|
import os |
|
import sys |
|
import cv2 |
|
from PIL import Image |
|
import numpy as np |
|
import gradio as gr |
|
|
|
from modules import processing, images |
|
from modules import scripts, script_callbacks, shared, devices, modelloader |
|
from modules.processing import Processed, StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img |
|
from modules.shared import opts, cmd_opts, state |
|
from modules.sd_models import model_hash |
|
from modules.paths import models_path |
|
from basicsr.utils.download_util import load_file_from_url |
|
|
|
dd_models_path = os.path.join(models_path, "mmdet") |
|
|
|
def list_models(model_path): |
|
model_list = modelloader.load_models(model_path=model_path, ext_filter=[".pth"]) |
|
|
|
def modeltitle(path, shorthash): |
|
abspath = os.path.abspath(path) |
|
|
|
if abspath.startswith(model_path): |
|
name = abspath.replace(model_path, '') |
|
else: |
|
name = os.path.basename(path) |
|
|
|
if name.startswith("\\") or name.startswith("/"): |
|
name = name[1:] |
|
|
|
shortname = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0] |
|
|
|
return f'{name} [{shorthash}]', shortname |
|
|
|
models = [] |
|
for filename in model_list: |
|
h = model_hash(filename) |
|
title, short_model_name = modeltitle(filename, h) |
|
models.append(title) |
|
|
|
return models |
|
|
|
def startup(): |
|
from launch import is_installed, run |
|
if not is_installed("mmdet"): |
|
python = sys.executable |
|
run(f'"{python}" -m pip install -U openmim', desc="Installing openmim", errdesc="Couldn't install openmim") |
|
run(f'"{python}" -m mim install mmcv-full', desc=f"Installing mmcv-full", errdesc=f"Couldn't install mmcv-full") |
|
run(f'"{python}" -m pip install mmdet', desc=f"Installing mmdet", errdesc=f"Couldn't install mmdet") |
|
|
|
if (len(list_models(dd_models_path)) == 0): |
|
print("No detection models found, downloading...") |
|
bbox_path = os.path.join(dd_models_path, "bbox") |
|
segm_path = os.path.join(dd_models_path, "segm") |
|
load_file_from_url("https://huggingface.co/dustysys/ddetailer/resolve/main/mmdet/bbox/mmdet_anime-face_yolov3.pth", bbox_path) |
|
load_file_from_url("https://huggingface.co/dustysys/ddetailer/raw/main/mmdet/bbox/mmdet_anime-face_yolov3.py", bbox_path) |
|
load_file_from_url("https://huggingface.co/dustysys/ddetailer/resolve/main/mmdet/segm/mmdet_dd-person_mask2former.pth", segm_path) |
|
load_file_from_url("https://huggingface.co/dustysys/ddetailer/raw/main/mmdet/segm/mmdet_dd-person_mask2former.py", segm_path) |
|
|
|
startup() |
|
|
|
def gr_show(visible=True): |
|
return {"visible": visible, "__type__": "update"} |
|
|
|
class DetectionDetailerScript(scripts.Script): |
|
def title(self): |
|
return "Detection Detailer" |
|
|
|
def show(self, is_img2img): |
|
return True |
|
|
|
def ui(self, is_img2img): |
|
import modules.ui |
|
|
|
model_list = list_models(dd_models_path) |
|
model_list.insert(0, "None") |
|
if is_img2img: |
|
info = gr.HTML("<p style=\"margin-bottom:0.75em\">Recommended settings: Use from inpaint tab, inpaint at full res ON, denoise <0.5</p>") |
|
else: |
|
info = gr.HTML("") |
|
with gr.Group(): |
|
with gr.Row(): |
|
dd_model_a = gr.Dropdown(label="Primary detection model (A)", choices=model_list,value = "None", visible=True, type="value") |
|
|
|
with gr.Row(): |
|
dd_conf_a = gr.Slider(label='Detection confidence threshold % (A)', minimum=0, maximum=100, step=1, value=30, visible=False) |
|
dd_dilation_factor_a = gr.Slider(label='Dilation factor (A)', minimum=0, maximum=255, step=1, value=4, visible=False) |
|
|
|
with gr.Row(): |
|
