198 lines
7.5 KiB
Markdown
198 lines
7.5 KiB
Markdown
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# Documentation
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- Class name: `ImageEffectsLensZoomBurst`
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- Category: `image/effects/lens`
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- Output node: `False`
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ImageEffectsLensZoomBurst节点用于为图像应用放大爆炸效果，通过动态缩放、旋转和可选的稳定处理来改变图像外观，从而创造出类似运动的视觉效果。该节点旨在通过模拟快速放大效果来增强视觉内容，常用于为图像特定区域添加戏剧性效果或焦点。
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# Input types
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## Required
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- **`images`**
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- 需要应用放大爆炸效果的输入图像。这些图像是变换的主要对象，效果参数将应用于其上以创造所需的视觉效果。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- **`scale`**
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- 定义放大爆炸效果的缩放因子，决定了在效果应用过程中图像会被放大或缩小的程度。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- **`samples`**
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- 指定创建放大爆炸效果时使用的样本数量，影响最终输出的平滑度和强度。
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- Comfy dtype: INT
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- Python dtype: int
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- **`position_x`**
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- 放大爆炸效果在图像中心的x坐标位置，允许针对特定图像区域进行有针对性的视觉强调。
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- Comfy dtype: FLOAT
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- Python dtype: int
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- **`position_y`**
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- 放大爆炸效果在图像中心的y坐标位置，允许针对特定图像区域进行有针对性的视觉强调。
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- Comfy dtype: FLOAT
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- Python dtype: int
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- **`rotation`**
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- 确定与放大效果一起应用于图像的旋转角度，为视觉效果增添动态扭转。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- **`method`**
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- 应用放大爆炸效果的方法，可能影响效果的外观和性能。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- **`stabilization`**
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- 指示是否应用稳定技术来减少潜在的模糊或失真效果，增强放大爆炸的视觉清晰度。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: bool
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# Output types
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- **`image`**
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- 应用放大爆炸效果后的输出图像，展示了经过变换的视觉外观，呈现出预期的动态、类似运动的视觉效果。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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## Usage tips
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- Infra type: `GPU`
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- Common nodes: unknown
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## Source code
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```python
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class ImageEffectsLensZoomBurst:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"images": ("IMAGE",),
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"scale": ("FLOAT", {
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"default": 1.5,
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"min": 1.0,
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"step": 0.01
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}),
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"samples": ("INT", {
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"default": 100,
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"min": 1,
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}),
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"position_x": ("FLOAT", {
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"default": 0.5,
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"max": 1.0,
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"step": 0.01
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}),
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"position_y": ("FLOAT", {
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"default": 0.5,
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"max": 1.0,
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"step": 0.01
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}),
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"rotation": ("FLOAT", {
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"default": 0.0,
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"min": 0.0,
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"max": 360.0,
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}),
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"method": (["circle", "point"],),
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"stabilization": (["true", "false"],),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "node"
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CATEGORY = "image/effects/lens"
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# noinspection PyUnresolvedReferences
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def zoom_burst(
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self,
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image,
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scale,
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samples,
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position,
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rotation,
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method,
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stabilization,
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):
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if scale < 1.0:
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raise ValueError("Parameter scale can't be smaller then initial image size.")
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h, w = image.shape[:2]
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x = np.arange(w)
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y = np.arange(h)
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xx, yy = np.meshgrid(x, y)
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cx = int(w * position[0])
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cy = int(h * position[1])
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if method == "circle":
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d = np.sqrt((xx - cx) ** 2 + (yy - cy) ** 2)
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max_d = np.sqrt((w / 2) ** 2 + (h / 2) ** 2)
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map_x_up = (xx - d * (scale - 1.0) / max_d * (xx - cx) / samples).astype(np.float32)
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map_y_up = (yy - d * (scale - 1.0) / max_d * (yy - cy) / samples).astype(np.float32)
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map_x_down = (xx + d * (scale - 1.0) / max_d * (xx - cx) / samples).astype(np.float32)
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map_y_down = (yy + d * (scale - 1.0) / max_d * (yy - cy) / samples).astype(np.float32)
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elif method == "point":
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map_x_up = (xx - (xx - cx) * (scale - 1.0) / samples).astype(np.float32)
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map_y_up = (yy - (yy - cy) * (scale - 1.0) / samples).astype(np.float32)
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map_x_down = (xx + (xx - cx) * (scale - 1.0) / samples).astype(np.float32)
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map_y_down = (yy + (yy - cy) * (scale - 1.0) / samples).astype(np.float32)
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else:
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raise ValueError("Unsupported method.")
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if rotation > 0.0:
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angle_step = rotation / samples
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rm_up = cv2.getRotationMatrix2D((cx, cy), angle_step, 1)
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rm_down = cv2.getRotationMatrix2D((cx, cy), -angle_step, 1)
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else:
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vibration_angle = 1.0
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vibration_step = vibration_angle / samples
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rm_up_even = cv2.getRotationMatrix2D((cx, cy), vibration_step, 1)
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rm_down_even = cv2.getRotationMatrix2D((cx, cy), -vibration_step, 1)
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rm_up_odd = cv2.getRotationMatrix2D((cx, cy), -vibration_step, 1)
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rm_down_odd = cv2.getRotationMatrix2D((cx, cy), vibration_step, 1)
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for i in range(samples):
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if stabilization:
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tmp_up = cv2.remap(image, map_x_up, map_y_up, cv2.INTER_LINEAR)
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tmp_down = cv2.remap(image, map_x_down, map_y_down, cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT)
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if rotation > 0.0:
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tmp_up = cv2.warpAffine(tmp_up, rm_up, (w, h), borderMode=cv2.BORDER_REFLECT)
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tmp_down = cv2.warpAffine(tmp_down, rm_down, (w, h), borderMode=cv2.BORDER_REFLECT)
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else:
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if i % 2 == 0:
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tmp_up = cv2.warpAffine(tmp_up, rm_up_even, (w, h), borderMode=cv2.BORDER_REFLECT)
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tmp_down = cv2.warpAffine(tmp_down, rm_down_even, (w, h), borderMode=cv2.BORDER_REFLECT)
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else:
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tmp_up = cv2.warpAffine(tmp_up, rm_up_odd, (w, h), borderMode=cv2.BORDER_REFLECT)
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tmp_down = cv2.warpAffine(tmp_down, rm_down_odd, (w, h), borderMode=cv2.BORDER_REFLECT)
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image = cv2.addWeighted(tmp_up, 0.5, tmp_down, 0.5, 0)
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else:
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tmp = cv2.remap(image, map_x_up, map_y_up, cv2.INTER_LINEAR)
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if rotation > 0.0:
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rm = cv2.getRotationMatrix2D((cx, cy), angle_step, 1)
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tmp = cv2.warpAffine(tmp, rm, (w, h), borderMode=cv2.BORDER_REFLECT)
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else:
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if i % 2 == 0:
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tmp = cv2.warpAffine(tmp, rm_up_even, (w, h), borderMode=cv2.BORDER_REFLECT)
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else:
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tmp = cv2.warpAffine(tmp, rm_up_odd, (w, h), borderMode=cv2.BORDER_REFLECT)
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image = cv2.addWeighted(tmp, 0.5, image, 0.5, 0)
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return image
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def node(self, images, scale, samples, position_x, position_y, rotation, method, stabilization):
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tensor = images.clone().detach()
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return (cv2_layer(tensor, lambda x: self.zoom_burst(
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x, scale, samples, (position_x, position_y), rotation, method, True if stabilization == "true" else False
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)),)
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```
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