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# import subprocess
# import re
# from typing import List, Tuple, Optional
# command = ["python", "setup.py", "build_ext", "--inplace"]
# result = subprocess.run(command, capture_output=True, text=True)
# print("Output:\n", result.stdout)
# print("Errors:\n", result.stderr)
# if result.returncode == 0:
# print("Command executed successfully.")
# else:
# print("Command failed with return code:", result.returncode)
import gc
import math
import os
os.environ["TORCH_CUDNN_SDPA_ENABLED"] = "1"
import shutil
import ffmpeg
import zipfile
import gradio as gr
import torch
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
from sam2.build_sam import build_sam2
from sam2.sam2_image_predictor import SAM2ImagePredictor
from sam2.build_sam import build_sam2_video_predictor
import cv2
def clean(Seg_Tracker):
if Seg_Tracker is not None:
predictor, inference_state, image_predictor = Seg_Tracker
predictor.reset_state(inference_state)
del predictor
del inference_state
del image_predictor
del Seg_Tracker
gc.collect()
torch.cuda.empty_cache()
return None, ({}, {}), None, None, 0, None, None, None, 0
def get_meta_from_video(Seg_Tracker, input_video, scale_slider, checkpoint):
output_dir = '/tmp/output_frames'
output_masks_dir = '/tmp/output_masks'
output_combined_dir = '/tmp/output_combined'
clear_folder(output_dir)
clear_folder(output_masks_dir)
clear_folder(output_combined_dir)
if input_video is None:
return None, ({}, {}), None, None, 0, None, None, None, 0
cap = cv2.VideoCapture(input_video)
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
cap.release()
output_frames = int(total_frames * scale_slider)
frame_interval = max(1, total_frames // output_frames)
ffmpeg.input(input_video, hwaccel='cuda').output(
os.path.join(output_dir, '%07d.jpg'), q=2, start_number=0,
vf=rf'select=not(mod(n\,{frame_interval}))', vsync='vfr'
).run()
first_frame_path = os.path.join(output_dir, '0000000.jpg')
first_frame = cv2.imread(first_frame_path)
first_frame_rgb = cv2.cvtColor(first_frame, cv2.COLOR_BGR2RGB)
if Seg_Tracker is not None:
del Seg_Tracker
Seg_Tracker = None
gc.collect()
torch.cuda.empty_cache()
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
if checkpoint == "tiny":
sam2_checkpoint = "segment-anything-2/checkpoints/sam2_hiera_tiny.pt"
model_cfg = "sam2_hiera_t.yaml"
elif checkpoint == "samll":
sam2_checkpoint = "segment-anything-2/checkpoints/sam2_hiera_small.pt"
model_cfg = "sam2_hiera_s.yaml"
elif checkpoint == "base-plus":
sam2_checkpoint = "segment-anything-2/checkpoints/sam2_hiera_base_plus.pt"
model_cfg = "sam2_hiera_b+.yaml"
elif checkpoint == "large":
sam2_checkpoint = "segment-anything-2/checkpoints/sam2_hiera_large.pt"
model_cfg = "sam2_hiera_l.yaml"
predictor = build_sam2_video_predictor(model_cfg, sam2_checkpoint, device="cuda")
sam2_model = build_sam2(model_cfg, sam2_checkpoint, device="cuda")
image_predictor = SAM2ImagePredictor(sam2_model)
inference_state = predictor.init_state(video_path=output_dir)
predictor.reset_state(inference_state)
return (predictor, inference_state, image_predictor), ({}, {}), first_frame_rgb, first_frame_rgb, 0, None, None, None, 0
def mask2bbox(mask):
if len(np.where(mask > 0)[0]) == 0:
print(f'not mask')
return np.array([0, 0, 0, 0]).astype(np.int64), False
x_ = np.sum(mask, axis=0)
y_ = np.sum(mask, axis=1)
x0 = np.min(np.nonzero(x_)[0])
x1 = np.max(np.nonzero(x_)[0])
y0 = np.min(np.nonzero(y_)[0])
y1 = np.max(np.nonzero(y_)[0])
return np.array([x0, y0, x1, y1]).astype(np.int64), True
def sam_stroke(Seg_Tracker, drawing_board, last_draw, frame_num, ann_obj_id):
predictor, inference_state, image_predictor = Seg_Tracker
image_path = f'/tmp/output_frames/{frame_num:07d}.jpg'
image = cv2.imread(image_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
display_image = drawing_board["image"]
image_predictor.set_image(image)
input_mask = drawing_board["mask"]
input_mask[input_mask != 0] = 255
