app.py

# 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 = "<center><strong><font size='8'>EdgeTAM CPU<font></strong> <a href='https://github.com/facebookresearch/EdgeTAM'><font size='6'>[GitHub]</font></a> </center>"

description_p = """# Instructions
                <ol>
                <li> Upload one video or click one example video</li>
                <li> Click 'include' point type, select the object to segment and track</li>
                <li> Click 'exclude' point type (optional), select the area you want to avoid segmenting and tracking</li>
                <li> Click the 'Track' button to obtain the masked video </li>
                </ol>
              """

# 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 first frame
    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,
        )

    # For CPU optimization - determine video properties
    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")
    
    # Determine if we need to resize for CPU performance
    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}")
    
    # Read frames - for CPU we'll be more selective about which frames to keep
    frame_number = 0
    first_frame = None
    all_frames = []
    
    # For CPU optimization, skip frames if video is too long
    frame_stride = 1
    if total_frames > 300:  # If more than 300 frames
        frame_stride = max(1, int(total_frames / 300))  # Process at most ~300 frames
        print(f"Video has {total_frames} frames, using stride of {frame_stride} to reduce processing load")
    
    while True:
        ret, frame = cap.read()
        if not ret:
            break
            
        if frame_number % frame_stride == 0:  # Process every frame_stride frames
            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)
                
                # Store the first 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()
    
    # Ensure we have at least one frame
    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]
    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))

    # 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
    )

    # Let's add a positive click at (x, y) = (210, 350) to get started
    points = np.array(session_state["input_points"], dtype=np.float32)
    # for labels, `1` means positive click and `0` means negative click
    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
        mask_array = (out_mask_logits[0] > 0.0).cpu().numpy()
        
        # Ensure the mask has the same size as the frame
        if mask_array.shape[:2] != (h, w):
            mask_array = cv2.resize(
                mask_array.astype(np.uint8), 
                (w, h), 
                interpolation=cv2.INTER_NEAREST
            ).astype(bool)
        
        mask_image = show_mask(mask_array)
        
        # Make sure mask_image has the same size as the background
        if mask_image.size != transparent_background.size:
            mask_image = mask_image.resize(transparent_background.size, Image.NEAREST)
            
        first_frame_output = Image.alpha_composite(transparent_background, mask_image)
    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, 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."""
    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])
    
    # Handle different mask shapes properly
    if len(mask.shape) == 2:
        h, w = mask.shape
    else:
        h, w = mask.shape[-2:]
    
    # Ensure correct reshaping based on mask dimensions
    mask_reshaped = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
    mask_rgba = (mask_reshaped * 255).astype(np.uint8)
    
    if convert_to_image:
        try:
            # Ensure the mask has correct RGBA shape (h, w, 4)
            if mask_rgba.shape[2] != 4:
                # If not RGBA, create a proper RGBA array
                proper_mask = np.zeros((h, w, 4), dtype=np.uint8)
                # Copy available channels
                proper_mask[:, :, :min(mask_rgba.shape[2], 4)] = mask_rgba[:, :, :min(mask_rgba.shape[2], 4)]
                mask_rgba = proper_mask
            
            # Create the PIL image
            return Image.fromarray(mask_rgba, "RGBA")
        except Exception as e:
            print(f"Error converting mask to image: {e}")
            # Fallback: create a blank transparent image of correct size
            blank = np.zeros((h, w, 4), dtype=np.uint8)
            return Image.fromarray(blank, "RGBA")
    
    return mask_rgba

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")
        
        progress.tqdm.reset()
        
        # Get the count for progress reporting
        all_frames_count = 0
        for _ in predictor.propagate_in_video(session_state["inference_state"], count_only=True):
            all_frames_count += 1
        
        print(f"Total frames to process: {all_frames_count}")
        progress.tqdm.total = all_frames_count
        
        # Now do the actual processing with progress updates
        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
                progress.tqdm.update(1)
                progress_percent = min(100, int((out_frame_idx + 1) / all_frames_count * 100))
                session_state["progress"] = progress_percent
                
                if out_frame_idx % 10 == 0:
                    print(f"Processed frame {out_frame_idx}/{all_frames_count} ({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
        progress.tqdm.reset()
        progress.tqdm.total = 2  # Two phases: rendering and video creation
        progress.tqdm.update(1)
        session_state["progress"] = 50
        
        # Limit to max 50 frames for CPU processing
        max_output_frames = 50
        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 = []
        progress.tqdm.reset()
        progress.tqdm.total = (total_frames // vis_frame_stride) + 1
        
        for out_frame_idx in range(0, total_frames, vis_frame_stride):
            if out_frame_idx not in video_segments or OBJ_ID not in video_segments[out_frame_idx]:
                progress.tqdm.update(1)
                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]
                transparent_background = Image.fromarray(frame).convert("RGBA")
                
                # Get the mask and ensure it's the right size
                out_mask = video_segments[out_frame_idx][OBJ_ID]
                
                # Ensure the mask is not empty and has the right dimensions
                if out_mask.size == 0:
                    print(f"Warning: Empty mask for frame {out_frame_idx}")
                    # Create an empty mask of the right size
                    out_mask = np.zeros((h, w), dtype=bool)
                
                # Resize mask if dimensions don't match
                mask_h, mask_w = out_mask.shape[:2]
                if mask_h != h or mask_w != w:
                    print(f"Resizing mask from {mask_h}x{mask_w} to {h}x{w}")
                    out_mask = cv2.resize(
                        out_mask.astype(np.uint8), 
                        (w, h), 
                        interpolation=cv2.INTER_NEAREST
                    ).astype(bool)
                
                mask_image = show_mask(out_mask)
                
                # Make sure mask has same dimensions as background
                if mask_image.size != transparent_background.size:
                    mask_image = mask_image.resize(transparent_background.size, Image.NEAREST)
                
                output_frame = Image.alpha_composite(transparent_background, mask_image)
                output_frame = np.array(output_frame)
                output_frames.append(output_frame)
                
                # Update progress
                progress.tqdm.update(1)
                progress_percent = 50 + min(50, int((len(output_frames) / (total_frames // vis_frame_stride)) * 50))
                session_state["progress"] = progress_percent
                
                # 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 on image to add new points
    points_map.select(
        fn=segment_with_points,
        inputs=[
            point_type,  # "include" or "exclude"
            session_state,
        ],
        outputs=[
            points_map,  # updated image with points
            output_image,
            session_state,
        ],
        queue=False,
    )

    # Clear every points clicked and added to the map
    clear_points_btn.click(
        fn=clear_points,
        inputs=session_state,
        outputs=[
            points_map,
            output_image,
            output_video,
            progress_bar,
            session_state,
        ],
        queue=False,
    )

    reset_btn.click(
        fn=reset,
        inputs=session_state,
        outputs=[
            video_in,
            video_in_drawer,
            points_map,
            output_image,
            output_video,
            progress_bar,
            session_state,
        ],
        queue=False,
    )

    propagate_btn.click(
        fn=update_ui,
        inputs=[],
        outputs=[output_video, progress_bar],
        queue=False,
    ).then(
        fn=propagate_to_all,
        inputs=[
            video_in,
            session_state,
        ],
        outputs=[
            output_video,
            progress_bar,
            session_state,
        ],
        queue=True,  # Use queue for CPU processing
    )


demo.queue()
demo.launch()