896 lines
31 KiB
Python
896 lines
31 KiB
Python
import os
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import sys
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import cv2
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import gradio as gr
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import numpy as np
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from basicsr.utils.download_util import load_file_from_url
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from PIL import Image
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from modules import (
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devices,
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images,
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modelloader,
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processing,
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script_callbacks,
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scripts,
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shared,
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)
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from modules.paths import models_path
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from modules.processing import (
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Processed,
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StableDiffusionProcessingImg2Img,
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StableDiffusionProcessingTxt2Img,
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)
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from modules.sd_models import model_hash
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from modules.shared import cmd_opts, opts, state
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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(("\\", "/")):
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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, python
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if not is_installed("mmdet"):
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run(f'"{python}" -m pip install openmim', desc="Installing openmim", errdesc="Couldn't install openmim")
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run(f'"{python}" -m mim install mmcv>=2.0.0 mmdet>=3.0.0', desc="Installing mmdet", errdesc="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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# bbox
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load_file_from_url(
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"https://huggingface.co/dustysys/ddetailer/resolve/main/mmdet/bbox/mmdet_anime-face_yolov3.pth",
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bbox_path,
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)
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load_file_from_url(
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"https://raw.githubusercontent.com/Bing-su/dddetailer/master/config/mmdet_anime-face_yolov3.py",
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bbox_path,
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)
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# segm
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load_file_from_url(
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"https://github.com/Bing-su/dddetailer/releases/download/segm/mmdet_dd-person_mask2former.pth",
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segm_path,
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)
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load_file_from_url(
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"https://raw.githubusercontent.com/Bing-su/dddetailer/master/config/mmdet_dd-person_mask2former.py",
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segm_path,
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)
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load_file_from_url(
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"https://raw.githubusercontent.com/Bing-su/dddetailer/master/config/mask2former_r50_8xb2-lsj-50e_coco-panoptic.py",
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segm_path,
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)
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load_file_from_url(
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"https://raw.githubusercontent.com/Bing-su/dddetailer/master/config/coco_panoptic.py",
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segm_path,
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)
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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(
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'<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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)
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else:
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info = gr.HTML("")
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dd_prompt = None
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with gr.Group():
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if not is_img2img:
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with gr.Row():
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dd_prompt = gr.Textbox(
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label="dd_prompt",
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elem_id="t2i_dd_prompt",
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show_label=False,
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lines=3,
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placeholder="Ddetailer Prompt",
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)
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with gr.Row():
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dd_neg_prompt = gr.Textbox(
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label="dd_neg_prompt",
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elem_id="t2i_dd_neg_prompt",
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show_label=False,
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lines=2,
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placeholder="Ddetailer Negative prompt",
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)
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with gr.Row():
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dd_model_a = gr.Dropdown(
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label="Primary detection model (A)",
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choices=model_list,
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value="None",
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visible=True,
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type="value",
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)
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with gr.Row():
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dd_conf_a = gr.Slider(
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label="Detection confidence threshold % (A)",
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minimum=0,
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maximum=100,
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step=1,
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value=30,
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visible=True,
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)
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dd_dilation_factor_a = gr.Slider(
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label="Dilation factor (A)",
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minimum=0,
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maximum=255,
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step=1,
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value=4,
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visible=True,
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)
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with gr.Row():
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dd_offset_x_a = gr.Slider(
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label="X offset (A)",
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minimum=-200,
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maximum=200,
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step=1,
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value=0,
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visible=True,
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)
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dd_offset_y_a = gr.Slider(
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label="Y offset (A)",
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minimum=-200,
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maximum=200,
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step=1,
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value=0,
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visible=True,
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)
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with gr.Row():
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dd_preprocess_b = gr.Checkbox(
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label="Inpaint model B detections before model A runs",
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value=False,
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visible=True,
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)
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dd_bitwise_op = gr.Radio(
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label="Bitwise operation",
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choices=["None", "A&B", "A-B"],
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value="None",
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visible=True,
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)
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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(
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label="Secondary detection model (B) (optional)",
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choices=model_list,
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value="None",
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visible=True,
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type="value",
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)
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with gr.Row():
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dd_conf_b = gr.Slider(
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label="Detection confidence threshold % (B)",
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minimum=0,
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maximum=100,
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step=1,
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value=30,
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visible=True,
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)
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dd_dilation_factor_b = gr.Slider(
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label="Dilation factor (B)",
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minimum=0,
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maximum=255,
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step=1,
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value=4,
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visible=True,
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)
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with gr.Row():
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dd_offset_x_b = gr.Slider(
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label="X offset (B)",
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minimum=-200,
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maximum=200,
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step=1,
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value=0,
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visible=True,
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)
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dd_offset_y_b = gr.Slider(
