Update app.py

app.py

@@ -81,10 +81,76 @@ sd_pipe = None
 lama_cleaner_model= None
 ram_model = None
 
+def get_sam_vit_h_4b8939():
+    if not os.path.exists('./sam_vit_h_4b8939.pth'):
+        logger.info(f"get 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}') 
+
+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)
+
+        try:
+            font = os.path.join(cv2.__path__[0],'qt','fonts','DejaVuSans.ttf')
+            font_size = 36
+            new_font = ImageFont.truetype(font, font_size)
+
+            draw.text((x0+2, y0+2), str(label), font=new_font, fill="white")
+        except Exception as e:
+            pass
+
+        mask_draw.rectangle([x0, y0, x1, y1], fill=255, width=6)
+
+
+    return image_pil, mask
+
 def load_image(image_path):
-    if isinstance(image_path, np.ndarray):
-        image_pil = Image.fromarray(image_path.astype(np.uint8))
-    elif isinstance(image_path, PIL.Image.Image):
+    # # load image
+    if isinstance(image_path, PIL.Image.Image):
         image_pil = image_path
     else:
         image_pil = Image.open(image_path).convert("RGB")  # load image
@@ -99,19 +165,16 @@ def load_image(image_path):
     image, _ = transform(image_pil, None)  # 3, h, w
     return image_pil, image
 
-
-def load_model_hf(model_config_path, repo_id, filename, device='cpu'):
-    args = SLConfig.fromfile(model_config_path) 
-    model = build_model(args)
+def load_model(model_config_path, model_checkpoint_path, device):
+    args = SLConfig.fromfile(model_config_path)
     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 = 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    
-    
+    return model
+
 def get_grounding_output(model, image, caption, box_threshold, text_threshold, with_logits=True, device="cpu"):
     caption = caption.lower()
     caption = caption.strip()
@@ -147,28 +210,503 @@ def get_grounding_output(model, image, caption, box_threshold, text_threshold, w
 
     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)
+
+def xywh_to_xyxy(box, sizeW, sizeH):
+    if isinstance(box, list):
+        box = torch.Tensor(box)
+    box = box * torch.Tensor([sizeW, sizeH, sizeW, sizeH])
+    box[:2] -= box[2:] / 2
+    box[2:] += box[:2]
+    box = box.numpy()
+    return box
+
+def mask_extend(img, box, extend_pixels=10, useRectangle=True):
+    box[0] = int(box[0])
+    box[1] = int(box[1])
+    box[2] = int(box[2])
+    box[3] = int(box[3])
+    region = img.crop(tuple(box))
+    new_width = box[2] - box[0] + 2*extend_pixels
+    new_height = box[3] - box[1] + 2*extend_pixels
+
+    region_BILINEAR = region.resize((int(new_width), int(new_height)))
+    if useRectangle:
+        region_draw = ImageDraw.Draw(region_BILINEAR)
+        region_draw.rectangle((0, 0, new_width, new_height), fill=(255, 255, 255))    
+    img.paste(region_BILINEAR, (int(box[0]-extend_pixels), int(box[1]-extend_pixels)))
+    return img
+
+def mix_masks(imgs):
+    re_img =  1 - np.asarray(imgs[0].convert("1"))
+    for i in range(len(imgs)-1):
+        re_img = np.multiply(re_img, 1 - np.asarray(imgs[i+1].convert("1")))
+    re_img =  1 - re_img
+    return  Image.fromarray(np.uint8(255*re_img))
+
+def set_device():
+    device = 'cuda' if torch.cuda.is_available() else 'cpu'
+    print(f'device={device}')
+
+def load_groundingdino_model():
+    # initialize groundingdino model
+    global groundingdino_model
+    logger.info(f"initialize groundingdino model...")
+    groundingdino_model = load_model_hf(config_file, ckpt_repo_id, ckpt_filenmae)
+
+def load_sam_model():
+    # initialize SAM
+    global sam_model, sam_predictor, sam_mask_generator, sam_device
+    logger.info(f"initialize SAM model...")
+    sam_device = device
+    sam_model = build_sam(checkpoint=sam_checkpoint).to(sam_device)
+    sam_predictor = SamPredictor(sam_model)
+    sam_mask_generator = SamAutomaticMaskGenerator(sam_model)
+
+def load_sd_model():
+    # initialize stable-diffusion-inpainting
+    global sd_pipe
+    logger.info(f"initialize stable-diffusion-inpainting...")
