231 lines
8.7 KiB
Python
231 lines
8.7 KiB
Python
import cv2
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import numpy as np
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import glob
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from PIL import Image
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import os
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import random
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import time
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from .image_process import images_process
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def load_images(image_path, stickers_path):
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"""
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加载矩形图片和贴纸图片。
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:param image_path: 矩形图片路径
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:param stickers_path: 贴纸图片文件夹路径
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:return: 矩形图片（RGBA格式）、贴纸列表
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"""
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shape_img = Image.open(image_path).convert("RGBA")
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sticker_files = glob.glob(os.path.join(stickers_path, '*'))
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stickers = [Image.open(sticker).convert("RGBA") for sticker in sticker_files]
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return shape_img, stickers
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def detect_rectangle(shape_image):
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"""
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检测矩形，并返回左上坐标和宽高。
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:param shape_image: 输入图片
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:return: 左上坐标和宽高
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"""
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# 转换为灰度图并二值化
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gray_img = np.array(shape_image.convert("L"))
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_, binary = cv2.threshold(gray_img, 127, 255, cv2.THRESH_BINARY)
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# 查找最大轮廓
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contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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cnt = max(contours, key=cv2.contourArea)
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# 获取矩形参数
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x, y, w, h = cv2.boundingRect(cnt)
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print("矩形参数 x, y, w, h:", x, y, w, h)
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return (x, y), (w, h)
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def generate_scaled_rectangles(location, size, n_scales = 4):
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"""
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生成n次同心缩放后的矩形轮廓。
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:param location: 左上角坐标
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:param size: 宽高
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:param n_scales: 缩放次数
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:param scale_factor: 缩放因子
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:return: 缩放后的矩形列表
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"""
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scaled_shapes = []
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x, y = location
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w, h = size
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print("原始矩形大小:", w, h)
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for i in range(n_scales):
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scaled_w = int(w * (1-i/n_scales))
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scaled_h = int(h * (1-i/n_scales))
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# 计算新的左上角位置,保持居中
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new_x = x + (w - scaled_w) // 2
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new_y = y + (h - scaled_h) // 2
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scaled_shapes.append(((new_x, new_y), (scaled_w, scaled_h)))
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return scaled_shapes
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def generate_grid(location, size, cell_size, sticker_size):
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"""
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在矩形中生成规则网格。
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:param location: 左上角坐标
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:param size: 矩形宽高
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:param cell_size: 网格单元大小
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:return: 网格点位置列表
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"""
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x, y = location
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w, h = size
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rows = h // cell_size
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cols = w // cell_size
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# 新增，需测试
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row_height = (h - sticker_size)//(rows-1)
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col_width = (w - sticker_size)//(cols-1)
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grid_positions = [(x + j * col_width, y + i * row_height)
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for i in range(rows) for j in range(cols)]
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# 新增，需测试
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'''
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grid_positions = [(x + j * cell_size, y + i * cell_size)
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for i in range(rows) for j in range(cols)]
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'''
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print('网格数量:', len(grid_positions))
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return grid_positions
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def transform_sticker(sticker, sticker_size, angle_open=True):
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"""
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对贴纸进行缩放和旋转变换。
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:param sticker: 贴纸图片
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:param sticker_size: 贴纸大小
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:return: 变换后的贴纸
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"""
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# 缩放贴纸
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long_side = max(sticker.size)
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scale_factor = (sticker_size / long_side)* random.uniform(0.95, 1.05)
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new_size = tuple(int(dim * scale_factor) for dim in sticker.size)
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sticker = sticker.resize(new_size, Image.Resampling.LANCZOS)
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# 随机旋转贴纸
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if angle_open:
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angle = random.uniform(-10, 10)
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sticker = sticker.rotate(angle, expand=True)
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return sticker
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def place_stickers(scaled_shapes, stickers, main_sticker, transparent_bg,
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sticker_size, main_sticker_size, cell_size_factor):
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"""
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在矩形内放置贴纸。
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:param scaled_shapes: 缩放后的矩形列表
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:param stickers: 贴纸列表
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:param main_sticker: 主贴纸
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:param transparent_bg: 透明背景
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:param sticker_size: 贴纸大小
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:param main_sticker_size: 主贴纸大小
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:param location: 矩形左上角坐标
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:param size: 矩形宽高
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:return: 贴纸拼贴结果
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"""
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'''
