import subprocess import re from typing import List, Tuple, Optional import spaces # Define the command to be executed command = ["python", "setup.py", "build_ext", "--inplace"] # Execute the command result = subprocess.run(command, capture_output=True, text=True) css=""" div#component-18, div#component-25, div#component-35, div#component-41{ align-items: stretch!important; } """ # Print the output and error (if any) print("Output:\n", result.stdout) print("Errors:\n", result.stderr) # Check if the command was successful if result.returncode == 0: print("Command executed successfully.") else: print("Command failed with return code:", result.returncode) import gradio as gr from datetime import datetime import os os.environ["TORCH_CUDNN_SDPA_ENABLED"] = "1" import torch import numpy as np import cv2 import matplotlib.pyplot as plt from PIL import Image, ImageFilter from sam2.build_sam import build_sam2_video_predictor from moviepy.editor import ImageSequenceClip def sparse_sampling(jpeg_images, original_fps, target_fps=6): # Calculate the frame interval for sampling based on the target fps frame_interval = int(original_fps // target_fps) # Sparse sample the jpeg_images by selecting every 'frame_interval' frame sampled_images = [jpeg_images[i] for i in range(0, len(jpeg_images), frame_interval)] return sampled_images def get_video_fps(video_path): # Open the video file cap = cv2.VideoCapture(video_path) if not cap.isOpened(): print("Error: Could not open video.") return None # Get the FPS of the video fps = cap.get(cv2.CAP_PROP_FPS) return fps def clear_points(image): # we clean all return [ image, # first_frame_path [], # tracking_points [], # trackings_input_label image, # points_map #gr.State() # stored_inference_state ] def preprocess_video_in(video_path): # Generate a unique ID based on the current date and time unique_id = datetime.now().strftime('%Y%m%d%H%M%S') # Set directory with this ID to store video frames extracted_frames_output_dir = f'frames_{unique_id}' # Create the output directory os.makedirs(extracted_frames_output_dir, exist_ok=True) ### Process video frames ### # Open the video file cap = cv2.VideoCapture(video_path) if not cap.isOpened(): print("Error: Could not open video.") return None # Get the frames per second (FPS) of the video fps = cap.get(cv2.CAP_PROP_FPS) # Calculate the number of frames to process (60 seconds of video) max_frames = int(fps * 60) frame_number = 0 first_frame = None while True: ret, frame = cap.read() if not ret or frame_number >= max_frames: break if frame_number % 6 == 0: # Format the frame filename as '00000.jpg' frame_filename = os.path.join(extracted_frames_output_dir, f'{frame_number:05d}.jpg') # Save the frame as a JPEG file cv2.imwrite(frame_filename, frame) # Store the first frame if frame_number == 0: first_frame = frame_filename frame_number += 1 # Release the video capture object cap.release() # scan all the JPEG frame names in this directory scanned_frames = [ p for p in os.listdir(extracted_frames_output_dir) if os.path.splitext(p)[-1] in [".jpg", ".jpeg", ".JPG", ".JPEG"] ] scanned_frames.sort(key=lambda p: int(os.path.splitext(p)[0])) # print(f"SCANNED_FRAMES: {scanned_frames}") return [ first_frame, # first_frame_path [], # tracking_points [], # trackings_input_label first_frame, # input_first_frame_image first_frame, # points_map extracted_frames_output_dir, # video_frames_dir scanned_frames, # scanned_frames None, # stored_inference_state None, # stored_frame_names gr.update(open=False) # video_in_drawer ] def get_point(point_type, tracking_points, trackings_input_label, input_first_frame_image, evt: gr.SelectData): print(f"You selected {evt.value} at {evt.index} from {evt.target}") tracking_points.append(evt.index) # tracking_points.value.append(evt.index) print(f"TRACKING POINT: {tracking_points}") if point_type == "include": trackings_input_label.append(1) # trackings_input_label.value.append(1) elif point_type == "exclude": trackings_input_label.append(0) # trackings_input_label.value.append(0) print(f"TRACKING INPUT LABEL: {trackings_input_label}") # Open the image and get its dimensions transparent_background = Image.open(input_first_frame_image).convert('RGBA') w, h = transparent_background.size # Define the circle radius as a fraction of the smaller dimension fraction = 0.02 # 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(tracking_points): if trackings_input_label[index] == 1: cv2.circle(transparent_layer, track, radius, (0, 255, 0, 255), -1) else: cv2.circle(transparent_layer, track, radius, (255, 0, 0, 255), -1) # 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) return tracking_points, trackings_input_label, selected_point_map def show_mask(mask, ax, obj_id=None, random_color=False): 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]) h, w = mask.shape[-2:] mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1) ax.imshow(mask_image) def show_points(coords, labels, ax, marker_size=200): pos_points = coords[labels==1] neg_points = coords[labels==0] ax.scatter(pos_points[:, 0], pos_points[:, 1], color='green', marker='*', s=marker_size, edgecolor='white', linewidth=1.25) ax.scatter(neg_points[:, 0], neg_points[:, 1], color='red', marker='*', s=marker_size, edgecolor='white', linewidth=1.25) def show_box(box, ax): x0, y0 = box[0], box[1] w, h = box[2] - box[0], box[3] - box[1] ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0, 0, 0, 0), lw=2)) def load_model(checkpoint): # Load model accordingly to user's choice if checkpoint == "tiny": sam2_checkpoint = "./checkpoints/sam2.1_hiera_tiny.pt" model_cfg = "configs/sam2.1/sam2.1_hiera_t.yaml" return [sam2_checkpoint, model_cfg] elif checkpoint == "samll": sam2_checkpoint = "./checkpoints/sam2.1_hiera_small.pt" model_cfg = "configs/sam2.1/sam2.1_hiera_s.yaml" return [sam2_checkpoint, model_cfg] elif checkpoint == "base-plus": sam2_checkpoint = "./checkpoints/sam2.1_hiera_base_plus.pt" model_cfg = "configs/sam2.1/sam2.1_hiera_b+.yaml" return [sam2_checkpoint, model_cfg] # elif checkpoint == "large": # sam2_checkpoint = "./checkpoints/sam2.1_hiera_large.pt" # model_cfg = "configs/sam2.1/sam2.1_hiera_l.yaml" # return [sam2_checkpoint, model_cfg] def get_mask_sam_process( stored_inference_state, input_first_frame_image, checkpoint, tracking_points, trackings_input_label, video_frames_dir, # extracted_frames_output_dir defined in 'preprocess_video_in' function scanned_frames, working_frame: str = None, # current frame being added points available_frames_to_check: List[str] = [], # progress=gr.Progress(track_tqdm=True) ): # get model and model config paths print(f"USER CHOSEN CHECKPOINT: {checkpoint}") sam2_checkpoint, model_cfg = load_model(checkpoint) print("MODEL LOADED") # set predictor predictor = build_sam2_video_predictor(model_cfg, sam2_checkpoint, device='cpu') print("PREDICTOR READY") # `video_dir` a directory of JPEG frames with filenames like `.jpg` # print(f"STATE FRAME OUTPUT DIRECTORY: {video_frames_dir}") video_dir = video_frames_dir # scan all the JPEG frame names in this directory frame_names = scanned_frames # print(f"STORED INFERENCE STEP: {stored_inference_state}") if stored_inference_state is None: # Init SAM2 inference_state inference_state = predictor.init_state(video_path=video_dir) inference_state['num_pathway'] = 3 inference_state['iou_thre'] = 0.3 inference_state['uncertainty'] = 2 print("NEW INFERENCE_STATE INITIATED") else: inference_state = stored_inference_state inference_state["device"] = 'cpu' # segment and track one object # predictor.reset_state(inference_state) # if any previous tracking, reset ### HANDLING WORKING FRAME # new_working_frame = None # Add new point if working_frame is None: ann_frame_idx = 0 # the frame index we interact with, 0 if it is the first frame working_frame = "00000.jpg" else: # Use a regular expression to find the integer match = re.search(r'frame_(\d+)', working_frame) if match: # Extract