+    sd_pipe = None
+    if os.environ.get('IS_MY_DEBUG') is None:
+        sd_pipe = StableDiffusionInpaintPipeline.from_pretrained(
+                "runwayml/stable-diffusion-inpainting", 
+                # revision="fp16",
+                # "stabilityai/stable-diffusion-2-inpainting",
+                torch_dtype=torch.float16,
+        )
+        sd_pipe = sd_pipe.to(device)
+
+def load_lama_cleaner_model():
+    # initialize lama_cleaner
+    global lama_cleaner_model
+    logger.info(f"initialize lama_cleaner...")
+
+    lama_cleaner_model = ModelManager(
+            name='lama',
+            device='cpu', # device,
+        )
+
+def lama_cleaner_process(image, mask, cleaner_size_limit=1080):
+    ori_image = image
+    if mask.shape[0] == image.shape[1] and mask.shape[1] == image.shape[0] and mask.shape[0] != mask.shape[1]:
+        # rotate image
+        ori_image = np.transpose(image[::-1, ...][:, ::-1], axes=(1, 0, 2))[::-1, ...]
+        image = ori_image
+    
+    original_shape = ori_image.shape
+    interpolation = cv2.INTER_CUBIC
+    
+    size_limit = cleaner_size_limit
+    if size_limit == -1:
+        size_limit = max(image.shape)
+    else:
+        size_limit = int(size_limit)
+
+    config = lama_Config(
+        ldm_steps=25,
+        ldm_sampler='plms',
+        zits_wireframe=True,
+        hd_strategy='Original',
+        hd_strategy_crop_margin=196,
+        hd_strategy_crop_trigger_size=1280,
+        hd_strategy_resize_limit=2048,
+        prompt='',
+        use_croper=False,
+        croper_x=0,
+        croper_y=0,
+        croper_height=512,
+        croper_width=512,
+        sd_mask_blur=5,
+        sd_strength=0.75,
+        sd_steps=50,
+        sd_guidance_scale=7.5,
+        sd_sampler='ddim',
+        sd_seed=42,
+        cv2_flag='INPAINT_NS',
+        cv2_radius=5,
+    )
+    
+    if config.sd_seed == -1:
+        config.sd_seed = random.randint(1, 999999999)
 
-def run_inference(input_image, text_prompt, box_threshold, text_threshold, config_file, ckpt_repo_id, ckpt_filenmae):
-
-    # Load the Grounding DINO model
-    model = load_model_hf(config_file, ckpt_repo_id, ckpt_filenmae)
-
-    # Load the input image
-    image_pil, image = load_image(input_image)
-
-    # Run the object detection and grounding model
-    boxes, labels = get_grounding_output(model, image, text_prompt, box_threshold, text_threshold)
-
-    # Convert the boxes and labels to a JSON format
-    result = []
-    for box, label in zip(boxes, labels):
-        result.append({
-            "box": box.tolist(),
-            "label": label
-        })
+    # logger.info(f"Origin image shape_0_: {original_shape} / {size_limit}")
+    image = resize_max_size(image, size_limit=size_limit, interpolation=interpolation)
+    # logger.info(f"Resized image shape_1_: {image.shape}")
+    
+    # logger.info(f"mask image shape_0_: {mask.shape} / {type(mask)}")
+    mask = resize_max_size(mask, size_limit=size_limit, interpolation=interpolation)
+    # logger.info(f"mask image shape_1_: {mask.shape} / {type(mask)}")
+
+    res_np_img = lama_cleaner_model(image, mask, config)
+    torch.cuda.empty_cache()
+  
+    image = Image.open(io.BytesIO(numpy_to_bytes(res_np_img, 'png')))
+    return  image
+
+class Ram_Predictor(RamPredictor):
+    def __init__(self, config, device='cpu'):
+        self.config = config
+        self.device = torch.device(device)
+        self._build_model()
+
+    def _build_model(self):
+        self.model = RamModel(**self.config.model).to(self.device)
+        if self.config.load_from is not None:
+            self.model.load_state_dict(torch.load(self.config.load_from, map_location=self.device))
+        self.model.train()
+
+def load_ram_model():
+    # load ram model
+    global ram_model
+    model_path = "./checkpoints/ram_epoch12.pth"
+    ram_config = dict(
+        model=dict(
+            pretrained_model_name_or_path='bert-base-uncased',
+            load_pretrained_weights=False,
+            num_transformer_layer=2,
+            input_feature_size=256,
