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---
license: mit
---
<p align="center" width="100%">
<img src="https://i.postimg.cc/MKmyP9wH/new-banner.png" width="80%" height="80%">
</p>
<div>
<div align="center">
<a href='https://brianboli.com/' target='_blank'>Bo Li*<sup>1</sup></a> 
<a href='https://zhangyuanhan-ai.github.io/' target='_blank'>Yuanhan Zhang*<sup>,1</sup></a> 
<a href='https://cliangyu.com/' target='_blank'>Liangyu Chen*<sup>,1</sup></a> 
<a href='https://king159.github.io/' target='_blank'>Jinghao Wang*<sup>,1</sup></a> 
<a href='https://pufanyi.github.io/' target='_blank'>Fanyi Pu*<sup>,1</sup></a> 
</br>
<a href='https://jingkang50.github.io/' target='_blank'>Jingkang Yang<sup>1</sup></a> 
<a href='https://chunyuan.li/' target='_blank'>Chunyuan Li<sup>2</sup></a> 
<a href='https://liuziwei7.github.io/' target='_blank'>Ziwei Liu<sup>1</sup></a>
</div>
<div>
<div align="center">
<sup>1</sup>S-Lab, Nanyang Technological University 
<sup>2</sup>Microsoft Research, Redmond
</div>
-----------------
[](https://hits.seeyoufarm.com)
An example of using this model to run on your video. Please first clone [Otter](https://github.com/Luodian/Otter) to your local disk. Place following script inside the `Otter` folder to make sure it has the access to `otter/modeling_otter.py`.
```python
import mimetypes
import os
from typing import Union
import cv2
import requests
import torch
import transformers
from PIL import Image
from otter.modeling_otter import OtterForConditionalGeneration
# Disable warnings
requests.packages.urllib3.disable_warnings()
# ------------------- Utility Functions -------------------
def get_content_type(file_path):
content_type, _ = mimetypes.guess_type(file_path)
return content_type
# ------------------- Image and Video Handling Functions -------------------
def extract_frames(video_path, num_frames=128):
video = cv2.VideoCapture(video_path)
total_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
frame_step = total_frames // num_frames
frames = []
for i in range(num_frames):
video.set(cv2.CAP_PROP_POS_FRAMES, i * frame_step)
ret, frame = video.read()
if ret:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame = Image.fromarray(frame).convert("RGB")
frames.append(frame)
video.release()
return frames
def get_image(url: str) -> Union[Image.Image, list]:
if "://" not in url: # Local file
content_type = get_content_type(url)
else: # Remote URL
content_type = requests.head(url, stream=True, verify=False).headers.get(
"Content-Type"
)
if "image" in content_type:
if "://" not in url: # Local file
return Image.open(url)
else: # Remote URL
return Image.open(requests.get(url, stream=True, verify=False).raw)
elif "video" in content_type:
video_path = "temp_video.mp4"
if "://" not in url: # Local file
video_path = url
else: # Remote URL
with open(video_path, "wb") as f:
f.write(requests.get(url, stream=True, verify=False).content)
frames = extract_frames(video_path)
if "://" in url: # Only remove the temporary video file if it was downloaded
os.remove(video_path)
return frames
else:
raise ValueError("Invalid content type. Expected image or video.")
# ------------------- OTTER Prompt and Response Functions -------------------
def get_formatted_prompt(prompt: str) -> str:
return f"<image>User: {prompt} GPT:<answer>"
def get_response(input_data, prompt: str, model=None, image_processor=None) -> str:
if isinstance(input_data, Image.Image):
vision_x = (
image_processor.preprocess([input_data], return_tensors="pt")[
"pixel_values"
]
.unsqueeze(1)
.unsqueeze(0)
)
elif isinstance(input_data, list): # list of video frames
vision_x = (
image_processor.preprocess(input_data, return_tensors="pt")["pixel_values"]
.unsqueeze(1)
.unsqueeze(0)
)
else:
raise ValueError(
"Invalid input data. Expected PIL Image or list of video frames."
)
lang_x = model.text_tokenizer(
[
get_formatted_prompt(prompt),
],
return_tensors="pt",
)
generated_text = model.generate(
vision_x=vision_x.to(model.device),
lang_x=lang_x["input_ids"].to(model.device),
attention_mask=lang_x["attention_mask"].to(model.device),
max_new_tokens=512,
num_beams=3,
no_repeat_ngram_size=3,
)
parsed_output = (
model.text_tokenizer.decode(generated_text[0])
.split("<answer>")[-1]
.lstrip()
.rstrip()
.split("<|endofchunk|>")[0]
.lstrip()
.rstrip()
.lstrip('"')
.rstrip('"')
)
return parsed_output
if __name__ == "__main__":
# ------------------- Main Function -------------------
load_bit = "fp16"
if load_bit == "fp16":
precision = {"torch_dtype": torch.float16}
elif load_bit == "bf16":
precision = {"torch_dtype": torch.bfloat16}
elif load_bit == "fp32":
precision = {"torch_dtype": torch.float32}
# This model version is trained on MIMIC-IT DC dataset.
model = OtterForConditionalGeneration.from_pretrained(
"luodian/otter-9b-dc-hf", device_map="auto", **precision
)
model.text_tokenizer.padding_side = "left"
tokenizer = model.text_tokenizer
image_processor = transformers.CLIPImageProcessor()
model.eval()
while True:
video_url = "demo.mp4" # Replace with the path to your video file
frames_list = get_image(video_url)
prompts_input = input("Enter prompts (comma-separated): ")
prompts = [prompt.strip() for prompt in prompts_input.split(",")]
for prompt in prompts:
print(f"\nPrompt: {prompt}")
response = get_response(frames_list, prompt, model, image_processor)
print(f"Response: {response}")
if prompts_input.lower() == "quit":
break
``` |