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right_boundary = location[0] + size[0]
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bottom_boundary = location[1] + size[1]
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cell_size = int(sticker_size * cell_size_factor)
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'''
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cell_size = int(sticker_size * cell_size_factor)
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# 贴纸计数器
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count = random.randint(0, len(stickers)-1)
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# 逐层放置贴纸
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for i, shape in enumerate(scaled_shapes):
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# 获取该层的边界值
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left_boundary = shape[0][0]
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top_boundary = shape[0][1]
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right_boundary = left_boundary + shape[1][0]
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bottom_boundary = top_boundary + shape[1][1]
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# 生成网格
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grid_positions = generate_grid(shape[0], shape[1], cell_size, sticker_size)
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# 在网格点放置贴纸
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for j, pos in enumerate(grid_positions):
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sticker = stickers[count % len(stickers)]
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# transformed = transform_sticker(sticker, sticker_size)
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# 贴纸左右偏移
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x_offset = random.randint(-5, 5)
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# 处理左侧边界对齐
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if pos[0] == left_boundary:
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angle_open = False
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transformed = transform_sticker(sticker, sticker_size, angle_open)
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if bottom_boundary - pos[1] < int(2*cell_size):
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pos = (pos[0], bottom_boundary - transformed.size[1])
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# 处理右侧边界对齐
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elif right_boundary - pos[0] < int(2*cell_size):
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angle_open = False
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transformed = transform_sticker(sticker, sticker_size, angle_open)
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pos = (right_boundary - transformed.size[0], pos[1])
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if bottom_boundary - pos[1] < int(2*cell_size):
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pos = (right_boundary - transformed.size[0], bottom_boundary - transformed.size[1])
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# 处理顶部边界对齐
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elif pos[1] == top_boundary:
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angle_open = False
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transformed = transform_sticker(sticker, sticker_size, angle_open)
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# 处理底部边界对齐
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elif bottom_boundary - pos[1] < int(2*cell_size):
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angle_open = False
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transformed = transform_sticker(sticker, sticker_size, angle_open)
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pos = (pos[0], bottom_boundary - transformed.size[1])
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else:
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transformed = transform_sticker(sticker, sticker_size)
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# 贴纸放置
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transparent_bg.paste(transformed, (pos[0] + x_offset, pos[1]), transformed)
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count += 1
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# 放置顶层主贴纸
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main_sticker = transform_sticker(random.choice(main_sticker), main_sticker_size)
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w, h = transparent_bg.size
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pos_x = w//2 - main_sticker.size[0]//2
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pos_y = h//2 - main_sticker.size[1]//2
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transparent_bg.paste(main_sticker, (pos_x, pos_y), main_sticker)
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return transparent_bg
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def rectangle_contour_main(image_path, stickers_path, main_sticker_path,
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n_scales=4, sticker_size=220, main_sticker_size=380, cell_size_factor=0.45, crop_image=False, padding=0):
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"""
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主函数。
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:param image_path: 矩形图片路径
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:param stickers_path: 贴纸文件夹路径
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:param main_sticker_path: 主贴纸路径
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:param n_scales: 缩放次数
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:param sticker_size: 贴纸大小
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:param main_sticker_size: 主贴纸大小
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:param crop_image: 是否裁剪图片
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:param padding: 裁剪时额外添加的边距
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"""
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# 加载图片和贴纸
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# shape_image, stickers = load_images(image_path, stickers_path)
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# _, main_sticker = load_images(image_path, main_sticker_path)
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shape_image = images_process(image_path)
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shape_image = shape_image[0]
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stickers = images_process(stickers_path, crop_image=crop_image, padding=padding)
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main_sticker = images_process(main_sticker_path, crop_image=crop_image, padding=padding)
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# 检测矩形并生成缩放序列
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location, size = detect_rectangle(shape_image)
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scaled_shapes = generate_scaled_rectangles(location, size, n_scales)
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# 创建透明背景并放置贴纸
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transparent_bg = Image.new("RGBA", shape_image.size, (0, 0, 0, 0))
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result = place_stickers(scaled_shapes, stickers, main_sticker,
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transparent_bg, sticker_size, main_sticker_size, cell_size_factor)
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return result
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if __name__ == "__main__":
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# 开始时间
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start_time = time.time()
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image_path = "images/rectangle.png"
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stickers_path = "images/baby_bear/all"
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main_sticker_path = "images/baby_bear/top"
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output_path = "images/output/output103.png"
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result_image = rectangle_contour_main(image_path, stickers_path, main_sticker_path,
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n_scales=4, sticker_size=280, main_sticker_size=450, cell_size_factor=0.45)
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result_image.save(output_path, format='PNG')
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print("处理完成")
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# 结束时间
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end_time = time.time()
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print(f"总时间：{end_time - start_time:.2f}秒")
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