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from transformers import CLIPProcessor, CLIPModel
from PIL import Image
import torch
class IdentificationModel:
def __init__(self):
self.model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
self.processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.model.to(self.device)
def identify_objects(self, image_path, text_descriptions):
# Load image
image = Image.open(image_path)
# Prepare inputs
inputs = self.processor(text=text_descriptions, images=image, return_tensors="pt", padding=True)
inputs = {k: v.to(self.device) for k, v in inputs.items()}
# Run inference
with torch.no_grad():
outputs = self.model(**inputs)
# Get logits and compute probabilities
logits_per_image = outputs.logits_per_image # this is the image-text similarity score
probs = logits_per_image.softmax(dim=1) # convert logits to probabilities
# Find the detection with the maximum probability
max_prob, max_idx = torch.max(probs[0], dim=0)
# Prepare the result for the highest probability detection
detection=[]
detection.append({
'description': text_descriptions[max_idx],
'probability': float(max_prob)
})
return detection
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