import colorsys import gc import os from typing import Optional import cv2 import gradio as gr import numpy as np import torch from gradio.themes import Soft from PIL import Image, ImageDraw, ImageFont from transformers import Sam3TrackerVideoModel, Sam3TrackerVideoProcessor, Sam3VideoModel, Sam3VideoProcessor def get_device_and_dtype() -> tuple[str, torch.dtype]: device = "cuda" if torch.cuda.is_available() else "cpu" dtype = torch.bfloat16 return device, dtype _GLOBAL_DEVICE, _GLOBAL_DTYPE = get_device_and_dtype() _GLOBAL_MODEL_REPO_ID = "facebook/sam3" _GLOBAL_TOKEN = os.getenv("HF_TOKEN") _GLOBAL_TRACKER_MODEL = Sam3TrackerVideoModel.from_pretrained( _GLOBAL_MODEL_REPO_ID, torch_dtype=_GLOBAL_DTYPE, device_map=_GLOBAL_DEVICE ).eval() _GLOBAL_TRACKER_PROCESSOR = Sam3TrackerVideoProcessor.from_pretrained(_GLOBAL_MODEL_REPO_ID, token=_GLOBAL_TOKEN) _GLOBAL_TEXT_VIDEO_MODEL = Sam3VideoModel.from_pretrained(_GLOBAL_MODEL_REPO_ID, token=_GLOBAL_TOKEN) _GLOBAL_TEXT_VIDEO_MODEL = _GLOBAL_TEXT_VIDEO_MODEL.to(_GLOBAL_DEVICE, dtype=_GLOBAL_DTYPE).eval() _GLOBAL_TEXT_VIDEO_PROCESSOR = Sam3VideoProcessor.from_pretrained(_GLOBAL_MODEL_REPO_ID, token=_GLOBAL_TOKEN) print("Models loaded successfully!") def try_load_video_frames(video_path_or_url: str) -> tuple[list[Image.Image], dict]: cap = cv2.VideoCapture(video_path_or_url) frames = [] while cap.isOpened(): ret, frame = cap.read() if not ret: break frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) frames.append(Image.fromarray(frame_rgb)) fps_val = cap.get(cv2.CAP_PROP_FPS) cap.release() info = { "num_frames": len(frames), "fps": float(fps_val) if fps_val and fps_val > 0 else None, } return frames, info def overlay_masks_on_frame( frame: Image.Image, masks_per_object: dict[int, np.ndarray], color_by_obj: dict[int, tuple[int, int, int]], alpha: float = 0.5, ) -> Image.Image: base = np.array(frame).astype(np.float32) / 255.0 height, width = base.shape[:2] overlay = base.copy() for obj_id, mask in masks_per_object.items(): if mask is None: continue if mask.dtype != np.float32: mask = mask.astype(np.float32) if mask.ndim == 3: mask = mask.squeeze() mask = np.clip(mask, 0.0, 1.0) color = np.array(color_by_obj.get(obj_id, (255, 0, 0)), dtype=np.float32) / 255.0 a = alpha m = mask[..., None] overlay = (1.0 - a * m) * overlay + (a * m) * color out = np.clip(overlay * 255.0, 0, 255).astype(np.uint8) return Image.fromarray(out) def pastel_color_for_object(obj_id: int) -> tuple[int, int, int]: golden_ratio_conjugate = 0.61 hue = (obj_id * golden_ratio_conjugate) % 1.0 saturation = 0.45 value = 1.0 r_f, g_f, b_f = colorsys.hsv_to_rgb(hue, saturation, value) return int(r_f * 255), int(g_f * 255), int(b_f * 255) def pastel_color_for_prompt(prompt_text: str) -> tuple[int, int, int]: """Generate a consistent color for a prompt text using a deterministic hash.""" # Use a deterministic hash by summing character codes # This ensures the same prompt always gets the same color char_sum = sum(ord(c) for c in prompt_text) # Use the sum to generate a hue that's well-distributed across the color spectrum # Multiply by a large prime to spread values out hue = ((char_sum * 2654435761) % 360) / 360.0 # Use pastel colors (lower saturation, high value) saturation = 0.5 value = 0.95 r_f, g_f, b_f = colorsys.hsv_to_rgb(hue, saturation, value) return int(r_f * 255), int(g_f * 255), int(b_f * 255) class AppState: def __init__(self): self.reset() def reset(self): self.video_frames: list[Image.Image] = [] self.inference_session = None self.video_fps: float | None = None self.masks_by_frame: dict[int, dict[int, np.ndarray]] = {} self.color_by_obj: dict[int, tuple[int, int, int]] = {} self.color_by_prompt: dict[str, tuple[int, int, int]] = {} self.clicks_by_frame_obj: dict[int, dict[int, list[tuple[int, int, int]]]] = {} self.boxes_by_frame_obj: dict[int, dict[int, list[tuple[int, int, int, int]]]] = {} self.text_prompts_by_frame_obj: dict[int, dict[int, str]] = {} self.composited_frames: dict[int, Image.Image] = {} self.current_frame_idx: int = 0 self.current_obj_id: int = 1 self.current_label: str = "positive" self.current_clear_old: bool = True self.current_prompt_type: str = "Points" self.pending_box_start: tuple[int, int] | None = None self.pending_box_start_frame_idx: int | None = None self.pending_box_start_obj_id: int | None = None self.active_tab: str = "point_box" def __repr__(self): return f"AppState(video_frames={len(self.video_frames)}, video_fps={self.video_fps}, masks_by_frame={len(self.masks_by_frame)}, color_by_obj={len(self.color_by_obj)})" @property def num_frames(self) -> int: return len(self.video_frames) def init_video_session( GLOBAL_STATE: gr.State, video: str | dict, active_tab: str = "point_box" ) -> tuple[AppState, int, int, Image.Image, str]: GLOBAL_STATE.video_frames = [] GLOBAL_STATE.masks_by_frame = {} GLOBAL_STATE.color_by_obj = {} GLOBAL_STATE.color_by_prompt = {} GLOBAL_STATE.text_prompts_by_frame_obj = {} GLOBAL_STATE.clicks_by_frame_obj = {} GLOBAL_STATE.boxes_by_frame_obj = {} GLOBAL_STATE.composited_frames = {} GLOBAL_STATE.inference_session = None GLOBAL_STATE.active_tab = active_tab device = _GLOBAL_DEVICE dtype = _GLOBAL_DTYPE video_path: Optional[str] = None if isinstance(video, dict): video_path = video.get("name") or video.get("path") or video.get("data") elif isinstance(video, str): video_path = video else: video_path = None if not video_path: raise gr.Error("Invalid video input.") frames, info = try_load_video_frames(video_path) if len(frames) == 0: raise gr.Error("No frames could be loaded from the video.") MAX_SECONDS = 8.0 trimmed_note = "" fps_in = info.get("fps") max_frames_allowed = int(MAX_SECONDS * fps_in) if fps_in else len(frames) if len(frames) > max_frames_allowed: frames = frames[:max_frames_allowed] trimmed_note = f" (trimmed to {int(MAX_SECONDS)}s = {len(frames)} frames)" if isinstance(info, dict): info["num_frames"] = len(frames) GLOBAL_STATE.video_frames = frames GLOBAL_STATE.video_fps = float(fps_in) if fps_in else None raw_video = [np.array(frame) for frame in frames] if active_tab == "text": processor = _GLOBAL_TEXT_VIDEO_PROCESSOR GLOBAL_STATE.inference_session = processor.init_video_session( video=frames, inference_device=device, processing_device="cpu", video_storage_device="cpu", dtype=dtype, ) else: processor = _GLOBAL_TRACKER_PROCESSOR GLOBAL_STATE.inference_session = processor.init_video_session( video=raw_video, inference_device=device, video_storage_device="cpu", processing_device="cpu", inference_state_device=device, dtype=dtype, ) first_frame = frames[0] max_idx = len(frames) - 1 if active_tab == "text": status = ( f"Loaded {len(frames)} frames @ {GLOBAL_STATE.video_fps or 'unknown'} fps{trimmed_note}. " f"Device: {device}, dtype: bfloat16. Ready for text prompting." ) else: status = ( f"Loaded {len(frames)} frames @ {GLOBAL_STATE.video_fps or 'unknown'} fps{trimmed_note}. " f"Device: {device}, dtype: bfloat16. Video session initialized." ) return GLOBAL_STATE, 0, max_idx, first_frame, status def compose_frame(state: AppState, frame_idx: int) -> Image.Image: if state is None or state.video_frames is None or len(state.video_frames) == 0: return None frame_idx = int(np.clip(frame_idx, 0, len(state.video_frames) - 1)) frame = state.video_frames[frame_idx] masks = state.masks_by_frame.get(frame_idx, {}) out_img = frame if len(masks) != 0: out_img = overlay_masks_on_frame(out_img, masks, state.color_by_obj, alpha=0.65) clicks_map = state.clicks_by_frame_obj.get(frame_idx) if clicks_map: draw = ImageDraw.Draw(out_img) cross_half = 6 for obj_id, pts in clicks_map.items(): for x, y, lbl in pts: color = (0, 255, 0) if int(lbl) == 1 else (255, 0, 0) draw.line([(x - cross_half, y), (x + cross_half, y)], fill=color, width=2) draw.line([(x, y - cross_half), (x, y + cross_half)], fill=color, width=2) if ( state.pending_box_start is not None and state.pending_box_start_frame_idx == frame_idx and state.pending_box_start_obj_id is not None ): draw = ImageDraw.Draw(out_img) x, y = state.pending_box_start cross_half = 6 color = state.color_by_obj.get(state.pending_box_start_obj_id, (255, 255, 255)) draw.line([(x - cross_half, y), (x + cross_half, y)], fill=color, width=2) draw.line([(x, y - cross_half), (x, y + cross_half)], fill=color, width=2) box_map = state.boxes_by_frame_obj.get(frame_idx) if box_map: draw = ImageDraw.Draw(out_img) for obj_id, boxes in box_map.items(): color = state.color_by_obj.get(obj_id, (255, 255, 255)) for x1, y1, x2, y2 in boxes: draw.rectangle([(x1, y1), (x2, y2)], outline=color, width=2) text_prompts_by_obj = {} for frame_texts in state.text_prompts_by_frame_obj.values(): for obj_id, text_prompt in frame_texts.items(): if obj_id not in text_prompts_by_obj: text_prompts_by_obj[obj_id] = text_prompt if text_prompts_by_obj and len(masks) > 0: draw = ImageDraw.Draw(out_img) # Calculate scale factor based on image size (reference: 720p height = 720) img_width, img_height = out_img.size reference_height = 720.0 scale_factor = img_height / reference_height # Scale font size (base size ~13 pixels for default font, scale proportionally) base_font_size = 13 font_size = max(10, int(base_font_size * scale_factor)) # Try to load a scalable font, fall back to default if not available try: # Try common system fonts font_paths = [ "/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf", "/System/Library/Fonts/Helvetica.ttc", "arial.ttf", ] font = None for font_path in font_paths: try: font = ImageFont.truetype(font_path, font_size) break except (OSError, IOError): continue if font is None: # Fallback to default font font = ImageFont.load_default() except Exception: font = ImageFont.load_default() for obj_id, text_prompt in text_prompts_by_obj.items(): obj_mask = masks.get(obj_id) if obj_mask is not None: mask_array = np.array(obj_mask) if mask_array.size > 0 and np.any(mask_array): rows = np.any(mask_array, axis=1) cols = np.any(mask_array, axis=0) if np.any(rows) and np.any(cols): y_min, y_max = np.where(rows)[0][[0, -1]] x_min, x_max = np.where(cols)[0][[0, -1]] label_x = int(x_min) # Scale vertical offset and padding vertical_offset = int(20 * scale_factor) padding = max(2, int(4 * scale_factor)) label_y = int(y_min) - vertical_offset label_y = max(int(5 * scale_factor), label_y) obj_color = state.color_by_obj.get(obj_id, (255, 255, 255)) # Include object ID in the label label_text = f"{text_prompt} - ID {obj_id}" bbox = draw.textbbox((label_x, label_y), label_text, font=font) draw.rectangle( [(bbox[0] - padding, bbox[1] - padding), (bbox[2] + padding, bbox[3] + padding)], fill=obj_color, outline=None, width=0, ) draw.text((label_x, label_y), label_text, fill=(255, 255, 255), font=font) state.composited_frames[frame_idx] = out_img return out_img def update_frame_display(state: AppState, frame_idx: int) -> Image.Image: if state is None or state.video_frames is None or len(state.video_frames) == 0: return None frame_idx = int(np.clip(frame_idx, 0, len(state.video_frames) - 1)) cached = state.composited_frames.get(frame_idx) if cached is not None: return cached return compose_frame(state, frame_idx) def _get_prompt_for_obj(state: AppState, obj_id: int) -> Optional[str]: """Get the prompt text associated with an object ID.""" # Priority 1: Check text_prompts_by_frame_obj (most reliable) for frame_texts in state.text_prompts_by_frame_obj.values(): if obj_id in frame_texts: return frame_texts[obj_id].strip() # Priority 2: Check inference session mapping if state.inference_session is not None: if ( hasattr(state.inference_session, "obj_id_to_prompt_id") and obj_id in state.inference_session.obj_id_to_prompt_id ): prompt_id = state.inference_session.obj_id_to_prompt_id[obj_id] if hasattr(state.inference_session, "prompts") and prompt_id in state.inference_session.prompts: return state.inference_session.prompts[prompt_id].strip() return None def _ensure_color_for_obj(state: AppState, obj_id: int): """Assign color to object based on its prompt if available, otherwise use object ID.""" prompt_text = _get_prompt_for_obj(state, obj_id) if prompt_text is not None: # Ensure prompt has a color assigned if prompt_text not in state.color_by_prompt: state.color_by_prompt[prompt_text] = pastel_color_for_prompt(prompt_text) # Always update to prompt-based color state.color_by_obj[obj_id] = state.color_by_prompt[prompt_text] elif obj_id not in state.color_by_obj: # Fallback to object ID-based color (for point/box prompting mode) state.color_by_obj[obj_id] = pastel_color_for_object(obj_id) def on_image_click( img: Image.Image | np.ndarray, state: AppState, frame_idx: int, obj_id: int, label: str, clear_old: bool, evt: gr.SelectData, ) -> Image.Image: if state is None or state.inference_session is None: return img model = _GLOBAL_TRACKER_MODEL processor = _GLOBAL_TRACKER_PROCESSOR x = y = None if evt is not None: try: if hasattr(evt, "index") and isinstance(evt.index, (list, tuple)) and len(evt.index) == 2: x, y = int(evt.index[0]), int(evt.index[1]) elif hasattr(evt, "value") and isinstance(evt.value, dict) and "x" in evt.value and "y" in evt.value: x, y = int(evt.value["x"]), int(evt.value["y"]) except Exception: x = y = None if x is None or y is None: raise gr.Error("Could not read click coordinates.") _ensure_color_for_obj(state, int(obj_id)) ann_frame_idx = int(frame_idx) ann_obj_id = int(obj_id) if state.current_prompt_type == "Boxes": if state.pending_box_start is None: frame_clicks = state.clicks_by_frame_obj.setdefault(ann_frame_idx, {}) frame_clicks[ann_obj_id] = [] state.composited_frames.pop(ann_frame_idx, None) state.pending_box_start = (int(x), int(y)) state.pending_box_start_frame_idx = ann_frame_idx state.pending_box_start_obj_id = ann_obj_id state.composited_frames.pop(ann_frame_idx, None) return update_frame_display(state, ann_frame_idx) else: x1, y1 = state.pending_box_start x2, y2 = int(x), int(y) state.pending_box_start = None state.pending_box_start_frame_idx = None state.pending_box_start_obj_id = None state.composited_frames.pop(ann_frame_idx, None) x_min, y_min = min(x1, x2), min(y1, y2) x_max, y_max = max(x1, x2), max(y1, y2) box = [[[x_min, y_min, x_max, y_max]]] processor.add_inputs_to_inference_session( inference_session=state.inference_session, frame_idx=ann_frame_idx, obj_ids=ann_obj_id, input_boxes=box, ) frame_boxes = state.boxes_by_frame_obj.setdefault(ann_frame_idx, {}) obj_boxes = frame_boxes.setdefault(ann_obj_id, []) obj_boxes.clear() obj_boxes.append((x_min, y_min, x_max, y_max)) state.composited_frames.pop(ann_frame_idx, None) else: label_int = 1 if str(label).lower().startswith("pos") else 0 frame_clicks = state.clicks_by_frame_obj.setdefault(ann_frame_idx, {}) obj_clicks = frame_clicks.setdefault(ann_obj_id, []) if bool(clear_old): obj_clicks.clear() frame_boxes = state.boxes_by_frame_obj.setdefault(ann_frame_idx, {}) frame_boxes[ann_obj_id] = [] if hasattr(state.inference_session, "reset_inference_session"): pass obj_clicks.append((int(x), int(y), int(label_int))) points = [[[[click[0], click[1]] for click in obj_clicks]]] labels = [[[click[2] for click in obj_clicks]]] processor.add_inputs_to_inference_session( inference_session=state.inference_session, frame_idx=ann_frame_idx, obj_ids=ann_obj_id, input_points=points, input_labels=labels, ) state.composited_frames.pop(ann_frame_idx, None) with