liuyizhang
update app.py
7957dbb
raw
history blame
13.1 kB
import subprocess, os, sys, time
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
result = subprocess.run(['pip', 'install', '-e', 'GroundingDINO'], check=True)
print(f'pip install GroundingDINO = {result}')
result = subprocess.run(['pip', 'list'], check=True)
print(f'pip list = {result}')
sys.path.insert(0, './GroundingDINO')
if not os.path.exists('./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}')
import gradio as gr
import argparse
import copy
import numpy as np
import torch
from PIL import Image, ImageDraw, ImageFont
# Grounding DINO
import GroundingDINO.groundingdino.datasets.transforms as T
from GroundingDINO.groundingdino.models import build_model
from GroundingDINO.groundingdino.util import box_ops
from GroundingDINO.groundingdino.util.slconfig import SLConfig
from GroundingDINO.groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap
# segment anything
from segment_anything import build_sam, SamPredictor
import cv2
import numpy as np
import matplotlib.pyplot as plt
# diffusers
import PIL
import requests
import torch
from io import BytesIO
from diffusers import StableDiffusionInpaintPipeline
from huggingface_hub import hf_hub_download
def get_device():
from numba import cuda
if cuda.is_available():
device = 'cuda:0' # cuda.get_current_device()
else:
device = 'cpu'
return device
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=device)
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 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)
draw.text((x0, y0), str(label), fill="white")
mask_draw.rectangle([x0, y0, x1, y1], fill=255, width=6)
return image_pil, mask
def load_image(image_path):
# # load image
# image_pil = Image.open(image_path).convert("RGB") # load image
image_pil = image_path
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(image_pil, None) # 3, h, w
return image_pil, image
def load_model(model_config_path, model_checkpoint_path, device):
args = SLConfig.fromfile(model_config_path)
args.device = device
model = build_model(args)
checkpoint = torch.load(model_checkpoint_path, map_location=device) #"cpu")
load_res = model.load_state_dict(clean_state_dict(checkpoint["model"]), strict=False)
print(load_res)
_ = model.eval()
return model
def get_grounding_output(model, image, caption, box_threshold, text_threshold, with_logits=True, device="cpu"):
caption = caption.lower()
caption = caption.strip()
if not caption.endswith("."):
caption = caption + "."
model = model.to(device)
image = image.to(device)
with torch.no_grad():
outputs = model(image[None], captions=[caption])
logits = outputs["pred_logits"].cpu().sigmoid()[0] # (nq, 256)
boxes = outputs["pred_boxes"].cpu()[0] # (nq, 4)
logits.shape[0]
# filter output
logits_filt = logits.clone()
boxes_filt = boxes.clone()
filt_mask = logits_filt.max(dim=1)[0] > box_threshold
logits_filt = logits_filt[filt_mask] # num_filt, 256
boxes_filt = boxes_filt[filt_mask] # num_filt, 4
logits_filt.shape[0]
# get phrase
tokenlizer = model.tokenizer
tokenized = tokenlizer(caption)
# build pred
pred_phrases = []
for logit, box in zip(logits_filt, boxes_filt):
pred_phrase = get_phrases_from_posmap(logit > text_threshold, tokenized, tokenlizer)
if with_logits:
pred_phrases.append(pred_phrase + f"({str(logit.max().item())[:4]})")
else:
pred_phrases.append(pred_phrase)
return boxes_filt, pred_phrases
def show_mask(mask, ax, random_color=False):
if random_color:
color = np.concatenate([np.random.random(3), np.array([0.6])], 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='green', facecolor=(0,0,0,0), lw=2))
ax.text(x0, y0, label)
config_file = 'GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py'
ckpt_repo_id = "ShilongLiu/GroundingDINO"
ckpt_filenmae = "groundingdino_swint_ogc.pth"
sam_checkpoint = './sam_vit_h_4b8939.pth'
output_dir = "outputs"
device = "cuda"
device = get_device()
print(f'device={device}')
# initialize groundingdino model
groundingdino_model = load_model_hf(config_file, ckpt_repo_id, ckpt_filenmae)
# initialize SAM
sam_predictor = SamPredictor(build_sam(checkpoint=sam_checkpoint))
# initialize stable-diffusion-inpainting
sd_pipe = StableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting",
torch_dtype=torch.float16
)
sd_pipe = sd_pipe.to(device)
def run_grounded_sam(image_path, text_prompt, task_type, inpaint_prompt, box_threshold, text_threshold):
assert text_prompt, 'text_prompt is not found!'
