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import csv
import gradio as gr
import glob
import pprint as pp
from sys import excepthook
from re import T
from urllib.parse import parse_qs, urlparse
import clip
import numpy as np
import requests
import torch
from sklearn.utils.extmath import softmax
import io
from IPython.display import Image, display
from PIL import Image, ImageFont
import os
import cv2
import torch
import glob
# Model
def predict(text,img):
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
img = cv2.imread(img)
results = model(img1)
dirpath = tempfile.mkdtemp()
results.crop(save_dir=dirpath)
path= dirpath+'/crops/**/*.jpg'
txtfiles = []
for file in glob.glob(path):
txtfiles.append(file)
import ipyplot
from PIL import Image
l = []
#keyList = list(range(len(txtfiles)))
for filename in glob.glob(path):
foo = Image.open(filename).convert('RGB')
#resized_image = foo.resize((250,250))
l.append(foo)
device = "cuda" if torch.cuda.is_available() else "cpu"
model, preprocess = clip.load("ViT-B/32", device=device)
images = torch.stack([preprocess(im) for im in l]).to(device)
with torch.no_grad():
image_features = model.encode_image(images)
image_features /= image_features.norm(dim=-1, keepdim=True)
image_features.cpu().numpy()
image_mean = torch.tensor([0.48145466, 0.4578275, 0.40821073]).cuda()
image_std = torch.tensor([0.26862954, 0.26130258, 0.27577711]).cuda()
images = [preprocess(im) for im in l]
image_input = torch.tensor(np.stack(images)).cuda()
image_input -= image_mean[:, None, None]
image_input /= image_std[:, None, None]
with torch.no_grad():
image_features = model.encode_image(image_input).float()
image_features /= image_features.norm(dim=-1, keepdim=True)
def get_top_N_semantic_similarity(similarity_list,N):
results = zip(range(len(similarity_list)), similarity_list)
results = sorted(results, key=lambda x: x[1],reverse= True)
top_N_images = []
scores=[]
for index,score in results[:N]:
scores.append(score)
top_N_images.append(l[index])
return scores,top_N_images
search_query = "White car"
with torch.no_grad():
# Encode and normalize the description using CLIP
text_encoded = model.encode_text(clip.tokenize(search_query).to(device))
text_encoded /= text_encoded.norm(dim=-1, keepdim=True)
similarity = text_encoded.cpu().numpy() @ image_features.cpu().numpy().T
similarity = similarity[0]
scores,imgs= get_top_N_semantic_similarity(similarity,N=1)
#print ("scores ",scores)
#ipyplot.plot_images(imgs,img_width=350)
return imgs
#text = gr.inputs.Textbox(lines=5, label="Context")
#img = gr.inputs.Image()
gr_interface = gr.Interface(fn=predict,["img", gr.inputs.Textbox(lines=1, label="Context")], outputs="image", title='Search inside image').launch(); |