the integer from the match frame_number = int(match.group(1)) ann_frame_idx = frame_number print(f"NEW_WORKING_FRAME PATH: {working_frame}") ann_obj_id = 1 # give a unique id to each object we interact with (it can be any integers) # Let's add a positive click at (x, y) = (210, 350) to get started points = np.array(tracking_points, dtype=np.float32) # for labels, `1` means positive click and `0` means negative click labels = np.array(trackings_input_label, np.int32) _, out_obj_ids, out_mask_logits = predictor.add_new_points( inference_state=inference_state, frame_idx=ann_frame_idx, obj_id=ann_obj_id, points=points, labels=labels, ) # Create the plot plt.figure(figsize=(12, 8)) plt.title(f"frame {ann_frame_idx}") plt.imshow(Image.open(os.path.join(video_dir, frame_names[ann_frame_idx]))) show_points(points, labels, plt.gca()) show_mask((out_mask_logits[0] > 0.0).cpu().numpy(), plt.gca(), obj_id=out_obj_ids[0]) # Save the plot as a JPG file first_frame_output_filename = "output_first_frame.jpg" plt.savefig(first_frame_output_filename, format='jpg') plt.close() # torch.cuda.empty_cache() # Assuming available_frames_to_check.value is a list if working_frame not in available_frames_to_check: available_frames_to_check.append(working_frame) print(available_frames_to_check) # return gr.update(visible=True), "output_first_frame.jpg", frame_names, predictor, inference_state, gr.update(choices=available_frames_to_check, value=working_frame, visible=True) return "output_first_frame.jpg", frame_names, predictor, inference_state, gr.update(choices=available_frames_to_check, value=working_frame, visible=False) @spaces.GPU def propagate_to_all(video_in, checkpoint, stored_inference_state, stored_frame_names, video_frames_dir, vis_frame_type, available_frames_to_check, working_frame, progress=gr.Progress(track_tqdm=True)): # use bfloat16 for the entire notebook torch.autocast(device_type="cuda", dtype=torch.bfloat16).__enter__() if torch.cuda.get_device_properties(0).major >= 8: # turn on tfloat32 for Ampere GPUs (https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices) torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cudnn.allow_tf32 = True #### PROPAGATION #### sam2_checkpoint, model_cfg = load_model(checkpoint) # set predictor inference_state = stored_inference_state if torch.cuda.is_available(): inference_state["device"] = 'cuda' predictor = build_sam2_video_predictor(model_cfg, sam2_checkpoint) else: inference_state["device"] = 'cpu' predictor = build_sam2_video_predictor(model_cfg, sam2_checkpoint, device='cpu') frame_names = stored_frame_names video_dir = video_frames_dir # Define a directory to save the JPEG images frames_output_dir = "frames_output_images" os.makedirs(frames_output_dir, exist_ok=True) # Initialize a list to store file paths of saved images jpeg_images = [] # run propagation throughout the video and collect the results in a dict video_segments = {} # video_segments contains the per-frame segmentation results # for out_frame_idx, out_obj_ids, out_mask_logits in 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) # } out_obj_ids, out_mask_logits = predictor.propagate_in_video(inference_state, start_frame_idx=0, reverse=False,) print(out_obj_ids) for frame_idx in range(0, inference_state['num_frames']): video_segments[frame_idx] = {out_obj_ids[0]: (out_mask_logits[frame_idx]> 0.0).cpu().numpy()} # output_scores_per_object[object_id][frame_idx] = out_mask_logits[frame_idx].cpu().numpy() # render the segmentation results every few frames if vis_frame_type == "check": vis_frame_stride = 15 elif vis_frame_type == "render": vis_frame_stride = 1 plt.close("all") for out_frame_idx in range(0, len(frame_names), vis_frame_stride): plt.figure(figsize=(6, 4)) plt.title(f"frame {out_frame_idx}") plt.imshow(Image.open(os.path.join(video_dir, frame_names[out_frame_idx]))) for out_obj_id, out_mask in video_segments[out_frame_idx].items(): show_mask(out_mask, plt.gca(), obj_id=out_obj_id) # Define