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label="Y offset (B)",
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minimum=-200,
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maximum=200,
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step=1,
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value=0,
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visible=True,
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)
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with gr.Group():
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with gr.Row():
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dd_mask_blur = gr.Slider(
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label="Mask blur ",
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minimum=0,
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maximum=64,
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step=1,
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value=4,
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visible=(not is_img2img),
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)
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dd_denoising_strength = gr.Slider(
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label="Denoising strength (Inpaint)",
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minimum=0.0,
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maximum=1.0,
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step=0.01,
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value=0.4,
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visible=(not is_img2img),
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)
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with gr.Row():
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dd_inpaint_full_res = gr.Checkbox(
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label="Inpaint at full resolution ",
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value=True,
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visible=(not is_img2img),
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)
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dd_inpaint_full_res_padding = gr.Slider(
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label="Inpaint at full resolution padding, pixels ",
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minimum=0,
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maximum=256,
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step=4,
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value=32,
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visible=(not is_img2img),
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)
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with gr.Row():
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dd_cfg_scale = gr.Slider(
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label="CFG Scale",
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minimum=0,
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maximum=30,
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step=0.5,
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value=7,
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visible=True,
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)
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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=[
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dd_model_b,
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dd_conf_a,
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dd_dilation_factor_a,
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dd_offset_x_a,
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dd_offset_y_a,
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],
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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=[
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dd_preprocess_b,
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dd_bitwise_op,
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dd_conf_b,
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dd_dilation_factor_b,
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dd_offset_x_b,
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dd_offset_y_b,
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],
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)
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if dd_prompt:
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self.infotext_fields = (
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(dd_prompt, "DDetailer prompt"),
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(dd_neg_prompt, "DDetailer neg prompt"),
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(dd_model_a, "DDetailer model a"),
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(dd_conf_a, "DDetailer conf a"),
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(dd_dilation_factor_a, "DDetailer dilation a"),
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(dd_offset_x_a, "DDetailer offset x a"),
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(dd_offset_y_a, "DDetailer offset y a"),
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(dd_preprocess_b, "DDetailer preprocess b"),
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(dd_bitwise_op, "DDetailer bitwise"),
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(dd_model_b, "DDetailer model b"),
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(dd_conf_b, "DDetailer conf b"),
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(dd_dilation_factor_b, "DDetailer dilation b"),
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(dd_offset_x_b, "DDetailer offset x b"),
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(dd_offset_y_b, "DDetailer offset y b"),
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(dd_mask_blur, "DDetailer mask blur"),
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(dd_denoising_strength, "DDetailer denoising"),
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(dd_inpaint_full_res, "DDetailer inpaint full"),
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(dd_inpaint_full_res_padding, "DDetailer inpaint padding"),
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(dd_cfg_scale, "DDetailer cfg"),
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)
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ret = [
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info,
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dd_model_a,
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dd_conf_a,
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dd_dilation_factor_a,
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dd_offset_x_a,
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dd_offset_y_a,
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dd_preprocess_b,
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dd_bitwise_op,
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br,
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dd_model_b,
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dd_conf_b,
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dd_dilation_factor_b,
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dd_offset_x_b,
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dd_offset_y_b,
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dd_mask_blur,
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dd_denoising_strength,
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dd_inpaint_full_res,
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dd_inpaint_full_res_padding,
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dd_cfg_scale,
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]
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if not is_img2img:
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ret += [dd_prompt, dd_neg_prompt]
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return ret
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def run(
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self,
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p,
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info,
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dd_model_a,
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dd_conf_a,
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dd_dilation_factor_a,
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dd_offset_x_a,
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dd_offset_y_a,
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dd_preprocess_b,
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dd_bitwise_op,
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br,
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dd_model_b,
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dd_conf_b,
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dd_dilation_factor_b,
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dd_offset_x_b,
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dd_offset_y_b,
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dd_mask_blur,
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dd_denoising_strength,
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dd_inpaint_full_res,
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dd_inpaint_full_res_padding,
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dd_cfg_scale,
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dd_prompt=None,
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dd_neg_prompt=None,
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):
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processing.fix_seed(p)
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dd_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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img2img_sampler_name = p_txt.sampler_name
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if p_txt.sampler_name in [
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"PLMS",
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"UniPC",
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]: # PLMS/UniPC do not support img2img so we just silently switch to DDIM
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img2img_sampler_name = "DDIM"
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p_txt_prompt = dd_prompt if dd_prompt else p_txt.prompt
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p_txt_neg_prompt = dd_neg_prompt if dd_neg_prompt else p_txt.negative_prompt
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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_neg_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=img2img_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=dd_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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info = processed.info
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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(
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init_image, dd_model_b, dd_conf_b / 100.0, label_b_pre
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)
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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(
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results_b_pre, init_image
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)
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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(
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segmask_preview_b,
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opts.outdir_ddetailer_previews,
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"",
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start_seed,
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p.prompt,
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opts.samples_format,
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p=p,
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)
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gen_count = len(masks_b_pre)
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state.job_count += gen_count
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print(
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f"Processing {gen_count} model {label_b_pre} detections for output generation {n + 1}."
