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import argparse | |
import cv2 | |
import os | |
from PIL import Image, ImageDraw, ImageFont, ImageOps | |
import numpy as np | |
from pathlib import Path | |
import gradio as gr | |
import matplotlib.pyplot as plt | |
from loguru import logger | |
import subprocess | |
import copy | |
import time | |
import warnings | |
import io | |
import random | |
import torch | |
from torchvision.ops import box_convert | |
warnings.filterwarnings("ignore") | |
# grounding DINO | |
from groundingdino.models import build_model | |
from groundingdino.util.slconfig import SLConfig | |
from groundingdino.util.utils import clean_state_dict | |
from groundingdino.util.inference import annotate, load_image, predict | |
import groundingdino.datasets.transforms as T | |
# segment anything | |
from segment_anything import build_sam, SamPredictor | |
# lama-cleaner | |
from lama_cleaner.model_manager import ModelManager | |
from lama_cleaner.schema import Config as lama_Config | |
from lama_cleaner.helper import load_img, numpy_to_bytes, resize_max_size | |
#stable diffusion | |
from diffusers import StableDiffusionInpaintPipeline | |
from huggingface_hub import hf_hub_download | |
if not os.path.exists('./inpaint_demo.jpg'): | |
os.system("wget https://github.com/IDEA-Research/Grounded-Segment-Anything/raw/main/assets/inpaint_demo.jpg") | |
if not os.path.exists('./sam_vit_h_4b8939.pth'): | |
logger.info(f"get sam_vit_h_4b8939.pth...") | |
result = subprocess.run(['wget', 'https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth'], check=True) | |
print(f'wget sam_vit_h_4b8939.pth result = {result}') | |
# Use this command for evaluate the GLIP-T model | |
config_file = "groundingdino/config/GroundingDINO_SwinT_OGC.py" | |
ckpt_repo_id = "ShilongLiu/GroundingDINO" | |
ckpt_filename = "groundingdino_swint_ogc.pth" | |
sam_checkpoint = './sam_vit_h_4b8939.pth' | |
output_dir = "outputs" | |
groundingdino_device = 'cpu' | |
device = 'cuda' if torch.cuda.is_available() else 'cpu' | |
print(f'device={device}') | |
# make dir | |
os.makedirs(output_dir, exist_ok=True) | |
def load_model_hf(model_config_path, repo_id, filename, device='cpu'): | |
args = SLConfig.fromfile(model_config_path) | |
model = build_model(args) | |
args.device = device | |
cache_file = hf_hub_download(repo_id=repo_id, filename=filename) | |
checkpoint = torch.load(cache_file, map_location='cpu') | |
log = model.load_state_dict(clean_state_dict(checkpoint['model']), strict=False) | |
print("Model loaded from {} \n => {}".format(cache_file, log)) | |
_ = model.eval() | |
return model | |
def load_image_and_transform(init_image): | |
init_image = init_image.convert("RGB") | |
transform = T.Compose([ | |
T.RandomResize([800], max_size=1333), | |
T.ToTensor(), | |
T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) | |
]) | |
image, _ = transform(init_image, None) # 3, h, w | |
return init_image, image | |
def image_transform_grounding_for_vis(init_image): | |
transform = T.Compose([ | |
T.RandomResize([800], max_size=1333), | |
]) | |
image, _ = transform(init_image, None) # 3, h, w | |
return image | |
def plot_boxes_to_image(image_pil, tgt): | |
H, W = tgt["size"] | |
boxes = tgt["boxes"] | |
labels = tgt["labels"] | |
assert len(boxes) == len(labels), "boxes and labels must have same length" | |
