app file

app.py

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+import os, sys
+import random
+import warnings
+
+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")
+sys.path.append(os.path.join(os.getcwd(), "GroundingDINO"))
+sys.path.append(os.path.join(os.getcwd(), "segment_anything"))
+sys.path.append(os.path.join(os.getcwd(), "Roboto-Regular"))
+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
+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, 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):
+    #caption="all plastic.all metal.all paper.all glass.all cardboard.all wood.all rubber"
+    caption="plastic.metal.paper.glass.cardboard.wood.rubber"
+    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()        
+        font = ImageFont.truetype("Roboto-Regular.ttf", 20)
+        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 = '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' if torch.cuda.is_available() else 'cpu'
+
+
+blip_processor = None
+blip_model = None
+groundingdino_model = None
+sam_predictor = None
+inpaint_pipeline = None
+
+
+def run_grounded_sam(input_image, text_prompt, task_type, box_threshold, text_threshold, iou_threshold):
+
+    global blip_processor, blip_model, groundingdino_model, sam_predictor, inpaint_pipeline
+    task_type="seg"
+    box_threshold=0.3
+    text_threshold= 0.25
+    iou_threshold= 0.8
+    # make dir
+    os.makedirs(output_dir, exist_ok=True)
+    # load image
+    image_pil = input_image.convert("RGB")
+    transformed_image = transform_image(image_pil)
+
+    if groundingdino_model is None:
+        groundingdino_model = load_model(
+            config_file, ckpt_filenmae, device=device)
+    
+
+    # 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 task_type == 'seg':
+        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)
+
+        image = np.array(image_pil)
+        sam_predictor.set_image(image)        
+
+        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,
+        )
+        # masks: [1, 1, 512, 512] 
+        
+    if task_type == 'seg':
+        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]   
+    else:
+        print("task_type:{} error!".format(task_type))
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser("Marine Litter", 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="all plastic.all metal.all paper.all glass.all cardboard.all wood.all rubber")
+                #inpaint_prompt = gr.Textbox(label="Inpaint Prompt", placeholder="A dinosaur, detailed, 4K.")
+                run_button = gr.Button(label="Run")               
+            with gr.Column():
+                gallery = gr.Gallery(
+                    label="Generated images", show_label=True, elem_id="gallery"
+                ).style(preview=True, grid=2, object_fit="scale-down")
+
+        run_button.click(fn=run_grounded_sam, inputs=[
+            input_image], outputs=gallery)
+
+    block.launch(debug=args.debug, share=args.share, show_error=True)
+    #block.launch(debug=args.debug, show_error=True)