import torch from transformers import AutoModelForCausalLM, AutoTokenizer from PIL import Image import numpy as np import os import tempfile import spaces import gradio as gr import subprocess import sys def install_flash_attn_wheel(): flash_attn_wheel_url = "https://github.com/Dao-AILab/flash-attention/releases/download/v2.6.3/flash_attn-2.6.3+cu123torch2.4cxx11abiFALSE-cp310-cp310-linux_x86_64.whl" try: # Call pip to install the wheel file subprocess.check_call([sys.executable, "-m", "pip", "install", flash_attn_wheel_url]) print("Wheel installed successfully!") except subprocess.CalledProcessError as e: print(f"Failed to install the flash attnetion wheel. Error: {e}") install_flash_attn_wheel() import cv2 try: from mmengine.visualization import Visualizer except ImportError: Visualizer = None print("Warning: mmengine is not installed, visualization is disabled.") # Load the model and tokenizer model_path = "ByteDance/Sa2VA-4B" model = AutoModelForCausalLM.from_pretrained( model_path, torch_dtype="auto", device_map="cuda:0", trust_remote_code=True, ).eval().cuda() tokenizer = AutoTokenizer.from_pretrained( model_path, trust_remote_code = True, ) from third_parts import VideoReader def read_video(video_path, video_interval): vid_frames = VideoReader(video_path)[::video_interval] temp_dir = tempfile.mkdtemp() os.makedirs(temp_dir, exist_ok=True) image_paths = [] # List to store paths of saved images for frame_idx in range(len(vid_frames)): frame_image = vid_frames[frame_idx] frame_image = frame_image[..., ::-1] # BGR (opencv system) to RGB (numpy system) frame_image = Image.fromarray(frame_image) vid_frames[frame_idx] = frame_image # Save the frame as a .jpg file in the temporary folder image_path = os.path.join(temp_dir, f"frame_{frame_idx:04d}.jpg") frame_image.save(image_path, format="JPEG") # Append the image path to the list image_paths.append(image_path) return vid_frames, image_paths def visualize(pred_mask, image_path, work_dir): visualizer = Visualizer() img = cv2.imread(image_path) visualizer.set_image(img) visualizer.draw_binary_masks(pred_mask, colors='g', alphas=0.4) visual_result = visualizer.get_image() output_path = os.path.join(work_dir, os.path.basename(image_path)) cv2.imwrite(output_path, visual_result) return output_path @spaces.GPU def image_vision(image_input_path, prompt): """Perform image-based visual question answering and segmentation. This function takes an image and a text prompt (instruction) as input, processes the image with a multimodal model, and returns a textual answer. If the model response includes a segmentation token ("[SEG]"), and segmentation visualization is available, a visual output is also generated. Args: image_input_path (str): The path to the input image file. prompt (str): The instruction or question about the image. Returns: Tuple[str, Optional[str]]: - A textual answer generated by the model. - If segmentation is requested (indicated by '[SEG]' in the answer), the path to the segmented image file; otherwise, returns None. """ image_path = image_input_path text_prompts = f"{prompt}" image = Image.open(image_path).convert('RGB') input_dict = { 'image': image, 'text': text_prompts, 'past_text': '', 'mask_prompts': None, 'tokenizer': tokenizer, } return_dict = model.predict_forward(**input_dict) print(return_dict) answer = return_dict["prediction"] # the text format answer seg_image = return_dict["prediction_masks"] if '[SEG]' in answer and Visualizer is not None: pred_masks = seg_image[0] temp_dir = tempfile.mkdtemp() pred_mask = pred_masks os.makedirs(temp_dir, exist_ok=True) seg_result = visualize(pred_mask, image_input_path, temp_dir) return answer, seg_result else: return answer, None @spaces.GPU(duration=80) def video_vision(video_input_path, prompt, video_interval): """Perform video-based visual question answering and segmentation. This function analyzes a video file using a multimodal vision-language model. It extracts frames based on a sampling interval, feeds the frames and prompt to the model, and returns a response. If segmentation is requested, it produces two videos: one with overlaid masks, and one with binary masks only. Args: video_input_path (str): The path to the input video file. prompt (str): The instruction or question about the video. video_interval (int): Frame sampling interval. A value of 1 processes every frame, 2 every second frame, etc. Returns: Tuple[str, Optional[str], Optional[str]]: - The model-generated textual answer. - If segmentation is requested (contains '[SEG]'), the path to the segmented output video file. - If segmentation is requested, the path to a binary mask-only video; otherwise, None. """ # Open the original video cap = cv2.VideoCapture(video_input_path) # Get original video properties original_fps = cap.get(cv2.CAP_PROP_FPS) frame_skip_factor = video_interval new_fps = None # Calculate new FPS if video_interval == 1: new_fps = original_fps else: new_fps = original_fps / frame_skip_factor vid_frames, image_paths = read_video(video_input_path, video_interval) # create a question ( is a placeholder for the video frames) question = f"{prompt}" result = model.predict_forward( video=vid_frames, text=question, tokenizer=tokenizer, ) prediction = result['prediction'] print(prediction) if '[SEG]' in prediction and Visualizer is not None: _seg_idx = 0 pred_masks = result['prediction_masks'][_seg_idx] seg_frames = [] masked_only_frames = [] # New list for masked-only frames for frame_idx in range(len(vid_frames)): pred_mask = pred_masks[frame_idx] temp_dir = tempfile.mkdtemp() os.makedirs(temp_dir, exist_ok=True) # Create visualized frame with segmentation overlay seg_frame = visualize(pred_mask, image_paths[frame_idx], temp_dir) seg_frames.append(seg_frame) # Create a binary mask image (white mask on black background) binary_mask = (pred_mask.astype('uint8') * 255) # Convert mask to 0/255 binary_mask_path = os.path.join(temp_dir, f"binary_mask_{frame_idx}.png") cv2.imwrite(binary_mask_path, binary_mask) masked_only_frames.append(binary_mask_path) output_video = "output_video.mp4" masked_video = "masked_only_video.mp4" # New video file for masked areas only # Read the first image to get the size (resolution) frame = cv2.imread(seg_frames[0]) height, width, layers = frame.shape # Define the video codec and create VideoWriter objects fourcc = cv2.VideoWriter_fourcc(*'mp4v') # Codec for MP4 video = cv2.VideoWriter(output_video, fourcc, new_fps, (width, height)) masked_video_writer = cv2.VideoWriter(masked_video, fourcc, new_fps, (width, height), isColor=False) # Write frames to the videos for idx, (seg_frame_path, mask_frame_path) in enumerate(zip(seg_frames, masked_only_frames)): seg_frame = cv2.imread(seg_frame_path) mask_frame = cv2.imread(mask_frame_path, cv2.IMREAD_GRAYSCALE) # Read the binary mask in grayscale video.write(seg_frame) masked_video_writer.write(mask_frame) # Release the video writers video.release() masked_video_writer.release() print(f"Video created successfully at {output_video}") print(f"Masked-only video created successfully at {masked_video}") return result['prediction'], output_video, masked_video else: return result['prediction'], None, None # Gradio UI with gr.Blocks(analytics_enabled=False) as demo: with gr.Column(): gr.Markdown("# Sa2VA: Marrying SAM2 with LLaVA for Dense Grounded Understanding of Images and Videos") gr.HTML("""
Duplicate this Space Follow me on HF
""") with gr.Tab("Single Image"): with gr.Row(): with gr.Column(): image_input = gr.Image(label="Image IN", type="filepath") with gr.Row(): instruction = gr.Textbox(label="Instruction", scale=4) submit_image_btn = gr.Button("Submit", scale=1) with gr.Column(): output_res = gr.Textbox(label="Response") output_image = gr.Image(label="Segmentation", type="numpy") submit_image_btn.click( fn = image_vision, inputs = [image_input, instruction], outputs = [output_res, output_image] ) with gr.Tab("Video"): with gr.Row(): with gr.Column(): video_input = gr.Video(label="Video IN") frame_interval = gr.Slider(label="Frame interval", step=1, minimum=1, maximum=12, value=6) with gr.Row(): vid_instruction = gr.Textbox(label="Instruction", scale=4) submit_video_btn = gr.Button("Submit", scale=1) with gr.Column(): vid_output_res = gr.Textbox(label="Response") output_video = gr.Video(label="Segmentation") masked_output = gr.Video(label="Masked video") submit_video_btn.click( fn = video_vision, inputs = [video_input, vid_instruction, frame_interval], outputs = [vid_output_res, output_video, masked_output] ) demo.queue().launch(show_api=True, show_error=True, ssr_mode=False, mcp_server=True)