draw = ImageDraw.Draw(image_pil) | |
mask = Image.new("L", image_pil.size, 0) | |
mask_draw = ImageDraw.Draw(mask) | |
# draw boxes and masks | |
for box, label in zip(boxes, labels): | |
# from 0..1 to 0..W, 0..H | |
box = box * torch.Tensor([W, H, W, H]) | |
# from xywh to xyxy | |
box[:2] -= box[2:] / 2 | |
box[2:] += box[:2] | |
# random color | |
color = tuple(np.random.randint(0, 255, size=3).tolist()) | |
# draw | |
x0, y0, x1, y1 = box | |
x0, y0, x1, y1 = int(x0), int(y0), int(x1), int(y1) | |
draw.rectangle([x0, y0, x1, y1], outline=color, width=6) | |
# draw.text((x0, y0), str(label), fill=color) | |
font = ImageFont.load_default() | |
if hasattr(font, "getbbox"): | |
bbox = draw.textbbox((x0, y0), str(label), font) | |
else: | |
w, h = draw.textsize(str(label), font) | |
bbox = (x0, y0, w + x0, y0 + h) | |
# bbox = draw.textbbox((x0, y0), str(label)) | |
draw.rectangle(bbox, fill=color) | |
font = os.path.join(cv2.__path__[0],'qt','fonts','DejaVuSans.ttf') | |
font_size = 20 | |
new_font = ImageFont.truetype(font, font_size) | |
draw.text((x0+2, y0+2), str(label), font=new_font, fill="white") | |
mask_draw.rectangle([x0, y0, x1, y1], fill=255, width=6) | |
return image_pil, mask | |
def show_mask(mask, ax, random_color=False): | |
if random_color: | |
color = np.concatenate([np.random.random(3), np.array([0.8])], axis=0) | |
else: | |
color = np.array([30/255, 144/255, 255/255, 0.6]) | |
h, w = mask.shape[-2:] | |
mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1) | |
ax.imshow(mask_image) | |
def show_box(box, ax, label): | |
x0, y0 = box[0], box[1] | |
w, h = box[2] - box[0], box[3] - box[1] | |
ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='red', facecolor=(0,0,0,0), lw=1)) | |
ax.text(x0, y0+20, label, fontdict={'fontsize': 6}, color="white") | |
def get_grounding_box(image_tensor, grounding_caption, box_threshold, text_threshold): | |
# run grounding | |
boxes, logits, phrases = predict(groundingDino_model, image_tensor, grounding_caption, box_threshold, text_threshold, device=groundingdino_device) | |
labels = [ | |
f"{phrase} ({logit:.2f})" | |
for phrase, logit | |
in zip(phrases, logits) | |
] | |
# annotated_frame = annotate(image_source=np.asarray(image_pil), boxes=boxes, logits=logits, phrases=phrases) | |
# image_with_box = Image.fromarray(cv2.cvtColor(annotated_frame, cv2.COLOR_BGR2RGB)) | |
return boxes, labels | |
def mask_extend(img, box, extend_pixels=10, useRectangle=True): | |
box[0] = int(box[0]) | |
box[1] = int(box[1]) | |
box[2] = int(box[2]) | |
box[3] = int(box[3]) | |
region = img.crop(tuple(box)) # crop based on bb box | |
new_width = box[2] - box[0] + 2*extend_pixels | |
new_height = box[3] - box[1] + 2*extend_pixels | |
region_BILINEAR = region.resize((int(new_width), int(new_height))) # resize the cropped region based on "extend_pixels" | |
if useRectangle: | |
region_draw = ImageDraw.Draw(region_BILINEAR) | |
region_draw.rectangle((0, 0, new_width, new_height), fill=(255, 255, 255)) # draw white rectangle | |
img.paste(region_BILINEAR, (int(box[0]-extend_pixels), int(box[1]-extend_pixels))) #pastes the resized region back into the original image at the same location as the original bounding box but with an additional padding of extend_pixels pixels on all sides | |
return img | |
def mix_masks(imgs): | |
re_img = 1 - np.asarray(imgs[0].convert("1")) | |
