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Update silver_custom.py

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Change testing on hand tracking image to mask
This commit is contained in:
Silversith 2023-04-28 12:27:16 +02:00
parent 8e9bcc378c
commit 72d262d11e
1 changed files with 28 additions and 15 deletions
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43
comfy_extras/silver_custom.py
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@ -6,43 +6,56 @@ class ExpandImageMask:
def INPUT_TYPES(s): def INPUT_TYPES(s):
return { return {
"required": { "required": {
"images": ("IMAGE",) "images": ("IMAGE", )
} }
} }
CATEGORY = "mask" CATEGORY = "mask"
RETURN_TYPES = ("IMAGE",) RETURN_TYPES = ("IMAGE", "MASK", )
FUNCTION = "image_to_mask_image" FUNCTION = "image_to_mask_image"
def image_to_mask_image(self, images): def image_to_mask_image(self, images):
mask_images = [] mask_images = []
for image in images: for image in images:
i = 255. * image.cpu().numpy() i = 255. * image.cpu().numpy()
# opencv_image = PIL.Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
# cv2.imwrite('opencv_image.png', i)
# Convert to grayscale
image_gray = cv2.cvtColor(i, cv2.COLOR_BGR2GRAY) image_gray = cv2.cvtColor(i, cv2.COLOR_BGR2GRAY)
image_gray = cv2.blur(image_gray, (10, 10))
# cv2.imwrite('image_gray1.png', image_gray)
image_gray = cv2.blur(image_gray, (20, 20))
# cv2.imwrite('image_gray2.png', image_gray)
# Convert the image to the expected data type # Apply blurring to grayscale image
image_gray = cv2.blur(image_gray, (10, 10))
image_gray = cv2.blur(image_gray, (20, 20))
# Convert image to the expected data type
image_gray = cv2.convertScaleAbs(image_gray) image_gray = cv2.convertScaleAbs(image_gray)
# Apply the threshold using the modified image # Apply threshold to grayscale image
(thresh, im_bw) = cv2.threshold(image_gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU) (thresh, im_bw) = cv2.threshold(image_gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
# Apply blurring to binary mask image
ksize = (50, 50) ksize = (50, 50)
im_bw = cv2.blur(im_bw, ksize) im_bw = cv2.blur(im_bw, ksize)
im_bw = cv2.threshold(im_bw, thresh, 255, cv2.THRESH_BINARY)[1]
im_bw = cv2.bitwise_not(im_bw)
# cv2.imwrite('im_bw.png', im_bw)
# Convert the binary mask image to a PyTorch tensor # Threshold binary mask image again
im_bw = cv2.threshold(im_bw, thresh, 255, cv2.THRESH_BINARY)[1]
# Invert binary mask image
# im_bw = cv2.bitwise_not(im_bw)
# Convert binary mask image to PyTorch tensor
img = torch.from_numpy(im_bw).unsqueeze(0).float() img = torch.from_numpy(im_bw).unsqueeze(0).float()
# Append mask image tensor to list
mask_images.append(img) mask_images.append(img)
return tuple(mask_images)
# Stack list of mask image tensors into a single tensor
mask_images_tensor = torch.cat(mask_images)
# Return tuple of mask images and single mask image
single_mask_image = mask_images_tensor[0, :, :]
return mask_images_tensor, single_mask_image
NODE_CLASS_MAPPINGS = { NODE_CLASS_MAPPINGS = {
"ExpandImageMask": ExpandImageMask "ExpandImageMask": ExpandImageMask