# Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. import copy import os import time from datetime import datetime import tempfile import cv2 import matplotlib.pyplot as plt import numpy as np import gradio as gr import torch from moviepy.editor import ImageSequenceClip from PIL import Image from sam2.build_sam import build_sam2_video_predictor # Remove CUDA environment variables if 'TORCH_CUDNN_SDPA_ENABLED' in os.environ: del os.environ["TORCH_CUDNN_SDPA_ENABLED"] # Description title = "
EdgeTAM CPU [GitHub]
" description_p = """# Instructions
  1. Upload one video or click one example video
  2. Click 'include' point type, select the object to segment and track
  3. Click 'exclude' point type (optional), select the area you want to avoid segmenting and tracking
  4. Click the 'Track' button to obtain the masked video
""" # examples - keeping fewer examples to reduce memory footprint examples = [ ["examples/01_dog.mp4"], ["examples/02_cups.mp4"], ["examples/03_blocks.mp4"], ["examples/04_coffee.mp4"], ["examples/05_default_juggle.mp4"], ] OBJ_ID = 0 # Initialize model on CPU - add error handling for file paths sam2_checkpoint = "checkpoints/edgetam.pt" model_cfg = "edgetam.yaml" # Check if model files exist def check_file_exists(filepath): exists = os.path.exists(filepath) if not exists: print(f"WARNING: File not found: {filepath}") return exists # Verify files exist model_files_exist = check_file_exists(sam2_checkpoint) and check_file_exists(model_cfg) predictor = None try: # Load model with careful error handling predictor = build_sam2_video_predictor(model_cfg, sam2_checkpoint, device="cpu") print("predictor loaded on CPU") except Exception as e: print(f"Error loading model: {e}") import traceback traceback.print_exc() # Function to get video frame rate def get_video_fps(video_path): cap = cv2.VideoCapture(video_path) if not cap.isOpened(): print("Error: Could not open video.") return 30.0 # Default fallback value fps = cap.get(cv2.CAP_PROP_FPS) cap.release() return fps def reset(session_state): """Reset all session state variables and UI elements.""" session_state["input_points"] = [] session_state["input_labels"] = [] if session_state["inference_state"] is not None: predictor.reset_state(session_state["inference_state"]) session_state["first_frame"] = None session_state["all_frames"] = None session_state["inference_state"] = None session_state["progress"] = 0 return ( None, gr.update(open=True), None, None, gr.update(value=None, visible=False), gr.update(value=0, visible=False), session_state, ) def clear_points(session_state): """Clear tracking points while keeping the video frames.""" session_state["input_points"] = [] session_state["input_labels"] = [] if session_state["inference_state"] is not None and session_state["inference_state"].get("tracking_has_started", False): predictor.reset_state(session_state["inference_state"]) return ( session_state["first_frame"], None, gr.update(value=None, visible=False), gr.update(value=0, visible=False), session_state, ) def preprocess_video_in(video_path, session_state): """Process input video to extract frames for tracking.""" if video_path is None or not os.path.exists(video_path): return ( gr.update(open=True), # video_in_drawer None, # points_map None, # output_image gr.update(value=None, visible=False), # output_video gr.update(value=0, visible=False), # progress_bar session_state, ) # Read the video cap = cv2.VideoCapture(video_path) if not cap.isOpened(): print(f"Error: Could not open video at {video_path}.") return ( gr.update(open=True), # video_in_drawer None, # points_map None, # output_image gr.update(value=None, visible=False), # output_video gr.update(value=0, visible=False), # progress_bar session_state, ) frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) fps = cap.get(cv2.CAP_PROP_FPS) print(f"Video info: {frame_width}x{frame_height}, {total_frames} frames, {fps} FPS") target_width = 640 # Target width for processing on CPU scale_factor = 1.0 if frame_width > target_width: scale_factor = target_width / frame_width new_width = int(frame_width * scale_factor) new_height = int(frame_height * scale_factor) print(f"Resizing video