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import os, sys
import random
import time
import warnings
from scipy.ndimage import binary_dilation
from PIL import Image, ImageDraw, ImageFont
def downloadStuff():
# os.system("python -m pip install -e segment_anything")
# os.system("python -m pip install -e GroundingDINO")
os.system("pip install --upgrade diffusers[torch]")
# os.system("pip install opencv-python pycocotools matplotlib onnxruntime onnx ipykernel")
# os.system("wget https://github.com/IDEA-Research/Grounded-Segment-Anything/raw/main/assets/demo1.jpg")
# os.system("wget https://huggingface.co/ShilongLiu/GroundingDINO/resolve/main/groundingdino_swint_ogc.pth")
# os.system("wget https://huggingface.co/spaces/mrtlive/segment-anything-model/resolve/main/sam_vit_h_4b8939.pth")
# os.system("wget https://huggingface.co/lkeab/hq-sam/resolve/main/sam_hq_vit_h.pth")
sys.path.append(os.path.join(os.getcwd(), "GroundingDINO"))
sys.path.append(os.path.join(os.getcwd(), "segment_anything"))
# downloadStuff()
warnings.filterwarnings("ignore")
import gradio as gr
import argparse
import numpy as np
import torch
import torchvision
from PIL import Image, ImageDraw, ImageFont
# Grounding DINO
from .GroundingDINO.groundingdino.datasets import 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.segment_anything import build_sam, SamPredictor
from .segment_anything.segment_anything.build_sam import build_sam
from .segment_anything.segment_anything.predictor import SamPredictor
import numpy as np
# diffusers
import torch
from diffusers import StableDiffusionInpaintPipeline
# 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):
image = image.to("cuda")
caption = caption.lower()
caption = caption.strip()
if not caption.endswith("."):
caption = caption + "."
start_time = time.time()
model.to("cuda")
with torch.no_grad():
outputs = model(image[None], captions=[caption])
print(f"Model forward time: {time.time() - start_time}")
start_time = time.time()
logits = outputs["pred_logits"].cpu().sigmoid()[0] # (nq, 256)
boxes = outputs["pred_boxes"].cpu()[0] # (nq, 4)
logits.shape[0]
print(f"Process output time: {time.time() - start_time}")
# filter output
start_time = time.time()
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]
print(f"Filter output time: {time.time() - start_time}")
# get phrase
start_time = time.time()
tokenlizer = model.tokenizer
tokenized = tokenlizer(caption)
print(f"Tokenize time: {time.time() - start_time}")
# build pred
pred_phrases = []
scores = []
start_time = time.time()
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())
print(f"Build pred time: {time.time() - start_time}")
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)
config_file = f'{os.environ.get("path", ".")}/grounded_sam/GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py'
ckpt_repo_id = "ShilongLiu/GroundingDINO"
ckpt_filenmae = f'{os.environ.get("path", ".")}/checkpoints/groundingdino_swint_ogc.pth'
sam_checkpoint = f'{os.environ.get("path", ".")}/checkpoints/sam_hq_vit_h.pth'
output_dir = "outputs"
device = 'cuda' if torch.cuda.is_available() else 'cpu'
blip_processor = None
blip_model = None
groundingdino_model = load_model(
config_file, ckpt_filenmae, device=device)
sam_predictor = None
inpaint_pipeline = None
def run_grounded_sam(input_image, text_prompt, task_type, inpaint_prompt, box_threshold, text_threshold, iou_threshold, inpaint_mode):
global blip_processor, blip_model, groundingdino_model, sam_predictor, inpaint_pipeline
# make dir
os.makedirs(output_dir, exist_ok=True)
# load image
start_time = time.time()
image_pil = input_image.convert("RGB")
transformed_image = transform_image(image_pil)
print(f"Transform image time: {time.time() - start_time}")
start_time = time.time()
print(f"Load model time: {time.time() - start_time}")
# run grounding dino model
start_time = time.time()
boxes_filt, scores, pred_phrases = get_grounding_output(
groundingdino_model, transformed_image, text_prompt, box_threshold, text_threshold
)
print(f"Run model time: {time.time() - start_time}")
size = image_pil.size
# process boxes
start_time = time.time()
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()
print(f"Process boxes time: {time.time() - start_time}")
# nms
start_time = time.time()
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")
print(f"NMS time: {time.time() - start_time}")
start_time = time.time()
if sam_predictor is None:
# initialize SAM
assert sam_checkpoint, 'sam_checkpoint is not found!'
