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import clip
import torch
import gradio as gr
import torchvision.transforms as T
from PIL import Image
try:
from torchvision.transforms import InterpolationMode
BICUBIC = InterpolationMode.BICUBIC
except ImportError:
BICUBIC = Image.BICUBIC
import warnings
warnings.filterwarnings("ignore")
device = "cuda" if torch.cuda.is_available() else "cpu"
model, preprocess = clip.load('ViT-L/14@336px')
model.to(device)
def zeroshot_detection(Press_Clear_Dont_Stack_Image):
inp = Press_Clear_Dont_Stack_Image
captions = "photo of a guardrail, no guardrail in the photo" #CHANGE THIS IF YOU WANT TO CHANGE THE PREDICTION: separate by commas
captions = captions.split(',')
caption = clip.tokenize(captions).to(device)
image = preprocess(inp).unsqueeze(0).to(device)
with torch.no_grad():
image_features = model.encode_image(image)
text_features = model.encode_text(caption)
image_features /= image_features.norm(dim=-1, keepdim=True)
text_features /= text_features.norm(dim=-1, keepdim=True)
similarity = (100.0 * image_features @ text_features.T).softmax(dim=-1)
values, indices = similarity[0].topk(len(captions))
return {captions[indices[i].item()]: float(values[i].item()) for i in range(len(values))}
gr.Interface(fn=zeroshot_detection,
inputs=[gr.Image(type="pil")],
outputs=gr.Label(num_top_classes=1)).launch()
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