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Zero
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import os
import gradio as gr
import cv2
import numpy as np
from PIL import Image
os.makedirs("./SAM2-Video-Predictor/checkpoints/", exist_ok=True)
os.makedirs("./model/", exist_ok=True)
from huggingface_hub import snapshot_download
def download_sam2():
snapshot_download(repo_id="facebook/sam2-hiera-large", local_dir="./SAM2-Video-Predictor/checkpoints/")
print("Download sam2 completed")
def download_remover():
snapshot_download(repo_id="zibojia/minimax-remover", local_dir="./model/")
print("Download minimax remover completed")
download_sam2()
download_remover()
import torch
import argparse
import random
import torch.nn.functional as F
import time
import random
from omegaconf import OmegaConf
from einops import rearrange
from diffusers.models import AutoencoderKLWan
import scipy
from transformer_minimax_remover import Transformer3DModel
from einops import rearrange
from diffusers.schedulers import UniPCMultistepScheduler
from pipeline_minimax_remover import Minimax_Remover_Pipeline
from diffusers.utils import export_to_video
from decord import VideoReader, cpu
from moviepy.editor import ImageSequenceClip
from sam2 import load_model
from sam2.build_sam import build_sam2, build_sam2_video_predictor
from sam2.sam2_image_predictor import SAM2ImagePredictor
import spaces
COLOR_PALETTE = [
(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0), (255, 0, 255),
(0, 255, 255), (255, 128, 0), (128, 0, 255), (0, 128, 255), (128, 255, 0)
]
random_seed = 42
video_length = 201
W = 1024
H = W
device = "cpu"
def get_pipe_image_and_video_predictor():
vae = AutoencoderKLWan.from_pretrained("./model/vae", torch_dtype=torch.float16)
transformer = Transformer3DModel.from_pretrained("./model/transformer", torch_dtype=torch.float16)
scheduler = UniPCMultistepScheduler.from_pretrained("./model/scheduler")
pipe = Minimax_Remover_Pipeline(transformer=transformer, vae=vae, scheduler=scheduler)
sam2_checkpoint = "./SAM2-Video-Predictor/checkpoints/sam2_hiera_large.pt"
config = "sam2_hiera_l.yaml"
video_predictor = build_sam2_video_predictor(config, sam2_checkpoint, device=device)
model = build_sam2(config, sam2_checkpoint, device=device)
model.image_size = 1024
image_predictor = SAM2ImagePredictor(sam_model=model)
return pipe, image_predictor, video_predictor
def get_video_info(video_path, video_state):
video_state["input_points"] = []
video_state["scaled_points"] = []
video_state["input_labels"] = []
video_state["frame_idx"] = 0
vr = VideoReader(video_path, ctx=cpu(0))
first_frame = vr[0].asnumpy()
del vr
if first_frame.shape[0] > first_frame.shape[1]:
W_ = W
H_ = int(W_ * first_frame.shape[0] / first_frame.shape[1])
else:
H_ = H
W_ = int(H_ * first_frame.shape[1] / first_frame.shape[0])
first_frame = cv2.resize(first_frame, (W_, H_))
video_state["origin_images"] = np.expand_dims(first_frame, axis=0)
video_state["inference_state"] = None
video_state["video_path"] = video_path
video_state["masks"] = None
video_state["painted_images"] = None
image = Image.fromarray(first_frame)
return image
def segment_frame(evt: gr.SelectData, label, video_state):
if video_state["origin_images"] is None:
return None
x, y = evt.index
new_point = [x, y]
label_value = 1 if label == "Positive" else 0
video_state["input_points"].append(new_point)
video_state["input_labels"].append(label_value)
height, width = video_state["origin_images"][0].shape[0:2]
scaled_points = []
