import os, sys
import random
import warnings
os.system("python -m pip install -e sam-hq")
os.system("python -m pip install -e GroundingDINO")
os.system("pip install opencv-python pycocotools matplotlib onnxruntime onnx ipykernel")
os.system("wget https://huggingface.co/ShilongLiu/GroundingDINO/resolve/main/groundingdino_swint_ogc.pth")
os.system("wget https://huggingface.co/lkeab/hq-sam/resolve/main/sam_hq_vit_l.pth")
os.system("wget https://raw.githubusercontent.com/SysCV/sam-hq/main/demo/input_imgs/example0.png")
os.system("wget https://raw.githubusercontent.com/SysCV/sam-hq/main/demo/input_imgs/example1.png")
os.system("wget https://raw.githubusercontent.com/SysCV/sam-hq/main/demo/input_imgs/example2.png")
os.system("wget https://raw.githubusercontent.com/SysCV/sam-hq/main/demo/input_imgs/example3.png")
os.system("wget https://raw.githubusercontent.com/SysCV/sam-hq/main/demo/input_imgs/example4.png")
os.system("wget https://raw.githubusercontent.com/SysCV/sam-hq/main/demo/input_imgs/example5.png")
os.system("wget https://raw.githubusercontent.com/SysCV/sam-hq/main/demo/input_imgs/example6.png")
os.system("wget https://raw.githubusercontent.com/SysCV/sam-hq/main/demo/input_imgs/example7.png")
sys.path.append(os.path.join(os.getcwd(), "GroundingDINO"))
sys.path.append(os.path.join(os.getcwd(), "sam-hq"))
warnings.filterwarnings("ignore")
import gradio as gr
import argparse
import numpy as np
import torch
import torchvision
from PIL import Image, ImageDraw, ImageFont
from scipy import ndimage
# Grounding DINO
import GroundingDINO.groundingdino.datasets.transforms as T
from GroundingDINO.groundingdino.models import build_model
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_vit_l, SamPredictor
import numpy as np
# BLIP
from transformers import BlipProcessor, BlipForConditionalGeneration
def generate_caption(processor, blip_model, raw_image):
# unconditional image captioning
inputs = processor(raw_image, return_tensors="pt").to(
"cuda", torch.float16)
out = blip_model.generate(**inputs)
caption = processor.decode(out[0], skip_special_tokens=True)
return caption
def transform_image(image_pil):
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
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="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):
caption = caption.lower()
caption = caption.strip()
if not caption.endswith("."):
caption = caption + "."
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 = []
scores = []
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)
scores.append(logit.max().item())
return boxes_filt, torch.Tensor(scores), pred_phrases
def draw_mask(mask, draw, random_color=False):
if random_color:
color = (random.randint(0, 255), random.randint(
0, 255), random.randint(0, 255), 153)
else:
color = (30, 144, 255, 153)
nonzero_coords = np.transpose(np.nonzero(mask))
for coord in nonzero_coords:
draw.point(coord[::-1], fill=color)
def draw_box(box, draw, label):
# random color
color = tuple(np.random.randint(0, 255, size=3).tolist())
draw.rectangle(((box[0], box[1]), (box[2], box[3])),
outline=color, width=2)
if label:
font = ImageFont.load_default()
if hasattr(font, "getbbox"):
bbox = draw.textbbox((box[0], box[1]), str(label), font)
else:
w, h = draw.textsize(str(label), font)
bbox = (box[0], box[1], w + box[0], box[1] + h)
draw.rectangle(bbox, fill=color)
draw.text((box[0], box[1]), str(label), fill="white")
draw.text((box[0], box[1]), label)
def draw_point(point, draw, r=10):
show_point = []
for p in point:
x,y = p
draw.ellipse((x-r, y-r, x+r, y+r), fill='green')
config_file = 'GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py'
ckpt_filenmae = "groundingdino_swint_ogc.pth"
sam_checkpoint = 'sam_hq_vit_l.pth'
output_dir = "outputs"
device = 'cuda' if torch.cuda.is_available() else 'cpu'
blip_processor = None
blip_model = None
groundingdino_model = None
sam_predictor = None
def run_grounded_sam(input_image, text_prompt, task_type, box_threshold, text_threshold, iou_threshold, hq_token_only):
global blip_processor, blip_model, groundingdino_model, sam_predictor
# make dir
os.makedirs(output_dir, exist_ok=True)
# load image
scribble = np.array(input_image["mask"])
image_pil = input_image["image"].convert("RGB")
transformed_image = transform_image(image_pil)
if groundingdino_model is None:
groundingdino_model = load_model(
config_file, ckpt_filenmae, device=device)
if task_type == 'automatic':