sam = build_sam(checkpoint=sam_checkpoint)
sam.to(device=device)
sam_predictor = SamPredictor(sam)
print(f"Initialize SAM time: {time.time() - start_time}")
image = np.array(image_pil)
sam_predictor.set_image(image)
start_time = time.time()
transformed_boxes = sam_predictor.transform.apply_boxes_torch(
boxes_filt, image.shape[:2]).to(device)
print(f"Transform boxes time: {time.time() - start_time}")
start_time = time.time()
masks, _, _ = sam_predictor.predict_torch(
point_coords=None,
point_labels=None,
boxes=transformed_boxes,
multimask_output=False,
)
print(f"Predict time: {time.time() - start_time}")
# 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)
for box, label in zip(boxes_filt, pred_phrases):
draw_box(box, image_draw, label)
image_pil = image_pil.convert('RGBA')
image_pil.alpha_composite(mask_image)
return [image_pil, mask_image]
def toBlackWhiteMask(input_pil):
if input_pil.mode != 'RGBA':
input_pil = input_pil.convert('RGBA')
new_image = Image.new('RGBA', input_pil.size, (255, 255, 255, 255))
input_data = input_pil.getdata()
new_data = []
for item in input_data:
if item[3] == 0:
new_data.append((0, 0, 0, 255))
else:
new_data.append((255, 255, 255, 255))
new_image.putdata(new_data)
return new_image
def expand_white_pixels(input_pil, expand_by=1):
# Convert the input image to grayscale
grayscale = input_pil.convert('L')
# Create a binary mask where white pixels are represented by 1
binary_mask = np.array(grayscale) > 245
# Apply the dilation operation to the binary mask
dilated_mask = binary_dilation(binary_mask, iterations=expand_by)
# Create a new PIL image from the dilated mask
expanded_image = Image.fromarray(np.uint8(dilated_mask * 255))
return expanded_image
def get_mask(input_pil, positive_prompt, expand_by=0):
result = run_grounded_sam(input_image=input_pil, text_prompt=positive_prompt, task_type="seg", inpaint_prompt=None, box_threshold=0.3,
text_threshold=0.25, iou_threshold=0.8, inpaint_mode="merge")
result = result[1]
black_white_result = toBlackWhiteMask(result)
if expand_by > 0:
black_white_result = expand_white_pixels(black_white_result, expand_by=expand_by)
return black_white_result
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:
with gr.Row():
with gr.Column():
input_image = gr.Image(
source='upload', type="pil", value="demo1.jpg")
task_type = gr.Dropdown(
["det", "seg", "inpainting", "automatic"], value="seg", label="task_type")
text_prompt = gr.Textbox(label="Text Prompt", placeholder="bear . beach .")
inpaint_prompt = gr.Textbox(label="Inpaint Prompt", placeholder="A dinosaur, detailed, 4K.")
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
)
inpaint_mode = gr.Dropdown(
["merge", "first"], value="merge", label="inpaint_mode")
with gr.Column():
gallery = gr.Gallery(
label="Generated images", show_label=False, elem_id="gallery"
).style(preview=True, grid=2, object_fit="scale-down")
run_button.click(fn=run_grounded_sam, inputs=[
input_image, text_prompt, task_type, inpaint_prompt, box_threshold, text_threshold, iou_threshold, inpaint_mode], outputs=gallery)
block.launch(debug=args.debug, share=args.share, show_error=True)
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