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)
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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(
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masks_b_pre[i],
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opts.outdir_ddetailer_masks,
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"",
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start_seed,
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p.prompt,
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opts.samples_format,
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p=p,
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)
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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]
|
|
init_image = processed.images[0]
|
|
|
|
else:
|
|
print(
|
|
f"No model B detections for output generation {n} with current settings."
|
|
)
|
|
|
|
# Primary run
|
|
if dd_model_a != "None":
|
|
label_a = "A"
|
|
if dd_model_b != "None" and dd_bitwise_op != "None":
|
|
label_a = dd_bitwise_op
|
|
results_a = inference(
|
|
init_image, dd_model_a, dd_conf_a / 100.0, label_a
|
|
)
|
|
masks_a = create_segmasks(results_a)
|
|
masks_a = dilate_masks(masks_a, dd_dilation_factor_a, 1)
|
|
masks_a = offset_masks(masks_a, dd_offset_x_a, dd_offset_y_a)
|
|
if dd_model_b != "None" and dd_bitwise_op != "None":
|
|
label_b = "B"
|
|
results_b = inference(
|
|
init_image, dd_model_b, dd_conf_b / 100.0, label_b
|
|
)
|
|
masks_b = create_segmasks(results_b)
|
|
masks_b = dilate_masks(masks_b, dd_dilation_factor_b, 1)
|
|
masks_b = offset_masks(masks_b, dd_offset_x_b, dd_offset_y_b)
|
|
if len(masks_b) > 0:
|
|
combined_mask_b = combine_masks(masks_b)
|
|
for i in reversed(range(len(masks_a))):
|
|
if dd_bitwise_op == "A&B":
|
|
masks_a[i] = bitwise_and_masks(
|
|
masks_a[i], combined_mask_b
|
|
)
|
|
elif dd_bitwise_op == "A-B":
|
|
masks_a[i] = subtract_masks(masks_a[i], combined_mask_b)
|
|
if is_allblack(masks_a[i]):
|
|
del masks_a[i]
|
|
for result in results_a:
|
|
del result[i]
|
|
|
|
else:
|
|
print("No model B detections to overlap with model A masks")
|
|
results_a = []
|
|
masks_a = []
|
|
|
|
if len(masks_a) > 0:
|
|
results_a = update_result_masks(results_a, masks_a)
|
|
segmask_preview_a = create_segmask_preview(results_a, init_image)
|
|
shared.state.current_image = segmask_preview_a
|
|
if opts.dd_save_previews:
|
|
images.save_image(
|
|
segmask_preview_a,
|
|
opts.outdir_ddetailer_previews,
|
|
"",
|
|
start_seed,
|
|
p.prompt,
|
|
opts.samples_format,
|
|
p=p,
|
|
)
|
|
gen_count = len(masks_a)
|
|
state.job_count += gen_count
|
|
print(
|
|
f"Processing {gen_count} model {label_a} detections for output generation {n + 1}."
