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import gradio as gr
import torch
import cv2
import time
from PIL import Image
# Images
torch.hub.download_url_to_file('https://cdn.pixabay.com/photo/2016/06/15/01/11/soccer-1457988_1280.jpg', 'soccer.jpg')
torch.hub.download_url_to_file('https://cdn.pixabay.com/photo/2016/11/21/14/31/vw-bus-1845719_1280.jpg', 'bus.jpg')
# Model
model = torch.hub.load('ultralytics/yolov3', 'yolov3') # or yolov3-spp, yolov3-tiny, custom
cam = cv2.VideoCapture(0)
def yolo(im, size=640):
g = (size / max(im.size)) # gain
im = im.resize((int(x * g) for x in im.size), Image.ANTIALIAS) # resize
results = model(im) # inference
results.render() # updates results.imgs with boxes and labels
return Image.fromarray(results.imgs[0])
#inputs = gr.inputs.Image(type='pil', label="Original Image")
title = "Определение элементов"
description = "Находит на картинке элементы"
article = "тест"
examples = [['soccer.jpg'], ['bus.jpg']]
while True:
#inputs=gr.inputs.Image(type="pil",source="webcam")
outputs = gr.outputs.Image(type="pil", label="Output Image")
#ret, inputs = cam.read()
img = cv2.imread("soccer.jpg")
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
inputs = Image.fromarray(img)
gr.Interface(yolo, inputs, outputs, title=title, description=description, article=article, examples=examples, theme="huggingface").launch(debug=True) |