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from fastai.vision.all import *
import gradio as gr
#to use load_learner() in windows:
# import pathlib
# temp = pathlib.PosixPath
# pathlib.PosixPath = pathlib.WindowsPath
cap_labels = (
'2S19 Msta artillery',
'BM-21 Grad artillery',
'BMP-2 vehicle',
'BTR-80 vehicle',
'Bayraktar TB2 UVAC drone',
'CH-5 Rainbow UVAC drone',
'G6 Rhino artillery',
'Hermes 900 drone',
'Heron TP drone',
'Humvee vehicle',
'LAV-25 vehicle',
'Leopard 2 tank',
'M1 Abrams tank',
'M109 artillery',
'M113 vehicle',
'M270 MLRS artillery',
'MQ-9 Reaper UVAC drone',
'MRAP vehicle',
'RQ-4 Global Hawk UVAC drone',
'T-72 tank',
'Type 99 tank',
'smerch artillery'
)
model = load_learner('models/ARMOR-classifier-v4.pkl')
def recognize_image(image):
pred, idx, probs = model.predict(image) #predict() returns category, it's index, probablity of all catg.
# print(pred)
return dict(zip(cap_labels, map(float, probs))) # for all categories
#input output gradio formatting set:
image = gr.inputs.Image(shape=(192,192))
label = gr.outputs.Label(num_top_classes=5)
#Answeres: bm21, humvee, t72, leopard 2
examples = [
'test_images/unknown_00.jpeg',
'test_images/unknown_01.jpg',
'test_images/unknown_02.jpg',
'test_images/unknown_03.webp',
'test_images/unknown_04.jpg'
]
#interface with i/o
iface = gr.Interface(
fn=recognize_image,
inputs=image,
outputs=label,
examples=examples,
title="A.R.M.O.R - Armament Models Recognizer",
description="A comprehensive security measure image classification model that classifies (for now) 22 different types of common military armaments around the world posing threat to civilians on land.<br>Tags: Computer Vision. Deep Learning, CNN, PyTorch."
)
iface.launch(inline=False) # share=True for colab |