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app.py
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app.py
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from flask import Flask,render_template,request,jsonify
from datetime import datetime
import time
import os,glob
import sys
import base64
import io
import pandas as pd
from io import BytesIO
from werkzeug.utils import secure_filename
from PIL import Image
sys.path.insert(1,'./')
from predict.predict_folder_richmodels import richmodels
from predict.predict_folder_pytorch import pytorch_predict
from predict.group_experiments import group
from predict.Catboost_predict import catboost_predict
from EXIF.exif_viewer import exif_viewer
from LSB_tool.stegano_LSB import decode_text
basedir = os.path.abspath(os.path.dirname(__file__))
app = Flask(__name__, template_folder='templates')
ALLOWED_EXTENSIONS = set(['png', 'jpg', 'JPG', 'PNG'])
def allowed_file(filename):
return '.' in filename and filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS
@app.route('/')
def home():
return render_template('index.html',result=2)
@app.route('/predict', methods=['POST'])
def predict():
time_start = time.time()
test_single_image = eval(request.form['test_single_image'])
folder = request.form['folder']
test_single_image = True if folder=="" else False
subset = request.form['subset']
device = request.form['device']
num_workers = int(request.form['num_workers'])
batch_size = int(request.form['batch_size'])
path0 = 'models_predictions/'+ subset
to_delete_files = glob.glob(os.path.join(path0, '*.csv'))
[(os.remove(f), to_delete_files.remove(f)) for f in to_delete_files]
def second_step(folder=folder, subset=subset, test_single_image=test_single_image, device=device, num_workers=num_workers, batch_size=batch_size, time_start=time_start):
if subset == '3Algorithms':
print('yes,I am here, entering prediction process for 3Algorithms')
richmodels(folder=folder, experiment='JRM', checkpoint='weights/rich_models/QF75_JRM_Y_ensemble_v7.mat',
test_single_image=test_single_image)
richmodels(folder=folder, experiment='DCTR', quality_factor=75,
checkpoint='weights/rich_models/QF75_DCTR_Y_ensemble_v7.mat',
test_single_image=test_single_image)
if not test_single_image:
richmodels(folder=folder, experiment='JRM', quality_factor=90,
checkpoint='weights/rich_models/QF90_JRM_Y_ensemble_v7.mat',
test_single_image=test_single_image)
richmodels(folder=folder, experiment='JRM', quality_factor=95,
checkpoint='weights/rich_models/QF95_JRM_Y_ensemble_v7.mat',
test_single_image=test_single_image)
richmodels(folder=folder, experiment='DCTR', quality_factor=90,
checkpoint='weights/rich_models/QF90_DCTR_Y_ensemble_v7.mat',
test_single_image=test_single_image)
richmodels(folder=folder, experiment='DCTR', quality_factor=95,
checkpoint='weights/rich_models/QF95_DCTR_Y_ensemble_v7.mat',
test_single_image=test_single_image)
pytorch_predict(folder=folder, model='efficientnet-b4', experiment='efficientnet_b4_NR', decoder='NR',
checkpoint='weights/efficientnet_b4_NR_mish/best-checkpoint-017epoch.bin',
test_single_image=test_single_image, device=device, num_workers=num_workers,
batch_size=batch_size)
pytorch_predict(folder=folder, model='efficientnet-b5', experiment='efficientnet_b5_NR', decoder='NR',
checkpoint='weights/efficientnet_b5_NR_mish/best-checkpoint-018epoch.bin',
test_single_image=test_single_image, device=device, num_workers=num_workers,
batch_size=batch_size)
pytorch_predict(folder=folder, model='mixnet_xl', experiment='mixnet_xL_NR_mish', decoder='NR',
