Question

️ Image Caption Generator using BLIP + PiCamera on Raspberry Pi

Turn your Raspberry Pi into a smart image captioning device using BLIP, an AI model that generates natural language descriptions of images.

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Overview

In this project, you will:

• Capture images using the PiCamera

• Use the BLIP (Bootstrapped Language Image Pretraining) model to generate captions

• Display or store the generated captions

• Optionally build a simple web interface with Gradio

This project works best with Raspberry Pi 4 or 5 and requires an internet connection (for downloading models or using APIs).

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Requirements

• Raspberry Pi 4 or 5 (Pi 3 will struggle with BLIP model inference)

• Raspberry Pi OS 64-bit (updated)

• PiCamera or USB camera

• Python 3.9+

• pip and virtualenv

• Git

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Step 1: Install System Dependencies

sudo apt update && sudo apt upgrade -y
sudo apt install python3-pip python3-venv libjpeg-dev libopenjp2-7-dev

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Step 2: Set Up Python Environment

mkdir ~/blip-caption
cd ~/blip-caption
python3 -m venv venv
source venv/bin/activate
pip install torch torchvision

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Step 3: Install HuggingFace Transformers and BLIP

pip install transformers timm pillow

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Step 4: Capture Image with PiCamera

If you're using the legacy PiCamera:

pip install picamera

And use this code to capture an image:

from picamera import PiCamera
from time import sleep

camera = PiCamera()
camera.start_preview()
sleep(2)
camera.capture('image.jpg')
camera.stop_preview()

For USB camera, use OpenCV:

pip install opencv-python

import cv2
cap = cv2.VideoCapture(0)
ret, frame = cap.read()
cv2.imwrite('image.jpg', frame)
cap.release()

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Step 5: Generate Caption Using BLIP

from transformers import BlipProcessor, BlipForConditionalGeneration
from PIL import Image
import torch

processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")

raw_image = Image.open("image.jpg").convert('RGB')
inputs = processor(raw_image, return_tensors="pt")
out = model.generate(**inputs)
caption = processor.decode(out[0], skip_special_tokens=True)
print("Caption:", caption)

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Optional: Add Gradio Interface

pip install gradio

import gradio as gr

def caption_image(image):
    inputs = processor(image, return_tensors="pt")
    out = model.generate(**inputs)
    return processor.decode(out[0], skip_special_tokens=True)

gr.Interface(fn=caption_image, inputs="image", outputs="text").launch()

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✅ Conclusion

You've built a working image caption generator using Raspberry Pi, PiCamera, and the BLIP AI model. You can now:

• Add automatic uploads or email integration

• Extend it into a surveillance or accessibility tool

• Use it in photo archiving projects

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