for pt in video_state["input_points"]:
sx = pt[0] / width
sy = pt[1] / height
scaled_points.append([sx, sy])
video_state["scaled_points"] = scaled_points
image_predictor.set_image(video_state["origin_images"][0])
mask, _, _ = image_predictor.predict(
point_coords=video_state["scaled_points"],
point_labels=video_state["input_labels"],
multimask_output=False,
normalize_coords=False,
)
mask = np.squeeze(mask)
mask = cv2.resize(mask, (width, height))
mask = mask[:,:,None]
color = np.array(COLOR_PALETTE[int(time.time()) % len(COLOR_PALETTE)], dtype=np.float32) / 255.0
color = color[None, None, :]
org_image = video_state["origin_images"][0].astype(np.float32) / 255.0
painted_image = (1 - mask * 0.5) * org_image + mask * 0.5 * color
painted_image = np.uint8(np.clip(painted_image * 255, 0, 255))
video_state["painted_images"] = np.expand_dims(painted_image, axis=0)
video_state["masks"] = np.expand_dims(mask[:,:,0], axis=0)
for i in range(len(video_state["input_points"])):
point = video_state["input_points"][i]
if video_state["input_labels"][i] == 0:
cv2.circle(painted_image, point, radius=3, color=(0, 0, 255), thickness=-1) # 红色点,半径为3
else:
cv2.circle(painted_image, point, radius=3, color=(255, 0, 0), thickness=-1)
return Image.fromarray(painted_image)
def clear_clicks(video_state):
video_state["input_points"] = []
video_state["input_labels"] = []
video_state["scaled_points"] = []
video_state["inference_state"] = None
video_state["masks"] = None
video_state["painted_images"] = None
return Image.fromarray(video_state["origin_images"][0]) if video_state["origin_images"] is not None else None
def preprocess_for_removal(images, masks):
out_images = []
out_masks = []
for img, msk in zip(images, masks):
if img.shape[0] > img.shape[1]:
img_resized = cv2.resize(img, (480, 832), interpolation=cv2.INTER_LINEAR)
else:
img_resized = cv2.resize(img, (832, 480), interpolation=cv2.INTER_LINEAR)
img_resized = img_resized.astype(np.float32) / 127.5 - 1.0 # [-1, 1]
out_images.append(img_resized)
if msk.shape[0] > msk.shape[1]:
msk_resized = cv2.resize(msk, (480, 832), interpolation=cv2.INTER_NEAREST)
else:
msk_resized = cv2.resize(msk, (832, 480), interpolation=cv2.INTER_NEAREST)
msk_resized = msk_resized.astype(np.float32)
msk_resized = (msk_resized > 0.5).astype(np.float32)
out_masks.append(msk_resized)
arr_images = np.stack(out_images)
arr_masks = np.stack(out_masks)
return torch.from_numpy(arr_images).half(), torch.from_numpy(arr_masks).half()
@spaces.GPU(duration=200)
def inference_and_return_video(dilation_iterations, num_inference_steps, video_state):
if video_state["origin_images"] is None or video_state["masks"] is None:
return None
images = video_state["origin_images"]
masks = video_state["masks"]
images = np.array(images)
masks = np.array(masks)
img_tensor, mask_tensor = preprocess_for_removal(images, masks)
img_tensor=img_tensor.to("cuda")
mask_tensor = mask_tensor[:,:,:,:1].to("cuda")
if mask_tensor.shape[1] < mask_tensor.shape[2]:
height = 480
width = 832
else:
height = 832
width = 480
pipe.to("cuda")
with torch.no_grad():
out = pipe(
images=img_tensor,
masks=mask_tensor,
num_frames=mask_tensor.shape[0],
height=height,
width=width,
num_inference_steps=int(num_inference_steps),
generator=torch.Generator(device=device).manual_seed(random_seed),
iterations=int(dilation_iterations)
).frames[0]
out = np.uint8(out * 255)
output_frames = [img for img in out]
video_file = f"/tmp/{time.time()}-{random.random()}-removed_output.mp4"
clip = ImageSequenceClip(output_frames, fps=15)