the output filename and save the figure as a JPEG file output_filename = os.path.join(frames_output_dir, f"frame_{out_frame_idx}.jpg") plt.savefig(output_filename, format='jpg') # Close the plot plt.close() # Append the file path to the list jpeg_images.append(output_filename) if f"frame_{out_frame_idx}.jpg" not in available_frames_to_check: available_frames_to_check.append(f"frame_{out_frame_idx}.jpg") torch.cuda.empty_cache() print(f"JPEG_IMAGES: {jpeg_images}") if vis_frame_type == "check": return gr.update(value=jpeg_images), gr.update(value=None), gr.update(choices=available_frames_to_check, value=working_frame, visible=True), available_frames_to_check, gr.update(visible=True) elif vis_frame_type == "render": # Create a video clip from the image sequence original_fps = get_video_fps(video_in) # sampled_images = sparse_sampling(jpeg_images, original_fps, target_fps=6) clip = ImageSequenceClip(jpeg_images, fps=original_fps//6) # clip = ImageSequenceClip(jpeg_images, fps=fps) # Write the result to a file final_vid_output_path = "output_video.mp4" # Write the result to a file clip.write_videofile( final_vid_output_path, codec='libx264' ) return gr.update(value=None), gr.update(value=final_vid_output_path), working_frame, available_frames_to_check, gr.update(visible=True) def update_ui(vis_frame_type): if vis_frame_type == "check": return gr.update(visible=True), gr.update(visible=False) elif vis_frame_type == "render": return gr.update(visible=False), gr.update(visible=True) def switch_working_frame(working_frame, scanned_frames, video_frames_dir): new_working_frame = None if working_frame == None: new_working_frame = os.path.join(video_frames_dir, scanned_frames[0]) else: # Use a regular expression to find the integer match = re.search(r'frame_(\d+)', working_frame) if match: # Extract the integer from the match frame_number = int(match.group(1)) ann_frame_idx = frame_number new_working_frame = os.path.join(video_frames_dir, scanned_frames[ann_frame_idx]) return gr.State([]), gr.State([]), new_working_frame, new_working_frame def reset_propagation(first_frame_path, predictor, stored_inference_state): predictor.reset_state(stored_inference_state) # print(f"RESET State: {stored_inference_state} ") return first_frame_path, [], [], gr.update(value=None, visible=False), stored_inference_state, None, ["frame_0.jpg"], first_frame_path, "frame_0.jpg", gr.update(visible=False) with gr.Blocks(css=css) as demo: first_frame_path = gr.State() tracking_points = gr.State([]) trackings_input_label = gr.State([]) video_frames_dir = gr.State() scanned_frames = gr.State() loaded_predictor = gr.State() stored_inference_state = gr.State() stored_frame_names = gr.State() available_frames_to_check = gr.State([]) with gr.Column(): gr.Markdown( """

🔥 SAM2Long Demo 🔥

""" ) gr.Markdown( """ This is a simple demo for video segmentation with [SAM2Long](https://github.com/Mark12Ding/SAM2Long). """ ) gr.Markdown( """ ### 📋 Instructions: It is largely built on the [SAM2-Video-Predictor](https://huggingface.co/spaces/fffiloni/SAM2-Video-Predictor). 1. **Upload your video** [MP4-24fps] 2. With **'include' point type** selected, click on the object to mask on the first frame 3. Switch to **'exclude' point type** if you want to specify an area to avoid 4. **Get Mask!** 5. **Check Propagation** every 15 frames 6. **Propagate with "render"** to render the final masked video 7. **Hit Reset** button if you want to refresh and start again *Note: Input video will be processed for up to 60 seconds only for demo purposes.