537 lines
23 KiB
Python
537 lines
23 KiB
Python
import os
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import sys
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import cv2
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from PIL import Image
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import numpy as np
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import gradio as gr
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from modules import processing, images
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from modules import scripts, script_callbacks, shared, devices, modelloader
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from modules.processing import Processed, StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
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from modules.shared import opts, cmd_opts, state
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from modules.sd_models import model_hash
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from modules.paths import models_path
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from basicsr.utils.download_util import load_file_from_url
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dd_models_path = os.path.join(models_path, "mmdet")
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def list_models(model_path):
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model_list = modelloader.load_models(model_path=model_path, ext_filter=[".pth"])
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def modeltitle(path, shorthash):
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abspath = os.path.abspath(path)
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if abspath.startswith(model_path):
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name = abspath.replace(model_path, '')
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else:
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name = os.path.basename(path)
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if name.startswith("\\") or name.startswith("/"):
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name = name[1:]
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shortname = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
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return f'{name} [{shorthash}]', shortname
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models = []
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for filename in model_list:
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h = model_hash(filename)
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title, short_model_name = modeltitle(filename, h)
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models.append(title)
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return models
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def startup():
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from launch import is_installed, run
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if not is_installed("mmdet"):
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python = sys.executable
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run(f'"{python}" -m pip install -U openmim', desc="Installing openmim", errdesc="Couldn't install openmim")
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run(f'"{python}" -m mim install mmcv-full', desc=f"Installing mmcv-full", errdesc=f"Couldn't install mmcv-full")
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run(f'"{python}" -m pip install mmdet', desc=f"Installing mmdet", errdesc=f"Couldn't install mmdet")
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if (len(list_models(dd_models_path)) == 0):
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print("No detection models found, downloading...")
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bbox_path = os.path.join(dd_models_path, "bbox")
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segm_path = os.path.join(dd_models_path, "segm")
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load_file_from_url("https://huggingface.co/dustysys/ddetailer/resolve/main/mmdet/bbox/mmdet_anime-face_yolov3.pth", bbox_path)
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load_file_from_url("https://huggingface.co/dustysys/ddetailer/raw/main/mmdet/bbox/mmdet_anime-face_yolov3.py", bbox_path)
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load_file_from_url("https://huggingface.co/dustysys/ddetailer/resolve/main/mmdet/segm/mmdet_dd-person_mask2former.pth", segm_path)
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load_file_from_url("https://huggingface.co/dustysys/ddetailer/raw/main/mmdet/segm/mmdet_dd-person_mask2former.py", segm_path)
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startup()
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def gr_show(visible=True):
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return {"visible": visible, "__type__": "update"}
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class DetectionDetailerScript(scripts.Script):
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def title(self):
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return "Detection Detailer"
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def show(self, is_img2img):
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return True
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def ui(self, is_img2img):
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import modules.ui
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model_list = list_models(dd_models_path)
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model_list.insert(0, "None")
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if is_img2img:
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info = gr.HTML("<p style=\"margin-bottom:0.75em\">Recommended settings: Use from inpaint tab, inpaint at full res ON, denoise <0.5</p>")
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else:
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info = gr.HTML("")
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with gr.Group():
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with gr.Row():
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dd_model_a = gr.Dropdown(label="Primary detection model (A)", choices=model_list,value = "None", visible=True, type="value")
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with gr.Row():
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dd_conf_a = gr.Slider(label='Detection confidence threshold % (A)', minimum=0, maximum=100, step=1, value=30, visible=False)
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dd_dilation_factor_a = gr.Slider(label='Dilation factor (A)', minimum=0, maximum=255, step=1, value=4, visible=False)
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with gr.Row():
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dd_offset_x_a = gr.Slider(label='X offset (A)', minimum=-200, maximum=200, step=1, value=0, visible=False)
