matrix_)*(50,50,50) |
| colors_fixed.append(gr.update(value=colored_map.astype(np.uint8))) |
|
|
| visibilities = [] |
| colors = [] |
| prompts = [] |
| for n in range(MAX_COLORS): |
| visibilities.append(gr.update(visible=False)) |
| colors.append(gr.update()) |
| prompts.append(gr.update()) |
|
|
| for n in range(len(colors_fixed)): |
| visibilities[n] = gr.update(visible=True) |
| colors[n] = colors_fixed[n] |
| prompts[n] = all_prompts[n+1] |
|
|
| return [gr.update(visible=True), binary_matrixes, *visibilities, *colors, *prompts, |
| gr.update(visible=True), gr.update(value=all_prompts[0]), int(seed_)] |