forked from mttu-developers/konabot
最新最热模糊
This commit is contained in:
@ -19,8 +19,6 @@ fx [滤镜名称] <参数1> <参数2> ...
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## 可用滤镜列表
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### 基础滤镜
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* ```fx 模糊 <半径=10>```
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* ```fx 马赛克 <像素大小=10>```
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* ```fx 轮廓```
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* ```fx 锐化```
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* ```fx 边缘增强```
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@ -33,6 +31,15 @@ fx [滤镜名称] <参数1> <参数2> ...
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* ```fx 素描```
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* ```fx 阴影 <x偏移量=10> <y偏移量=10> <模糊量=10> <不透明度=0.5> <阴影颜色=black>```
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### 模糊滤镜
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* ```fx 模糊 <半径=10>```
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* ```fx 马赛克 <像素大小=10>```
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* ```fx 径向模糊 <强度=3.0> <采样量=6>```
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* ```fx 旋转模糊 <强度=30.0> <采样量=6>```
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* ```fx 方向模糊 <角度=0.0> <距离=20> <采样量=6>```
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* ```fx 缩放模糊 <强度=0.1> <采样量=6>```
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* ```fx 边缘模糊 <半径=10.0>```
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### 色彩处理滤镜
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* ```fx 反色```
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* ```fx 黑白```
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@ -566,4 +566,325 @@ class ImageFilterImplement:
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result.paste(image, image_position, image)
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return result
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@staticmethod
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def apply_radial_blur(image: Image.Image, strength: float = 3.0, samples: int = 6) -> Image.Image:
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"""
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快速径向模糊 - 使用预计算网格和向量化
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"""
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if image.mode != 'RGBA':
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image = image.convert('RGBA')
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width, height = image.size
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arr = np.array(image, dtype=np.uint8)
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# 转换为float32并归一化
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arr_float = arr.astype(np.float32) / 255.0
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# 计算中心点
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center_x = width / 2
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center_y = height / 2
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# 预计算坐标网格（只计算一次）
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x_indices = np.arange(width, dtype=np.float32)
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y_indices = np.arange(height, dtype=np.float32)
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x_grid, y_grid = np.meshgrid(x_indices, y_indices)
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# 预计算相对坐标和距离
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dx = x_grid - center_x
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dy = y_grid - center_y
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distance = np.sqrt(dx*dx + dy*dy + 1e-6) # 避免除零
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max_dist = np.max(distance)
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# 生成采样强度
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if samples > 1:
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strengths = np.linspace(-strength/2, strength/2, samples) / 100
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else:
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strengths = np.array([0])
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# 初始化结果
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result = np.zeros_like(arr_float)
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for s in strengths:
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# 计算缩放因子
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scale = 1.0 + s * distance / max_dist
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# 计算变形坐标
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new_x = np.clip(center_x + dx * scale, 0, width - 1)
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new_y = np.clip(center_y + dy * scale, 0, height - 1)
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# 快速双线性插值
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x0 = np.floor(new_x).astype(np.int32)
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x1 = np.minimum(x0 + 1, width - 1)
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y0 = np.floor(new_y).astype(np.int32)
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y1 = np.minimum(y0 + 1, height - 1)
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# 计算权重
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wx = new_x - x0
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wy = new_y - y0
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w00 = (1 - wx) * (1 - wy)
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w01 = wx * (1 - wy)
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w10 = (1 - wx) * wy
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w11 = wx * wy
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# 向量化插值所有通道
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for c in range(4):
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result[:, :, c] += (
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arr_float[y0, x0, c] * w00 +
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arr_float[y0, x1, c] * w01 +
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arr_float[y1, x0, c] * w10 +
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arr_float[y1, x1, c] * w11
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)
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# 平均并转换
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result /= len(strengths)
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result = np.clip(result * 255, 0, 255).astype(np.uint8)
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return Image.fromarray(result, 'RGBA')
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@staticmethod
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def apply_spin_blur(image: Image.Image, strength: float = 30.0, samples: int = 6) -> Image.Image:
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"""
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快速旋转模糊
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"""
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if image.mode != 'RGBA':
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image = image.convert('RGBA')
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width, height = image.size
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arr = np.array(image, dtype=np.uint8)
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arr_float = arr.astype(np.float32) / 255.0
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# 计算中心点
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center_x = width / 2
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center_y = height / 2