torch.no_grad(): outputs = model( inference_session=state.inference_session, frame_idx=ann_frame_idx, ) out_mask_logits = processor.post_process_masks( [outputs.pred_masks], [[state.inference_session.video_height, state.inference_session.video_width]], binarize=False, )[0] mask_2d = (out_mask_logits[0] > 0.0).cpu().numpy() masks_for_frame = state.masks_by_frame.setdefault(ann_frame_idx, {}) masks_for_frame[ann_obj_id] = mask_2d state.composited_frames.pop(ann_frame_idx, None) return update_frame_display(state, ann_frame_idx) def on_text_prompt( state: AppState, frame_idx: int, text_prompt: str, ) -> tuple[Image.Image, str, str]: if state is None or state.inference_session is None: return None, "Upload a video and enter text prompt.", "**Active prompts:** None" model = _GLOBAL_TEXT_VIDEO_MODEL processor = _GLOBAL_TEXT_VIDEO_PROCESSOR if not text_prompt or not text_prompt.strip(): active_prompts = _get_active_prompts_display(state) return update_frame_display(state, int(frame_idx)), "Please enter a text prompt.", active_prompts frame_idx = int(np.clip(frame_idx, 0, len(state.video_frames) - 1)) # Parse comma-separated prompts or single prompt prompt_texts = [p.strip() for p in text_prompt.split(",") if p.strip()] if not prompt_texts: active_prompts = _get_active_prompts_display(state) return update_frame_display(state, int(frame_idx)), "Please enter a valid text prompt.", active_prompts # Add text prompt(s) - supports both single string and list of strings state.inference_session = processor.add_text_prompt( inference_session=state.inference_session, text=prompt_texts, # Pass as list to add multiple at once ) masks_for_frame = state.masks_by_frame.setdefault(frame_idx, {}) frame_texts = state.text_prompts_by_frame_obj.setdefault(int(frame_idx), {}) num_objects = 0 detected_obj_ids = [] prompt_to_obj_ids_summary = {} with torch.no_grad(): for model_outputs in model.propagate_in_video_iterator( inference_session=state.inference_session, start_frame_idx=frame_idx, max_frame_num_to_track=1, ): processed_outputs = processor.postprocess_outputs( state.inference_session, model_outputs, ) current_frame_idx = model_outputs.frame_idx if current_frame_idx == frame_idx: object_ids = processed_outputs["object_ids"] masks = processed_outputs["masks"] scores = processed_outputs["scores"] prompt_to_obj_ids = processed_outputs.get("prompt_to_obj_ids", {}) # Update prompt_to_obj_ids summary for status message for prompt, obj_ids in prompt_to_obj_ids.items(): if prompt not in prompt_to_obj_ids_summary: prompt_to_obj_ids_summary[prompt] = [] prompt_to_obj_ids_summary[prompt].extend( [int(oid) for oid in obj_ids if int(oid) not in prompt_to_obj_ids_summary[prompt]] ) num_objects = len(object_ids) if num_objects > 0: if len(scores) > 0: sorted_indices = torch.argsort(scores, descending=True).cpu().tolist() else: sorted_indices = list(range(num_objects)) for mask_idx in sorted_indices: current_obj_id = int(object_ids[mask_idx].item()) detected_obj_ids.append(current_obj_id) mask_2d = masks[mask_idx].float().cpu().numpy() if mask_2d.ndim == 3: mask_2d = mask_2d.squeeze() mask_2d = (mask_2d > 0.0).astype(np.float32) masks_for_frame[current_obj_id] = mask_2d # Find which prompt detected this object detected_prompt = None for prompt, obj_ids in prompt_to_obj_ids.items(): if current_obj_id in obj_ids: detected_prompt = prompt break # Store prompt and assign color if detected_prompt: frame_texts[current_obj_id] = detected_prompt.strip() _ensure_color_for_obj(state, current_obj_id) state.composited_frames.pop(frame_idx, None) # Build status message with prompt breakdown if detected_obj_ids: status_parts = [f"Processed text prompt(s) on frame {frame_idx}. Found {num_objects} object(s):"] for prompt, obj_ids in prompt_to_obj_ids_summary.items(): if obj_ids: obj_ids_str = ", ".join(map(str, sorted(obj_ids))) status_parts.append(f" • '{prompt}': {len(obj_ids)} object(s) (IDs: {obj_ids_str})") status = "\n".join(status_parts) else: prompts_str = ", ".join([f"'{p}'" for p in prompt_texts]) status = f"Processed text prompt(s) {prompts_str} on frame {frame_idx}. No objects detected." active_prompts = _get_active_prompts_display(state) return update_frame_display(state, int(frame_idx)), status, active_prompts def _get_active_prompts_display(state: AppState) -> str: """Get a formatted string showing all active prompts in the inference session.""" if state is None or state.inference_session is None: return "**Active prompts:** None" if hasattr(state.inference_session, "prompts") and state.inference_session.prompts: prompts_list = sorted(set(state.inference_session.prompts.values())) if prompts_list: prompts_str = ", ".join([f"'{p}'" for p in prompts_list]) return f"**Active prompts:** {prompts_str}" return "**Active prompts:** None" def propagate_masks(GLOBAL_STATE: gr.State): if GLOBAL_STATE is None: return GLOBAL_STATE, "Load a video first.", gr.update() if GLOBAL_STATE.active_tab != "text" and