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): 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): # 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 # Calculate new FPS 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 = [] 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) seg_frame = visualize(pred_mask, image_paths[frame_idx], temp_dir) seg_frames.append(seg_frame) output_video = "output_video.mp4" # 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 object fourcc = cv2.VideoWriter_fourcc(*'mp4v') # Codec for MP4 video = cv2.VideoWriter(output_video, fourcc, new_fps, (width, height)) # Iterate over the image paths and write to the video for img_path in seg_frames: frame = cv2.imread(img_path) video.write(frame) # Release the video writer video.release() print(f"Video created successfully at {output_video}") return result['prediction'], output_video else: return result['prediction'], 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") submit_video_btn.click( fn = video_vision, inputs = [video_input, vid_instruction, frame_interval], outputs = [vid_output_res, output_video] ) demo.queue().launch(show_api=False, show_error=True)