|
|
)
|
|
p.seed = start_seed
|
|
p.init_images = [init_image]
|
|
|
|
for i in range(gen_count):
|
|
p.image_mask = masks_a[i]
|
|
if opts.dd_save_masks:
|
|
images.save_image(
|
|
masks_a[i],
|
|
opts.outdir_ddetailer_masks,
|
|
"",
|
|
start_seed,
|
|
p.prompt,
|
|
opts.samples_format,
|
|
p=p,
|
|
)
|
|
|
|
processed = processing.process_images(p)
|
|
if dd_info is None:
|
|
dd_info = info + (
|
|
f', DDetailer prompt: "{dd_prompt}", DDetailer neg prompt: "{dd_neg_prompt}", '
|
|
f'DDetailer model a: "{dd_model_a}", DDetailer conf a: {dd_conf_a}, '
|
|
f"DDetailer dilation a: {dd_dilation_factor_a}, DDetailer offset x a: {dd_offset_x_a}, DDetailer offset y a: {dd_offset_y_a}, "
|
|
f'DDetailer preprocess b: {dd_preprocess_b}, DDetailer bitwise: {dd_bitwise_op}, DDetailer model b: "{dd_model_b}", '
|
|
f"DDetailer conf b: {dd_conf_b}, DDetailer dilation b: {dd_dilation_factor_b}, DDetailer offset x b: {dd_offset_x_b}, "
|
|
f"DDetailer offset y b: {dd_offset_y_b}, DDetailer mask blur: {dd_mask_blur}, DDetailer denoising: {dd_denoising_strength}, "
|
|
f"DDetailer inpaint full: {dd_inpaint_full_res}, DDetailer inpaint padding: {dd_inpaint_full_res_padding}, "
|
|
f"DDetailer cfg: {dd_cfg_scale}"
|
|
).replace("\n", " ")
|
|
p.seed = processed.seed + 1
|
|
p.init_images = processed.images
|
|
|
|
if gen_count > 0:
|
|
output_images[n] = processed.images[0]
|
|
if opts.samples_save:
|
|
images.save_image(
|
|
processed.images[0],
|
|
p.outpath_samples,
|
|
"",
|
|
start_seed,
|
|
p.prompt,
|
|
opts.samples_format,
|
|
info=dd_info,
|
|
p=p,
|
|
)
|
|
|
|
else:
|
|
print(
|
|
f"No model {label_a} detections for output generation {n} with current settings."
|
|
)
|
|
state.job = f"Generation {n + 1} out of {state.job_count}"
|
|
if dd_info is None:
|
|
dd_info = info + ", No detections found."
|
|
|
|
return Processed(p, output_images, seed, dd_info)
|
|
|
|
|
|
def modeldataset(model_shortname):
|
|
path = modelpath(model_shortname)
|
|
dataset = "coco" if "mmdet" in path and "segm" in path else "bbox"
|
|
return dataset
|
|
|
|
|
|
def modelpath(model_shortname):
|
|
model_list = modelloader.load_models(model_path=dd_models_path, ext_filter=[".pth"])
|
|
model_h = model_shortname.split("[")[-1].split("]")[0]
|
|
for path in model_list:
|
|
if model_hash(path) == model_h:
|
|
return path
|
|
return None
|
|
|
|
|
|
def update_result_masks(results, masks):
|
|
for i in range(len(masks)):
|
|
boolmask = np.array(masks[i], dtype=bool)
|
|
results[2][i] = boolmask
|
|
return results
|
|
|
|
|
|
def create_segmask_preview(results, image):
|
|
labels = results[0]
|
|
bboxes = results[1]
|
|
segms = results[2]
|
|
scores = results[3]
|
|
|
|
cv2_image = np.array(image)
|
|
cv2_image = cv2_image[:, :, ::-1].copy()
|
|
|
|
for i in range(len(segms)):
|
|
color = np.full_like(
|
|
cv2_image, np.random.randint(100, 256, (1, 3), dtype=np.uint8)
|
|
)
|
|
alpha = 0.2
|
|
color_image = cv2.addWeighted(cv2_image, alpha, color, 1 - alpha, 0)
|
|
cv2_mask = segms[i].astype(np.uint8) * 255
|
|
cv2_mask_bool = np.array(segms[i], dtype=bool)
|
|
centroid = np.mean(np.argwhere(cv2_mask_bool), axis=0)
|
|
centroid_x, centroid_y = int(centroid[1]), int(centroid[0])
|
|
|
|
cv2_mask_rgb = cv2.merge((cv2_mask, cv2_mask, cv2_mask))
|
|
cv2_image = np.where(cv2_mask_rgb == 255, color_image, cv2_image)
|
|