checkpoint='weights/mixnet_xL_NR_mish/best-checkpoint-021epoch.bin',
test_single_image=test_single_image, surgery=1, device=device, num_workers=num_workers,
batch_size=batch_size)
pytorch_predict(folder=folder, model='efficientnet-b2', experiment='efficientnet_b2_NR', decoder='NR',
checkpoint='weights/efficientnet_b2/NR/best-checkpoint-028epoch.bin',
test_single_image=test_single_image, device=device, num_workers=num_workers,
batch_size=batch_size)
pytorch_predict(folder=folder, model='efficientnet-b2', experiment='efficientnet_b2_R',
checkpoint='weights/efficientnet_b2/R/best-checkpoint-028epoch.bin',
test_single_image=test_single_image, device=device, num_workers=num_workers,
batch_size=batch_size)
pytorch_predict(folder=folder, model='mixnet_s', experiment='mixnet_S_R_seed0',
checkpoint='weights/mixnet_S/R_seed0/best-checkpoint-033epoch.bin',
test_time_augmentation=1, test_single_image=test_single_image, device=device,
num_workers=num_workers, batch_size=batch_size)
pytorch_predict(folder=folder, model='mixnet_s', experiment='mixnet_S_R_seed1',
checkpoint='weights/mixnet_S/R_seed1/best-checkpoint-035epoch.bin',
test_time_augmentation=1, test_single_image=test_single_image, device=device,
num_workers=num_workers, batch_size=batch_size)
pytorch_predict(folder=folder, model='mixnet_s', experiment='mixnet_S_R_seed2',
checkpoint='weights/mixnet_S/R_seed2/best-checkpoint-036epoch.bin',
test_time_augmentation=1, test_single_image=test_single_image, device=device,
num_workers=num_workers, batch_size=batch_size)
pytorch_predict(folder=folder, model='mixnet_s', experiment='mixnet_S_R_seed3',
checkpoint='weights/mixnet_S/R_seed3/best-checkpoint-038epoch.bin',
test_time_augmentation=1, test_single_image=test_single_image, device=device,
num_workers=num_workers, batch_size=batch_size)
pytorch_predict(folder=folder, model='mixnet_s', experiment='mixnet_S_R_seed4',
checkpoint='weights/mixnet_S/R_seed4/best-checkpoint-035epoch.bin',
test_time_augmentation=1, test_single_image=test_single_image, device=device,
num_workers=num_workers, batch_size=batch_size)
pytorch_predict(folder=folder, model='mixnet_s', experiment='mixnet_S_NR', decoder='NR',
checkpoint='weights/mixnet_S/NR/best-checkpoint-058epoch.bin', test_time_augmentation=1,
test_single_image=test_single_image, device=device, num_workers=num_workers,
batch_size=batch_size)
id = '0000'
group(id=id, test_single_image=test_single_image)
parent_dir = basedir + '/models_predictions/' + subset + '/'
result = catboost_predict(zoo_file=parent_dir + 'probabilities_zoo_Test_' + id + '.csv',
test_single_image=test_single_image)
else:
print('yes,I am here, entering prediction process for nsf5')
richmodels(folder=folder, experiment='JRM',
checkpoint='weights/nsf5/rich_models_for_nsf5/JRM_Y_ensemble_v7.mat', subset='for_nsf5',
test_single_image=test_single_image)
richmodels(folder=folder, experiment='DCTR',
checkpoint='weights/nsf5/rich_models_for_nsf5/DCTR_Y_ensemble_v7.mat', subset='for_nsf5',
test_single_image=test_single_image)
pytorch_predict(folder=folder, model='efficientnet-b4', experiment='efficientnet_b4',
checkpoint='weights/nsf5/efficientnet_b4/best-checkpoint-001epoch.bin', subset='for_nsf5',
test_single_image=test_single_image, device=device, num_workers=num_workers,
batch_size=batch_size)
pytorch_predict(folder=folder, model='efficientnet-b5', experiment='efficientnet_b5',
checkpoint='weights/nsf5/efficientnet_b5/best-checkpoint-003epoch.bin', subset='for_nsf5',
test_single_image=test_single_image, device=device, num_workers=num_workers,