clip.write_videofile(video_file, codec='libx264', audio=False, verbose=False, logger=None)
return video_file
@spaces.GPU(duration=100)
def track_video(n_frames,video_state):
input_points = video_state["input_points"]
input_labels = video_state["input_labels"]
frame_idx = video_state["frame_idx"]
obj_id = video_state["obj_id"]
scaled_points = video_state["scaled_points"]
vr = VideoReader(video_state["video_path"], ctx=cpu(0))
height, width = vr[0].shape[0:2]
images = [vr[i].asnumpy() for i in range(min(len(vr), n_frames))]
del vr
if images[0].shape[0] > images[0].shape[1]:
W_ = W
H_ = int(W_ * images[0].shape[0] / images[0].shape[1])
else:
H_ = H
W_ = int(H_ * images[0].shape[1] / images[0].shape[0])
images = [cv2.resize(img, (W_, H_)) for img in images]
video_state["origin_images"] = images
images = np.array(images)
sam2_checkpoint = "./SAM2-Video-Predictor/checkpoints/sam2_hiera_large.pt"
config = "sam2_hiera_l.yaml"
video_predictor_local = build_sam2_video_predictor(config, sam2_checkpoint, device="cuda")
inference_state = video_predictor_local.init_state(images=images/255, device="cuda")
#video_state["inference_state"] = inference_state #cause bug
if len(torch.from_numpy(video_state["masks"][0]).shape) == 3:
mask = torch.from_numpy(video_state["masks"][0])[:,:,0]
else:
mask = torch.from_numpy(video_state["masks"][0])
video_predictor_local.add_new_mask(
inference_state=inference_state,
frame_idx=0,
obj_id=obj_id,
mask=mask
)
output_frames = []
mask_frames = []
color = np.array(COLOR_PALETTE[int(time.time()) % len(COLOR_PALETTE)], dtype=np.float32) / 255.0
color = color[None, None, :]
for out_frame_idx, out_obj_ids, out_mask_logits in video_predictor_local.propagate_in_video(inference_state):
frame = images[out_frame_idx].astype(np.float32) / 255.0
mask = np.zeros((H, W, 3), dtype=np.float32)
for i, logit in enumerate(out_mask_logits):
out_mask = logit.cpu().squeeze().detach().numpy()
out_mask = (out_mask[:,:,None] > 0).astype(np.float32)
mask += out_mask
mask = np.clip(mask, 0, 1)
mask = cv2.resize(mask, (W_, H_))
mask_frames.append(mask)
painted = (1 - mask * 0.5) * frame + mask * 0.5 * color
painted = np.uint8(np.clip(painted * 255, 0, 255))
output_frames.append(painted)
video_state["masks"] =mask_frames
video_file = f"/tmp/{time.time()}-{random.random()}-tracked_output.mp4"
clip = ImageSequenceClip(output_frames, fps=15)
clip.write_videofile(video_file, codec='libx264', audio=False, verbose=False, logger=None)
print("line 286 done")
return video_file,video_state
text = """
<div style='text-align:center; font-size:32px; font-family: Arial, Helvetica, sans-serif;'>
Minimax-Remover: Taming Bad Noise Helps Video Object Removal
</div>
<div style="display: flex; justify-content: center; align-items: center; gap: 10px; flex-wrap: nowrap;">
<a href="https://huggingface.co/zibojia/minimax-remover"><img alt="Huggingface Model" src="https://img.shields.io/badge/%F0%9F%A4%97%20Huggingface-Model-brightgreen"></a>
<a href="https://github.com/zibojia/MiniMax-Remover"><img alt="Github" src="https://img.shields.io/badge/MiniMaxRemover-github-black"></a>
<a href="https://huggingface.co/spaces/PengWeixuanSZU/MiniMax-Remover"><img alt="Huggingface Space" src="https://img.shields.io/badge/%F0%9F%A4%97%20Huggingface-Space-1e90ff"></a>
<a href="https://arxiv.org/abs/2505.24873"><img alt="arXiv" src="https://img.shields.io/badge/MiniMaxRemover-arXiv-b31b1b"></a>
<a href="https://www.youtube.com/watch?v=KaU5yNl6CTc"><img alt="YouTube" src="https://img.shields.io/badge/Youtube-video-ff0000"></a>
