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import os
import subprocess
import glob
import gradio as gr
from gradio.components import Gallery, Video, Textbox
from classificatsion_video_demo import process
def get_car_numbers(result_folder):
# change current working directory to binare/linux/mu gdfg
current_dir = os.getcwd()
os.chdir("binaries/linux/x86_64")
# construct and run the command
# command = "python ../../../python/setup.py build_ext --inplace -v"
# process = subprocess.Popen(command, shell=True, stdin=subprocess.PIPE)
command = "PYTHONPATH=$PYTHONPATH:.:../../../python LD_LIBRARY_PATH=.:$LD_LIBRARY_PATH python ../../../scripts/alpr/license_plate_recognizer.py --image ../../../"+result_folder+" --assets ../../../assets"
process = subprocess.Popen(command, shell=True, stdin=subprocess.PIPE)
# send newline character to standard input
process.communicate(input='\n'.encode())
# wait for the subprocess to exit
process.wait()
os.chdir(current_dir)
return 0
def predict(video_path):
"""
Gradio interface orqali yuklab olingan videodan problem framelarni ajratib olinadi va resultat sifatida Galleryga chiqariladi
:param video_path:
:return: file
"""
# Your image processing code here
# print(video_path)
_path = "assets/result_imgs"
images = glob.glob(f'{_path}/*.jpg')
for image in images:
os.remove(image)
problem, good, result = process(video_path)
images = glob.glob(f'{_path}/*.jpg')
get_car_numbers(result)
# # selected_images = [images[0],images[len(images)//2],images[-1]]
return problem+good, problem, good,images
# return 0, 0, 0, images
my_example = [
# ['video/vid_39_1284-2_1202.mp4']
# ['video/vid_39_1284-2_1174.mp4']
]
my_title = "Video Klassifikatsiya"
my_description = "128-4 qoida `To’xtash chizig’ini bosish` bo'yicha video analiz"
all_frame = Textbox(label="Umumiy framelar soni")
problem_frame = Textbox(label="Muammoli framelar soni")
good_frame = Textbox(label="Muammosiz framelar soni")
problem_frames = Gallery(label="Muammoli rasmlar", elem_id="gallery").style(
grid=[3], height="auto"
)
input_video = Video(label="Kiruvchi video") # Create input video component
gr.Interface(fn=predict,
inputs=input_video,
outputs=[all_frame,problem_frame,good_frame,problem_frames],
title=my_title,
examples=my_example,
description=my_description).launch()
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