batch_size=batch_size)
pytorch_predict(folder=folder, model='mixnet_xl', experiment='mixnet_xl',
checkpoint='weights/nsf5/mixnet_xL_R/last-checkpoint.bin',
test_single_image=test_single_image, surgery=1, device=device, num_workers=num_workers,
subset='for_nsf5',
batch_size=batch_size)
pytorch_predict(folder=folder, model='efficientnet-b2', experiment='efficientnet_b2',
checkpoint='weights/nsf5/efficientnet_b2/best-checkpoint-003epoch.bin', subset='for_nsf5',
test_single_image=test_single_image, device=device, num_workers=num_workers,
batch_size=batch_size)
pytorch_predict(folder=folder, model='mixnet_s', experiment='mixnet_S',
checkpoint='weights/nsf5/mixnet_s_R/best-checkpoint-000epoch.bin',
test_time_augmentation=1, test_single_image=test_single_image, device=device,
num_workers=num_workers, batch_size=batch_size, subset='for_nsf5')
id = '0000'
group(id=id, test_single_image=test_single_image, subset=subset)
parent_dir = basedir + '/models_predictions/' + subset + '/'
result = catboost_predict(zoo_file=parent_dir + 'probabilities_zoo_Test_' + id + '.csv',
test_single_image=test_single_image, subset=subset,
weights_path='weights/nsf5/catboost/best_catboost.cmb')
time_end = time.time()
running_time = time_end - time_start
return result, running_time
if test_single_image:
files = request.files.getlist('file')
image_size =len(files)
number = 0
img = list(range(2))
# img[1] = None if len(files) == 1 else 1
results = []
running_time = 0
exifs = []
file_names = []
for f in files:
if not (f and allowed_file(f.filename)):
return jsonify({"error": 1001, "msg": "Please check the format of uploaded images"})
file_name = secure_filename(f.filename)
filename = datetime.now().strftime("%Y%m%d%H%M%S") + "." + "jpg"
print('new filename is: ', filename)
file_path = basedir +"/uploaded_images/"
os.makedirs(file_path, exist_ok=True)
f.save(file_path+filename)
folder = file_path + filename
# to show the image
byteImgIO = io.BytesIO()
byteImg = Image.open(folder)
byteImg.save(byteImgIO, "JPEG")
print(number)
img[number] = base64.b64encode(byteImgIO.getvalue()).decode('ascii')
exif = exif_viewer(folder)
result, running_time_temp = second_step(folder=folder)
print(result)
number +=1
results.append(result)
exifs.append(exif)
file_names.append(file_name)
running_time += running_time_temp
path0 = 'models_predictions/' + subset
to_delete_files = glob.glob(os.path.join(path0, '*.csv'))
[(os.remove(f), to_delete_files.remove(f)) for f in to_delete_files]
if len(files)==2:
return render_template('index_with_multiple_images.html',img1=img[0],img2 = img[1], exif=exifs,
message='We processed %d images, time cost : %.3f sec' % (image_size, running_time),
prediction_text='Detected result for {} is {}'.format(file_names,
results))
else:
return render_template('index_with_images.html', img1=img[0], exif=exifs,
message='We processed %d images, time cost : %.3f sec' % (image_size, running_time),
prediction_text='Detected result for {} is {}'.format(file_names,
result))
else:
DATA_ROOT_PATH = os.environ.get('DATA_ROOT_PATH')
filename = DATA_ROOT_PATH+'/'+subset+'Test_qf_dicts.p'
try:
os.remove(filename)
except OSError:
pass
exif = ""
image_size = len(os.listdir(os.path.join(DATA_ROOT_PATH,folder)))
print('We are processing {} images'.format(image_size))
result, running_time = second_step()
return render_template('index.html', exif=exif,
message='We processed %d images, time cost : %.3f sec' % (image_size, running_time),
prediction_text='Detection result is saved.')