+            output_feature_size=768,
+            cls_feature_size=512,
+            num_relation_classes=56,
+            pred_type='attention',
+            loss_type='multi_label_ce',
+        ),
+        load_from=model_path,
+    )
+    ram_config = mmengine_Config(ram_config)
+    ram_model = Ram_Predictor(ram_config, device)
+
+# visualization
+def draw_selected_mask(mask, draw):
+    color = (255, 0, 0, 153)
+    nonzero_coords = np.transpose(np.nonzero(mask))
+    for coord in nonzero_coords:
+        draw.point(coord[::-1], fill=color)
+
+def draw_object_mask(mask, draw):
+    color = (0, 0, 255, 153)
+    nonzero_coords = np.transpose(np.nonzero(mask))
+    for coord in nonzero_coords:
+        draw.point(coord[::-1], fill=color)
+
+def create_title_image(word1, word2, word3, width, font_path='./assets/OpenSans-Bold.ttf'):
+    # Define the colors to use for each word
+    color_red = (255, 0, 0)
+    color_black = (0, 0, 0)
+    color_blue = (0, 0, 255)
+
+    # Define the initial font size and spacing between words
+    font_size = 40
+
+    # Create a new image with the specified width and white background
+    image = Image.new('RGB', (width, 60), (255, 255, 255))
+
+    try:
+        # Load the specified font
+        font = ImageFont.truetype(font_path, font_size)
+
+        # Keep increasing the font size until all words fit within the desired width
+        while True:
+            # Create a draw object for the image
+            draw = ImageDraw.Draw(image)
+            
+            word_spacing = font_size / 2
+            # Draw each word in the appropriate color
+            x_offset = word_spacing
+            draw.text((x_offset, 0), word1, color_red, font=font)
+            x_offset += font.getsize(word1)[0] + word_spacing
+            draw.text((x_offset, 0), word2, color_black, font=font)
+            x_offset += font.getsize(word2)[0] + word_spacing
+            draw.text((x_offset, 0), word3, color_blue, font=font)
+            
+            word_sizes = [font.getsize(word) for word in [word1, word2, word3]]
+            total_width = sum([size[0] for size in word_sizes]) + word_spacing * 3
+
+            # Stop increasing font size if the image is within the desired width
+            if total_width <= width:
+                break
+                
+            # Increase font size and reset the draw object
+            font_size -= 1
+            image = Image.new('RGB', (width, 50), (255, 255, 255))
+            font = ImageFont.truetype(font_path, font_size)
+            draw = None
+    except Exception as e:
+        pass
+
+    return image
+
+def concatenate_images_vertical(image1, image2):
+    # Get the dimensions of the two images
+    width1, height1 = image1.size
+    width2, height2 = image2.size
+
+    # Create a new image with the combined height and the maximum width
+    new_image = Image.new('RGBA', (max(width1, width2), height1 + height2))
+
+    # Paste the first image at the top of the new image
+    new_image.paste(image1, (0, 0))
+
+    # Paste the second image below the first image
+    new_image.paste(image2, (0, height1))
+
+    return new_image
+
+def relate_anything(input_image, k):    
+    logger.info(f'relate_anything_1_{input_image.size}_')
+    w, h = input_image.size
+    max_edge = 1500
+    if w > max_edge or h > max_edge:
+        ratio = max(w, h) / max_edge
+        new_size = (int(w / ratio), int(h / ratio))
+        input_image.thumbnail(new_size)
+    
+    logger.info(f'relate_anything_2_')
+    # load image
+    pil_image = input_image.convert('RGBA')
+    image = np.array(input_image)
+    sam_masks = sam_mask_generator.generate(image)
+    filtered_masks = sort_and_deduplicate(sam_masks)
+
+    logger.info(f'relate_anything_3_')
+    feat_list = []
+    for fm in filtered_masks:
+        feat = torch.Tensor(fm['feat']).unsqueeze(0).unsqueeze(0).to(device)
+        feat_list.append(feat)
+    feat = torch.cat(feat_list, dim=1).to(device)
+    matrix_output, rel_triplets = ram_model.predict(feat)
+
+    logger.info(f'relate_anything_4_')
