import os import torch import numpy as np import gradio as gr from PIL import Image from transformers import AutoProcessor, AutoModelForCausalLM from sam2.build_sam import build_sam2_video_predictor, build_sam2 from sam2.sam2_image_predictor import SAM2ImagePredictor import cv2 import traceback import matplotlib.pyplot as plt # CUDA optimizations torch.autocast(device_type="cuda", dtype=torch.bfloat16).__enter__() if torch.cuda.get_device_properties(0).major >= 8: torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cudnn.allow_tf32 = True # Initialize models sam2_checkpoint = "../checkpoints/sam2_hiera_large.pt" model_cfg = "sam2_hiera_l.yaml" video_predictor = build_sam2_video_predictor(model_cfg, sam2_checkpoint) sam2_model = build_sam2(model_cfg, sam2_checkpoint, device="cuda") image_predictor = SAM2ImagePredictor(sam2_model) model_id = 'microsoft/Florence-2-large' florence_model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True, torch_dtype=torch.bfloat16).eval().cuda() florence_processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True) def apply_color_mask(frame, mask, obj_id): cmap = plt.get_cmap("tab10") color = np.array(cmap(obj_id % 10)[:3]) # Use modulo 10 to cycle through colors # Ensure mask has the correct shape if mask.ndim == 4: mask = mask.squeeze() # Remove singleton dimensions if mask.ndim == 3 and mask.shape[0] == 1: mask = mask[0] # Take the first channel if it's a single-channel 3D array # Reshape mask to match frame dimensions mask = cv2.resize(mask.astype(np.float32), (frame.shape[1], frame.shape[0]), interpolation=cv2.INTER_LINEAR) # Expand dimensions of mask and color for broadcasting mask = np.expand_dims(mask, axis=2) color = color.reshape(1, 1, 3) colored_mask = mask * color return frame * (1 - mask) + colored_mask * 255 def run_florence(image, text_input): with torch.cuda.amp.autocast(dtype=torch.bfloat16): task_prompt = '' prompt = task_prompt + text_input inputs = florence_processor(text=prompt, images=image, return_tensors="pt").to('cuda', torch.bfloat16) generated_ids = florence_model.generate( input_ids=inputs["input_ids"].cuda(), pixel_values=inputs["pixel_values"].cuda(), max_new_tokens=1024, early_stopping=False, do_sample=False, num_beams=3, ) generated_text = florence_processor.batch_decode(generated_ids, skip_special_tokens=False)[0] parsed_answer = florence_processor.post_process_generation( generated_text, task=task_prompt, image_size=(image.width, image.height) ) return parsed_answer[task_prompt]['bboxes'][0] def remove_directory_contents(directory): for root, dirs, files in os.walk(directory, topdown=False): for name in files: os.remove(os.path.join(root, name)) for name in dirs: os.rmdir(os.path.join(root, name)) def process_video(video_path, prompt, chunk_size=30): try: video = cv2.VideoCapture(video_path) if not video.isOpened(): raise ValueError("Unable to open video file") fps = video.get(cv2.CAP_PROP_FPS) frame_count = int(video.get(cv2.CAP_PROP_FRAME_COUNT)) # Process video in chunks all_segmented_frames = [] for chunk_start in range(0, frame_count, chunk_size): chunk_end = min(chunk_start + chunk_size, frame_count) frames = [] video.set(cv2.CAP_PROP_POS_FRAMES, chunk_start) for _ in range(chunk_end - chunk_start): ret, frame = video.read() if not ret: break frames.append(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) if not frames: print(f"No frames extracted for chunk starting at {chunk_start}") continue # Florence detection on first frame of the chunk first_frame = Image.fromarray(frames[0]) mask_box = run_florence(first_frame, prompt) print("Original mask box:", mask_box) # Convert mask_box to numpy array and ensure it's in the correct format mask_box = np.array(mask_box) print("Reshaped mask box:", mask_box) # SAM2 segmentation on first frame with torch.cuda.amp.autocast(dtype=torch.bfloat16): image_predictor.set_image(first_frame) masks, _, _ = image_predictor.predict( point_coords=None, point_labels=None, box=mask_box[None, :], multimask_output=False, ) print("masks.shape",masks.shape) mask = masks.squeeze().astype(bool) print("Mask shape:", mask.shape) print("Frame shape:", frames[0].shape) # SAM2 video propagation temp_dir = f"temp_frames_{chunk_start}" os.makedirs(temp_dir, exist_ok=True) for i, frame in enumerate(frames): cv2.imwrite(os.path.join(temp_dir, f"{i:04d}.jpg"), cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)) with torch.cuda.amp.autocast(dtype=torch.bfloat16): inference_state = video_predictor.init_state(video_path=temp_dir) _, _, _ = video_predictor.add_new_mask( inference_state=inference_state, frame_idx=0, obj_id=1, mask=mask ) video_segments = {} for out_frame_idx, out_obj_ids, out_mask_logits in video_predictor.propagate_in_video(inference_state): 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) } print('segmenting for main vid done') # Apply segmentation masks to frames for i, frame in enumerate(frames): if i in video_segments: for out_obj_id, mask in video_segments[i].items(): frame = apply_color_mask(frame, mask, out_obj_id) all_segmented_frames.append(frame.astype(np.uint8)) else: all_segmented_frames.append(frame) # Clean up temporary files remove_directory_contents(temp_dir) os.rmdir(temp_dir) video.release() if not all_segmented_frames: raise ValueError("No frames were processed successfully") # Create video from segmented frames output_path = "segmented_video.mp4" out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (all_segmented_frames[0].shape[1], all_segmented_frames[0].shape[0])) for frame in all_segmented_frames: out.write(cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)) out.release() return output_path except Exception as e: print(f"Error in process_video: {str(e)}") print(traceback.format_exc()) # This will print the full stack trace return None def segment_video(video_file, prompt, chunk_size): if video_file is None: return None output_video = process_video(video_file, prompt, int(chunk_size)) return output_video demo = gr.Interface( fn=segment_video, inputs=[ gr.Video(label="Upload Video"), gr.Textbox(label="Enter prompt (e.g., 'a gymnast')"), gr.Slider(minimum=10, maximum=100, step=10, value=30, label="Chunk Size (frames)") ], outputs=gr.Video(label="Segmented Video"), title="Video Object Segmentation with Florence and SAM2", description="Upload a video and provide a text prompt to segment a specific object throughout the video." ) demo.launch()