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dd_offset_y_a = gr.Slider(label='Y offset (A)', minimum=-200, maximum=200, step=1, value=0, visible=False)
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with gr.Row():
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dd_preprocess_b = gr.Checkbox(label='Inpaint model B detections before model A runs', value=False, visible=False)
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dd_bitwise_op = gr.Radio(label='Bitwise operation', choices=['None', 'A&B', 'A-B'], value="None", visible=False)
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br = gr.HTML("<br>")
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with gr.Group():
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with gr.Row():
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dd_model_b = gr.Dropdown(label="Secondary detection model (B) (optional)", choices=model_list,value = "None", visible =False, type="value")
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with gr.Row():
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dd_conf_b = gr.Slider(label='Detection confidence threshold % (B)', minimum=0, maximum=100, step=1, value=30, visible=False)
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dd_dilation_factor_b = gr.Slider(label='Dilation factor (B)', minimum=0, maximum=255, step=1, value=4, visible=False)
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with gr.Row():
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dd_offset_x_b = gr.Slider(label='X offset (B)', minimum=-200, maximum=200, step=1, value=0, visible=False)
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dd_offset_y_b = gr.Slider(label='Y offset (B)', minimum=-200, maximum=200, step=1, value=0, visible=False)
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with gr.Group():
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with gr.Row():
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dd_mask_blur = gr.Slider(label='Mask blur ', minimum=0, maximum=64, step=1, value=4, visible=(not is_img2img))
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dd_denoising_strength = gr.Slider(label='Denoising strength (Inpaint)', minimum=0.0, maximum=1.0, step=0.01, value=0.4, visible=(not is_img2img))
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with gr.Row():
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dd_inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution ', value=True, visible = (not is_img2img))
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dd_inpaint_full_res_padding = gr.Slider(label='Inpaint at full resolution padding, pixels ', minimum=0, maximum=256, step=4, value=32, visible=(not is_img2img))
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dd_model_a.change(
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lambda modelname: {
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dd_model_b:gr_show( modelname != "None" ),
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dd_conf_a:gr_show( modelname != "None" ),
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dd_dilation_factor_a:gr_show( modelname != "None"),
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dd_offset_x_a:gr_show( modelname != "None" ),
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dd_offset_y_a:gr_show( modelname != "None" )
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},
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inputs= [dd_model_a],
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outputs =[dd_model_b, dd_conf_a, dd_dilation_factor_a, dd_offset_x_a, dd_offset_y_a]
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)
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dd_model_b.change(
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lambda modelname: {
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dd_preprocess_b:gr_show( modelname != "None" ),
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dd_bitwise_op:gr_show( modelname != "None" ),
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dd_conf_b:gr_show( modelname != "None" ),
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dd_dilation_factor_b:gr_show( modelname != "None"),
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dd_offset_x_b:gr_show( modelname != "None" ),
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dd_offset_y_b:gr_show( modelname != "None" )
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},
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inputs= [dd_model_b],
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outputs =[dd_preprocess_b, dd_bitwise_op, dd_conf_b, dd_dilation_factor_b, dd_offset_x_b, dd_offset_y_b]
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)
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return [info,
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dd_model_a,
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dd_conf_a, dd_dilation_factor_a,
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dd_offset_x_a, dd_offset_y_a,
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dd_preprocess_b, dd_bitwise_op,
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br,
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dd_model_b,
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dd_conf_b, dd_dilation_factor_b,
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dd_offset_x_b, dd_offset_y_b,
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dd_mask_blur, dd_denoising_strength,
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dd_inpaint_full_res, dd_inpaint_full_res_padding
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]
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def run(self, p, info,
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dd_model_a,
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dd_conf_a, dd_dilation_factor_a,
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dd_offset_x_a, dd_offset_y_a,
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dd_preprocess_b, dd_bitwise_op,
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br,
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dd_model_b,
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dd_conf_b, dd_dilation_factor_b,
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dd_offset_x_b, dd_offset_y_b,
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dd_mask_blur, dd_denoising_strength,
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dd_inpaint_full_res, dd_inpaint_full_res_padding):
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processing.fix_seed(p)
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initial_info = None
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seed = p.seed
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p.batch_size = 1
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ddetail_count = p.n_iter
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p.n_iter = 1
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p.do_not_save_grid = True
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p.do_not_save_samples = True