+    pil_image_list = []
+    for i, rel in enumerate(rel_triplets[:k]):
+        s,o,r = int(rel[0]),int(rel[1]),int(rel[2])
+        relation = relation_classes[r]
+
+        mask_image = Image.new('RGBA', pil_image.size, color=(0, 0, 0, 0))
+        mask_draw = ImageDraw.Draw(mask_image)
+            
+        draw_selected_mask(filtered_masks[s]['segmentation'], mask_draw)
+        draw_object_mask(filtered_masks[o]['segmentation'], mask_draw)
+
+        current_pil_image = pil_image.copy()
+        current_pil_image.alpha_composite(mask_image)
+                
+        title_image = create_title_image('Red', relation, 'Blue', current_pil_image.size[0])
+        concate_pil_image = concatenate_images_vertical(current_pil_image, title_image)
+        pil_image_list.append(concate_pil_image)
+
+    logger.info(f'relate_anything_5_{len(pil_image_list)}')
+    return pil_image_list
+
+mask_source_draw = "draw a mask on input image"
+mask_source_segment = "type what to detect below"
+
+def run_anything_task(input_image, text_prompt, task_type, inpaint_prompt, box_threshold, text_threshold, 
+            iou_threshold, inpaint_mode, mask_source_radio, remove_mode, remove_mask_extend, num_relation, cleaner_size_limit=1080):
+    if (task_type == 'relate anything'):
+        output_images = relate_anything(input_image['image'], num_relation)
+        return output_images, gr.Gallery.update(label='relate images')
+
+    text_prompt = text_prompt.strip()
+    if not ((task_type == 'inpainting' or task_type == 'remove') and mask_source_radio == mask_source_draw):
+        if text_prompt == '':
+            return [], gr.Gallery.update(label='Detection prompt is not found!😂😂😂😂')
+
+    if input_image is None:
+            return [], gr.Gallery.update(label='Please upload a image!😂😂😂😂')
+
+    file_temp = int(time.time())
+    logger.info(f'run_anything_task_[{file_temp}]_{task_type}/{inpaint_mode}/[{mask_source_radio}]/{remove_mode}/{remove_mask_extend}_[{text_prompt}]/[{inpaint_prompt}]___1_')
+
+    output_images = []
+
+    # load image
+    if mask_source_radio == mask_source_draw:
+        input_mask_pil = input_image['mask']
+        input_mask = np.array(input_mask_pil.convert("L"))  
+    
+    if isinstance(input_image, dict):
+        image_pil, image = load_image(input_image['image'].convert("RGB"))
+        input_img = input_image['image']
+        output_images.append(input_image['image'])
+    else:
+        image_pil, image = load_image(input_image.convert("RGB"))
+        input_img = input_image
+        output_images.append(input_image)
 
-    return result
+    size = image_pil.size
 
+    pred_dict = {
+    }          
+    
+    # run grounding dino model
+    if (task_type == 'inpainting' or task_type == 'remove') and mask_source_radio == mask_source_draw:
+        pass
+    else:
+        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!")
+
+        boxes_filt, pred_phrases = get_grounding_output(
+            groundingdino_model, image, text_prompt, box_threshold, text_threshold, device=groundingdino_device
+        )
+        if boxes_filt.size(0) == 0:
+            logger.info(f'run_anything_task_[{file_temp}]_{task_type}_[{text_prompt}]_1_[No objects detected, please try others.]_')
+            return [], gr.Gallery.update(label='No objects detected, please try others.😂😂😂😂')
+        boxes_filt_ori = copy.deepcopy(boxes_filt)
+
+        pred_dict = {
+            "boxes": boxes_filt,
+            "size": [size[1], size[0]],  # H,W
+            "labels": pred_phrases,
+        }
+
+        image_with_box = plot_boxes_to_image(copy.deepcopy(image_pil), pred_dict)[0]
+        output_images.append(image_with_box)
+
+    logger.info(f'run_anything_task_[{file_temp}]_{task_type}_2_')
+    if task_type == 'segment' or ((task_type == 'inpainting' or task_type == 'remove') and mask_source_radio == mask_source_segment):
+        image = np.array(input_img)
+        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.to(sam_device)
+        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: [9, 1, 512, 512]
