resolve groundingdino install problem

install.py

@@ -8,7 +8,6 @@ with open(req_file) as file:
     for lib in file:
         lib = lib.strip()
         if not launch.is_installed(lib):
-            if lib == "groundingdino":
-                lib = "git+https://github.com/IDEA-Research/GroundingDINO"
             launch.run_pip(
-                f"install {lib}", f"sd-webui-segment-anything requirement: {lib}")
+                f"install {lib}",
+                f"sd-webui-segment-anything requirement: {lib}")

requirements.txt

@@ -1,2 +1 @@
-segment_anything
+segment_anything
-groundingdino

scripts/dino.py

@@ -8,12 +8,6 @@ from collections import OrderedDict
 from modules import scripts, shared
 from modules.devices import device, torch_gc, cpu
 
-# Grounding DINO
-import groundingdino.datasets.transforms as T
-from groundingdino.models import build_model
-from groundingdino.util.slconfig import SLConfig
-from groundingdino.util.utils import clean_state_dict
-
 
 dino_model_cache = OrderedDict()
 dino_model_dir = os.path.join(scripts.basedir(), "models/grounding-dino")
@@ -33,6 +27,23 @@ dino_model_info = {
 }
 
 
+def install_goundingdino():
+    import launch
+    if launch.is_installed("groundingdino"):
+        return True
+    try:
+        launch.run_pip(
+            f"install git+https://github.com/IDEA-Research/GroundingDINO",
+            f"sd-webui-segment-anything requirement: groundingdino")
+        print("GroundingDINO install success.")
+        return True
+    except Exception:
+        import traceback
+        print(traceback.print_exc())
+        print("GroundingDINO install failed. Submit an issue to https://github.com/continue-revolution/sd-webui-segment-anything/issues.")
+        return False
+
+
 def show_boxes(image_np, boxes, color=(255, 0, 0, 255), thickness=2, show_index=False):
     if boxes is None:
         return image_np
@@ -64,6 +75,9 @@ def load_dino_model(dino_checkpoint):
             dino.to(device=device)
     else:
         clear_dino_cache()
+        from groundingdino.models import build_model
+        from groundingdino.util.slconfig import SLConfig
+        from groundingdino.util.utils import clean_state_dict
         args = SLConfig.fromfile(dino_model_info[dino_checkpoint]["config"])
         dino = build_model(args)
         checkpoint = torch.hub.load_state_dict_from_url(
@@ -77,6 +91,7 @@ def load_dino_model(dino_checkpoint):
 
 
 def load_dino_image(image_pil):
+    import groundingdino.datasets.transforms as T
     transform = T.Compose(
         [
             T.RandomResize([800], max_size=1333),
@@ -112,6 +127,9 @@ def get_grounding_output(model, image, caption, box_threshold):
 
 
 def dino_predict_internal(input_image, dino_model_name, text_prompt, box_threshold):
+    install_success = install_goundingdino()
+    if not install_success:
+        return None, False
     print("Running GroundingDINO Inference")
     dino_image = load_dino_image(input_image.convert("RGB"))
     dino_model = load_dino_model(dino_model_name)
@@ -127,4 +145,4 @@ def dino_predict_internal(input_image, dino_model_name, text_prompt, box_thresho
         boxes_filt[i][2:] += boxes_filt[i][:2]
     gc.collect()
     torch_gc()
-    return boxes_filt
+    return boxes_filt, True

scripts/sam.py

@@ -17,6 +17,7 @@ from modules.paths import models_path
 from segment_anything import SamPredictor, sam_model_registry
 from scripts.dino import dino_model_list, dino_predict_internal, show_boxes, clear_dino_cache
 