@app.route('/exif', methods=['POST'])
def exif():
test_single_image = eval(request.form['test_single_image'])
folder = request.form['folder']
test_single_image = True if folder == "" else False
if test_single_image:
files = request.files.getlist('file')
number = 0
img = list(range(2))
file_names = []
predicts = []
for f in files:
# if not (f and allowed_file(f.filename)):
# return jsonify({"error": 1001, "msg": "Please check the format of uploaded images"})
file_name = secure_filename(f.filename)
filename = datetime.now().strftime("%Y%m%d%H%M%S") + "." + "jpg"
print('new filename is: ', filename)
file_path = basedir + "/uploaded_images/"
os.makedirs(file_path, exist_ok=True)
folder = file_path + filename
f.save(folder)
message = exif_viewer(folder)
# to show the image
byteImgIO = io.BytesIO()
byteImg = Image.open(folder)
byteImg.save(byteImgIO, "JPEG")
img[number] = base64.b64encode(byteImgIO.getvalue()).decode('ascii')
number +=1
file_names.append(file_name)
predicts.append(message)
message = 'EXIF results for {}: '.format(file_names)
if len(files)==2:
return render_template('index_with_multiple_images.html', img1=img[0],img2=img[1],message=message, prediction_text=predicts)
else:
return render_template('index_with_images.html', img1=img[0],message=message,prediction_text = predicts)
else:
DATA_ROOT_PATH = os.environ.get('DATA_ROOT_PATH')
folder = os.path.join(DATA_ROOT_PATH, folder)
image_size = len(os.listdir(folder))
alllist = os.listdir(folder)
df = pd.DataFrame(columns=['name', 'EXIF'])
for file in alllist:
message = exif_viewer(os.path.join(folder, file))
df.loc[len(df.index)] = [file, message]
df.to_csv('final_results_for_EXIF.csv')
return render_template('index.html',
message='We processed %d images' % (image_size),
prediction_text='Decoded result is saved.')
@app.route('/lsb', methods=['POST'])
def lsb():
test_single_image = eval(request.form['test_single_image'])
folder = request.form['folder']
test_single_image = True if folder == "" else False
if test_single_image:
files = request.files.getlist('file')
number=0
img=list(range(2))
# img[1] = None if len(files) ==1 else 1
messages = []
for f in files:
if not (f and allowed_file(f.filename)):
return jsonify({"error": 1001, "msg": "Please check the format of uploaded images"})
file_name = secure_filename(f.filename)
filename = datetime.now().strftime("%Y%m%d%H%M%S") + "." + "png"
print('new filename is: ', filename)
file_path = basedir + "/uploaded_images/"
os.makedirs(file_path, exist_ok=True)
f.save(file_path + filename)
folder = file_path + filename
message = decode_text(folder)
if message == "" or len(message) > 50:
message = 'Result for {} is :no LSB steganography detected'.format(file_name)
else:
message = 'Result for {} the decoded message: '.format(file_name) +message
print(message)
# to show the image
byteImgIO = io.BytesIO()
byteImg = Image.open(folder)
byteImg.save(byteImgIO, "PNG")
img[number] = base64.b64encode(byteImgIO.getvalue()).decode('ascii')
messages.append(message)
number+=1
# imgs.append(img)
if len(files) == 2:
return render_template('index_with_multiple_images.html', img1=img[0], img2=img[1], message=messages)
else:
return render_template('index_with_images.html', img1=img[0], message=messages)
else:
DATA_ROOT_PATH = os.environ.get('DATA_ROOT_PATH')
folder = os.path.join(DATA_ROOT_PATH,folder)
image_size = len(os.listdir(folder))
alllist = os.listdir(folder)
df = pd.DataFrame(columns=['name', 'decoded message'])
for file in alllist:
message = decode_text(os.path.join(folder,file))
df.loc[len(df.index)] = [file,message]
df.to_csv('final_results_for_lsb.csv')
return render_template('index.html',
message='We processed %d images' % (image_size),
prediction_text='Decoded result is saved.')
def main():
app.run(host='0.0.0.0',port=8000,debug=True)
if __name__ == "__main__":
main()