GLOBAL_STATE.inference_session is None: return GLOBAL_STATE, "Load a video first.", gr.update() total = max(1, GLOBAL_STATE.num_frames) processed = 0 yield GLOBAL_STATE, f"Propagating masks: {processed}/{total}", gr.update() last_frame_idx = 0 with torch.no_grad(): if GLOBAL_STATE.active_tab == "text": if GLOBAL_STATE.inference_session is None: yield GLOBAL_STATE, "Text video model not loaded.", gr.update() return model = _GLOBAL_TEXT_VIDEO_MODEL processor = _GLOBAL_TEXT_VIDEO_PROCESSOR # Collect all unique prompts from existing frame annotations text_prompt_to_obj_ids = {} for frame_idx, frame_texts in GLOBAL_STATE.text_prompts_by_frame_obj.items(): for obj_id, text_prompt in frame_texts.items(): if text_prompt not in text_prompt_to_obj_ids: text_prompt_to_obj_ids[text_prompt] = [] if obj_id not in text_prompt_to_obj_ids[text_prompt]: text_prompt_to_obj_ids[text_prompt].append(obj_id) # Also check if there are prompts already in the inference session if hasattr(GLOBAL_STATE.inference_session, "prompts") and GLOBAL_STATE.inference_session.prompts: for prompt_text in GLOBAL_STATE.inference_session.prompts.values(): if prompt_text not in text_prompt_to_obj_ids: text_prompt_to_obj_ids[prompt_text] = [] for text_prompt in text_prompt_to_obj_ids: text_prompt_to_obj_ids[text_prompt].sort() if not text_prompt_to_obj_ids: yield GLOBAL_STATE, "No text prompts found. Please add a text prompt first.", gr.update() return # Add all prompts to the inference session (processor handles deduplication) for text_prompt in text_prompt_to_obj_ids.keys(): GLOBAL_STATE.inference_session = processor.add_text_prompt( inference_session=GLOBAL_STATE.inference_session, text=text_prompt, ) earliest_frame = ( min(GLOBAL_STATE.text_prompts_by_frame_obj.keys()) if GLOBAL_STATE.text_prompts_by_frame_obj else 0 ) frames_to_track = GLOBAL_STATE.num_frames - earliest_frame outputs_per_frame = {} for model_outputs in model.propagate_in_video_iterator( inference_session=GLOBAL_STATE.inference_session, start_frame_idx=earliest_frame, max_frame_num_to_track=frames_to_track, ): processed_outputs = processor.postprocess_outputs( GLOBAL_STATE.inference_session, model_outputs, ) frame_idx = model_outputs.frame_idx outputs_per_frame[frame_idx] = processed_outputs object_ids = processed_outputs["object_ids"] masks = processed_outputs["masks"] scores = processed_outputs["scores"] prompt_to_obj_ids = processed_outputs.get("prompt_to_obj_ids", {}) masks_for_frame = GLOBAL_STATE.masks_by_frame.setdefault(frame_idx, {}) frame_texts = GLOBAL_STATE.text_prompts_by_frame_obj.setdefault(frame_idx, {}) num_objects = len(object_ids) if num_objects > 0: if len(scores) > 0: sorted_indices = torch.argsort(scores, descending=True).cpu().tolist() else: sorted_indices = list(range(num_objects)) for mask_idx in sorted_indices: current_obj_id = int(object_ids[mask_idx].item()) mask_2d = masks[mask_idx].float().cpu().numpy() if mask_2d.ndim == 3: mask_2d = mask_2d.squeeze() mask_2d = (mask_2d > 0.0).astype(np.float32) masks_for_frame[current_obj_id] = mask_2d # Find which prompt detected this object found_prompt = None for prompt, obj_ids in prompt_to_obj_ids.items(): if current_obj_id in obj_ids: found_prompt = prompt break # Store prompt and assign color if found_prompt: frame_texts[current_obj_id] = found_prompt.strip() _ensure_color_for_obj(GLOBAL_STATE, current_obj_id) GLOBAL_STATE.composited_frames.pop(frame_idx, None) last_frame_idx = frame_idx processed += 1 if processed % 30 == 0 or processed == total: yield GLOBAL_STATE, f"Propagating masks: {processed}/{total}", gr.update(value=frame_idx) else: if GLOBAL_STATE.inference_session is None: yield GLOBAL_STATE, "Tracker model not loaded.", gr.update() return model = _GLOBAL_TRACKER_MODEL processor = _GLOBAL_TRACKER_PROCESSOR for sam2_video_output in model.propagate_in_video_iterator( inference_session=GLOBAL_STATE.inference_session ): video_res_masks = processor.post_process_masks( [sam2_video_output.pred_masks], original_sizes=[ [GLOBAL_STATE.inference_session.video_height, GLOBAL_STATE.inference_session.video_width] ], )[0] frame_idx = sam2_video_output.frame_idx for i, out_obj_id in enumerate(GLOBAL_STATE.inference_session.obj_ids): _ensure_color_for_obj(GLOBAL_STATE, int(out_obj_id)) mask_2d = video_res_masks[i].cpu().numpy() masks_for_frame = GLOBAL_STATE.masks_by_frame.setdefault(frame_idx, {}) masks_for_frame[int(out_obj_id)] = mask_2d GLOBAL_STATE.composited_frames.pop(frame_idx, None) last_frame_idx = frame_idx processed += 1 if processed % 30 == 0 or processed == total: yield GLOBAL_STATE, f"Propagating masks: {processed}/{total}", gr.update(value=frame_idx) text = f"Propagated masks across {processed} frames." yield GLOBAL_STATE, text, gr.update(value=last_frame_idx) def reset_prompts(GLOBAL_STATE: gr.State) -> tuple[AppState, Image.Image, str, str]: """Reset prompts and all outputs, but keep processed frames and cached vision features.""" if GLOBAL_STATE is None or GLOBAL_STATE.inference_session is None: active_prompts = _get_active_prompts_display(GLOBAL_STATE) return