dd_offset_x_a = gr.Slider(label='X offset (A)', minimum=-200, maximum=200, step=1, value=0, visible=False) |
|
dd_offset_y_a = gr.Slider(label='Y offset (A)', minimum=-200, maximum=200, step=1, value=0, visible=False) |
|
|
|
with gr.Row(): |
|
dd_preprocess_b = gr.Checkbox(label='Inpaint model B detections before model A runs', value=False, visible=False) |
|
dd_bitwise_op = gr.Radio(label='Bitwise operation', choices=['None', 'A&B', 'A-B'], value="None", visible=False) |
|
|
|
br = gr.HTML("<br>") |
|
|
|
with gr.Group(): |
|
with gr.Row(): |
|
dd_model_b = gr.Dropdown(label="Secondary detection model (B) (optional)", choices=model_list,value = "None", visible =False, type="value") |
|
|
|
with gr.Row(): |
|
dd_conf_b = gr.Slider(label='Detection confidence threshold % (B)', minimum=0, maximum=100, step=1, value=30, visible=False) |
|
dd_dilation_factor_b = gr.Slider(label='Dilation factor (B)', minimum=0, maximum=255, step=1, value=4, visible=False) |
|
|
|
with gr.Row(): |
|
dd_offset_x_b = gr.Slider(label='X offset (B)', minimum=-200, maximum=200, step=1, value=0, visible=False) |
|
dd_offset_y_b = gr.Slider(label='Y offset (B)', minimum=-200, maximum=200, step=1, value=0, visible=False) |
|
|
|
with gr.Group(): |
|
with gr.Row(): |
|
dd_mask_blur = gr.Slider(label='Mask blur ', minimum=0, maximum=64, step=1, value=4, visible=(not is_img2img)) |
|
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)) |
|
|
|
with gr.Row(): |
|
dd_inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution ', value=True, visible = (not is_img2img)) |
|
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)) |
|
|
|
dd_model_a.change( |
|
lambda modelname: { |
|
dd_model_b:gr_show( modelname != "None" ), |
|
dd_conf_a:gr_show( modelname != "None" ), |
|
dd_dilation_factor_a:gr_show( modelname != "None"), |
|
dd_offset_x_a:gr_show( modelname != "None" ), |
|
dd_offset_y_a:gr_show( modelname != "None" ) |
|
|
|
}, |
|
inputs= [dd_model_a], |
|
outputs =[dd_model_b, dd_conf_a, dd_dilation_factor_a, dd_offset_x_a, dd_offset_y_a] |
|
) |
|
|
|
dd_model_b.change( |
|
lambda modelname: { |
|
dd_preprocess_b:gr_show( modelname != "None" ), |
|
dd_bitwise_op:gr_show( modelname != "None" ), |
|
dd_conf_b:gr_show( modelname != "None" ), |
|
dd_dilation_factor_b:gr_show( modelname != "None"), |
|
dd_offset_x_b:gr_show( modelname != "None" ), |
|
dd_offset_y_b:gr_show( modelname != "None" ) |
|
}, |
|
inputs= [dd_model_b], |
|
outputs =[dd_preprocess_b, dd_bitwise_op, dd_conf_b, dd_dilation_factor_b, dd_offset_x_b, dd_offset_y_b] |
|
) |
|
|
|
return [info, |
|
dd_model_a, |
|
dd_conf_a, dd_dilation_factor_a, |
|
dd_offset_x_a, dd_offset_y_a, |
|
dd_preprocess_b, dd_bitwise_op, |
|
br, |
|
dd_model_b, |
|
dd_conf_b, dd_dilation_factor_b, |
|
dd_offset_x_b, dd_offset_y_b, |
|
dd_mask_blur, dd_denoising_strength, |
|
dd_inpaint_full_res, dd_inpaint_full_res_padding |
|
] |
|
|
|
def run(self, p, info, |
|
dd_model_a, |
|
dd_conf_a, dd_dilation_factor_a, |
|
dd_offset_x_a, dd_offset_y_a, |
|
dd_preprocess_b, dd_bitwise_op, |
|
br, |
|
dd_model_b, |
|
dd_conf_b, dd_dilation_factor_b, |
|
dd_offset_x_b, dd_offset_y_b, |
|
dd_mask_blur, dd_denoising_strength, |
|
dd_inpaint_full_res, dd_inpaint_full_res_padding): |
|
|
|
processing.fix_seed(p) |
|
initial_info = None |
|
seed = p.seed |
|
p.batch_size = 1 |
|
ddetail_count = p.n_iter |
|
p.n_iter = 1 |
|
p.do_not_save_grid = True |
|
p.do_not_save_samples = True |
|
is_txt2img = isinstance(p, StableDiffusionProcessingTxt2Img) |
|
if (not is_txt2img): |
|
orig_image = p.init_images[0] |
|
else: |
|
p_txt = p |
|