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is_txt2img = isinstance(p, StableDiffusionProcessingTxt2Img)
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if (not is_txt2img):
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orig_image = p.init_images[0]
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else:
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p_txt = p
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p = StableDiffusionProcessingImg2Img(
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init_images = None,
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resize_mode = 0,
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denoising_strength = dd_denoising_strength,
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mask = None,
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mask_blur= dd_mask_blur,
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inpainting_fill = 1,
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inpaint_full_res = dd_inpaint_full_res,
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inpaint_full_res_padding= dd_inpaint_full_res_padding,
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inpainting_mask_invert= 0,
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sd_model=p_txt.sd_model,
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outpath_samples=p_txt.outpath_samples,
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outpath_grids=p_txt.outpath_grids,
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prompt=p_txt.prompt,
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negative_prompt=p_txt.negative_prompt,
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styles=p_txt.styles,
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seed=p_txt.seed,
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subseed=p_txt.subseed,
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subseed_strength=p_txt.subseed_strength,
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seed_resize_from_h=p_txt.seed_resize_from_h,
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seed_resize_from_w=p_txt.seed_resize_from_w,
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sampler_name=p_txt.sampler_name,
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n_iter=p_txt.n_iter,
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steps=p_txt.steps,
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cfg_scale=p_txt.cfg_scale,
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width=p_txt.width,
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height=p_txt.height,
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tiling=p_txt.tiling,
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)
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p.do_not_save_grid = True
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p.do_not_save_samples = True
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output_images = []
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state.job_count = ddetail_count
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for n in range(ddetail_count):
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devices.torch_gc()
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start_seed = seed + n
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if ( is_txt2img ):
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print(f"Processing initial image for output generation {n + 1}.")
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p_txt.seed = start_seed
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processed = processing.process_images(p_txt)
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init_image = processed.images[0]
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else:
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init_image = orig_image
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output_images.append(init_image)
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masks_a = []
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masks_b_pre = []
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# Optional secondary pre-processing run
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if (dd_model_b != "None" and dd_preprocess_b):
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label_b_pre = "B"
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results_b_pre = inference(init_image, dd_model_b, dd_conf_b/100.0, label_b_pre)
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masks_b_pre = create_segmasks(results_b_pre)
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masks_b_pre = dilate_masks(masks_b_pre, dd_dilation_factor_b, 1)
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masks_b_pre = offset_masks(masks_b_pre,dd_offset_x_b, dd_offset_y_b)
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if (len(masks_b_pre) > 0):
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results_b_pre = update_result_masks(results_b_pre, masks_b_pre)
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segmask_preview_b = create_segmask_preview(results_b_pre, init_image)
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shared.state.current_image = segmask_preview_b
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if ( opts.dd_save_previews):
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images.save_image(segmask_preview_b, opts.outdir_ddetailer_previews, "", start_seed, p.prompt, opts.samples_format, p=p)
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gen_count = len(masks_b_pre)
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state.job_count += gen_count
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print(f"Processing {gen_count} model {label_b_pre} detections for output generation {n + 1}.")
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p.seed = start_seed
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p.init_images = [init_image]
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for i in range(gen_count):
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p.image_mask = masks_b_pre[i]
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if ( opts.dd_save_masks):
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images.save_image(masks_b_pre[i], opts.outdir_ddetailer_masks, "", start_seed, p.prompt, opts.samples_format, p=p)
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processed = processing.process_images(p)
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p.seed = processed.seed + 1
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p.init_images = processed.images
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if (gen_count > 0):
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output_images[n] = processed.images[0]
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init_image = processed.images[0]
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else:
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print(f"No model B detections for output generation {n} with current settings.")