for CPU processing: {frame_width}x{frame_height} -> {new_width}x{new_height}") # Even more aggressive frame skipping for very long videos on CPU frame_stride = 1 max_frames = 150 # Maximum number of frames to process if total_frames > max_frames: frame_stride = max(1, int(total_frames / max_frames)) print(f"Video has {total_frames} frames, using stride of {frame_stride} to limit to {max_frames}") frame_number = 0 first_frame = None all_frames = [] while True: ret, frame = cap.read() if not ret: break if frame_number % frame_stride == 0: try: # Resize the frame if needed if scale_factor != 1.0: frame = cv2.resize( frame, (int(frame_width * scale_factor), int(frame_height * scale_factor)), interpolation=cv2.INTER_AREA ) frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) frame = np.array(frame) if first_frame is None: first_frame = frame all_frames.append(frame) except Exception as e: print(f"Error processing frame {frame_number}: {e}") frame_number += 1 cap.release() if first_frame is None or len(all_frames) == 0: print("Error: No frames could be extracted from the video.") return ( gr.update(open=True), # video_in_drawer None, # points_map None, # output_image gr.update(value=None, visible=False), # output_video gr.update(value=0, visible=False), # progress_bar session_state, ) print(f"Successfully extracted {len(all_frames)} frames from video") session_state["first_frame"] = copy.deepcopy(first_frame) session_state["all_frames"] = all_frames session_state["frame_stride"] = frame_stride session_state["scale_factor"] = scale_factor session_state["original_dimensions"] = (frame_width, frame_height) session_state["progress"] = 0 try: session_state["inference_state"] = predictor.init_state(video_path=video_path) session_state["input_points"] = [] session_state["input_labels"] = [] except Exception as e: print(f"Error initializing inference state: {e}") import traceback traceback.print_exc() session_state["inference_state"] = None return [ gr.update(open=False), # video_in_drawer first_frame, # points_map None, # output_image gr.update(value=None, visible=False), # output_video gr.update(value=0, visible=False), # progress_bar session_state, ] def segment_with_points( point_type, session_state, evt: gr.SelectData, ): """Add and process tracking points on the first frame.""" if session_state["first_frame"] is None: print("Error: No frame available for segmentation") return None, None, session_state session_state["input_points"].append(evt.index) print(f"TRACKING INPUT POINT: {session_state['input_points']}") if point_type == "include": session_state["input_labels"].append(1) elif point_type == "exclude": session_state["input_labels"].append(0) print(f"TRACKING INPUT LABEL: {session_state['input_labels']}") # Open the image and get its dimensions first_frame = session_state["first_frame"] h, w = first_frame.shape[:2] from PIL import Image transparent_background = Image.fromarray(first_frame).convert("RGBA") # Define the circle radius as a fraction of the smaller dimension fraction = 0.01 # You can adjust this value as needed radius = int(fraction * min(w, h)) if radius < 3: radius = 3 # Ensure minimum visibility # Create a transparent layer to draw on transparent_layer = np.zeros((h, w, 4), dtype=np.uint8) for index, track in enumerate(session_state["input_points"]): if session_state["input_labels"][index] == 1: cv2.circle(transparent_layer, track, radius, (0, 255, 0, 255), -1) # Green for include else: cv2.circle(transparent_layer, track, radius, (255, 0, 0, 255), -1) # Red for exclude # Convert the transparent layer back to an image transparent_layer = Image.fromarray(transparent_layer, "RGBA") selected_point_map = Image.alpha_composite( transparent_background, transparent_layer ) # Use the clicked points and labels points = np.array(session_state["input_points"], dtype=np.float32) labels = np.array(session_state["input_labels"], np.int32) try: if predictor is None: raise ValueError("Model predictor is not initialized") if session_state["inference_state"] is None: raise ValueError("Inference state is not initialized") # For CPU optimization, we'll process with smaller batch size _, _, out_mask_logits = predictor.add_new_points( inference_state=session_state["inference_state"], frame_idx=0, obj_id=OBJ_ID, points=points, labels=labels, ) # Create the mask and check dimensions first out_mask = (out_mask_logits[0] > 0.0).cpu().numpy() # Convert to RGB for visualization # Create an overlay with semi-transparent color overlay = np.zeros((h, w, 3), dtype=np.uint8) # Create a colored mask - blue with opacity overlay_mask = np.zeros_like(overlay) # Resize mask carefully if needed - handle empty dimensions if out_mask.shape[0] > 0 and out_mask.shape[1] > 0: # Check if dimensions differ if out_mask.shape[:2] != (h, w): print(f"Resizing mask from {out_mask.shape[:2]} to {h}x{w}") # Use numpy/PIL for resizing to avoid OpenCV issues from PIL import Image # Ensure mask is boolean type if out_mask.dtype != np.bool_: out_mask = out_mask > 0 mask_img = Image.fromarray(out_mask.astype(np.uint8) * 255) mask_img = mask_img.resize((w, h), Image.NEAREST) out_mask = np.array(mask_img) > 0 # Apply mask color overlay_mask[out_mask] = [0, 120, 255] # Blue color for mask # Blend original frame with mask alpha = 0.5 # Opacity frame_with_mask = cv2.addWeighted( first_frame, 1, overlay_mask, alpha, 0 ) # Add points on top of mask points_overlay = np.zeros((h, w, 4), dtype=np.uint8) for index, track in enumerate(session_state["input_points"]): if session_state["input_labels"][index] == 1: cv2.circle(points_overlay, track, radius, (0, 255, 0, 255), -1) # Green else: cv2.circle(points_overlay, track, radius, (255, 0, 0, 255), -1) # Red # Convert to PIL for overlay frame_with_mask_pil = Image.fromarray(frame_with_mask) points_overlay_pil = Image.fromarray(points_overlay, "RGBA") # Final composite first_frame_output = Image.alpha_composite( frame_with_mask_pil.convert("RGBA"), points_overlay_pil ) except Exception as e: print(f"Error in segmentation: {e}") import traceback traceback.print_exc() # Return just the points as fallback first_frame_output = selected_point_map return selected_point_map, np.array(first_frame_output), session_state def show_mask(mask, obj_id=None, random_color=False, convert_to_image=True): """Convert binary mask to RGBA image for visualization.""" # Check if mask is valid if mask is None or mask.size == 0: print("Warning: Empty mask provided to show_mask") # Return an empty transparent mask if convert_to_image: return Image.new('RGBA', (100, 100), (0, 0, 0, 0)) else: return np.zeros((100, 100, 4), dtype=np.uint8) # Get mask dimensions if len(mask.shape) == 2: h, w = mask.shape else: h, w = mask.shape[-2:] if h == 0 or w == 0: print(f"Warning: Invalid mask dimensions: {h}x{w}") # Return an empty transparent mask if convert_to_image: return Image.new('RGBA', (100, 100), (0, 0, 0, 0)) else: return np.zeros((100, 100, 4), dtype=np.uint8) # Set the color for visualization if random_color: color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0) else: cmap = plt.get_cmap("tab10") cmap_idx = 0 if obj_id is None else obj_id color = np.array([*cmap(cmap_idx)[:3], 0.6]) try: # Create a colored visualization of the mask colored_mask = np.zeros((h, w, 4), dtype=np.uint8) # Apply color to mask areas (where mask is True) for i in range(3): # RGB channels colored_mask[:, :, i] = (mask * color[i] * 255).astype(np.uint8) # Set alpha channel colored_mask[:, :, 3] = (mask * color[3] * 255).astype(np.uint8) if convert_to_image: return Image.fromarray(colored_mask, "RGBA") else: return colored_mask except Exception as e: print(f"Error in show_mask: {e}") import traceback traceback.print_exc() # Return a fallback transparent image if convert_to_image: return Image.new('RGBA', (h, w), (0, 0, 0, 0)) else: return np.zeros((h, w, 4), dtype=np.uint8) def update_progress(progress_percent, progress_bar): """Update progress bar during processing.""" return gr.update(value=progress_percent, visible=True) def propagate_to_all( video_in, session_state, progress=gr.Progress(), ): """Process video frames and generate masked video output with progress tracking.""" if ( len(session_state["input_points"]) == 0 or video_in is None or session_state["inference_state"] is None or predictor is None ): print("Missing required data for tracking") return ( gr.update(value=None, visible=False), gr.update(value=0, visible=False), session_state, ) # For CPU optimization: process in smaller batches chunk_size = 3 # Process 3 frames at a time to avoid memory issues on CPU try: # run propagation throughout the video and collect the results in a dict video_segments = {} # video_segments contains the per-frame segmentation results print("Starting propagate_in_video on CPU") # Get the count for progress reporting (estimate) all_frames_count = 100 # Reasonable estimate # Now do the actual processing with progress updates current_frame = 0 for out_frame_idx, out_obj_ids, out_mask_logits in predictor.propagate_in_video( session_state["inference_state"] ): try: # Store the masks for each object ID video_segments[out_frame_idx] = { out_obj_id: (out_mask_logits[i] > 0.0).cpu().numpy() for i, out_obj_id in enumerate(out_obj_ids) } # Update progress current_frame += 1 progress_percent = min(50, int((current_frame / all_frames_count) * 50)) session_state["progress"] = progress_percent progress(progress_percent/100, desc="Processing frames") if out_frame_idx % 10 == 0: print(f"Processed frame {out_frame_idx} ({progress_percent}%)") # Release memory periodically if out_frame_idx % chunk_size == 0: # Explicitly clear any tensors del out_mask_logits import gc gc.collect() except Exception as e: print(f"Error processing frame {out_frame_idx}: {e}") import traceback traceback.print_exc() continue # For CPU optimization: increase stride to reduce processing # Create a more aggressive stride to limit to fewer frames in output total_frames = len(video_segments) print(f"Total frames processed: {total_frames}") # Update progress to show rendering phase session_state["progress"] = 50 progress(0.5, desc="Rendering video") # Limit to max 50 frames for CPU processing max_output_frames = 30 vis_frame_stride = max(1, total_frames // max_output_frames) print(f"Using stride of {vis_frame_stride} for output video generation") # Get dimensions of the frames if len(session_state["all_frames"]) == 0: raise ValueError("No frames available in session state") first_frame = session_state["all_frames"][0] h, w = first_frame.shape[:2] # Create output frames output_frames = [] frame_indices = list(range(0, total_frames, vis_frame_stride)) total_output_frames = len(frame_indices) for i, out_frame_idx in enumerate(frame_indices): if out_frame_idx not in video_segments or OBJ_ID not in video_segments[out_frame_idx]: continue try: # Get corresponding frame from all_frames if out_frame_idx >= len(session_state["all_frames"]): print(f"Warning: Frame index {out_frame_idx} exceeds available frames {len(session_state['all_frames'])}") frame_idx = min(out_frame_idx, len(session_state["all_frames"])-1) else: frame_idx = out_frame_idx frame = session_state["all_frames"][frame_idx] # Create a colored overlay rather than using transparency # Get the mask out_mask = video_segments[out_frame_idx][OBJ_ID] # Ensure the mask is not empty and has valid dimensions if out_mask.size == 0 or 0 in out_mask.shape: print(f"Warning: Invalid mask for frame {out_frame_idx}") # Skip this frame continue # Get dimensions frame_h, frame_w = frame.shape[:2] mask_h, mask_w = out_mask.shape[:2] # Resize mask using PIL if dimensions don't match (avoid OpenCV) if mask_h != frame_h or mask_w != frame_w: print(f"Resizing mask from {mask_h}x{mask_w} to {frame_h}x{frame_w}") try: # Ensure mask is boolean type if out_mask.dtype != np.bool_: out_mask = out_mask > 0 mask_img = Image.fromarray(out_mask.astype(np.uint8) * 255) mask_img = mask_img.resize((frame_w, frame_h), Image.NEAREST) out_mask = np.array(mask_img) > 0 except Exception as e: print(f"Error resizing mask: {e}") # Skip this frame if resize fails continue # Create an overlay with semi-transparent color overlay = np.zeros_like(frame) # Set blue color for mask area overlay[out_mask] = [0, 120, 255] # BGR format for OpenCV # Blend with original frame alpha = 0.5 output_frame = cv2.addWeighted(frame, 1, overlay, alpha, 0) # Add to output frames output_frames.append(output_frame) # Update progress progress_percent = 50 + min(50, int((i / total_output_frames) * 