* """ ) with gr.Row(): with gr.Column(): with gr.Group(): with gr.Group(): with gr.Row(): point_type = gr.Radio(label="point type", choices=["include", "exclude"], value="include", scale=2) clear_points_btn = gr.Button("Clear Points", scale=1) input_first_frame_image = gr.Image(label="input image", interactive=False, type="filepath", visible=False) points_map = gr.Image( label="Point n Click map", type="filepath", interactive=False ) with gr.Group(): with gr.Row(): checkpoint = gr.Dropdown(label="Checkpoint", choices=["tiny", "small", "base-plus"], value="tiny") submit_btn = gr.Button("Get Mask", size="lg") with gr.Accordion("Your video IN", open=True) as video_in_drawer: video_in = gr.Video(label="Video IN", format="mp4") gr.HTML(""" Duplicate this Space to skip queue and avoid OOM errors from heavy public load """) with gr.Column(): with gr.Group(): # with gr.Group(): # with gr.Row(): working_frame = gr.Dropdown(label="working frame ID", choices=["frame_0.jpg"], value="frame_0.jpg", visible=False, allow_custom_value=False, interactive=True) # change_current = gr.Button("change current", visible=False) # working_frame = [] output_result = gr.Image(label="current working mask ref") with gr.Group(): with gr.Row(): vis_frame_type = gr.Radio(label="Propagation level", choices=["check", "render"], value="check", scale=2) propagate_btn = gr.Button("Propagate", scale=2) reset_prpgt_brn = gr.Button("Reset", visible=False) output_propagated = gr.Gallery(label="Propagated Mask samples gallery", columns=4, visible=False) output_video = gr.Video(visible=False) # output_result_mask = gr.Image() # When new video is uploaded video_in.upload( fn = preprocess_video_in, inputs = [video_in], outputs = [ first_frame_path, tracking_points, # update Tracking Points in the gr.State([]) object trackings_input_label, # update Tracking Labels in the gr.State([]) object input_first_frame_image, # hidden component used as ref when clearing points points_map, # Image component where we add new tracking points video_frames_dir, # Array where frames from video_in are deep stored scanned_frames, # Scanned frames by SAM2 stored_inference_state, # Sam2 inference state stored_frame_names, # video_in_drawer, # Accordion to hide uploaded video player ], queue = False ) # triggered when we click on image to add new points points_map.select( fn = get_point, inputs = [ point_type, # "include" or "exclude" tracking_points, # get tracking_points values trackings_input_label, # get tracking label values input_first_frame_image, # gr.State() first frame path ], outputs = [ tracking_points, # updated with new points trackings_input_label, # updated with corresponding labels points_map, # updated image with points ], queue = False ) # Clear every points clicked and added to the map clear_points_btn.click( fn = clear_points, inputs = input_first_frame_image, # we get the untouched hidden image outputs = [ first_frame_path, tracking_points, trackings_input_label, points_map, #stored_inference_state, ], queue=False ) # change_current.click( # fn = switch_working_frame, # inputs = [working_frame, scanned_frames, video_frames_dir], # outputs = [tracking_points, trackings_input_label, input_first_frame_image, points_map], # queue=False # ) submit_btn.click( fn = get_mask_sam_process, inputs = [ stored_inference_state, input_first_frame_image, checkpoint, tracking_points, trackings_input_label, video_frames_dir, scanned_frames, working_frame, available_frames_to_check, ], outputs = [ output_result, stored_frame_names, loaded_predictor, stored_inference_state, working_frame, ], queue=False ) reset_prpgt_brn.click( fn = reset_propagation, inputs = [first_frame_path, loaded_predictor, stored_inference_state], outputs = [points_map, tracking_points, trackings_input_label, output_propagated, stored_inference_state, output_result, available_frames_to_check, input_first_frame_image, working_frame, reset_prpgt_brn], queue=False ) propagate_btn.click( fn = update_ui, inputs = [vis_frame_type], outputs = [output_propagated, output_video], queue=False ).then( fn = propagate_to_all, inputs = [video_in, checkpoint, stored_inference_state, stored_frame_names, video_frames_dir, vis_frame_type, available_frames_to_check, working_frame], outputs = [output_propagated, output_video, working_frame, available_frames_to_check, reset_prpgt_brn] ) demo.launch()