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# 预计算坐标网格
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x_indices = np.arange(width, dtype=np.float32)
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y_indices = np.arange(height, dtype=np.float32)
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x_grid, y_grid = np.meshgrid(x_indices, y_indices)
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# 预计算相对坐标
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dx = x_grid - center_x
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dy = y_grid - center_y
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# 生成角度采样
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if samples > 1:
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angles = np.linspace(-strength/2, strength/2, samples) * np.pi / 180
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else:
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angles = np.array([0])
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result = np.zeros_like(arr_float)
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for angle in angles:
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# 预计算三角函数值
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cos_a = math.cos(angle)
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sin_a = math.sin(angle)
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# 计算旋转坐标
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new_x = center_x + dx * cos_a - dy * sin_a
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new_y = center_y + dx * sin_a + dy * cos_a
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# 边界裁剪
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new_x = np.clip(new_x, 0, width - 1)
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new_y = np.clip(new_y, 0, height - 1)
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# 快速双线性插值
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x0 = np.floor(new_x).astype(np.int32)
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x1 = np.minimum(x0 + 1, width - 1)
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y0 = np.floor(new_y).astype(np.int32)
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y1 = np.minimum(y0 + 1, height - 1)
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wx = new_x - x0
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wy = new_y - y0
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w00 = (1 - wx) * (1 - wy)
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w01 = wx * (1 - wy)
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w10 = (1 - wx) * wy
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w11 = wx * wy
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# 向量化插值
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for c in range(4):
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result[:, :, c] += (
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arr_float[y0, x0, c] * w00 +
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arr_float[y0, x1, c] * w01 +
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arr_float[y1, x0, c] * w10 +
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arr_float[y1, x1, c] * w11
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)
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result /= len(angles)
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result = np.clip(result * 255, 0, 255).astype(np.uint8)
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return Image.fromarray(result, 'RGBA')
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@staticmethod
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def apply_directional_blur(image: Image.Image, angle: float = 0.0,
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distance: int = 20, samples: int = 6) -> Image.Image:
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"""
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快速方向模糊 - 使用累积缓冲区技术
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"""
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if image.mode != 'RGBA':
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image = image.convert('RGBA')
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width, height = image.size
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arr = np.array(image, dtype=np.uint8)
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arr_float = arr.astype(np.float32) / 255.0
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# 计算角度和步长
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rad = math.radians(angle)
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cos_a = math.cos(rad)
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sin_a = math.sin(rad)
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# 生成偏移位置
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if samples > 1:
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offsets = np.linspace(-distance/2, distance/2, samples)
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else:
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offsets = np.array([0])
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result = np.zeros_like(arr_float)
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for offset in offsets:
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# 计算偏移量
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shift_x = offset * cos_a
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shift_y = offset * sin_a
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# 预计算变形坐标
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x_indices = np.arange(width, dtype=np.float32)
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y_indices = np.arange(height, dtype=np.float32)
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x_grid, y_grid = np.meshgrid(x_indices, y_indices)
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new_x = x_grid - shift_x
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new_y = y_grid - shift_y
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# 边界裁剪
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new_x = np.clip(new_x, 0, width - 1)
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new_y = np.clip(new_y, 0, height - 1)
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# 快速双线性插值
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x0 = np.floor(new_x).astype(np.int32)
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x1 = np.minimum(x0 + 1, width - 1)
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y0 = np.floor(new_y).astype(np.int32)
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y1 = np.minimum(y0 + 1, height - 1)
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wx = new_x - x0
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wy = new_y - y0
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w00 = (1 - wx) * (1 - wy)
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w01 = wx * (1 - wy)
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w10 = (1 - wx) * wy
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w11 = wx * wy
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# 向量化插值
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for c in range(4):
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result[:, :, c] += (
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arr_float[y0, x0, c] * w00 +
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arr_float[y0, x1, c] * w01 +
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arr_float[y1, x0, c] * w10 +
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arr_float[y1, x1, c] * w11
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)
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result /= len(offsets)