for i in range(len(imgs)-1): | |
re_img = np.multiply(re_img, 1 - np.asarray(imgs[i+1].convert("1"))) | |
re_img = 1 - re_img | |
return Image.fromarray(np.uint8(255*re_img)) | |
def lama_cleaner_process(image, mask): | |
ori_image = image | |
if mask.shape[0] == image.shape[1] and mask.shape[1] == image.shape[0] and mask.shape[0] != mask.shape[1]: | |
# rotate image | |
ori_image = np.transpose(image[::-1, ...][:, ::-1], axes=(1, 0, 2))[::-1, ...] | |
image = ori_image | |
original_shape = ori_image.shape | |
interpolation = cv2.INTER_CUBIC | |
size_limit = 1080 | |
if size_limit == "Original": | |
size_limit = max(image.shape) | |
else: | |
size_limit = int(size_limit) | |
config = lama_Config( | |
ldm_steps=25, | |
ldm_sampler='plms', | |
zits_wireframe=True, | |
hd_strategy='Original', | |
hd_strategy_crop_margin=196, | |
hd_strategy_crop_trigger_size=1280, | |
hd_strategy_resize_limit=2048, | |
prompt='', | |
use_croper=False, | |
croper_x=0, | |
croper_y=0, | |
croper_height=512, | |
croper_width=512, | |
sd_mask_blur=5, | |
sd_strength=0.75, | |
sd_steps=50, | |
sd_guidance_scale=7.5, | |
sd_sampler='ddim', | |
sd_seed=42, | |
cv2_flag='INPAINT_NS', | |
cv2_radius=5, | |
) | |
if config.sd_seed == -1: | |
config.sd_seed = random.randint(1, 999999999) | |
# logger.info(f"Origin image shape_0_: {original_shape} / {size_limit}") | |
image = resize_max_size(image, size_limit=size_limit, interpolation=interpolation) | |
# logger.info(f"Resized image shape_1_: {image.shape}") | |
# logger.info(f"mask image shape_0_: {mask.shape} / {type(mask)}") | |
mask = resize_max_size(mask, size_limit=size_limit, interpolation=interpolation) | |
# logger.info(f"mask image shape_1_: {mask.shape} / {type(mask)}") | |
res_np_img = lama_cleaner_model(image, mask, config) | |
torch.cuda.empty_cache() | |
image = Image.open(io.BytesIO(numpy_to_bytes(res_np_img, 'png'))) | |
return image | |
def xywh_to_xyxy(box, sizeW, sizeH): | |
if isinstance(box, list): | |
box = torch.Tensor(box) | |
box = box * torch.Tensor([sizeW, sizeH, sizeW, sizeH]) | |
box[:2] -= box[2:] / 2 # top left corner | |
box[2:] += box[:2] # bottom right corner | |
box = box.numpy() | |
return box | |
def to_extend_mask(segment_mask, boxes_filt, size, remove_mask_extend, remove_mode): | |
# remove from mask | |
mask_imgs = [] | |
masks_shape = segment_mask.shape | |
boxes_filt_ori_array = boxes_filt.numpy() | |
if inpaint_mode == 'merge': | |
extend_shape_0 = masks_shape[0] | |
extend_shape_1 = masks_shape[1] | |
else: | |
extend_shape_0 = 1 | |
extend_shape_1 = 1 | |
for i in range(extend_shape_0): | |
for j in range(extend_shape_1): | |
mask = segment_mask[i][j].cpu().numpy() | |
mask_pil = Image.fromarray(mask) | |
if remove_mode == 'segment': | |
useRectangle = False | |
else: | |
useRectangle = True | |
try: | |
remove_mask_extend = int(remove_mask_extend) | |
except: | |
remove_mask_extend = 10 | |
mask_pil_exp = mask_extend(copy.deepcopy(mask_pil).convert("RGB"), | |
xywh_to_xyxy(torch.tensor(boxes_filt_ori_array[i]), size[0], size[1]), | |
extend_pixels=remove_mask_extend, useRectangle=useRectangle) | |
mask_imgs.append(mask_pil_exp) | |
mask_pil = mix_masks(mask_imgs) | |
return mask_pil | |
def run_anything_task(input_image, text_prompt, task_type, inpaint_prompt, box_threshold, text_threshold, | |