if last_draw is not None:
diff_mask = cv2.absdiff(input_mask, last_draw)
input_mask = diff_mask
bbox, hasMask = mask2bbox(input_mask[:, :, 0])
if not hasMask :
return Seg_Tracker, display_image, display_image
masks, scores, logits = image_predictor.predict( point_coords=None, point_labels=None, box=bbox[None, :], multimask_output=False,)
mask = masks > 0.0
masked_frame = show_mask(mask, display_image, ann_obj_id)
masked_with_rect = draw_rect(masked_frame, bbox, ann_obj_id)
frame_idx, object_ids, masks = predictor.add_new_mask(inference_state, frame_idx=frame_num, obj_id=ann_obj_id, mask=mask[0])
last_draw = drawing_board["mask"]
return Seg_Tracker, masked_with_rect, masked_with_rect, last_draw
def draw_rect(image, bbox, obj_id):
cmap = plt.get_cmap("tab10")
color = np.array(cmap(obj_id)[:3])
rgb_color = tuple(map(int, (color[:3] * 255).astype(np.uint8)))
inv_color = tuple(map(int, (255 - color[:3] * 255).astype(np.uint8)))
x0, y0, x1, y1 = bbox
image_with_rect = cv2.rectangle(image.copy(), (x0, y0), (x1, y1), inv_color, thickness=2)
return image_with_rect
def sam_click(Seg_Tracker, frame_num, point_mode, click_stack, ann_obj_id, evt: gr.SelectData):
points_dict, labels_dict = click_stack
predictor, inference_state, image_predictor = Seg_Tracker
ann_frame_idx = frame_num # the frame index we interact with
print(f'ann_frame_idx: {ann_frame_idx}')
point = np.array([[evt.index[0], evt.index[1]]], dtype=np.float32)
if point_mode == "Positive":
label = np.array([1], np.int32)
else:
label = np.array([0], np.int32)
if ann_frame_idx not in points_dict:
points_dict[ann_frame_idx] = {}
if ann_frame_idx not in labels_dict:
labels_dict[ann_frame_idx] = {}
if ann_obj_id not in points_dict[ann_frame_idx]:
points_dict[ann_frame_idx][ann_obj_id] = np.empty((0, 2), dtype=np.float32)
if ann_obj_id not in labels_dict[ann_frame_idx]:
labels_dict[ann_frame_idx][ann_obj_id] = np.empty((0,), dtype=np.int32)
points_dict[ann_frame_idx][ann_obj_id] = np.append(points_dict[ann_frame_idx][ann_obj_id], point, axis=0)
labels_dict[ann_frame_idx][ann_obj_id] = np.append(labels_dict[ann_frame_idx][ann_obj_id], label, axis=0)
click_stack = (points_dict, labels_dict)
frame_idx, 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_dict[ann_frame_idx][ann_obj_id],
labels=labels_dict[ann_frame_idx][ann_obj_id],
)
image_path = f'/tmp/output_frames/{ann_frame_idx:07d}.jpg'
image = cv2.imread(image_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
masked_frame = image.copy()
for i, obj_id in enumerate(out_obj_ids):
mask = (out_mask_logits[i] > 0.0).cpu().numpy()
masked_frame = show_mask(mask, image=masked_frame, obj_id=obj_id)
masked_frame_with_markers = draw_markers(masked_frame, points_dict[ann_frame_idx], labels_dict[ann_frame_idx])
return Seg_Tracker, masked_frame_with_markers, masked_frame_with_markers, click_stack
def draw_markers(image, points_dict, labels_dict):
cmap = plt.get_cmap("tab10")
image_h, image_w = image.shape[:2]
marker_size = max(1, int(min(image_h, image_w) * 0.05))
for obj_id in points_dict:
color = np.array(cmap(obj_id)[:3])
rgb_color = tuple(map(int, (color[:3] * 255).astype(np.uint8)))
inv_color = tuple(map(int, (255 - color[:3] * 255).astype(np.uint8)))
for point, label in zip(points_dict[obj_id], labels_dict[obj_id]):
x, y = int(point[0]), int(point[1])
if label == 1:
cv2.drawMarker(image, (x, y), inv_color, markerType=cv2.MARKER_CROSS, markerSize=marker_size, thickness=2)
else:
cv2.drawMarker(image, (x, y), inv_color, markerType=cv2.MARKER_TILTED_CROSS, markerSize=int(marker_size / np.sqrt(2)), thickness=2)
return image
def show_mask(mask, image=None, obj_id=None):
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)
mask_image = (mask_image * 255).astype(np.uint8)
if image is not None:
image_h, image_w = image.shape[:2]
if (image_h, image_w) != (h, w):
raise ValueError(f"Image dimensions ({image_h}, {image_w}) and mask dimensions ({h}, {w}) do not match")
colored_mask = np.zeros_like(image, dtype=np.uint8)