GLOBAL_STATE, None, "No active session to reset.", active_prompts if GLOBAL_STATE.active_tab != "text": active_prompts = _get_active_prompts_display(GLOBAL_STATE) return GLOBAL_STATE, None, "Reset prompts is only available for text prompting mode.", active_prompts # Reset inference session tracking data but keep cache and processed frames if hasattr(GLOBAL_STATE.inference_session, "reset_tracking_data"): GLOBAL_STATE.inference_session.reset_tracking_data() # Manually clear prompts (reset_tracking_data doesn't clear prompts themselves) if hasattr(GLOBAL_STATE.inference_session, "prompts"): GLOBAL_STATE.inference_session.prompts.clear() if hasattr(GLOBAL_STATE.inference_session, "prompt_input_ids"): GLOBAL_STATE.inference_session.prompt_input_ids.clear() if hasattr(GLOBAL_STATE.inference_session, "prompt_embeddings"): GLOBAL_STATE.inference_session.prompt_embeddings.clear() if hasattr(GLOBAL_STATE.inference_session, "prompt_attention_masks"): GLOBAL_STATE.inference_session.prompt_attention_masks.clear() if hasattr(GLOBAL_STATE.inference_session, "obj_id_to_prompt_id"): GLOBAL_STATE.inference_session.obj_id_to_prompt_id.clear() # Reset detection-tracking fusion state if hasattr(GLOBAL_STATE.inference_session, "obj_id_to_score"): GLOBAL_STATE.inference_session.obj_id_to_score.clear() if hasattr(GLOBAL_STATE.inference_session, "obj_id_to_tracker_score_frame_wise"): GLOBAL_STATE.inference_session.obj_id_to_tracker_score_frame_wise.clear() if hasattr(GLOBAL_STATE.inference_session, "obj_id_to_last_occluded"): GLOBAL_STATE.inference_session.obj_id_to_last_occluded.clear() if hasattr(GLOBAL_STATE.inference_session, "max_obj_id"): GLOBAL_STATE.inference_session.max_obj_id = -1 if hasattr(GLOBAL_STATE.inference_session, "obj_first_frame_idx"): GLOBAL_STATE.inference_session.obj_first_frame_idx.clear() if hasattr(GLOBAL_STATE.inference_session, "unmatched_frame_inds"): GLOBAL_STATE.inference_session.unmatched_frame_inds.clear() if hasattr(GLOBAL_STATE.inference_session, "overlap_pair_to_frame_inds"): GLOBAL_STATE.inference_session.overlap_pair_to_frame_inds.clear() if hasattr(GLOBAL_STATE.inference_session, "trk_keep_alive"): GLOBAL_STATE.inference_session.trk_keep_alive.clear() if hasattr(GLOBAL_STATE.inference_session, "removed_obj_ids"): GLOBAL_STATE.inference_session.removed_obj_ids.clear() if hasattr(GLOBAL_STATE.inference_session, "suppressed_obj_ids"): GLOBAL_STATE.inference_session.suppressed_obj_ids.clear() if hasattr(GLOBAL_STATE.inference_session, "hotstart_removed_obj_ids"): GLOBAL_STATE.inference_session.hotstart_removed_obj_ids.clear() # Clear all app state outputs GLOBAL_STATE.masks_by_frame.clear() GLOBAL_STATE.text_prompts_by_frame_obj.clear() GLOBAL_STATE.composited_frames.clear() GLOBAL_STATE.color_by_obj.clear() GLOBAL_STATE.color_by_prompt.clear() # Update display current_idx = int(getattr(GLOBAL_STATE, "current_frame_idx", 0)) current_idx = max(0, min(current_idx, GLOBAL_STATE.num_frames - 1)) preview_img = update_frame_display(GLOBAL_STATE, current_idx) active_prompts = _get_active_prompts_display(GLOBAL_STATE) status = "Prompts and outputs reset. Processed frames and cached vision features preserved." return GLOBAL_STATE, preview_img, status, active_prompts def reset_session(GLOBAL_STATE: gr.State) -> tuple[AppState, Image.Image, int, int, str, str]: if not GLOBAL_STATE.video_frames: return GLOBAL_STATE, None, 0, 0, "Session reset. Load a new video.", "**Active prompts:** None" if GLOBAL_STATE.active_tab == "text": if GLOBAL_STATE.video_frames: processor = _GLOBAL_TEXT_VIDEO_PROCESSOR GLOBAL_STATE.inference_session = processor.init_video_session( video=GLOBAL_STATE.video_frames, inference_device=_GLOBAL_DEVICE, processing_device="cpu", video_storage_device="cpu", dtype=_GLOBAL_DTYPE, ) elif GLOBAL_STATE.inference_session is not None and hasattr( GLOBAL_STATE.inference_session, "reset_inference_session" ): GLOBAL_STATE.inference_session.reset_inference_session() else: if GLOBAL_STATE.video_frames: processor = _GLOBAL_TRACKER_PROCESSOR raw_video = [np.array(frame) for frame in GLOBAL_STATE.video_frames] GLOBAL_STATE.inference_session = processor.init_video_session( video=raw_video, inference_device=_GLOBAL_DEVICE, video_storage_device="cpu", processing_device="cpu", dtype=_GLOBAL_DTYPE, ) GLOBAL_STATE.masks_by_frame.clear() GLOBAL_STATE.clicks_by_frame_obj.clear() GLOBAL_STATE.boxes_by_frame_obj.clear() GLOBAL_STATE.text_prompts_by_frame_obj.clear() GLOBAL_STATE.composited_frames.clear() GLOBAL_STATE.color_by_obj.clear() GLOBAL_STATE.color_by_prompt.clear() GLOBAL_STATE.pending_box_start = None GLOBAL_STATE.pending_box_start_frame_idx = None GLOBAL_STATE.pending_box_start_obj_id = None gc.collect() current_idx = int(getattr(GLOBAL_STATE, "current_frame_idx", 0)) current_idx = max(0, min(current_idx, GLOBAL_STATE.num_frames - 1)) preview_img = update_frame_display(GLOBAL_STATE, current_idx) slider_minmax = gr.update(minimum=0, maximum=max(GLOBAL_STATE.num_frames - 1, 0), interactive=True) slider_value = gr.update(value=current_idx) status = "Session reset. Prompts