【深度学习】inpaint图像中的alpha混合图的边缘处理

比如原图是：

红圈内就是文字水印，经过inpaint后得到图和原图混合，如何处理边界呢，这个代码可以干这事：

越是中心就直接用inpaint图，否则就用原图，这样进行alpha混合。

import numpy as np
import cv2
import pyclipper
from shapely.geometry import Polygon


def expand_polygon_pyclipper(polygon, expand_ratio):
    polygon_shape = Polygon(polygon)
    distance = (
            polygon_shape.area * (np.power(expand_ratio, 2) - 1) / polygon_shape.length
    )
    subject = [tuple(l) for l in polygon]
    padding = pyclipper.PyclipperOffset()
    padding.AddPath(subject, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON)
    expanded = padding.Execute(distance)
    if expanded == []:
        expanded = np.array(expanded)
    else:
        expanded = np.array(expanded[0]).reshape(-1, 2)
    return expanded


def shrink_polygon_pyclipper(polygon, expand_ratio):
    polygon_shape = Polygon(polygon)
    distance = (
            polygon_shape.area * (np.power(expand_ratio, 2) - 1) / polygon_shape.length
    )
    subject = [tuple(l) for l in polygon]
    padding = pyclipper.PyclipperOffset()
    padding.AddPath(subject, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON)
    expanded = padding.Execute(-distance)
    if expanded == []:
        expanded = np.array(expanded)
    else:
        expanded = np.array(expanded[0]).reshape(-1, 2)
    return expanded


# 定义函数
def func(x):
    return 0.25 * (np.sin(x - np.pi / 2) + 1)


# 生成0到pi的10个均匀分布的点
x_values = np.linspace(0, np.pi, 11)[1:][::-1]
y_values = func(x_values) + 1
print(f"面积{len(y_values)}", y_values)
x_values = np.linspace(0, np.pi, 13)[1:]
weight_inpaint = func(x_values) * 2
print(f"权重{len(weight_inpaint)}", weight_inpaint)


def draw_heatmap(image, polygons, num_levels=10, initial_color=(255, 0, 0)):
    # 计算函数值并加上1

    # 搞个和image一样大的二维矩阵
    inpaint_image_weights = np.zeros(image.shape[:2], dtype=np.float32)

    for i in range(num_levels):
        for polygon in polygons:
            expanded_polygon = expand_polygon_pyclipper(polygon, y_values[i])
            if expanded_polygon.size > 0:
                cv2.fillPoly(inpaint_image_weights, [expanded_polygon.astype(np.int32)], weight_inpaint[i])
    # 收缩0.95区域用白色填充
    for polygon in polygons:
        for indx, shk in enumerate([0.99, 0.98]):
            expanded_polygon = shrink_polygon_pyclipper(polygon, shk)
            if expanded_polygon.size > 0:
                cv2.fillPoly(inpaint_image_weights, [expanded_polygon.astype(np.int32)], weight_inpaint[10 + indx])
    return inpaint_image_weights


if __name__ == "__main__":
    # 示例图像
    image = np.ones((200, 200, 3), dtype=np.uint8) * 255

    # 示例文本框多边形
    text_polys = [
        np.array([[50, 50], [150, 50], [150, 100], [50, 100]]),
        np.array([[60, 120], [140, 120], [140, 160], [60, 160]])
    ]

    # 生成热力等高线图
    inpaint_image_weights = draw_heatmap(image, text_polys, num_levels=10, initial_color=(255, 0, 0))
    print(inpaint_image_weights.shape)

    # plt显示图
    import matplotlib.pyplot as plt

    plt.imshow(inpaint_image_weights, cmap='hot', interpolation='nearest')
    plt.colorbar()
    plt.show()

原文地址：https://blog.csdn.net/x1131230123/article/details/140501959

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