import os,sys
import magic
from PIL import Image, ImageChops, ImageEnhance


def ela(filename, output_path):
  print("****ELA is starting****")
  if magic.from_file(filename, mime=True) == "image/jpeg":
    # set tmp_image's quality_level to be resaved
    quality_level = 80
    # get fileRealName,.postfix
    (filerealname, extension) = os.path.splitext(os.path.basename(filename))
    # set tmp_image & ela_image path
    tmp_path = os.path.join(output_path,filerealname+"_tmp.jpg")
    ela_path = os.path.join(output_path,filerealname+"_ela.jpg")

    # resave image
    image = Image.open(filename)
    image.save(tmp_path, 'JPEG', quality=quality_level)


    tmp_image = Image.open(tmp_path)
    # return abs of difference
    ela_image = ImageChops.difference(image, tmp_image)
    # return (min,max) two-truples with RGB 3 elements, eg. ((0,255),(0,255),(0,255))
    extrema = ela_image.getextrema()
    # get the max of RGB max
    max_diff = max([ex[1] for ex in extrema])
    # set scale to enhance
    scale = 255/max_diff
    # 'Brightness' indicates we will brignten img
    # 'enhance' indicates the scale of brightness
    # An enhancement factor of 0.0 gives a black image. A factor of 1.0 gives the original image.
    ela_image = ImageEnhance.Brightness(ela_image).enhance(scale)

    ela_image.save(ela_path)
    os.remove(tmp_path)  # if remove this code, image will be resaved as tem_image and won't be removed.
    print("****ELA has been completed****")
  else:
    print("ELA works only with JPEG")

if __name__ == "__main__":
  filename = "./img/webOriginalImg.jpg"
  output_path = "./img"
  ela(filename, output_path)

# use OpenCV to detect face from images & save them

import cv2
import os
import time

resize_x = 256
resize_y = 256
cantFindFaceImgs = []

# Detect face rects
def detect(img, cascade, list):
    rects = cascade.detectMultiScale(img, scaleFactor = 1.3, minNeighbors = 4,
                                     flags = cv2.CASCADE_SCALE_IMAGE)
    if len(rects) == 0:
        print("I haven't found a face in %s"%(list))
        cantFindFaceImgs.append(list)
        return []
    rects[:, 2:] += rects[:, :2]
    return rects

if __name__ == '__main__':
    start_time =time.clock()
    # OpenCV Classifier
    cascade = cv2.CascadeClassifier("E:\Program Files\Python\Python36\Lib\site-packages\opencv-master\data\haarcascades\haarcascade_frontalface_default.xml")
    original_path = 'D:/Celeba/devel'
    new_path = 'D:/Celeba_face/devel'
    # os.listdir show all the filename(including extension)
    imglist = os.listdir(original_path) 


    for list in imglist:
        img = cv2.imread(original_path+'/'+list)
        rects = detect(img, cascade, list)
        if len(rects) == 0:
            print(list)
        for x1, y1, x2, y2 in rects:
            face = img[y1:y2, x1:x2]
            resized_face = cv2.resize(face,(resize_x, resize_y))
            # Save new img, named as original name in new directory, then we can find which are not be detected 
            cv2.imwrite(new_path+'/CV_'+list, resized_face)

    end_time = time.clock()
    print("I haven't found a face in these images: %s"%(cantFindFaceImgs))
    print('Running time using OpenCV is: %s Seconds'%(end_time-start_time))

# use face-recognition to detect face from images & save them

from PIL import Image
import face_recognition
import os
import time

resize_x = 256
resize_y = 256
cantFindFaceImgs = []

# Detect face rects
def detect(img, new_path, list):
    image = face_recognition.load_image_file(img)
    face_locations = face_recognition.face_locations(image)
    if len(face_locations) == 0:
        print("I haven't found a face in %s"%(list))
        cantFindFaceImgs.append(list)
        return []
    for i,face_location in enumerate(face_locations):

        # Get the location of each face in this image
        top, right, bottom, left = face_location
        face_image = image[top:bottom, left:right]
        pil_image = Image.fromarray(face_image)
        resized_face = pil_image.resize((resize_x, resize_y))
        (filename, extension) = os.path.splitext(list)
        resized_face.save(new_path+'/FR_'+filename+'_'+str(i)+extension)

if __name__ == '__main__':
    start_time =time.clock()
    original_path = 'D:/Celeba/devel'
    new_path = 'D:/Celeba_face/devel'
    # os.listdir show all the filename(including extension)
    imglist = os.listdir(original_path) 

    for list in imglist:
        img = original_path+'/'+list
        detect(img, new_path, list)

    end_time = time.clock()
    print("I haven't found a face in these images: %s"%(cantFindFaceImgs))
    print('Running time using Face-recognition is: %s Seconds'%(end_time-start_time))

import os
import cv2
import time
import datetime

def framing():
    input_path = 'D:/test'
    output_path = 'D:/test_face'
    txt_path = output_path+'/log.txt'
    with open(txt_path, "a", encoding="utf-8") as fi:
        fi.write('\n AllVideosFullName \t Index \t Frame \t Picture\n')

    videos = os.listdir(input_path)
    videos.sort(key = lambda x: x[:-4])

    if len(videos) != 0:
        video_num = 0
        for each_video in videos:
            print('Video {} is running ...'.format(video_num))
            each_video_input = input_path+'/'+str(each_video)
            each_video_output_path = output_path+'/'+str(each_video[:-4])
            if not os.path.exists(each_video_output_path):
                os.mkdir(each_video_output_path)
            
            capture = cv2.VideoCapture(each_video_input)
            if capture.isOpened():
                real = True
            else:
                real = False

            frame_step = 10
            frame_num = 0
            picture_num = 0

            while real:
                real, frame = capture.read()
                if(frame_num % frame_step == 0):
                    cv2.imwrite(each_video_output_path+'/Frame'+str(frame_num)+'_Pic'+str(picture_num)+'.jpg',frame)
                    picture_num += 1
                frame_num += 1
                cv2.waitKey(1)

            video_num += 1
            with open(txt_path, "a", encoding="utf-8") as fi:
                fi.write('{} \t {} \t {} \t {}\n'.format(each_video[:-4], video_num, frame_num, picture_num ))
            capture.release()

        print('Running log has been saved here: '+txt_path)

    else:
        print('Empty Directory')


if __name__ == '__main__':
    start_time = time.clock()
    framing()
    end_time = time.clock()
    delta_time = datetime.timedelta(seconds = (end_time-start_time))
    print('Running time is: %s '%(delta_time))