text_color = tuple([int(x) for x in (color[0][0] - 100)])
|
|
name = labels[i]
|
|
score = scores[i]
|
|
score = str(score)[:4]
|
|
text = name + ":" + score
|
|
cv2.putText(
|
|
cv2_image,
|
|
text,
|
|
(centroid_x - 30, centroid_y),
|
|
cv2.FONT_HERSHEY_DUPLEX,
|
|
0.4,
|
|
text_color,
|
|
1,
|
|
cv2.LINE_AA,
|
|
)
|
|
|
|
if len(segms) > 0:
|
|
preview_image = Image.fromarray(cv2.cvtColor(cv2_image, cv2.COLOR_BGR2RGB))
|
|
else:
|
|
preview_image = image
|
|
|
|
return preview_image
|
|
|
|
|
|
def is_allblack(mask):
|
|
cv2_mask = np.array(mask)
|
|
return cv2.countNonZero(cv2_mask) == 0
|
|
|
|
|
|
def bitwise_and_masks(mask1, mask2):
|
|
cv2_mask1 = np.array(mask1)
|
|
cv2_mask2 = np.array(mask2)
|
|
cv2_mask = cv2.bitwise_and(cv2_mask1, cv2_mask2)
|
|
mask = Image.fromarray(cv2_mask)
|
|
return mask
|
|
|
|
|
|
def subtract_masks(mask1, mask2):
|
|
cv2_mask1 = np.array(mask1)
|
|
cv2_mask2 = np.array(mask2)
|
|
cv2_mask = cv2.subtract(cv2_mask1, cv2_mask2)
|
|
mask = Image.fromarray(cv2_mask)
|
|
return mask
|
|
|
|
|
|
def dilate_masks(masks, dilation_factor, iter=1):
|
|
if dilation_factor == 0:
|
|
return masks
|
|
dilated_masks = []
|
|
kernel = np.ones((dilation_factor, dilation_factor), np.uint8)
|
|
for i in range(len(masks)):
|
|
cv2_mask = np.array(masks[i])
|
|
dilated_mask = cv2.dilate(cv2_mask, kernel, iter)
|
|
dilated_masks.append(Image.fromarray(dilated_mask))
|
|
return dilated_masks
|
|
|
|
|
|
def offset_masks(masks, offset_x, offset_y):
|
|
if offset_x == 0 and offset_y == 0:
|
|
return masks
|
|
offset_masks = []
|
|
for i in range(len(masks)):
|
|
cv2_mask = np.array(masks[i])
|
|
offset_mask = cv2_mask.copy()
|
|
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
|
|
|
|
|
|
from mmdet.apis import inference_detector, init_detector
|
|
from mmdet.evaluation import get_classes
|
|
|
|
|
|
def get_device():
|
|
device_id = shared.cmd_opts.device_id
|
|
cuda_device = f"cuda:{device_id}" if device_id is not None else "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, palette="random", device=model_device
|
|
)
|
|
mmdet_results = inference_detector(model, np.array(image)).pred_instances
|
|
bboxes = mmdet_results.bboxes.numpy()
|
|
segms = mmdet_results.masks.numpy()
|
|
scores = mmdet_results.scores.numpy()
|
|
dataset = modeldataset(modelname)
|
|
classes = get_classes(dataset)
|
|
|
|
n, m = bboxes.shape
|
|
if n == 0:
|
|
return [[], [], [], []]
|
|
labels = mmdet_results.labels
|
|
filter_inds = np.where(mmdet_results.scores > 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])
|
|
results[3].append(scores[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, palette="random", device=model_device
|
|
)
|
|
output = inference_detector(model, np.array(image)).pred_instances
|
|
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 in output.bboxes:
|
|
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 = output.bboxes.shape
|
|
if n == 0:
|
|
return [[], [], [], []]
|
|
bboxes = output.bboxes.numpy()
|
|
scores = output.scores.numpy()
|
|
filter_inds = np.where(scores > conf_thres)[0]
|
|
results = [[], [], [], []]
|
|
for i in filter_inds:
|
|
results[0].append(label)
|
|
results[1].append(bboxes[i])
|
|
results[2].append(segms[i])
|
|
results[3].append(scores[i])
|
|
|
|
return results
|
|
|
|
|
|
script_callbacks.on_ui_settings(on_ui_settings)
|