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result = np.clip(result * 255, 0, 255).astype(np.uint8)
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return Image.fromarray(result, 'RGBA')
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@staticmethod
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def apply_zoom_blur(image: Image.Image, strength: float = 0.1, samples: int = 6) -> Image.Image:
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"""
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快速缩放模糊
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"""
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if image.mode != 'RGBA':
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image = image.convert('RGBA')
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width, height = image.size
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arr = np.array(image, dtype=np.uint8)
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arr_float = arr.astype(np.float32) / 255.0
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# 计算中心点
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center_x = width / 2
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center_y = height / 2
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# 预计算坐标网格
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x_indices = np.arange(width, dtype=np.float32)
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y_indices = np.arange(height, dtype=np.float32)
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x_grid, y_grid = np.meshgrid(x_indices, y_indices)
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# 预计算相对坐标
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dx = x_grid - center_x
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dy = y_grid - center_y
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# 生成缩放因子
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if samples > 1:
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scales = np.linspace(1 - strength, 1 + strength, samples)
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else:
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scales = np.array([1.0])
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result = np.zeros_like(arr_float)
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for scale in scales:
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# 计算缩放坐标
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new_x = center_x + dx / scale
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new_y = center_y + dy / scale
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# 边界裁剪
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new_x = np.clip(new_x, 0, width - 1)
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new_y = np.clip(new_y, 0, height - 1)
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# 快速双线性插值
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x0 = np.floor(new_x).astype(np.int32)
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x1 = np.minimum(x0 + 1, width - 1)
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y0 = np.floor(new_y).astype(np.int32)
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y1 = np.minimum(y0 + 1, height - 1)
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wx = new_x - x0
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wy = new_y - y0
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w00 = (1 - wx) * (1 - wy)
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w01 = wx * (1 - wy)
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w10 = (1 - wx) * wy
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w11 = wx * wy
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# 向量化插值
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for c in range(4):
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result[:, :, c] += (
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arr_float[y0, x0, c] * w00 +
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arr_float[y0, x1, c] * w01 +
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arr_float[y1, x0, c] * w10 +
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arr_float[y1, x1, c] * w11
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)
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result /= len(scales)
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result = np.clip(result * 255, 0, 255).astype(np.uint8)
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return Image.fromarray(result, 'RGBA')
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# 中心清晰，边缘模糊
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@staticmethod
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def apply_focus_blur(image: Image.Image, radius: float = 10.0) -> Image.Image:
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if image.mode != 'RGBA':
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image = image.convert('RGBA')
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width, height = image.size
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arr = np.array(image)
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# 创建坐标网格
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y_coords, x_coords = np.mgrid[0:height, 0:width]
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# 计算中心点
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center_x = width / 2
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center_y = height / 2
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# 计算归一化坐标
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norm_x = (x_coords - center_x) / (width / 2)
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norm_y = (y_coords - center_y) / (height / 2)
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distance = np.sqrt(norm_x**2 + norm_y**2)
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# 创建模糊图像
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blurred_image = image.filter(ImageFilter.GaussianBlur(radius))
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blurred_arr = np.array(blurred_image)
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# 创建结果图像（向量化）
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result = np.zeros_like(arr, dtype=np.float32)
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# 根据距离混合原图和模糊图
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for c in range(4): # 对每个通道
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result[:, :, c] = arr[:, :, c] * (1 - distance) + blurred_arr[:, :, c] * distance
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return Image.fromarray(np.clip(result, 0, 255).astype(np.uint8), 'RGBA')
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@ -35,6 +35,11 @@ class ImageFilterManager:
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"素描": ImageFilterImplement.apply_sketch,
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"叠加颜色": ImageFilterImplement.apply_gradient_overlay,
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"阴影": ImageFilterImplement.apply_shadow,
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"径向模糊": ImageFilterImplement.apply_radial_blur,
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"旋转模糊": ImageFilterImplement.apply_spin_blur,
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"方向模糊": ImageFilterImplement.apply_directional_blur,
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"边缘模糊": ImageFilterImplement.apply_focus_blur,
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"缩放模糊": ImageFilterImplement.apply_zoom_blur,
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}
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@classmethod
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Reference in New Issue
Block a user