5.2 KiB
5.2 KiB
Documentation
- Class name: WAS_Canny_Filter
- Category: WAS Suite/Image/Filter
- Output node: False
- Repo Ref: https://github.com/WASasquatch/was-node-suite-comfyui
WAS_Canny_Filter 节点旨在对输入图像应用Canny边缘检测算法，增强图像数据中边缘的可见性。它通过使用包括高斯模糊、梯度计算和阈值处理的多阶段算法来处理每个图像，以识别和突出重要边缘。
Input types
Required
- images
- ‘images’参数至关重要，因为它是Canny边缘检测过程的输入。它是节点将处理以识别和增强边缘的原始图像数据。
- Comfy dtype: IMAGE
- Python dtype: torch.Tensor
Optional
- enable_threshold
- ‘enable_threshold’参数决定是否在Canny算法中应用阈值步骤。这对于控制边缘检测的灵敏度很重要。
- Comfy dtype: COMBO['false', 'true']
- Python dtype: str
- threshold_low
- ‘threshold_low’参数设置边缘检测的下限阈值。它是决定哪些边缘足够强以包含在最终输出中的关键因素。
- Comfy dtype: FLOAT
- Python dtype: float
- threshold_high
- ‘threshold_high’参数设置边缘检测的上限阈值。它与下限阈值一起工作，以微调边缘检测过程。
- Comfy dtype: FLOAT
- Python dtype: float
Output types
- images
- ‘images’输出参数包含使用Canny滤波器突出显示边缘的已处理图像。这个输出很重要，因为它代表了边缘检测操作的最终结果。
- Comfy dtype: IMAGE
- Python dtype: torch.Tensor
Usage tips
- Infra type: CPU
Source code
class WAS_Canny_Filter:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {'required': {'images': ('IMAGE',), 'enable_threshold': (['false', 'true'],), 'threshold_low': ('FLOAT', {'default': 0.0, 'min': 0.0, 'max': 1.0, 'step': 0.01}), 'threshold_high': ('FLOAT', {'default': 1.0, 'min': 0.0, 'max': 1.0, 'step': 0.01})}}
RETURN_TYPES = ('IMAGE',)
RETURN_NAMES = ('images',)
FUNCTION = 'canny_filter'
CATEGORY = 'WAS Suite/Image/Filter'
def canny_filter(self, images, threshold_low, threshold_high, enable_threshold):
if enable_threshold == 'false':
threshold_low = None
threshold_high = None
batch_tensor = []
for image in images:
image_canny = Image.fromarray(self.Canny_detector(255.0 * image.cpu().numpy().squeeze(), threshold_low, threshold_high)).convert('RGB')
batch_tensor.append(pil2tensor(image_canny))
batch_tensor = torch.cat(batch_tensor, dim=0)
return (pil2tensor(image_canny),)
def Canny_detector(self, img, weak_th=None, strong_th=None):
import cv2
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img = cv2.GaussianBlur(img, (5, 5), 1.4)
gx = cv2.Sobel(np.float32(img), cv2.CV_64F, 1, 0, 3)
gy = cv2.Sobel(np.float32(img), cv2.CV_64F, 0, 1, 3)
(mag, ang) = cv2.cartToPolar(gx, gy, angleInDegrees=True)
mag_max = np.max(mag)
if not weak_th:
weak_th = mag_max * 0.1
if not strong_th:
strong_th = mag_max * 0.5
(height, width) = img.shape
for i_x in range(width):
for i_y in range(height):
grad_ang = ang[i_y, i_x]
grad_ang = abs(grad_ang - 180) if abs(grad_ang) > 180 else abs(grad_ang)
(neighb_1_x, neighb_1_y) = (-1, -1)
(neighb_2_x, neighb_2_y) = (-1, -1)
if grad_ang <= 22.5:
(neighb_1_x, neighb_1_y) = (i_x - 1, i_y)
(neighb_2_x, neighb_2_y) = (i_x + 1, i_y)
elif grad_ang > 22.5 and grad_ang <= 22.5 + 45:
(neighb_1_x, neighb_1_y) = (i_x - 1, i_y - 1)
(neighb_2_x, neighb_2_y) = (i_x + 1, i_y + 1)
elif grad_ang > 22.5 + 45 and grad_ang <= 22.5 + 90:
(neighb_1_x, neighb_1_y) = (i_x, i_y - 1)
(neighb_2_x, neighb_2_y) = (i_x, i_y + 1)
elif grad_ang > 22.5 + 90 and grad_ang <= 22.5 + 135:
(neighb_1_x, neighb_1_y) = (i_x - 1, i_y + 1)
(neighb_2_x, neighb_2_y) = (i_x + 1, i_y - 1)
elif grad_ang > 22.5 + 135 and grad_ang <= 22.5 + 180:
(neighb_1_x, neighb_1_y) = (i_x - 1, i_y)
(neighb_2_x, neighb_2_y) = (i_x + 1, i_y)
if width > neighb_1_x >= 0 and height > neighb_1_y >= 0:
if mag[i_y, i_x] < mag[neighb_1_y, neighb_1_x]:
mag[i_y, i_x] = 0
continue
if width > neighb_2_x >= 0 and height > neighb_2_y >= 0:
if mag[i_y, i_x] < mag[neighb_2_y, neighb_2_x]:
mag[i_y, i_x] = 0
weak_ids = np.zeros_like(img)
strong_ids = np.zeros_like(img)
ids = np.zeros_like(img)
for i_x in range(width):
for i_y in range(height):
grad_mag = mag[i_y, i_x]
if grad_mag < weak_th:
mag[i_y, i_x] = 0
elif strong_th > grad_mag >= weak_th:
ids[i_y, i_x] = 1
else:
ids[i_y, i_x] = 2
return mag