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Txt2mask padding dilate #38

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Txt2mask padding dilate #38

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acb8306
Fix RGBA images throwing error in txt2mask
credman0 Dec 26, 2022
8fbf63b
Fix typo I made "cleaning it up" on github...
credman0 Dec 26, 2022
639d1c8
txt2mask pad with cv2 dilation
credman0 Dec 26, 2022
9e3ba8f
Dilate before smooth
credman0 Dec 26, 2022
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27 changes: 15 additions & 12 deletions shortcodes/stable_diffusion/txt2mask.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,6 +14,8 @@ def run_block(self, pargs, kwargs, context, content):
from matplotlib import pyplot as plt
import cv2
import numpy
from modules.images import flatten
from modules.shared import opts

brush_mask_mode = self.Unprompted.parse_advanced(kwargs["mode"],context) if "mode" in kwargs else "add"
self.show = True if "show" in pargs else False
Expand All @@ -24,6 +26,14 @@ def run_block(self, pargs, kwargs, context, content):
if "smoothing" in kwargs:
radius = int(kwargs["smoothing"])
smoothing_kernel = numpy.ones((radius,radius),numpy.float32)/(radius*radius)



# Pad the mask by applying a dilation
mask_padding = int(self.Unprompted.parse_advanced(kwargs["padding"],context) if "padding" in kwargs else 0)
padding_dilation_kernel = None
if (mask_padding > 0):
padding_dilation_kernel = numpy.ones((mask_padding, mask_padding), numpy.uint8)

prompts = content.split(self.Unprompted.Config.syntax.delimiter)
prompt_parts = len(prompts)
Expand Down Expand Up @@ -60,6 +70,7 @@ def process_mask_parts(these_preds,these_prompt_parts,mode,final_img = None):
# TODO: Figure out how to convert the plot above to numpy instead of re-loading image
img = cv2.imread(filename)

if padding_dilation_kernel is not None: img = cv2.dilate(img,padding_dilation_kernel,iterations=1)
if smoothing_kernel is not None: img = cv2.filter2D(img,-1,smoothing_kernel)

gray_image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
Expand Down Expand Up @@ -99,7 +110,8 @@ def get_mask():
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
transforms.Resize((512, 512)),
])
img = transform(self.Unprompted.shortcode_user_vars["init_images"][0]).unsqueeze(0)
flattened_input = flatten(self.Unprompted.shortcode_user_vars["init_images"][0], opts.img2img_background_color)
img = transform(flattened_input).unsqueeze(0)

# predict
with torch.no_grad():
Expand Down Expand Up @@ -133,16 +145,7 @@ def get_mask():
if (pixel_data[0] == 0 and pixel_data[1] == 0 and pixel_data[2] == 0): black_pixels += 1
subject_size = 1 - black_pixels / total_pixels
self.Unprompted.shortcode_user_vars[kwargs["size_var"]] = subject_size

# Increase mask size with padding
mask_padding = int(self.Unprompted.parse_advanced(kwargs["padding"],context) if "padding" in kwargs else 0)
if (mask_padding > 0):
aspect_ratio = self.Unprompted.shortcode_user_vars["init_images"][0].width / self.Unprompted.shortcode_user_vars["init_images"][0].height
new_width = self.Unprompted.shortcode_user_vars["init_images"][0].width+mask_padding*2
new_height = round(new_width / aspect_ratio)
final_img = final_img.resize((new_width,new_height))
final_img = center_crop(final_img,self.Unprompted.shortcode_user_vars["init_images"][0].width,self.Unprompted.shortcode_user_vars["init_images"][0].height)


return final_img

# Set up processor parameters correctly
Expand All @@ -162,4 +165,4 @@ def after(self,p=None,processed=None):
processed.images.append(self.image_mask)
self.image_mask = None
self.show = False
return processed
return processed