iou_threshold, inpaint_mode, mask_source_radio, remove_mode, remove_mask_extend): | |
text_prompt = text_prompt.strip() | |
# user guidance messages | |
if not (task_type == 'inpainting' or task_type == 'remove'): | |
if text_prompt == '': | |
return [], gr.Gallery.update(label='Please input detection prompt~~') | |
if input_image is None: | |
return [], gr.Gallery.update(label='Please upload a image~~') | |
file_temp = int(time.time()) | |
# load mask | |
input_mask_pil = input_image['mask'] | |
input_mask = np.array(input_mask_pil.convert("L")) | |
# load image | |
image_pil, image_tensor = load_image_and_transform(input_image['image']) | |
output_images = [] | |
output_images.append(input_image['image']) | |
# RUN GROUNDINGDINO: we skip DINO if we draw mask on the image | |
if (task_type == 'inpainting' or task_type == 'remove') and mask_source_radio == mask_source_draw: | |
pass | |
else: | |
boxes, phrases = get_grounding_box(image_tensor, text_prompt, box_threshold, text_threshold) | |
if boxes.size(0) == 0: | |
logger.info(f'run_grounded_sam_[]_{task_type}_[{text_prompt}]_1_[No objects detected, please try others.]_') | |
return [], gr.Gallery.update(label='No objects detected, please try others!') | |
boxes_filt_ori = copy.deepcopy(boxes) | |
size = image_pil.size | |
pred_dict = { | |
"boxes": boxes, | |
"size": [size[1], size[0]], # H,W | |
"labels": phrases, | |
} | |
# store and save DINO output | |
image_with_box = plot_boxes_to_image(copy.deepcopy(image_pil), pred_dict)[0] | |
image_path = os.path.join(output_dir, f"grounding_dino_output_{file_temp}.jpg") | |
image_with_box.save(image_path) | |
detection_image_result = cv2.cvtColor(cv2.imread(image_path), cv2.COLOR_BGR2RGB) | |
os.remove(image_path) | |
output_images.append(detection_image_result) | |
# if mask is detected from DINO | |
logger.info(f'run_anything_task_[{file_temp}]_{task_type}_2_') | |
if task_type == 'segment' or ((task_type == 'inpainting' or task_type == 'remove') | |
and mask_source_radio == mask_source_segment): | |
image = np.array(input_image['image']) | |
sam_predictor.set_image(image) | |
# map the bounding boxes from dino to original size | |
h, w = size[1], size[0] | |
boxes = boxes * torch.Tensor([w, h, w, h]) | |
boxes = box_convert(boxes=boxes, in_fmt="cxcywh", out_fmt="xyxy") | |
# can use box_convert function or below | |
# for i in range(boxes.size(0)): | |
# boxes[i] = boxes[i] * torch.Tensor([W, H, W, H]) | |
# boxes[i][:2] -= boxes[i][2:] / 2 # top left corner | |
# boxes[i][2:] += boxes[i][:2] # bottom left corner | |
# transform boxes from original ratio to sam's zoomed ratio | |
transformed_boxes = sam_predictor.transform.apply_boxes_torch(boxes, image.shape[:2]) | |
# predict masks/segmentation | |
# masks: [number of masks, C, H, W] but note that H and W is 512 | |
masks, _, _ = sam_predictor.predict_torch( | |
point_coords = None, | |
point_labels = None, | |
boxes = transformed_boxes, | |
multimask_output = False, | |
) | |
# draw output image | |
plt.figure() | |
plt.imshow(image) | |
for mask in masks: | |
show_mask(mask.cpu().numpy(), plt.gca(), random_color=True) | |
for box, label in zip(boxes, phrases): | |
show_box(box.numpy(), plt.gca(), label) | |
plt.axis('off') | |
image_path = os.path.join(output_dir, f"grounding_seg_output_{file_temp}.jpg") | |
plt.savefig(image_path, bbox_inches="tight") | |