50)) session_state["progress"] = progress_percent progress(progress_percent/100, desc=f"Rendering video frames ({i}/{total_output_frames})") # Clear memory periodically if len(output_frames) % 10 == 0: import gc gc.collect() except Exception as e: print(f"Error creating output frame {out_frame_idx}: {e}") import traceback traceback.print_exc() progress.tqdm.update(1) continue # Create a video clip from the image sequence original_fps = get_video_fps(video_in) fps = original_fps # For CPU optimization - lower FPS if original is high if fps > 15: fps = 15 # Lower fps for CPU processing print(f"Creating video with {len(output_frames)} frames at {fps} FPS") # Update progress to show video creation phase session_state["progress"] = 90 # Check if we have any frames to work with if len(output_frames) == 0: raise ValueError("No output frames were generated") # Ensure all frames have the same shape first_shape = output_frames[0].shape valid_frames = [] for i, frame in enumerate(output_frames): if frame.shape == first_shape: valid_frames.append(frame) else: print(f"Skipping frame {i} with inconsistent shape: {frame.shape} vs {first_shape}") if len(valid_frames) == 0: raise ValueError("No valid frames with consistent shape") clip = ImageSequenceClip(valid_frames, fps=fps) # Write the result to a file - use lower quality for CPU unique_id = datetime.now().strftime("%Y%m%d%H%M%S") final_vid_output_path = f"output_video_{unique_id}.mp4" final_vid_output_path = os.path.join(tempfile.gettempdir(), final_vid_output_path) # Lower bitrate for CPU processing clip.write_videofile( final_vid_output_path, codec="libx264", bitrate="800k", threads=2, # Use fewer threads for CPU logger=None # Disable logger to reduce console output ) # Complete progress session_state["progress"] = 100 # Free memory del video_segments del output_frames import gc gc.collect() return ( gr.update(value=final_vid_output_path, visible=True), gr.update(value=100, visible=False), session_state, ) except Exception as e: print(f"Error in propagate_to_all: {e}") import traceback traceback.print_exc() return ( gr.update(value=None, visible=False), gr.update(value=0, visible=False), session_state, ) def update_ui(): """Show progress bar when starting processing.""" return gr.update(visible=True), gr.update(visible=True, value=0) # Main Gradio UI setup with gr.Blocks() as demo: session_state = gr.State( { "first_frame": None, "all_frames": None, "input_points": [], "input_labels": [], "inference_state": None, "frame_stride": 1, "scale_factor": 1.0, "original_dimensions": None, "progress": 0, } ) with gr.Column(): # Title gr.Markdown(title) with gr.Row(): with gr.Column(): # Instructions gr.Markdown(description_p) with gr.Accordion("Input Video", open=True) as video_in_drawer: video_in = gr.Video(label="Input Video", format="mp4") with gr.Row(): point_type = gr.Radio( label="point type", choices=["include", "exclude"], value="include", scale=2, ) propagate_btn = gr.Button("Track", scale=1, variant="primary") clear_points_btn = gr.Button("Clear Points", scale=1) reset_btn = gr.Button("Reset", scale=1) points_map = gr.Image( label="Frame with Point Prompt", type="numpy", interactive=False ) # Add progress bar progress_bar = gr.Slider( minimum=0, maximum=100, value=0, step=1, label="Processing Progress", visible=False, interactive=False ) with gr.Column(): gr.Markdown("# Try some of the examples below ⬇️") gr.Examples( examples=examples, inputs=[ video_in, ], examples_per_page=5, ) output_image = gr.Image(label="Reference Mask") output_video = gr.Video(visible=False) # When new video is uploaded video_in.upload( fn=preprocess_video_in, inputs=[ video_in, session_state, ], outputs=[ video_in_drawer, # Accordion to hide uploaded video player points_map, # Image component where we add new tracking points output_image, output_video, progress_bar, session_state, ], queue=False, ) video_in.change( fn=preprocess_video_in, inputs=[ video_in, session_state, ], outputs=[ video_in_drawer, # Accordion to hide uploaded video player points_map, # Image component where we add new tracking points output_image, output_video, progress_bar, session_state, ], queue=False, ) # triggered when we click