+        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.cpu().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")
+        segment_image_result = cv2.cvtColor(cv2.imread(image_path), cv2.COLOR_BGR2RGB)
+        os.remove(image_path)
+        output_images.append(segment_image_result)        
+
+    logger.info(f'run_anything_task_[{file_temp}]_{task_type}_3_')
+    if task_type == 'detection' or task_type == 'segment':
+        logger.info(f'run_anything_task_[{file_temp}]_{task_type}_9_')
+        return pred_dict
+    elif task_type == 'inpainting' or task_type == 'remove':
+        if inpaint_prompt.strip() == '' and mask_source_radio == mask_source_segment:
+            task_type = 'remove'
+
+        logger.info(f'run_anything_task_[{file_temp}]_{task_type}_4_')  
+        if mask_source_radio == mask_source_draw:
+            mask_pil = input_mask_pil
+            mask = input_mask          
+        else:
+            masks_ori = copy.deepcopy(masks)
+            if inpaint_mode == 'merge':
+                masks = torch.sum(masks, dim=0).unsqueeze(0)
+                masks = torch.where(masks > 0, True, False)
+            mask = masks[0][0].cpu().numpy()
+            mask_pil = Image.fromarray(mask)   
+        output_images.append(mask_pil.convert("RGB"))
+
+        if task_type == 'inpainting':
+            # inpainting pipeline
+            image_source_for_inpaint = image_pil.resize((512, 512))
+            image_mask_for_inpaint = mask_pil.resize((512, 512))
+            image_inpainting = sd_pipe(prompt=inpaint_prompt, image=image_source_for_inpaint, mask_image=image_mask_for_inpaint).images[0]
+        else:
+            # remove from mask
+            logger.info(f'run_anything_task_[{file_temp}]_{task_type}_5_')
+            if mask_source_radio == mask_source_segment:
+                mask_imgs = []
+                masks_shape = masks_ori.shape        
+                boxes_filt_ori_array = boxes_filt_ori.numpy()
+                if inpaint_mode == 'merge':
+                    extend_shape_0 = masks_shape[0]
+                    extend_shape_1 = masks_shape[1]
+                else:
+                    extend_shape_0 = 1
+                    extend_shape_1 = 1
+                for i in range(extend_shape_0):
+                    for j in range(extend_shape_1):                
+                        mask = masks_ori[i][j].cpu().numpy()
+                        mask_pil = Image.fromarray(mask)
+                    
+                        if remove_mode == 'segment':
+                            useRectangle = False
+                        else:
+                            useRectangle = True
+
+                        try:
+                            remove_mask_extend = int(remove_mask_extend)
+                        except:
+                            remove_mask_extend = 10
+                        mask_pil_exp = mask_extend(copy.deepcopy(mask_pil).convert("RGB"), 
+                                        xywh_to_xyxy(torch.tensor(boxes_filt_ori_array[i]), size[0], size[1]),
+                                        extend_pixels=remove_mask_extend, useRectangle=useRectangle)
+                        mask_imgs.append(mask_pil_exp)
+                mask_pil = mix_masks(mask_imgs)
+                output_images.append(mask_pil.convert("RGB"))   
+
+            logger.info(f'run_anything_task_[{file_temp}]_{task_type}_6_')            
+            image_inpainting = lama_cleaner_process(np.array(image_pil), np.array(mask_pil.convert("L")), cleaner_size_limit)
+            # output_images.append(image_inpainting)
+
+        logger.info(f'run_anything_task_[{file_temp}]_{task_type}_7_')
+        image_inpainting = image_inpainting.resize((image_pil.size[0], image_pil.size[1]))
+        output_images.append(image_inpainting)
+        logger.info(f'run_anything_task_[{file_temp}]_{task_type}_9_')
+        return output_images, gr.Gallery.update(label='result images')        
+    else:
+        logger.info(f"task_type:{task_type} error!")