+
 sam_model_cache = OrderedDict()
 scripts_sam_model_dir = os.path.join(scripts.basedir(), "models/sam") 
 sd_sam_model_dir = os.path.join(models_path, "sam")
@@ -132,11 +133,18 @@ def sam_predict(sam_model_name, input_image, positive_points, negative_points,
     image_np = np.array(input_image)
     image_np_rgb = image_np[..., :3]
     dino_enabled = dino_checkbox and text_prompt is not None
-
+    boxes_filt = None
-    boxes_filt = dino_predict_internal(input_image, dino_model_name, text_prompt, box_threshold) if dino_enabled else None
+    sam_predict_result = " done."
-    if dino_enabled and dino_preview_checkbox is not None and dino_preview_checkbox and dino_preview_boxes_selection is not None:
+    if dino_enabled:
-        valid_indices = [int(i) for i in dino_preview_boxes_selection if int(i) < boxes_filt.shape[0]]
+        boxes_filt, install_success = dino_predict_internal(input_image, dino_model_name, text_prompt, box_threshold)
-        boxes_filt = boxes_filt[valid_indices]
+        if install_success and dino_preview_checkbox is not None and dino_preview_checkbox and dino_preview_boxes_selection is not None:
+            valid_indices = [int(i) for i in dino_preview_boxes_selection if int(i) < boxes_filt.shape[0]]
+            boxes_filt = boxes_filt[valid_indices]
+        if not install_success:
+            if len(positive_points) == 0 and len(negative_points) == 0:
+                return [], "GroundingDINO installment has failed. Check your terminal for more detail and submit an issue to https://github.com/continue-revolution/sd-webui-segment-anything/issues."
+            else:
+                sam_predict_result += " However, GroundingDINO installment has failed. Your process automatically fall back to point prompt only. Check your terminal for more detail and submit an issue to https://github.com/continue-revolution/sd-webui-segment-anything/issues."
 
     sam = init_sam_model(sam_model_name)
 
@@ -145,7 +153,8 @@ def sam_predict(sam_model_name, input_image, positive_points, negative_points,
     predictor.set_image(image_np_rgb)
 
     if dino_enabled and boxes_filt.shape[0] > 1:
-        print(f"SAM inference with {boxes_filt.shape[0]} boxes, point prompts disgarded.")
+        sam_predict_status = f"SAM inference with {boxes_filt.shape[0]} boxes, point prompts disgarded"
+        print(sam_predict_status)
         transformed_boxes = predictor.transform.apply_boxes_torch(boxes_filt, image_np.shape[:2])
         masks, _, _ = predictor.predict_torch(
             point_coords=None,
@@ -156,7 +165,8 @@ def sam_predict(sam_model_name, input_image, positive_points, negative_points,
         
         masks = masks.permute(1, 0, 2, 3).cpu().numpy()
     else:
-        print(f"SAM inference with {0 if boxes_filt is None else boxes_filt.shape[0]} box, {len(positive_points)} positive prompts, {len(negative_points)} negative prompts.")
+        sam_predict_status = f"SAM inference with {0 if boxes_filt is None else boxes_filt.shape[0]} box, {len(positive_points)} positive prompts, {len(negative_points)} negative prompts"
+        print(sam_predict_status)
         point_coords = np.array(positive_points + negative_points)
         point_labels = np.array([1] * len(positive_points) + [0] * len(negative_points))
 
@@ -190,14 +200,17 @@ def sam_predict(sam_model_name, input_image, positive_points, negative_points,
             image_np_copy[~np.any(mask, axis=0)] = np.array([0, 0, 0, 0])
             matted_images.append(Image.fromarray(image_np_copy))
 
-    return mask_images + masks_gallery + matted_images
+    return mask_images + masks_gallery + matted_images, sam_predict_status + sam_predict_result
 
 
 def dino_predict(input_image, dino_model_name, text_prompt, box_threshold):
     image_np = np.array(input_image)
-    boxes_filt = dino_predict_internal(input_image, dino_model_name, text_prompt, box_threshold).numpy()
+    boxes_filt, install_success = dino_predict_internal(input_image, dino_model_name, text_prompt, box_threshold)
+    if not install_success:
+        return None, gr.update(), gr.update(visible=True, value="GroundingDINO installment failed. Preview failed. See your terminal for more detail and submit an issue to https://github.com/continue-revolution/sd-webui-segment-anything/issues.")
+    boxes_filt = boxes_filt.numpy()
     boxes_choice = [str(i) for i in range(boxes_filt.shape[0])]
-    return Image.fromarray(show_boxes(image_np, boxes_filt.astype(int), show_index=True)), gr.update(choices=boxes_choice, value=boxes_choice)
+    return Image.fromarray(show_boxes(image_np, boxes_filt.astype(int), show_index=True)), gr.update(choices=boxes_choice, value=boxes_choice), gr.update(visible=False)
 