# generate caption and tags
# use Tag2Text can generate better captions
# https://huggingface.co/spaces/xinyu1205/Tag2Text
# but there are some bugs...
blip_processor = blip_processor or BlipProcessor.from_pretrained(
"Salesforce/blip-image-captioning-large")
blip_model = blip_model or BlipForConditionalGeneration.from_pretrained(
"Salesforce/blip-image-captioning-large", torch_dtype=torch.float16).to("cuda")
text_prompt = generate_caption(blip_processor, blip_model, image_pil)
print(f"Caption: {text_prompt}")
# run grounding dino model
boxes_filt, scores, pred_phrases = get_grounding_output(
groundingdino_model, transformed_image, text_prompt, box_threshold, text_threshold
)
size = image_pil.size
# process boxes
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()
# nms
print(f"Before NMS: {boxes_filt.shape[0]} boxes")
nms_idx = torchvision.ops.nms(
boxes_filt, scores, iou_threshold).numpy().tolist()
boxes_filt = boxes_filt[nms_idx]
pred_phrases = [pred_phrases[idx] for idx in nms_idx]
print(f"After NMS: {boxes_filt.shape[0]} boxes")
if sam_predictor is None:
# initialize SAM
assert sam_checkpoint, 'sam_checkpoint is not found!'
sam = build_sam_vit_l(checkpoint=sam_checkpoint)
sam.to(device=device)
sam_predictor = SamPredictor(sam)
image = np.array(image_pil)
sam_predictor.set_image(image)
hq_token_only = (hq_token_only=='True') # str2bool
if task_type == 'automatic':
# use NMS to handle overlapped boxes
print(f"Revise caption with number: {text_prompt}")
if task_type == 'text' or task_type == 'automatic' or task_type == 'scribble_box':
if task_type == 'scribble_box':
scribble = scribble.transpose(2, 1, 0)[0]
labeled_array, num_features = ndimage.label(scribble >= 255)
centers = ndimage.center_of_mass(scribble, labeled_array, range(1, num_features+1))
centers = np.array(centers)
### (x1, y1, x2, y2)
x_min = centers[:, 0].min()
x_max = centers[:, 0].max()
y_min = centers[:, 1].min()
y_max = centers[:, 1].max()
bbox = np.array([x_min, y_min, x_max, y_max])
bbox = torch.tensor(bbox).unsqueeze(0)
transformed_boxes = sam_predictor.transform.apply_boxes_torch(bbox, image.shape[:2]).to(device)
else:
transformed_boxes = sam_predictor.transform.apply_boxes_torch(
boxes_filt, image.shape[:2]).to(device)
masks, _, _ = sam_predictor.predict_torch(
point_coords=None,
point_labels=None,
boxes=transformed_boxes,
multimask_output=False,
hq_token_only=hq_token_only,
)
# masks: [1, 1, 512, 512]
mask_image = Image.new('RGBA', size, color=(0, 0, 0, 0))
mask_draw = ImageDraw.Draw(mask_image)
for mask in masks:
draw_mask(mask[0].cpu().numpy(), mask_draw, random_color=True)
image_draw = ImageDraw.Draw(image_pil)
if task_type == 'scribble_box':
for box in bbox:
draw_box(box, image_draw, None)
else:
for box, label in zip(boxes_filt, pred_phrases):
draw_box(box, image_draw, label)
if task_type == 'automatic':
image_draw.text((10, 10), text_prompt, fill='black')
image_pil = image_pil.convert('RGBA')
image_pil.alpha_composite(mask_image)
return [image_pil, mask_image]
elif task_type == 'scribble_point':
scribble = scribble.transpose(2, 1, 0)[0]
labeled_array, num_features = ndimage.label(scribble >= 255)
centers = ndimage.center_of_mass(scribble, labeled_array, range(1, num_features+1))
centers = np.array(centers)
point_coords = centers
point_labels = np.ones(point_coords.shape[0])
masks, _, _ = sam_predictor.predict(
point_coords=point_coords,
point_labels=point_labels,
box=None,
multimask_output=False,
