clip_component.py

import cv2
import torch
import os
from PIL import Image
import clip

similarity_threshold = 22.00
def get_token_from_clip(image):
    text_inputs = ["apple", "banana", "cereal", "milk", "lemon", "orange",  "salad", "juice", "chicken", "bread"]
    text_tokens = clip.tokenize(text_inputs)

    device = "cpu"
    model, preprocess = clip.load("ViT-B/32")

    print("device: ", device)

    text_features = model.encode_text(text_tokens).float()
    text_features /= text_features.norm(dim=-1, keepdim=True)

    image_pil = Image.fromarray(image.astype('uint8'))
    image_input = preprocess(image_pil).unsqueeze(0).to(device)  # Add batch dimension

    with torch.no_grad():
        image_feature = model.encode_image(image_input)
        image_feature /= image_feature.norm(dim=-1, keepdim=True)

    with torch.no_grad():
        similarity = text_features.cpu().numpy() @ image_feature.cpu().numpy().T

    results = []


    for i in range(similarity.shape[0]):
        similarity_num = (100.0 * similarity[i][0])
        text_input = text_inputs[i]
        results.append({"text_input": text_input, "similarity": similarity_num})
        # print(similarity_num)

    results.sort(key=lambda x: x["similarity"], reverse=True)
    # Print the caption for each text input along with their similarity scores

    detect_food = ""
    for result in results:
        print(f"Text input: {result['text_input']}, Similarity: {result['similarity']:.2f}")
        if result['similarity'] >= similarity_threshold:
            detect_food += " " + text_input + " ."

    detect_food_list = detect_food[1:]
    return detect_food_list