process.py

import os
import shutil
import subprocess
from PIL import Image
import directories as Dir

def clearDir():

    #생성되는 파일들을 삭제함으로써 중복 출력 방지
    # 1. User input
    #   "/TextDetection/cookie/user_input.jpg" 삭제
    # 2. Cropped images
    #   TextDetection/runs/detect/ 하위 파일 전부 삭제
    # 3. Recognition Result
    #   /TextRecognition/log_demo_result.txt 삭제

    user_input = "/home/user/app"+ Dir.yolo_dir +"/cookie/user_input.jpg"
    crops = "/home/user/app" + Dir.yolo_dir + Dir.cropped_img_path
    result = "/home/user/app" + Dir.txt_file_path

    if os.path.isfile(user_input):
        os.remove(user_input)
        return print('Message: user input clear')
    else:
        print('Message: no user input')

    #cropped_img_path = "/runs/detect/" + cropped_img_folder_name
    if os.path.isdir(crops):
        shutil.rmtree(crops) #'/runs/detect/user_output'
        return print('Message: crop folder clear')
    else:
        print('Message: no crop folder')
    
    #txt_file_path = "/log_demo_result.txt"  
    if os.path.isfile(result):
        os.remove(result)
        return print('Message: result clear')
    else:
        print('Message: no result')

def textDetection(im):

    #change dir to yolo folder
    #yolo_dir = "/HCR/TextDetection/"
    #subprocess.call('pwd', shell=True)
    #subprocess.call('ls', shell=True)
    ## subprocess.call('sudo cd '+ Dir.yolo_dir, shell=True)

    #transfrom ndarray type to PIL type
    im = Image.fromarray(im)

    # save input image to cookie folder
    ## subprocess.call('cd cookie', shell=True)
    im.save("/home/user/app"+Dir.yolo_dir+"/cookie/user_input.jpg", 'JPEG')

    #yolo_dir = "/HCR/TextDetection/"
    ## subprocess.call('cd '+ Dir.yolo_dir, shell=True)

    # (Shell) run detect.py to get cropped word images
    subprocess.call(['python', "/home/user/app"+Dir.yolo_dir+'/detect.py',
                  #User Input Data : /text_detection/cookie
                 '--source',"/home/user/app"+Dir.yolo_dir+'/cookie',
                  #Text Detection Model : /runs/wordDetection/weights/best.pt
                 '--weights', "/home/user/app"+Dir.detect_model_dir,
                 '--conf','0.25',
                  #Output Images Save Directory /runs/detect/user_output
                 '--name', Dir.cropped_img_folder_name,
                 '--save-crop',
                 '--save-conf'])

    #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])

def textRearrange():
    ## subprocess.call('cd ' + Dir.DBSCAN_dir, shell=True)
    subprocess.call(['python', "/home/user/app"+Dir.DBSCAN_dir+'/DBSCAN.py'])

def textRecognition():
    #%cd /content/drive/MyDrive/KITA/Text/lmdb/deep-text-recognition-benchmark
    ## subprocess.call('cd '+Dir.recog_dir, shell=True)
    #!CUDA_VISIBLE_DEVICES=0 python3 demo.py --Transformation TPS --FeatureExtraction ResNet --SequenceModeling BiLSTM --Prediction Attn --image_folder /content/drive/MyDrive/KITA/Text/YOLO/runs/detect/youtube_data2/crops/word --saved_model /content/drive/MyDrive/KITA/Text/best_accuracy_s/best_accuracy_s.pth
    subprocess.call('CUDA_VISIBLE_DEVICES="" python3 '+ "/home/user/app"+ Dir.recog_dir +'/demo.py --Transformation TPS --FeatureExtraction ResNet --SequenceModeling BiLSTM --Prediction Attn --image_folder /home/user/app' + Dir.yolo_dir + Dir.cropped_img_path + '/crops/word --saved_model /home/user/app'+Dir.recog_model_dir, shell=True)

def getHcrResult(file_path):#*#
    texts = ""
    with open(file_path, 'r') as file:
        lines = file.readlines()
    for line in lines[3:]:
      line = line.replace("\t","*",1)
      line = line.replace(" ","*",1)
      parts = line.replace(" ","")
      parts2 = parts.split("*",2)
      #print(len(parts2))
      texts = texts +" "+ str(parts2[1:2])[2:-2]

    return texts