<a href="https://minimax-remover.github.io"><img alt="Demo Page" src="https://img.shields.io/badge/Website-Demo%20Page-yellow"></a>
</div>
<div style='text-align:center; font-size:20px; margin-top: 10px; font-family: Arial, Helvetica, sans-serif;'>
Bojia Zi<sup>*</sup>, Weixuan Peng<sup>*</sup>, Xianbiao Qi<sup>†</sup>, Jianan Wang, Shihao Zhao, Rong Xiao, Kam-Fai Wong
</div>
<div style='text-align:center; font-size:14px; color: #888; margin-top: 5px; font-family: Arial, Helvetica, sans-serif;'>
<sup>*</sup> Equal contribution <sup>†</sup> Corresponding author
</div>
"""
pipe, image_predictor, video_predictor = get_pipe_image_and_video_predictor()
with gr.Blocks() as demo:
video_state = gr.State({
"origin_images": None,
"inference_state": None,
"masks": None, # Store user-generated masks
"painted_images": None,
"video_path": None,
"input_points": [],
"scaled_points": [],
"input_labels": [],
"frame_idx": 0,
"obj_id": 1
})
gr.Markdown(f"<div style='text-align:center;'>{text}</div>")
with gr.Column():
video_input = gr.Video(label="Upload Video", elem_id="my-video1")
get_info_btn = gr.Button("Extract First Frame", elem_id="my-btn")
gr.Examples(
examples=[
["./cartoon/0.mp4"],
["./cartoon/1.mp4"],
["./cartoon/2.mp4"],
["./cartoon/3.mp4"],
["./cartoon/4.mp4"],
["./normal_videos/0.mp4"],
["./normal_videos/1.mp4"],
["./normal_videos/3.mp4"],
["./normal_videos/4.mp4"],
["./normal_videos/5.mp4"],
],
inputs=[video_input],
label="Choose a video to remove.",
elem_id="my-btn2"
)
image_output = gr.Image(label="First Frame Segmentation", interactive=True, elem_id="my-video")#, height="35%", width="60%")
demo.css = """
#my-btn {
width: 60% !important;
margin: 0 auto;
}
#my-video1 {
width: 60% !important;
height: 35% !important;
margin: 0 auto;
}
#my-video {
width: 60% !important;
height: 35% !important;
margin: 0 auto;
}
#my-md {
margin: 0 auto;
}
#my-btn2 {
width: 60% !important;
margin: 0 auto;
}
#my-btn2 button {
width: 120px !important;
max-width: 120px !important;
min-width: 120px !important;
height: 70px !important;
max-height: 70px !important;
min-height: 70px !important;
margin: 8px !important;
border-radius: 8px !important;
overflow: hidden !important;
white-space: normal !important;
}
"""
with gr.Row(elem_id="my-btn"):
point_prompt = gr.Radio(["Positive", "Negative"], label="Click Type", value="Positive")
clear_btn = gr.Button("Clear All Clicks")
with gr.Row(elem_id="my-btn"):
n_frames_slider = gr.Slider(minimum=1, maximum=201, value=81, step=1, label="Tracking Frames N")
track_btn = gr.Button("Tracking")
video_output = gr.Video(label="Tracking Result", elem_id="my-video")
with gr.Column(elem_id="my-btn"):
dilation_slider = gr.Slider(minimum=1, maximum=20, value=6, step=1, label="Mask Dilation")
inference_steps_slider = gr.Slider(minimum=1, maximum=100, value=6, step=1, label="Num Inference Steps")
remove_btn = gr.Button("Remove", elem_id="my-btn")
remove_video = gr.Video(label="Remove Results", elem_id="my-video")
remove_btn.click(
inference_and_return_video,
inputs=[dilation_slider, inference_steps_slider, video_state],
outputs=remove_video
)
get_info_btn.click(get_video_info, inputs=[video_input, video_state], \
outputs=image_output)
image_output.select(fn=segment_frame, inputs=[point_prompt, video_state], outputs=image_output)
clear_btn.click(clear_clicks, inputs=video_state, outputs=image_output)
track_btn.click(track_video, inputs=[n_frames_slider,video_state], outputs=[video_output,video_state])
demo.launch() |