for c in range(3):
colored_mask[..., c] = mask_image[..., c]
alpha_mask = mask_image[..., 3] / 255.0
for c in range(3):
image[..., c] = np.where(alpha_mask > 0, (1 - alpha_mask) * image[..., c] + alpha_mask * colored_mask[..., c], image[..., c])
return image
return mask_image
def show_res_by_slider(frame_per, click_stack):
image_path = '/tmp/output_frames'
output_combined_dir = '/tmp/output_combined'
combined_frames = sorted([os.path.join(output_combined_dir, img_name) for img_name in os.listdir(output_combined_dir)])
if combined_frames:
output_masked_frame_path = combined_frames
else:
original_frames = sorted([os.path.join(image_path, img_name) for img_name in os.listdir(image_path)])
output_masked_frame_path = original_frames
total_frames_num = len(output_masked_frame_path)
if total_frames_num == 0:
print("No output results found")
return None, None
else:
frame_num = math.floor(total_frames_num * frame_per / 100)
if frame_per == 100:
frame_num = frame_num - 1
chosen_frame_path = output_masked_frame_path[frame_num]
print(f"{chosen_frame_path}")
chosen_frame_show = cv2.imread(chosen_frame_path)
chosen_frame_show = cv2.cvtColor(chosen_frame_show, cv2.COLOR_BGR2RGB)
points_dict, labels_dict = click_stack
if frame_num in points_dict and frame_num in labels_dict:
chosen_frame_show = draw_markers(chosen_frame_show, points_dict[frame_num], labels_dict[frame_num])
return chosen_frame_show, chosen_frame_show, frame_num
def clear_folder(folder_path):
if os.path.exists(folder_path):
shutil.rmtree(folder_path)
os.makedirs(folder_path)
def zip_folder(folder_path, output_zip_path):
with zipfile.ZipFile(output_zip_path, 'w', zipfile.ZIP_STORED) as zipf:
for root, _, files in os.walk(folder_path):
for file in files:
file_path = os.path.join(root, file)
zipf.write(file_path, os.path.relpath(file_path, folder_path))
def tracking_objects(Seg_Tracker, frame_num, input_video):
output_dir = '/tmp/output_frames'
output_masks_dir = '/tmp/output_masks'
output_combined_dir = '/tmp/output_combined'
output_video_path = '/tmp/output_video.mp4'
output_zip_path = '/tmp/output_masks.zip'
clear_folder(output_masks_dir)
clear_folder(output_combined_dir)
if os.path.exists(output_video_path):
os.remove(output_video_path)
if os.path.exists(output_zip_path):
os.remove(output_zip_path)
video_segments = {}
predictor, inference_state, image_predictor = Seg_Tracker
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)
}
frame_files = sorted([f for f in os.listdir(output_dir) if f.endswith('.jpg')])
# for frame_idx in sorted(video_segments.keys()):
for frame_file in frame_files:
frame_idx = int(os.path.splitext(frame_file)[0])
frame_path = os.path.join(output_dir, frame_file)
image = cv2.imread(frame_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
masked_frame = image.copy()
if frame_idx in video_segments:
for obj_id, mask in video_segments[frame_idx].items():
masked_frame = show_mask(mask, image=masked_frame, obj_id=obj_id)
mask_output_path = os.path.join(output_masks_dir, f'{obj_id}_{frame_idx:07d}.png')
cv2.imwrite(mask_output_path, show_mask(mask))
combined_output_path = os.path.join(output_combined_dir, f'{frame_idx:07d}.png')
combined_image_bgr = cv2.cvtColor(masked_frame, cv2.COLOR_RGB2BGR)
cv2.imwrite(combined_output_path, combined_image_bgr)
if frame_idx == frame_num:
final_masked_frame = masked_frame
cap = cv2.VideoCapture(input_video)
fps = cap.get(cv2.CAP_PROP_FPS)
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
cap.release()
# output_frames = int(total_frames * scale_slider)
output_frames = len([name for name in os.listdir(output_combined_dir) if os.path.isfile(os.path.join(output_combined_dir, name)) and name.endswith('.png')])
out_fps = fps * output_frames / total_frames
# ffmpeg.input(os.path.join(output_combined_dir, '%07d.png'), framerate=out_fps).output(output_video_path, vcodec='h264_nvenc', pix_fmt='yuv420p').run()
fourcc = cv2.VideoWriter_fourcc(*"mp4v")
