app.py

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)