segment_image_result = cv2.cvtColor(cv2.imread(image_path), cv2.COLOR_BGR2RGB) | |
os.remove(image_path) | |
output_images.append(segment_image_result) | |
logger.info(f'run_anything_task_[{file_temp}]_{task_type}_3_') | |
if task_type == 'segment': | |
logger.info(f'run_anything_task_[{file_temp}]_{task_type}_Final_') | |
return output_images, gr.Gallery.update(label='result images') | |
elif task_type == 'inpainting' or task_type == 'remove': | |
# if no inpaint prompt is entered, we treat it as remove | |
if inpaint_prompt.strip() == '' and mask_source_radio == mask_source_segment: | |
task_type = 'remove' | |
logger.info(f'run_anything_task_[{file_temp}]_{task_type}_4_') | |
if mask_source_radio == mask_source_draw: | |
mask_pil = input_mask_pil | |
mask = input_mask | |
else: | |
masks_ori = copy.deepcopy(masks) | |
# inpainting pipeline | |
if inpaint_mode == 'merge': | |
masks = torch.sum(masks, dim=0).unsqueeze(0) | |
masks = torch.where(masks > 0, True, False) | |
# simply choose the first mask, which will be refine in the future release | |
mask = masks[0][0].cpu().numpy() | |
mask_pil = Image.fromarray(mask) | |
output_images.append(mask_pil.convert("RGB")) | |
if task_type == 'inpainting': | |
# inpainting pipeline | |
image_source_for_inpaint = image_pil.resize((512, 512)) | |
if remove_mask_extend: | |
mask_pil = to_extend_mask(masks_ori, boxes_filt_ori, size, remove_mask_extend, remove_mode) | |
output_images.append(mask_pil.convert("RGB")) | |
image_mask_for_inpaint = mask_pil.resize((512, 512)) | |
image_inpainting = sd_pipe(prompt=inpaint_prompt, image=image_source_for_inpaint, mask_image=image_mask_for_inpaint).images[0] | |
else: | |
# remove from mask | |
if mask_source_radio == mask_source_segment: | |
if remove_mask_extend: | |
mask_pil = to_extend_mask(masks_ori, boxes_filt_ori, size, remove_mask_extend, remove_mode) | |
output_images.append(mask_pil.convert("RGB")) | |
image_inpainting = lama_cleaner_process(np.array(image_pil), np.array(mask_pil.convert("L"))) | |
image_inpainting = image_inpainting.resize((image_pil.size[0], image_pil.size[1])) | |
output_images.append(image_inpainting) | |
return output_images, gr.Gallery.update(label='result images') | |
else: | |
logger.info(f"task_type:{task_type} error!") | |
logger.info(f'run_anything_task_[{file_temp}]_Final_Inpainting_') | |
return output_images, gr.Gallery.update(label='result images') | |
def change_radio_display(task_type, mask_source_radio): | |
text_prompt_visible = True | |
inpaint_prompt_visible = False | |
mask_source_radio_visible = False | |
if task_type == "inpainting": | |
inpaint_prompt_visible = True | |
if task_type == "inpainting" or task_type == "remove": | |
mask_source_radio_visible = True | |
if mask_source_radio == mask_source_draw: | |
text_prompt_visible = False | |
return gr.Textbox.update(visible=text_prompt_visible), gr.Textbox.update(visible=inpaint_prompt_visible), gr.Radio.update(visible=mask_source_radio_visible) | |
# model initialization | |
groundingDino_model = load_model_hf(config_file, ckpt_repo_id, ckpt_filename, groundingdino_device) | |
sam_predictor = SamPredictor(build_sam(checkpoint=sam_checkpoint)) | |
lama_cleaner_model = ModelManager(name='lama',device='cpu') | |
# initialize stable-diffusion-inpainting | |
logger.info(f"initialize stable-diffusion-inpainting...") | |
sd_pipe = None | |