out = cv2.VideoWriter(output_video_path, fourcc, out_fps, (frame_width, frame_height))
for i in range(output_frames):
frame_path = os.path.join(output_combined_dir, f'{i:07d}.png')
frame = cv2.imread(frame_path)
out.write(frame)
out.release()
zip_folder(output_masks_dir, output_zip_path)
print("done")
return final_masked_frame, final_masked_frame, output_video_path, output_video_path, output_zip_path
def increment_ann_obj_id(ann_obj_id):
ann_obj_id += 1
return ann_obj_id
def drawing_board_get_input_first_frame(input_first_frame):
return input_first_frame
def seg_track_app():
##########################################################
###################### Front-end ########################
##########################################################
css = """
#input_output_video video {
max-height: 550px;
max-width: 100%;
height: auto;
}
"""
app = gr.Blocks(css=css)
with app:
gr.Markdown(
'''
<div style="text-align:center; margin-bottom:20px;">
<span style="font-size:3em; font-weight:bold;">SAM2 for Video Segmentation 🔥</span>
</div>
<div style="text-align:center; margin-bottom:10px;">
<span style="font-size:1.5em; font-weight:bold;">Segment Anything in Medical Images and Videos: Benchmark and Deployment</span>
</div>
<div style="text-align:center; margin-bottom:20px;">
<a href="https://github.com/bowang-lab/MedSAM/tree/MedSAM2">
<img src="https://badges.aleen42.com/src/github.svg" alt="GitHub" style="display:inline-block; margin-right:10px;">
</a>
<a href="https://arxiv.org/abs/2408.03322">
<img src="https://img.shields.io/badge/arXiv-2408.03322-green?style=plastic" alt="Paper" style="display:inline-block; margin-right:10px;">
</a>
<a href="https://github.com/bowang-lab/MedSAMSlicer/tree/SAM2">
<img src="https://img.shields.io/badge/3D-Slicer-Plugin" alt="3D Slicer Plugin" style="display:inline-block; margin-right:10px;">
</a>
<a href="https://drive.google.com/drive/folders/1EXzRkxZmrXbahCFA8_ImFRM6wQDEpOSe?usp=sharing">
<img src="https://img.shields.io/badge/Video-Tutorial-green?style=plastic" alt="Video Tutorial" style="display:inline-block; margin-right:10px;">
</a>
</div>
<div style="text-align:left; margin-bottom:20px;">
This API supports using box (generated by scribble) and point prompts for video segmentation with
<a href="https://ai.meta.com/sam2/" target="_blank">SAM2</a>.
</div>
<div style="margin-bottom:20px;">
<ol style="list-style:none; padding-left:0;">
<li>1. Upload video file</li>
<li>2. Select model size and downsample frame rate and run <b>Preprocess</b></li>
<li>3. Use <b>Stroke to Box Prompt</b> to draw box on the first frame or <b>Point Prompt</b> to click on the first frame.</li>
<li> Note: The bounding rectangle of the stroke should be able to cover the segmentation target.</li>
<li>4. Click <b>Segment</b> to get the segmentation result</li>
<li>5. Click <b>Add New Object</b> to add new object</li>
<li>6. Click <b>Start Tracking</b> to track objects in the video</li>
<li>7. Click <b>Reset</b> to reset the app</li>
<li>8. Download the video with segmentation results</li>
</ol>
</div>
'''
)
click_stack = gr.State(({}, {}))
Seg_Tracker = gr.State(None)
frame_num = gr.State(value=(int(0)))
ann_obj_id = gr.State(value=(int(0)))
last_draw = gr.State(None)
with gr.Row():
with gr.Column(scale=0.5):
with gr.Row():
tab_video_input = gr.Tab(label="Video input")
with tab_video_input:
input_video = gr.Video(label='Input video', elem_id="input_output_video")
with gr.Row():
checkpoint = gr.Dropdown(label="Model Size", choices=["tiny", "small", "base-plus", "large"], value="tiny")
scale_slider = gr.Slider(
label="Downsampe Frame Rate",
minimum=0.0,
maximum=1.0,
step=0.25,
value=1.0,
interactive=True
)
preprocess_button = gr.Button(
value="Preprocess",
interactive=True,
)
with gr.Row():
tab_stroke = gr.Tab(label="Stroke to Box Prompt")
with tab_stroke:
drawing_board = gr.Image(label='Drawing Board', tool="sketch", brush_radius=10, interactive=True)
with gr.Row():
seg_acc_stroke = gr.Button(value="Segment", interactive=True)