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# Primary run
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if (dd_model_a != "None"):
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label_a = "A"
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if (dd_model_b != "None" and dd_bitwise_op != "None"):
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label_a = dd_bitwise_op
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results_a = inference(init_image, dd_model_a, dd_conf_a/100.0, label_a)
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masks_a = create_segmasks(results_a)
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masks_a = dilate_masks(masks_a, dd_dilation_factor_a, 1)
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masks_a = offset_masks(masks_a,dd_offset_x_a, dd_offset_y_a)
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if (dd_model_b != "None" and dd_bitwise_op != "None"):
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label_b = "B"
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results_b = inference(init_image, dd_model_b, dd_conf_b/100.0, label_b)
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masks_b = create_segmasks(results_b)
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masks_b = dilate_masks(masks_b, dd_dilation_factor_b, 1)
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masks_b = offset_masks(masks_b,dd_offset_x_b, dd_offset_y_b)
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if (len(masks_b) > 0):
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combined_mask_b = combine_masks(masks_b)
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for i in reversed(range(len(masks_a))):
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if (dd_bitwise_op == "A&B"):
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masks_a[i] = bitwise_and_masks(masks_a[i], combined_mask_b)
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elif (dd_bitwise_op == "A-B"):
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masks_a[i] = subtract_masks(masks_a[i], combined_mask_b)
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if (is_allblack(masks_a[i])):
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del masks_a[i]
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for result in results_a:
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del result[i]
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else:
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print("No model B detections to overlap with model A masks")
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results_a = []
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masks_a = []
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if (len(masks_a) > 0):
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results_a = update_result_masks(results_a, masks_a)
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segmask_preview_a = create_segmask_preview(results_a, init_image)
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shared.state.current_image = segmask_preview_a
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if ( opts.dd_save_previews):
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images.save_image(segmask_preview_a, opts.outdir_ddetailer_previews, "", start_seed, p.prompt, opts.samples_format, p=p)
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gen_count = len(masks_a)
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state.job_count += gen_count
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print(f"Processing {gen_count} model {label_a} detections for output generation {n + 1}.")
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p.seed = start_seed
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p.init_images = [init_image]
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for i in range(gen_count):
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p.image_mask = masks_a[i]
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if ( opts.dd_save_masks):
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images.save_image(masks_a[i], opts.outdir_ddetailer_masks, "", start_seed, p.prompt, opts.samples_format, p=p)
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processed = processing.process_images(p)
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if initial_info is None:
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initial_info = processed.info
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p.seed = processed.seed + 1
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p.init_images = processed.images
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if (gen_count > 0):
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output_images[n] = processed.images[0]
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if ( opts.samples_save ):
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images.save_image(processed.images[0], p.outpath_samples, "", start_seed, p.prompt, opts.samples_format, info=initial_info, p=p)
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else:
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print(f"No model {label_a} detections for output generation {n} with current settings.")
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state.job = f"Generation {n + 1} out of {state.job_count}"
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if (initial_info is None):
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initial_info = "No detections found."