cleared; video preserved." active_prompts = _get_active_prompts_display(GLOBAL_STATE) return GLOBAL_STATE, preview_img, slider_minmax, slider_value, status, active_prompts def _on_video_change_pointbox(GLOBAL_STATE: gr.State, video): GLOBAL_STATE, min_idx, max_idx, first_frame, status = init_video_session(GLOBAL_STATE, video, "point_box") return ( GLOBAL_STATE, gr.update(minimum=min_idx, maximum=max_idx, value=min_idx, interactive=True), first_frame, status, ) def _on_video_change_text(GLOBAL_STATE: gr.State, video): GLOBAL_STATE, min_idx, max_idx, first_frame, status = init_video_session(GLOBAL_STATE, video, "text") active_prompts = _get_active_prompts_display(GLOBAL_STATE) return ( GLOBAL_STATE, gr.update(minimum=min_idx, maximum=max_idx, value=min_idx, interactive=True), first_frame, status, active_prompts, ) theme = Soft(primary_hue="blue", secondary_hue="rose", neutral_hue="slate") with gr.Blocks(title="SAM3", theme=theme) as demo: GLOBAL_STATE = gr.State(AppState()) gr.Markdown( """ ### SAM3 Video Tracking · powered by Hugging Face 🤗 Transformers Segment and track objects across a video with SAM3 (Segment Anything 3). This demo runs the official implementation from the Hugging Face Transformers library for interactive, promptable video segmentation with point, box, and text prompts. """ ) with gr.Tabs() as main_tabs: with gr.Tab("Text Prompting"): with gr.Row(): with gr.Column(): gr.Markdown( """ **Quick start** - **Load a video**: Upload your own or pick an example below. - Select a frame and enter text description(s) to segment objects (e.g., "red car", "penguin"). You can add multiple prompts separated by commas (e.g., "person, bed, lamp") or add them one by one. The text prompt will return all the instances of the object in the frame and not specific ones (e.g. not "penguin on the left" but "penguin"). """ ) with gr.Column(): gr.Markdown( """ **Working with results** - **Preview**: Use the slider to navigate frames and see the current masks. - **Propagate**: Click "Propagate across video" to track all defined objects through the entire video. - **Export**: Render an MP4 for smooth playback using the original video FPS. """ ) with gr.Row(): with gr.Column(scale=1): video_in_text = gr.Video(label="Upload video", sources=["upload", "webcam"], interactive=True) load_status_text = gr.Markdown(visible=True) reset_btn_text = gr.Button("Reset Session", variant="secondary") with gr.Column(scale=2): preview_text = gr.Image(label="Preview", interactive=True) with gr.Row(): frame_slider_text = gr.Slider( label="Frame", minimum=0, maximum=0, step=1, value=0, interactive=True ) with gr.Column(scale=0): propagate_btn_text = gr.Button("Propagate across video", variant="primary") propagate_status_text = gr.Markdown(visible=True) with gr.Row(): text_prompt_input = gr.Textbox( label="Text Prompt(s)", placeholder="Enter text description(s) (e.g., 'person' or 'person, bed, lamp' for multiple)", lines=2, ) with gr.Column(scale=0): text_apply_btn = gr.Button("Apply Text Prompt(s)", variant="primary") reset_prompts_btn = gr.Button("Reset Prompts", variant="secondary") active_prompts_display = gr.Markdown("**Active prompts:** None", visible=True) text_status = gr.Markdown(visible=True) with gr.Row(): render_btn_text = gr.Button("Render MP4 for smooth playback", variant="primary") playback_video_text = gr.Video(label="Rendered Playback", interactive=False) examples_list_text = [ [None, "./deers.mp4"], [None, "./penguins.mp4"], [None, "./foot.mp4"], ] with gr.Row(): gr.Examples( examples=examples_list_text, inputs=[GLOBAL_STATE, video_in_text], fn=_on_video_change_text, outputs=[GLOBAL_STATE, frame_slider_text, preview_text, load_status_text, active_prompts_display], label="Examples", cache_examples=False, examples_per_page=5, ) with gr.Tab("Point/Box Prompting"): with gr.Row(): with gr.Column(): gr.Markdown( """ **Quick start** - **Load a video**: Upload your own or pick an example below. - Select an Object ID and point label (positive/negative), then click the frame to add guidance. You can add **multiple points per object** and define **multiple objects** across frames. """ ) with gr.Column(): gr.Markdown( """ **Working with results** - **Preview**: Use the slider to navigate frames and see the current masks. - **Propagate**: Click "Propagate across video" to track all defined objects through the entire video. - **Export**: Render an MP4 for smooth playback using the original video FPS. """ ) with gr.Row(): with gr.Column(scale=1): video_in_pointbox = gr.Video( label="Upload video", sources=["upload", "webcam"], interactive=True, max_length=7 ) load_status_pointbox = gr.Markdown(visible=True) reset_btn_pointbox = gr.Button("Reset Session", variant="secondary") with gr.Column(scale=2): preview_pointbox = gr.Image(label="Preview", interactive=True) with gr.Row(): frame_slider_pointbox = gr.Slider( label="Frame", minimum=0, maximum=0, step=1, value=0, interactive=True ) with gr.Column(scale=0): propagate_btn_pointbox = gr.Button("Propagate across video", variant="primary") propagate_status_pointbox = gr.Markdown(visible=True) with gr.Row(): obj_id_inp = gr.Number(value=1, precision=0, label="Object ID", scale=0) label_radio = gr.Radio(choices=["positive", "negative"], value="positive", label="Point label") clear_old_chk = gr.Checkbox(value=False, label="Clear old inputs for this object") prompt_type = gr.Radio(choices=["Points", "Boxes"], value="Points", label="Prompt type") with gr.Row(): render_btn_pointbox = gr.Button("Render MP4 for smooth playback", variant="primary") playback_video_pointbox = gr.Video(label="Rendered Playback", interactive=False) examples_list_pointbox = [ [None, "./deers.mp4"], [None, "./penguins.mp4"], [None, "./foot.mp4"], ] with gr.Row(): gr.Examples( examples=examples_list_pointbox, inputs=[GLOBAL_STATE, video_in_pointbox], fn=_on_video_change_pointbox, outputs=[GLOBAL_STATE, frame_slider_pointbox, preview_pointbox, load_status_pointbox], label="Examples", cache_examples=False, examples_per_page=5, ) video_in_pointbox.change( _on_video_change_pointbox, inputs=[GLOBAL_STATE, video_in_pointbox], outputs=[GLOBAL_STATE, frame_slider_pointbox, preview_pointbox, load_status_pointbox], show_progress=True, ) def _sync_frame_idx_pointbox(state_in: AppState, idx: int): if state_in is not None: state_in.current_frame_idx = int(idx) return update_frame_display(state_in, int(idx)) frame_slider_pointbox.change( _sync_frame_idx_pointbox, inputs=[GLOBAL_STATE, frame_slider_pointbox], outputs=preview_pointbox, ) video_in_text.change( _on_video_change_text, inputs=[GLOBAL_STATE, video_in_text], outputs=[GLOBAL_STATE, frame_slider_text, preview_text, load_status_text, active_prompts_display], show_progress=True, ) def _sync_frame_idx_text(state_in: AppState, idx: int): if state_in is not None: state_in.current_frame_idx = int(idx) return update_frame_display(state_in, int(idx)) frame_slider_text.change( _sync_frame_idx_text, inputs=[GLOBAL_STATE, frame_slider_text], outputs=preview_text, ) def _sync_obj_id(s: AppState, oid): if s is not None and oid is not None: s.current_obj_id = int(oid) return gr.update() obj_id_inp.change(_sync_obj_id, inputs=[GLOBAL_STATE, obj_id_inp], outputs=[]) def _sync_label(s: AppState, lab: str): if s is not None and lab is not None: s.current_label = str(lab) return gr.update() label_radio.change(_sync_label, inputs=[GLOBAL_STATE, label_radio], outputs=[]) def _sync_prompt_type(s: AppState, val: str): if s is not None and val is not None: s.current_prompt_type = str(val) s.pending_box_start = None is_points = str(val).lower() == "points" updates = [ gr.update(visible=is_points), gr.update(interactive=is_points) if is_points else gr.update(value=True, interactive=False), ] return updates prompt_type.change( _sync_prompt_type, inputs=[GLOBAL_STATE, prompt_type], outputs=[label_radio, clear_old_chk], ) preview_pointbox.select( on_image_click, [preview_pointbox, GLOBAL_STATE, frame_slider_pointbox, obj_id_inp, label_radio, clear_old_chk], preview_pointbox, ) def _on_text_apply(state: AppState, frame_idx: int, text: str): img, status, active_prompts = on_text_prompt(state, frame_idx, text) return img, status, active_prompts text_apply_btn.click( _on_text_apply, inputs=[GLOBAL_STATE, frame_slider_text, text_prompt_input], outputs=[preview_text, text_status, active_prompts_display], ) reset_prompts_btn.click( reset_prompts, inputs=[GLOBAL_STATE], outputs=[GLOBAL_STATE, preview_text, text_status, active_prompts_display], ) def _render_video(s: AppState): if s is None or s.num_frames == 0: raise gr.Error("Load a video first.") fps = s.video_fps if s.video_fps and s.video_fps > 0 else 12 frames_np = [] first = compose_frame(s, 0) h, w = first.size[1], first.size[0] for idx in range(s.num_frames): img = s.composited_frames.get(idx) if img is None: img = compose_frame(s, idx) frames_np.append(np.array(img)[:, :, ::-1]) if (idx + 1) % 60 == 0: gc.collect() out_path = "/tmp/sam3_playback.mp4" try: fourcc = cv2.VideoWriter_fourcc(*"mp4v") writer = cv2.VideoWriter(out_path, fourcc, fps, (w, h)) for fr_bgr in frames_np: writer.write(fr_bgr) writer.release() return out_path except Exception as e: print(f"Failed to render video with cv2: {e}") raise gr.Error(f"Failed to render video: {e}") render_btn_pointbox.click(_render_video, inputs=[GLOBAL_STATE], outputs=[playback_video_pointbox]) render_btn_text.click(_render_video, inputs=[GLOBAL_STATE], outputs=[playback_video_text]) propagate_btn_pointbox.click( propagate_masks, inputs=[GLOBAL_STATE], outputs=[GLOBAL_STATE, propagate_status_pointbox, frame_slider_pointbox], ) propagate_btn_text.click( propagate_masks, inputs=[GLOBAL_STATE], outputs=[GLOBAL_STATE, propagate_status_text, frame_slider_text], ) reset_btn_pointbox.click( reset_session, inputs=GLOBAL_STATE, outputs=[GLOBAL_STATE, preview_pointbox, frame_slider_pointbox, frame_slider_pointbox, load_status_pointbox], ) reset_btn_text.click( reset_session, inputs=GLOBAL_STATE, outputs=[ GLOBAL_STATE, preview_text, frame_slider_text, frame_slider_text, load_status_text, active_prompts_display, ], ) demo.queue(api_open=False).launch()