To process and analyze video files using LangChain, you can follow some best practices and examples from the LangChain cookbook and related resources. Here's a comprehensive approach to accomplish video analysis and search:
First, you need to extract frames from the video, which can be done using a library like OpenCV:
import cv2
import os
def extract_frames(video_path, output_folder):
cap = cv2.VideoCapture(video_path)
frame_rate = cap.get(cv2.CAP_PROP_FPS)
success, frame = cap.read()
count = 0
while success:
cv2.imwrite(os.path.join(output_folder, f"frame_{count}.jpg"), frame)
success, frame = cap.read()
count += 1
cap.release()
Use a pre-trained model like ResNet to extract features from each frame. These features can be embedded into a vector space for similarity search:
import numpy as np
from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input
from tensorflow.keras.preprocessing import image
model = ResNet50(weights='imagenet', include_top=False)
def extract_features(img_path):
img = image.load_img(img_path, target_size=(224, 224))
img_data = image.img_to_array(img)
img_data = np.expand_dims(img_data, axis=0)
img_data = preprocess_input(img_data)
features = model.predict(img_data)
return features.flatten()
Store the extracted features in Weaviate, and use LangChain to manage and query the data:
import weaviate
from langchain.vectorstores import WeaviateVectorStore
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import Runnable
client = weaviate.Client("http://localhost:8080")
schema = {
"classes": [
{
"class": "Frame",
"properties": [
{"name": "frame_id", "dataType": ["int"]},
{"name": "features", "dataType": ["blob"]}
]
}
]
}
client.schema.create(schema)
def store_features(frame_id, features):
client.data_object.create({
"frame_id": frame_id,
"features": features.tolist()
}, "Frame")
output_folder = "path_to_extracted_frames"
for frame_file in os.listdir(output_folder):
frame_path = os.path.join(output_folder, frame_file)
features = extract_features(frame_path)
frame_id = int(frame_file.split('_')[1].split('.')[0])
store_features(frame_id, features)
query_vector = extract_features("path_to_query_frame.jpg")
result = client.query.get("Frame", ["frame_id"]).with_near_vector(query_vector).do()
print(result)
You can use LangChain's components to handle video captioning, as shown in this example from the LangChain cookbook:
import openai
from langchain.prompts import ChatPromptTemplate
from langchain.output_parsers.openai_tools import PydanticToolsParser
from langchain.language_models import OpenAI
video_path = "path_to_video.mp4"
with open(video_path, "rb") as video_file:
video_data = video_file.read()
prompt = ChatPromptTemplate.from_messages([
("system", "Caption the following video."),
("user", "{input}")
])
llm = OpenAI(model_name="text-davinci-002")
chain = prompt | llm | PydanticToolsParser()
response = chain.invoke({"input": video_data})
print(response)
- LangChain GitHub Repository
- LangChain Audio/Video Structured Extraction
- Video Captioning with LangChain
These steps provide a structured approach to processing, analyzing, and searching video files using LangChain and related tools. This workflow leverages the strengths of various components to provide comprehensive video analysis capabilities.