p = StableDiffusionProcessingImg2Img( |
|
init_images = None, |
|
resize_mode = 0, |
|
denoising_strength = dd_denoising_strength, |
|
mask = None, |
|
mask_blur= dd_mask_blur, |
|
inpainting_fill = 1, |
|
inpaint_full_res = dd_inpaint_full_res, |
|
inpaint_full_res_padding= dd_inpaint_full_res_padding, |
|
inpainting_mask_invert= 0, |
|
sd_model=p_txt.sd_model, |
|
outpath_samples=p_txt.outpath_samples, |
|
outpath_grids=p_txt.outpath_grids, |
|
prompt=p_txt.prompt, |
|
negative_prompt=p_txt.negative_prompt, |
|
styles=p_txt.styles, |
|
seed=p_txt.seed, |
|
subseed=p_txt.subseed, |
|
subseed_strength=p_txt.subseed_strength, |
|
seed_resize_from_h=p_txt.seed_resize_from_h, |
|
seed_resize_from_w=p_txt.seed_resize_from_w, |
|
sampler_name=p_txt.sampler_name, |
|
n_iter=p_txt.n_iter, |
|
steps=p_txt.steps, |
|
cfg_scale=p_txt.cfg_scale, |
|
width=p_txt.width, |
|
height=p_txt.height, |
|
tiling=p_txt.tiling, |
|
) |
|
p.do_not_save_grid = True |
|
p.do_not_save_samples = True |
|
output_images = [] |
|
state.job_count = ddetail_count |
|
for n in range(ddetail_count): |
|
devices.torch_gc() |
|
start_seed = seed + n |
|
if ( is_txt2img ): |
|
print(f"Processing initial image for output generation {n + 1}.") |
|
p_txt.seed = start_seed |
|
processed = processing.process_images(p_txt) |
|
init_image = processed.images[0] |
|
else: |
|
init_image = orig_image |
|
|
|
output_images.append(init_image) |
|
masks_a = [] |
|
masks_b_pre = [] |
|
|
|
|
|
if (dd_model_b != "None" and dd_preprocess_b): |
|
label_b_pre = "B" |
|
results_b_pre = inference(init_image, dd_model_b, dd_conf_b/100.0, label_b_pre) |
|
masks_b_pre = create_segmasks(results_b_pre) |
|
masks_b_pre = dilate_masks(masks_b_pre, dd_dilation_factor_b, 1) |
|
masks_b_pre = offset_masks(masks_b_pre,dd_offset_x_b, dd_offset_y_b) |
|
if (len(masks_b_pre) > 0): |
|
results_b_pre = update_result_masks(results_b_pre, masks_b_pre) |
|
segmask_preview_b = create_segmask_preview(results_b_pre, init_image) |
|
shared.state.current_image = segmask_preview_b |
|
if ( opts.dd_save_previews): |
|
images.save_image(segmask_preview_b, opts.outdir_ddetailer_previews, "", start_seed, p.prompt, opts.samples_format, p=p) |
|
gen_count = len(masks_b_pre) |
|
state.job_count += gen_count |
|
print(f"Processing {gen_count} model {label_b_pre} 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_b_pre[i] |
|
if ( opts.dd_save_masks): |
|
images.save_image(masks_b_pre[i], opts.outdir_ddetailer_masks, "", start_seed, p.prompt, opts.samples_format, p=p) |
|
processed = processing.process_images(p) |
|
p.seed = processed.seed + 1 |
|
p.init_images = processed.images |
|
|
|
if (gen_count > 0): |
|
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.") |
|
|
|
|
|
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 initial_info is None: |
|
initial_info = processed.info |
|
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=initial_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 (initial_info is None): |
|
initial_info = "No detections found." |
|
|
|
return Processed(p, output_images, seed, initial_info) |
|
|
|
def modeldataset(model_shortname): |
|
path = modelpath(model_shortname) |
|
if ("mmdet" in path and "segm" in path): |
|
dataset = 'coco' |
|
else: |
|
dataset = '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 |
|
|
|
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] |
|
|
|
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 = bboxes[i][4] |
|
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 |
|
|
|
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) |
|
|
