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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
import io
from IPython.display import Image, display
from PIL import Image, ImageFont
import os
import cv2
import torch
import glob
# Model
def predict(img,text):
import tempfile
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
results = model(img)
dirpath = tempfile.mkdtemp()
results.crop(save_dir=dirpath)
path= dirpath+'/crops/**/*.jpg'
txtfiles = []
for file in glob.glob(path):
txtfiles.append(file)
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])
image_std = torch.tensor([0.26862954, 0.26130258, 0.27577711])
images = [preprocess(im) for im in l]
image_input = torch.tensor(np.stack(images))
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 = text
with torch.no_grad():
# Encode and normalize the description using CLIP
text_encoded = model.encode_text(clip.tokenize(text).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[0]
#text = gr.inputs.Textbox(lines=1, label="Text query", placeholder="Introduce the search text...",)
#img = gr.inputs.Image()
#img = "image"
gr.Interface(predict, ["image", gr.inputs.Textbox(lines=1, label="Text query", placeholder="Type here...",)], outputs="image", title="Crop-CLIP", description ="Search subjects/objects in an image using simple text description and get cropped results.This is done by combining Object detection Yolov5 and OpenAI's CLIP model.").launch();
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