tab_click = gr.Tab(label="Point Prompt")
with tab_click:
input_first_frame = gr.Image(label='Segment result of first frame',interactive=True).style(height=550)
with gr.Row():
point_mode = gr.Radio(
choices=["Positive", "Negative"],
value="Positive",
label="Point Prompt",
interactive=True)
with gr.Row():
with gr.Column():
frame_per = gr.Slider(
label = "Percentage of Frames Viewed",
minimum= 0.0,
maximum= 100.0,
step=0.01,
value=0.0,
)
new_object_button = gr.Button(
value="Add New Object",
interactive=True
)
track_for_video = gr.Button(
value="Start Tracking",
interactive=True,
)
reset_button = gr.Button(
value="Reset",
interactive=True,
)
with gr.Column(scale=0.5):
output_video = gr.Video(label='Visualize Results', elem_id="input_output_video")
output_mp4 = gr.File(label="Predicted video")
output_mask = gr.File(label="Predicted masks")
with gr.Tab(label='Video examples'):
gr.Examples(
label="",
examples=[
"assets/12fps_Dancing_cells_trimmed.mp4",
"assets/clip_012251_fps5_07_25.mp4",
"assets/FLARE22_Tr_0004.mp4",
"assets/FLARE22_Tr_0016.mp4",
"assets/FLARE22_Tr_0046.mp4",
"assets/c_elegans_mov_cut_fps12.mp4",
"assets/12fps_Dylan_Burnette_neutrophil.mp4",
],
inputs=[input_video],
)
gr.Examples(
label="",
examples=[
"assets/12fps_volvox_microcystis_play_trimmed.mp4",
"assets/12fps_neuron_time_lapse.mp4",
"assets/12fps_macrophages_phagocytosis.mp4",
"assets/12fps_worm_eats_organism_3.mp4",
"assets/12fps_worm_eats_organism_4.mp4",
"assets/12fps_worm_eats_organism_5.mp4",
"assets/12fps_worm_eats_organism_6.mp4",
"assets/12fps_02_cups.mp4",
],
inputs=[input_video],
)
gr.Markdown(
'''
<div style="text-align:center; margin-top: 20px;">
The authors of this work highly appreciate Meta AI for making SAM2 publicly available to the community.
The interface was built on <a href="https://github.com/z-x-yang/Segment-and-Track-Anything/blob/main/tutorial/tutorial%20for%20WebUI-1.0-Version.md" target="_blank">SegTracker</a>.
<a href="https://docs.google.com/document/d/1idDBV0faOjdjVs-iAHr0uSrw_9_ZzLGrUI2FEdK-lso/edit?usp=sharing" target="_blank">Data source</a>
</div>
'''
)
##########################################################
###################### back-end #########################
##########################################################
# listen to the preprocess button click to get the first frame of video with scaling
preprocess_button.click(
fn=get_meta_from_video,
inputs=[
Seg_Tracker,
input_video,
scale_slider,
checkpoint
],
outputs=[
Seg_Tracker, click_stack, input_first_frame, drawing_board, frame_per, output_video, output_mp4, output_mask, ann_obj_id
]
)
frame_per.release(
fn=show_res_by_slider,
inputs=[
frame_per, click_stack
],
outputs=[
input_first_frame, drawing_board, frame_num
]
)
# Interactively modify the mask acc click
input_first_frame.select(
fn=sam_click,
inputs=[
Seg_Tracker, frame_num, point_mode, click_stack, ann_obj_id
],
outputs=[
Seg_Tracker, input_first_frame, drawing_board, click_stack
]
)
# Track object in video
track_for_video.click(
fn=tracking_objects,
inputs=[
Seg_Tracker,
frame_num,
input_video,
],
outputs=[
input_first_frame,
drawing_board,
output_video,
output_mp4,
output_mask
]
)
reset_button.click(
fn=clean,
inputs=[
Seg_Tracker
],
outputs=[
Seg_Tracker, click_stack, input_first_frame, drawing_board, frame_per, output_video, output_mp4, output_mask, ann_obj_id
]
)
new_object_button.click(
fn=increment_ann_obj_id,
inputs=[
ann_obj_id
],
outputs=[
ann_obj_id
]
)
tab_stroke.select(
fn=drawing_board_get_input_first_frame,
inputs=[input_first_frame,],
outputs=[drawing_board,],
)
seg_acc_stroke.click(
fn=sam_stroke,
inputs=[
Seg_Tracker, drawing_board, last_draw, frame_num, ann_obj_id
],
outputs=[
Seg_Tracker, input_first_frame, drawing_board, last_draw
]
)
app.queue(concurrency_count=1)
app.launch(debug=True, enable_queue=True, share=True)
if __name__ == "__main__":
seg_track_app()
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