+    logger.info(f'run_anything_task_[{file_temp}]_9_9_')
+    return output_images, gr.Gallery.update(label='result images')
+
+def change_radio_display(task_type, mask_source_radio):
+    text_prompt_visible = True
+    inpaint_prompt_visible = False
+    mask_source_radio_visible = False
+    num_relation_visible = False
+    if task_type == "inpainting":
+        inpaint_prompt_visible = True
+    if task_type == "inpainting" or task_type == "remove":
+        mask_source_radio_visible = True   
+        if mask_source_radio == mask_source_draw:
+            text_prompt_visible = False
+    if task_type == "relate anything":
+        text_prompt_visible = False
+        num_relation_visible = True
+    return  gr.Textbox.update(visible=text_prompt_visible), gr.Textbox.update(visible=inpaint_prompt_visible), gr.Radio.update(visible=mask_source_radio_visible), gr.Slider.update(visible=num_relation_visible) 
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser("Grounded SAM demo", add_help=True)
@@ -177,28 +715,60 @@ if __name__ == "__main__":
     args = parser.parse_args()
     print(f'args = {args}')
 
-    model_config_file = 'GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py'
-    model_ckpt_repo_id = "ShilongLiu/GroundingDINO"
-    model_ckpt_filenmae = "groundingdino_swint_ogc.pth"
-
-    def inference_func(input_image, text_prompt):
-        result = run_inference(input_image, text_prompt, 0.3, 0.25, model_config_file, model_ckpt_repo_id, model_ckpt_filenmae)
-        return result
-
-    # Create the Gradio interface for the model
-    interface = gr.Interface(
-        fn=inference_func,
-        inputs=[
-            gr.inputs.Image(label="Input Image"),
-            gr.inputs.Textbox(label="Detection Prompt")
-        ],
-        outputs=gr.outputs.Dataframe(type="pandas"),
-        title="Object Detection and Grounding",
-        description="A Gradio app to detect objects in an image and ground them to captions using Grounding DINO.",
-        server_name='0.0.0.0',
-        debug=args.debug,
-        share=args.share
-    )
-
-    # Launch the interface
-    interface.launch()
+    set_device()
+    get_sam_vit_h_4b8939()
+    load_groundingdino_model()
+    load_sam_model()
+    load_sd_model()
+    load_lama_cleaner_model()
+    load_ram_model()
+
+    os.system("pip list")
+
+    block = gr.Blocks().queue()
+    with block:
+        with gr.Row():
+            with gr.Column():
+                input_image = gr.Image(source='upload', elem_id="image_upload", tool='sketch', type='pil', label="Upload")    
+                task_type = gr.Radio(["detection", "segment", "inpainting", "remove", "relate anything"],  value="detection", 
+                                                label='Task type', visible=True) 
+                mask_source_radio = gr.Radio([mask_source_draw, mask_source_segment], 
+                                    value=mask_source_segment, label="Mask from",
+                                    visible=False) 
+                text_prompt = gr.Textbox(label="Detection Prompt[To detect multiple objects, seperating each name with '.', like this: cat . dog . chair ]", placeholder="Cannot be empty")                                                
+                inpaint_prompt = gr.Textbox(label="Inpaint Prompt (if this is empty, then remove)", visible=False)
+                num_relation = gr.Slider(label="How many relations do you want to see", minimum=1, maximum=20, value=5, step=1, visible=False)
+                run_button = gr.Button(label="Run", visible=True)
+                with gr.Accordion("Advanced options", open=False) as advanced_options:
+                    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.Radio(["merge", "first"], value="merge", label="inpaint_mode")
+                    with gr.Row():
+                        with gr.Column(scale=1):
+                            remove_mode = gr.Radio(["segment", "rectangle"],  value="segment", label='remove mode') 
+                        with gr.Column(scale=1):
+                            remove_mask_extend = gr.Textbox(label="remove_mask_extend", value='10')
+
+            run_button.click(fn=run_anything_task, inputs=[
+                            input_image, text_prompt, task_type, inpaint_prompt, box_threshold, text_threshold, iou_threshold, inpaint_mode, mask_source_radio, remove_mode, remove_mask_extend, num_relation], outputs=gr.outputs.Dataframe(type="pandas"), show_progress=True, queue=True)
+            
+            mask_source_radio.change(fn=change_radio_display, inputs=[task_type, mask_source_radio], outputs=[text_prompt, inpaint_prompt, mask_source_radio, num_relation])
+            task_type.change(fn=change_radio_display, inputs=[task_type, mask_source_radio], outputs=[text_prompt, inpaint_prompt, mask_source_radio, num_relation])
+
+        DESCRIPTION = f'### This demo from [Grounded-Segment-Anything](https://github.com/IDEA-Research/Grounded-Segment-Anything). <br>'
+        DESCRIPTION += f'RAM from [RelateAnything](https://github.com/Luodian/RelateAnything). <br>'
+        DESCRIPTION += f'Remove(cleaner) from [lama-cleaner](https://github.com/Sanster/lama-cleaner). <br>'
+        DESCRIPTION += f'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)
+
+    computer_info()
+    block.launch(server_name='0.0.0.0', debug=args.debug, share=args.share)