# make dir
os.makedirs(output_dir, exist_ok=True)
# load image
image_pil, image = load_image(image_path.convert("RGB"))
file_temp = int(time.time())
# visualize raw image
# image_pil.save(os.path.join(output_dir, f"raw_image_{file_temp}.jpg"))
# run grounding dino model
groundingdino_device = 'cpu'
if device != 'cpu':
try:
from groundingdino import _C
groundingdino_device = 'cuda:0'
except:
warnings.warn("Failed to load custom C++ ops. Running on CPU mode Only in groundingdino!")
groundingdino_device = 'cpu'
boxes_filt, pred_phrases = get_grounding_output(
groundingdino_model, image, text_prompt, box_threshold, text_threshold, device=groundingdino_device
)
size = image_pil.size
if task_type == 'segment' or task_type == 'inpainting':
image = np.array(image_path)
sam_predictor.set_image(image)
H, W = size[1], size[0]
for i in range(boxes_filt.size(0)):
boxes_filt[i] = boxes_filt[i] * torch.Tensor([W, H, W, H])
boxes_filt[i][:2] -= boxes_filt[i][2:] / 2
boxes_filt[i][2:] += boxes_filt[i][:2]
boxes_filt = boxes_filt.cpu()
transformed_boxes = sam_predictor.transform.apply_boxes_torch(boxes_filt, image.shape[:2])
masks, _, _ = sam_predictor.predict_torch(
point_coords = None,
point_labels = None,
boxes = transformed_boxes,
multimask_output = False,
)
# masks: [1, 1, 512, 512]
if task_type == 'detection':
pred_dict = {
"boxes": boxes_filt,
"size": [size[1], size[0]], # H,W
"labels": pred_phrases,
}
# import ipdb; ipdb.set_trace()
image_with_box = plot_boxes_to_image(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)
image_result = cv2.cvtColor(cv2.imread(image_path), cv2.COLOR_BGR2RGB)
os.remove(image_path)
return image_result
elif task_type == 'segment':
assert sam_checkpoint, 'sam_checkpoint is not found!'
# draw output image
plt.figure(figsize=(10, 10))
plt.imshow(image)
for mask in masks:
show_mask(mask.cpu().numpy(), plt.gca(), random_color=True)
for box, label in zip(boxes_filt, pred_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")
image_result = cv2.cvtColor(cv2.imread(image_path), cv2.COLOR_BGR2RGB)
os.remove(image_path)
return image_result
elif task_type == 'inpainting':
assert inpaint_prompt, 'inpaint_prompt is not found!'
# inpainting pipeline
mask = masks[0][0].cpu().numpy() # simply choose the first mask, which will be refine in the future release
mask_pil = Image.fromarray(mask)
image_pil = Image.fromarray(image)
image = sd_pipe(prompt=inpaint_prompt, image=image_pil, mask_image=mask_pil).images[0]
image_path = os.path.join(output_dir, f"grounded_sam_inpainting_output_{file_temp}.jpg")
image.save(image_path)
image_result = cv2.cvtColor(cv2.imread(image_path), cv2.COLOR_BGR2RGB)
os.remove(image_path)
return image_result
else:
print("task_type:{} error!".format(task_type))
def change_task_type(task_type):
if task_type == "inpainting":
return gr.Textbox.update(visible=True)
else:
return gr.Textbox.update(visible=False)
if __name__ == "__main__":
parser = argparse.ArgumentParser("Grounded 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:
with gr.Row():
with gr.Column():
input_image = gr.Image(source='upload', type="pil")
task_type = gr.Radio(["detection", "segment", "inpainting"], value="detection",
label='Task type',interactive=True, visible=True)
text_prompt = gr.Textbox(label="Detection Prompt", placeholder="Cannot be empty")
inpaint_prompt = gr.Textbox(label="Inpaint Prompt", visible=True)
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=0.3, step=0.001
)
text_threshold = gr.Slider(
label="Text Threshold", minimum=0.0, maximum=1.0, value=0.25, step=0.001
)
with gr.Column():
gallery = gr.outputs.Image(
type="pil",
).style(full_width=True, full_height=True)
run_button.click(fn=run_grounded_sam, inputs=[
input_image, text_prompt, task_type, inpaint_prompt, box_threshold, text_threshold], outputs=[gallery])
# task_type.change(fn=change_task_type, inputs=[task_type], outputs=[inpaint_prompt])
DESCRIPTION = '### This demo from [Grounded-Segment-Anything](https://github.com/IDEA-Research/Grounded-Segment-Anything). Thanks for their excellent work.'
DESCRIPTION += f'<p>For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings. <a href="https://huggingface.co/spaces/yizhangliu/Grounded-Segment-Anything?duplicate=true"><img style="display: inline; margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space" /></a></p>'
gr.Markdown(DESCRIPTION)
block.launch(server_name='0.0.0.0', debug=args.debug, share=args.share)