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import cv2
import numpy as np
import json
from rotation_adjust import rotate_bound
import os
base_path = os.path.dirname(os.path.abspath(__file__))
template_config_path = os.path.join(base_path, 'assets', 'template_config.json')
def generte_template_photo(template_name: str, input_image: np.ndarray) -> np.ndarray:
"""
生成模板照片
:param template_name: 模板名称
:param input_image: 输入图像
:return: 模板照片
"""
# 读取模板配置json
with open(template_config_path, 'r') as f:
template_config_dict = json.load(f)
# 获取对应该模板的配置
template_config = template_config_dict[template_name]
template_width = template_config['width']
template_height = template_config['height']
anchor_points = template_config['anchor_points']
rotation = anchor_points['rotation']
left_top = anchor_points['left_top']
right_top = anchor_points['right_top']
left_bottom = anchor_points['left_bottom']
right_bottom = anchor_points['right_bottom']
if rotation < 0:
height = right_bottom[1] - left_top[1]
width = right_top[0] - left_bottom[0]
else:
height = left_top[1] - right_bottom[1]
width = left_bottom[0] - right_top[0]
# 读取模板图像
template_image_path = os.path.join(base_path, 'assets', f'{template_name}.png')
template_image = cv2.imread(template_image_path, cv2.IMREAD_UNCHANGED)
# 无损旋转
rotated_image = rotate_bound(input_image, -1 * rotation)[0]
rotated_image_height, rotated_image_width, _ = rotated_image.shape
# 计算缩放比例
scale_x = width / rotated_image_width
scale_y = height / rotated_image_height
scale = max(scale_x, scale_y)
resized_image = cv2.resize(rotated_image, None, fx=scale, fy=scale)
resized_height, resized_width, _ = resized_image.shape
# 创建一个与template_image大小相同的背景，使用白色填充
result = np.full((template_height, template_width, 3), 255, dtype=np.uint8)
# 计算粘贴位置
paste_x = left_bottom[0]
paste_y = left_top[1]
# 确保不会超出边界
paste_height = min(resized_height, template_height - paste_y)
paste_width = min(resized_width, template_width - paste_x)
# 将旋转后的图像粘贴到结果图像上
result[paste_y:paste_y+paste_height, paste_x:paste_x+paste_width] = resized_image[:paste_height, :paste_width]
template_image = cv2.cvtColor(template_image, cv2.COLOR_BGRA2RGBA)
# 将template_image叠加到结果图像上
if template_image.shape[2] == 4: # 确保template_image有alpha通道
alpha = template_image[:, :, 3] / 255.0
for c in range(0, 3):
result[:, :, c] = result[:, :, c] * (1 - alpha) + template_image[:, :, c] * alpha
return result
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