Add video mask demo. (#149)

Author: wkzhang636

mlcd_vl/downstream/README.md

@@ -7,6 +7,7 @@
 ## Example:
 
 ![output](https://github.com/user-attachments/assets/85c023a1-3e0c-4ea5-a764-1eb9ee0fbddf)
+<video src="https://github.com/user-attachments/assets/380dee0d-47c4-4e01-8ff0-e69e62cccd7c" alt="output" width="1024"></video>
 
 
 ## RefCOCO Segmentation Evaluation Results:
@@ -56,6 +57,86 @@ pred_mask = model.seg(seg_img, seg_prompt, tokenizer, force_seg=False)
 
 ```
 
+If you want to use this code in video, please refer to this sample below
+```python
+from transformers import AutoModel, AutoTokenizer
+from PIL import Image
+import torch
+from torchvision import transforms
+import subprocess
+import os
+
+# video path
+video_path = "updownfunk.mp4"
+input_dir = "frames"
+output_dir = "mask_frames"
+os.makedirs(input_dir, exist_ok=True)
+os.makedirs(output_dir, exist_ok=True)
+
+# assert you have ffmpeg installed, mp4 -> jpg
+cmd = [
+    "ffmpeg",
+    "-i", video_path,
+    "-vf", "fps=30",    # 30FPS
+    "-qscale:v", "1",  
+    os.path.join(input_dir, "frame_%04d.jpg") 
+]
+subprocess.run(cmd)
+
+# model path
+
+model_path = "/DeepGlint-AI/MLCD-Seg/" # or use your local path
+mlcd_seg = AutoModel.from_pretrained(
+    model_path,
+    torch_dtype=torch.float16,
+    trust_remote_code=True
+).cuda()
+tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
+
+
+# read jpgs
+image_files = sorted([f for f in os.listdir(input_dir) if f.endswith(('.jpg', '.png', '.jpeg'))])
+
+for idx, filename in enumerate(image_files, start=1):
+
+    src_path = os.path.join(input_dir, filename)
+    seg_img = Image.open(src_path).convert('RGB')
+
+    seg_prompt = "This <video> depicts a group of people dancing.\nCould you provide a segmentation mask for the man in pink suit?"
+    pred_mask = mlcd_seg.predict_forward(seg_img, seg_prompt, tokenizer, force_seg=True)
+
+    # Mask visualization
+    pred_mask = pred_mask.squeeze(0).cpu()
+    pred_mask = (pred_mask > 0.5).float()
+    img_tensor = transforms.ToTensor()(seg_img)
+    alpha = 0.2  # 20% transparency
+    red_mask = torch.tensor([0.0, 1.0, 0.0]).view(3, 1, 1).to(img_tensor.device)  # green mask
+    black_bg = torch.zeros_like(img_tensor)  # black background
+    masked_area = red_mask * alpha + img_tensor * (1 - alpha)
+    background = black_bg * alpha + img_tensor * (1 - alpha)
+    combined = torch.where(pred_mask.unsqueeze(0).bool(), masked_area, background)
+    combined = combined.cpu()  # [3, H, W], CPU
+
+    # Save masked jpgs
+    new_name = f"{idx:04d}{os.path.splitext(filename)[1]}"
+    dst_path = os.path.join(output_dir, new_name)
+    transforms.ToPILImage()(combined.clamp(0, 1)).save(dst_path)
+
+cmd = [
+    "ffmpeg",
+    "-y",  
+    "-framerate", str(30),  # fps
+    "-i", os.path.join(output_dir, "%04d.jpg"), 
+    "-c:v", "libx264",
+    "-crf", str(23), 
+    "-pix_fmt", "yuv420p", 
+    "-vf", "fps=" + str(23), 
+    "updownfunk_mask.mp4"  # output video
+]
+# jpgs -> mp4    
+subprocess.run(cmd, check=True)
+```
+
 If you want to use this code measurement dataset (e.g. refcoco), then you need to use the following method
 ```python
 from transformers import AutoModel, AutoTokenizer