 def dino_batch_process(
     batch_sam_model_name, batch_dino_model_name, batch_text_prompt, batch_box_threshold, batch_dilation_amt,
@@ -216,7 +229,9 @@ def dino_batch_process(
         image_np = np.array(input_image)
         image_np_rgb = image_np[..., :3]
 
-        boxes_filt = dino_predict_internal(input_image, batch_dino_model_name, batch_text_prompt, batch_box_threshold)
+        boxes_filt, install_success = dino_predict_internal(input_image, batch_dino_model_name, batch_text_prompt, batch_box_threshold)
+        if not install_success:
+            return "GroundingDINO installment failed. Batch processing failed. See your terminal for more detail and submit an issue to https://github.com/continue-revolution/sd-webui-segment-anything/issues."
 
         predictor.set_image(image_np_rgb)
         transformed_boxes = predictor.transform.apply_boxes_torch(boxes_filt, image_np.shape[:2])
@@ -301,20 +316,21 @@ class Script(scripts.Script):
                                     dino_preview_boxes_button = gr.Button(value="Generate bounding box", elem_id="dino_run_button")
                                 dino_preview_boxes_selection = gr.CheckboxGroup(label="Select your favorite boxes: ", elem_id="dino_preview_boxes_selection")
                                 dino_preview_boxes_selection.change(fn=lambda _: None, inputs=[dino_preview_boxes_selection], outputs=None, _js="onChangeDinoPreviewBoxesSelection")
+                                dino_preview_result = gr.Text(value="", show_label=False, visible=False)
 
                                 dino_preview_boxes_button.click(
                                     fn=dino_predict,
                                     _js="submit_dino",
                                     inputs=[input_image, dino_model_name, text_prompt, box_threshold],
-                                    outputs=[dino_preview_boxes, dino_preview_boxes_selection]
+                                    outputs=[dino_preview_boxes, dino_preview_boxes_selection, dino_preview_result])
-                                )
 
                         mask_image = gr.Gallery(label='Segment Anything Output', show_label=False, elem_id='sam_gallery').style(grid=3)
 
                         with gr.Row(elem_id="sam_generate_box", elem_classes="generate-box"):
                             gr.Button(value="Add dot prompt or enable GroundingDINO with text prompts to preview segmentation", elem_id="sam_no_button")
                             run_button = gr.Button(value="Preview Segmentation", elem_id="sam_run_button")
-                            
+                        run_result = gr.Text(value="", show_label=False)
+
                         gr.Checkbox(value=False, label="Preview automatically when add/remove points", elem_id="sam_realtime_preview_checkbox")
                             
                         with gr.Row():
@@ -376,7 +392,7 @@ class Script(scripts.Script):
                         dummy_component, dummy_component,   # Point prompts
                         dino_checkbox, dino_model_name, text_prompt, box_threshold, # DINO prompts
                         dino_preview_checkbox, dino_preview_boxes_selection],  # DINO preview prompts
-                outputs=[mask_image],)
+                outputs=[mask_image, run_result])
             
             dino_checkbox.change(
                 fn=gr_show,
@@ -394,21 +410,18 @@ class Script(scripts.Script):
                 fn=lambda _: None,
                 _js="switchToInpaintUpload",
                 inputs=[dummy_component],
-                outputs=None
+                outputs=None)
-            )
 
             remove_dots.click(
                 fn=lambda _: None,
                 _js="removeDots",
                 inputs=[dummy_component],
-                outputs=None
+                outputs=None)
-            )
             
             unload.click(
                 fn=clear_cache,
                 inputs=[],
-                outputs=[]
+                outputs=[])
-            )
 
             dilation_checkbox.change(
                 fn=gr_show,