if os.environ.get('IS_MY_DEBUG') is None: | |
sd_pipe = StableDiffusionInpaintPipeline.from_pretrained( | |
"runwayml/stable-diffusion-inpainting", | |
torch_dtype=torch.float16 | |
) | |
sd_pipe = sd_pipe.to(device) | |
if __name__ == "__main__": | |
mask_source_draw = "Draw mask on image." | |
mask_source_segment = "Segment based on prompt and inpaint." | |
parser = argparse.ArgumentParser("Grounding SAM demo", add_help=True) | |
parser.add_argument("--debug", action="store_true", help="using debug mode") | |
parser.add_argument("--share", action="store_true", help="share the app") | |
args = parser.parse_args() | |
print(f'args = {args}') | |
block = gr.Blocks().queue() | |
with block: | |
gr.Markdown("# GroundingDino SAM and Stable Diffusion") | |
with gr.Row(): | |
with gr.Column(): | |
input_image = gr.Image( | |
source="upload", elem_id="image_upload", type="pil", tool="sketch", value="inpaint_demo.jpg", label="Upload") | |
task_type = gr.Radio(["segment", "inpainting", "remove"], value="segment", | |
label='Task type', visible=True) | |
mask_source_radio = gr.Radio([mask_source_draw, mask_source_segment], | |
value=mask_source_segment, label="Mask from", | |
visible=False) | |
text_prompt = gr.Textbox(label="Detection Prompt, seperating each name with dot '.', i.e.: bear.cat.dog.chair ]", \ | |
value='dog', placeholder="Cannot be empty") | |
inpaint_prompt = gr.Textbox(label="Inpaint Prompt (if this is empty, then remove)", visible=False) | |
run_button = gr.Button(label="Run") | |
with gr.Accordion("Advanced options", open=False): | |
box_threshold = gr.Slider( | |
label="Box Threshold", minimum=0.0, maximum=1.0, value=0.25, step=0.001 | |
) | |
text_threshold = gr.Slider( | |
label="Text Threshold", minimum=0.0, maximum=1.0, value=0.25, step=0.001 | |
) | |
iou_threshold = gr.Slider( | |
label="IOU Threshold", minimum=0.0, maximum=1.0, value=0.8, step=0.001 | |
) | |
inpaint_mode = gr.Radio(["merge", "first"], value="merge", label="inpaint_mode") | |
with gr.Row(): | |
with gr.Column(scale=1): | |
remove_mode = gr.Radio(["segment", "rectangle"], value="segment", label='remove mode') | |
with gr.Column(scale=1): | |
remove_mask_extend = gr.Textbox(label="Enlarge Mask (Empty: no mask extension, default: 10)", value=10) | |
with gr.Column(): | |
gallery = gr.Gallery(label="result images", show_label=True, elem_id="gallery", visible=True | |
).style(preview=True, columns=[5], object_fit="scale-down", height="auto") | |
task_type.change(fn=change_radio_display, inputs=[task_type, mask_source_radio], outputs=[text_prompt, inpaint_prompt, mask_source_radio]) | |
mask_source_radio.change(fn=change_radio_display, inputs=[task_type, mask_source_radio], outputs=[text_prompt, inpaint_prompt, mask_source_radio]) | |
DESCRIPTION = '### This demo from [Grounded-Segment-Anything](https://github.com/IDEA-Research/Grounded-Segment-Anything) and kudos to thier excellent works. Welcome everyone to try this out and learn together!' | |
gr.Markdown(DESCRIPTION) | |
run_button.click(fn=run_anything_task, inputs=[ | |
input_image, text_prompt, task_type, inpaint_prompt, | |
box_threshold,text_threshold, iou_threshold, inpaint_mode, | |
mask_source_radio, remove_mode, remove_mask_extend], | |
outputs=[gallery, gallery], show_progress=True, queue=True) | |
block.launch(debug=args.debug, share=args.share, show_api=False, show_error=True) |