hq_token_only=hq_token_only,
)
mask_image = Image.new('RGBA', size, color=(0, 0, 0, 0))
mask_draw = ImageDraw.Draw(mask_image)
for mask in masks:
draw_mask(mask, mask_draw, random_color=True)
image_draw = ImageDraw.Draw(image_pil)
draw_point(point_coords,image_draw)
image_pil = image_pil.convert('RGBA')
image_pil.alpha_composite(mask_image)
return [image_pil, mask_image]
else:
print("task_type:{} error!".format(task_type))
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")
parser.add_argument('--no-gradio-queue', action="store_true",
help='path to the SAM checkpoint')
args = parser.parse_args()
print(args)
block = gr.Blocks()
if not args.no_gradio_queue:
block = block.queue()
with block:
gr.Markdown(
"""
# Segment Anything in High Quality
[[`ArXiv`](https://arxiv.org/abs/2306.01567)]
[[`Code`](https://github.com/SysCV/sam-hq)]
Welcome to the SAM-HQ demo <br/>
You may select different prompt types to get the output mask of target instance.
## Usage
You may check the instruction below, or check our github page about more details.
<details>
You may select an example image or upload your image to start, we support 4 prompt types:
**automatic**: Automaticly generate text prompt and the corresponding box input with BLIP and Grounding-DINO.
**scribble_point**: Click an point on the target instance.
**scribble_box**: Click on two points, the top-left point and the bottom-right point to represent a bounding box of the target instance.
**text**: Send text prompt to identify the target instance in the `Text prompt` box.
We also support a hyper-paramter **hq_token_only**. False means use hq output to correct SAM output. True means use hq output only. Default: False.
To achieve best visualization effect, for images contain multiple objects (like typical coco images), we suggest to set hq_token_only=False. For images contain single object, we suggest to set hq_token_only = True.
</details>
""")
with gr.Row():
with gr.Column():
input_image = gr.Image(
source='upload', type="pil", value="example4.png", tool="sketch",brush_radius=20)
task_type = gr.Dropdown(
["automatic", "scribble_point", "scribble_box", "text"], value="automatic", label="task_type")
text_prompt = gr.Textbox(label="Text Prompt", placeholder="bench .")
hq_token_only = gr.Dropdown(
[False, True], value=False, label="hq_token_only"
)
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
)
iou_threshold = gr.Slider(
label="IOU Threshold", minimum=0.0, maximum=1.0, value=0.8, step=0.001
)
with gr.Column():
gallery = gr.Gallery(
label="Generated images", show_label=False, elem_id="gallery"
).style(preview=True, grid=2, object_fit="scale-down")
with gr.Row():
with gr.Column():
gr.Examples(["example0.png"], inputs=input_image)
with gr.Column():
gr.Examples(["example1.png"], inputs=input_image)
with gr.Column():
gr.Examples(["example2.png"], inputs=input_image)
with gr.Column():
gr.Examples(["example3.png"], inputs=input_image)
with gr.Column():
gr.Examples(["example4.png"], inputs=input_image)
with gr.Column():
gr.Examples(["example5.png"], inputs=input_image)
with gr.Column():
gr.Examples(["example6.png"], inputs=input_image)
with gr.Column():
gr.Examples(["example7.png"], inputs=input_image)
run_button.click(fn=run_grounded_sam, inputs=[
input_image, text_prompt, task_type, box_threshold, text_threshold, iou_threshold, hq_token_only], outputs=gallery)
block.launch(debug=args.debug, share=args.share, show_error=True)