Update app.py

app.py

@@ -466,79 +466,22 @@ def concatenate_images_vertical(image1, image2):
 
     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')
+def run_anything_task(input_image, text_prompt, box_threshold, text_threshold, iou_threshold, cleaner_size_limit=1080):
 
     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 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']
@@ -550,166 +493,38 @@ def run_anything_task(input_image, text_prompt, task_type, inpaint_prompt, box_t
 
     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) 
+    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}]_[{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)
+
+
+    return pred_dict
+ 
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser("Grounded SAM demo", add_help=True)
+    parser = argparse.ArgumentParser("VideoQuest segmentation module", 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")
     args = parser.parse_args()
@@ -730,14 +545,9 @@ if __name__ == "__main__":
         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(
@@ -749,25 +559,11 @@ if __name__ == "__main__":
                     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.JSON()], 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>'
+                            input_image, text_prompt, box_threshold, text_threshold, iou_threshold], outputs=[gr.outputs.JSON()], show_progress=True, queue=True)
+
+        DESCRIPTION = f'### This space is used by the experimental VideoQuest game. <br> It is based on <a href="https://huggingface.co/spaces/yizhangliu/Grounded-Segment-Anything?duplicate=true">Grounded-Segment-Anything</a>'
         gr.Markdown(DESCRIPTION)
 
     computer_info()