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return Processed(p, output_images, seed, initial_info)
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def modeldataset(model_shortname):
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path = modelpath(model_shortname)
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if ("mmdet" in path and "segm" in path):
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dataset = 'coco'
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else:
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dataset = 'bbox'
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return dataset
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def modelpath(model_shortname):
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model_list = modelloader.load_models(model_path=dd_models_path, ext_filter=[".pth"])
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model_h = model_shortname.split("[")[-1].split("]")[0]
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for path in model_list:
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if ( model_hash(path) == model_h):
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return path
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def update_result_masks(results, masks):
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for i in range(len(masks)):
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boolmask = np.array(masks[i], dtype=bool)
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results[2][i] = boolmask
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return results
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def create_segmask_preview(results, image):
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labels = results[0]
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bboxes = results[1]
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segms = results[2]
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cv2_image = np.array(image)
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cv2_image = cv2_image[:, :, ::-1].copy()
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for i in range(len(segms)):
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color = np.full_like(cv2_image, np.random.randint(100, 256, (1, 3), dtype=np.uint8))
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alpha = 0.2
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color_image = cv2.addWeighted(cv2_image, alpha, color, 1-alpha, 0)
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cv2_mask = segms[i].astype(np.uint8) * 255
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cv2_mask_bool = np.array(segms[i], dtype=bool)
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centroid = np.mean(np.argwhere(cv2_mask_bool),axis=0)
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centroid_x, centroid_y = int(centroid[1]), int(centroid[0])
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cv2_mask_rgb = cv2.merge((cv2_mask, cv2_mask, cv2_mask))
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cv2_image = np.where(cv2_mask_rgb == 255, color_image, cv2_image)
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text_color = tuple([int(x) for x in ( color[0][0] - 100 )])
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name = labels[i]
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score = bboxes[i][4]
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score = str(score)[:4]
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text = name + ":" + score
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cv2.putText(cv2_image, text, (centroid_x - 30, centroid_y), cv2.FONT_HERSHEY_DUPLEX, 0.4, text_color, 1, cv2.LINE_AA)
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if ( len(segms) > 0):
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preview_image = Image.fromarray(cv2.cvtColor(cv2_image, cv2.COLOR_BGR2RGB))
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else:
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preview_image = image
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return preview_image
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def is_allblack(mask):
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cv2_mask = np.array(mask)
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return cv2.countNonZero(cv2_mask) == 0
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def bitwise_and_masks(mask1, mask2):
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cv2_mask1 = np.array(mask1)
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cv2_mask2 = np.array(mask2)
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cv2_mask = cv2.bitwise_and(cv2_mask1, cv2_mask2)
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mask = Image.fromarray(cv2_mask)
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return mask
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def subtract_masks(mask1, mask2):
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cv2_mask1 = np.array(mask1)
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cv2_mask2 = np.array(mask2)
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cv2_mask = cv2.subtract(cv2_mask1, cv2_mask2)
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mask = Image.fromarray(cv2_mask)
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return mask
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|
|
|
def dilate_masks(masks, dilation_factor, iter=1):
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|
if dilation_factor == 0:
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return masks
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|
dilated_masks = []
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|
kernel = np.ones((dilation_factor,dilation_factor), np.uint8)
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|
for i in range(len(masks)):
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cv2_mask = np.array(masks[i])
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dilated_mask = cv2.dilate(cv2_mask, kernel, iter)
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dilated_masks.append(Image.fromarray(dilated_mask))
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return dilated_masks
|
|
|
|
def offset_masks(masks, offset_x, offset_y):
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|
if (offset_x == 0 and offset_y == 0):
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|
return masks
|
|
offset_masks = []
|
|
for i in range(len(masks)):
|
|
cv2_mask = np.array(masks[i])
|
|
offset_mask = cv2_mask.copy()
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|
offset_mask = np.roll(offset_mask, -offset_y, axis=0)
|
|
offset_mask = np.roll(offset_mask, offset_x, axis=1)
|
|
|
|
offset_masks.append(Image.fromarray(offset_mask))
|
|
return offset_masks
|
|
|
|
def combine_masks(masks):
|
|
initial_cv2_mask = np.array(masks[0])
|
|
combined_cv2_mask = initial_cv2_mask
|
|
for i in range(1, len(masks)):
|
|
cv2_mask = np.array(masks[i])
|
|
combined_cv2_mask = cv2.bitwise_or(combined_cv2_mask, cv2_mask)
|
|
|
|
combined_mask = Image.fromarray(combined_cv2_mask)
|
|
return combined_mask
|
|
|
|
def on_ui_settings():
|
|
shared.opts.add_option("dd_save_previews", shared.OptionInfo(False, "Save mask previews", section=("ddetailer", "Detection Detailer")))
|
|
shared.opts.add_option("outdir_ddetailer_previews", shared.OptionInfo("extensions/ddetailer/outputs/masks-previews", 'Output directory for mask previews', section=("ddetailer", "Detection Detailer")))
|
|
shared.opts.add_option("dd_save_masks", shared.OptionInfo(False, "Save masks", section=("ddetailer", "Detection Detailer")))
|
|
shared.opts.add_option("outdir_ddetailer_masks", shared.OptionInfo("extensions/ddetailer/outputs/masks", 'Output directory for masks', section=("ddetailer", "Detection Detailer")))
|
|
|
|
def create_segmasks(results):
|
|
segms = results[2]
|
|
segmasks = []
|
|
for i in range(len(segms)):
|
|
cv2_mask = segms[i].astype(np.uint8) * 255
|
|
mask = Image.fromarray(cv2_mask)
|
|
segmasks.append(mask)
|
|
|
|
return segmasks
|
|
|
|
import mmcv
|
|
from mmdet.core import get_classes
|
|
from mmdet.apis import (inference_detector,
|
|
init_detector)
|
|
|
|
def get_device():
|
|
device_id = shared.cmd_opts.device_id
|
|
if device_id is not None:
|
|
cuda_device = f"cuda:{device_id}"
|
|
else:
|
|
cuda_device = "cpu"
|
|
return cuda_device
|
|
|
|
def inference(image, modelname, conf_thres, label):
|
|
path = modelpath(modelname)
|
|
if ( "mmdet" in path and "bbox" in path ):
|
|
results = inference_mmdet_bbox(image, modelname, conf_thres, label)
|
|
elif ( "mmdet" in path and "segm" in path):
|
|
results = inference_mmdet_segm(image, modelname, conf_thres, label)
|
|
return results
|
|
|
|
def inference_mmdet_segm(image, modelname, conf_thres, label):
|
|
model_checkpoint = modelpath(modelname)
|
|
model_config = os.path.splitext(model_checkpoint)[0] + ".py"
|
|
model_device = get_device()
|
|
model = init_detector(model_config, model_checkpoint, device=model_device)
|
|
mmdet_results = inference_detector(model, np.array(image))
|
|
bbox_results, segm_results = mmdet_results
|
|
dataset = modeldataset(modelname)
|
|
classes = get_classes(dataset)
|
|
labels = [
|
|
np.full(bbox.shape[0], i, dtype=np.int32)
|
|
for i, bbox in enumerate(bbox_results)
|
|
]
|
|
n,m = bbox_results[0].shape
|
|
if (n == 0):
|
|
return [[],[],[]]
|
|
labels = np.concatenate(labels)
|
|
bboxes = np.vstack(bbox_results)
|
|
segms = mmcv.concat_list(segm_results)
|
|
filter_inds = np.where(bboxes[:,-1] > conf_thres)[0]
|
|
results = [[],[],[]]
|
|
for i in filter_inds:
|
|
results[0].append(label + "-" + classes[labels[i]])
|
|
results[1].append(bboxes[i])
|
|
results[2].append(segms[i])
|
|
|
|
return results
|
|
|
|
def inference_mmdet_bbox(image, modelname, conf_thres, label):
|
|
model_checkpoint = modelpath(modelname)
|
|
model_config = os.path.splitext(model_checkpoint)[0] + ".py"
|
|
model_device = get_device()
|
|
model = init_detector(model_config, model_checkpoint, device=model_device)
|
|
results = inference_detector(model, np.array(image))
|
|
cv2_image = np.array(image)
|
|
cv2_image = cv2_image[:, :, ::-1].copy()
|
|
cv2_gray = cv2.cvtColor(cv2_image, cv2.COLOR_BGR2GRAY)
|
|
|
|
segms = []
|
|
for (x0, y0, x1, y1, conf) in results[0]:
|
|
cv2_mask = np.zeros((cv2_gray.shape), np.uint8)
|
|
cv2.rectangle(cv2_mask, (int(x0), int(y0)), (int(x1), int(y1)), 255, -1)
|
|
cv2_mask_bool = cv2_mask.astype(bool)
|
|
segms.append(cv2_mask_bool)
|
|
|
|
n,m = results[0].shape
|
|
if (n == 0):
|
|
return [[],[],[]]
|
|
bboxes = np.vstack(results[0])
|
|
filter_inds = np.where(bboxes[:,-1] > conf_thres)[0]
|
|
results = [[],[],[]]
|
|
for i in filter_inds:
|
|
results[0].append(label)
|
|
results[1].append(bboxes[i])
|
|
results[2].append(segms[i])
|
|
|
|
return results
|
|
|
|
script_callbacks.on_ui_settings(on_ui_settings)
|