Esempio n. 1
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    def bot_init(self):

        ############################
        # REQUIRED: LOGIN DETAILS! #
        ############################

        self.config['api_key'] = os.environ['TWITTER_API_KEY']
        self.config['api_secret'] = os.environ['TWITTER_API_SECRET']
        self.config['access_key'] = os.environ['TWITTER_ACCESS_KEY']
        self.config['access_secret'] = os.environ['TWITTER_ACCESS_SECRET']

        ######################################
        # SEMI-OPTIONAL: OTHER CONFIG STUFF! #
        ######################################

        # how often to tweet, in seconds
        self.config['tweet_interval'] = 30 * 60     # default: 30 minutes

        # only include bot followers (and original tweeter) in @-replies
        self.config['reply_followers_only'] = True

        # fav any tweets that mention this bot?
        self.config['autofav_mentions'] = False

        # fav any tweets containing these keywords?
        self.config['autofav_keywords'] = []

        # follow back all followers?
        self.config['autofollow'] = True

        ###########################################
        # CUSTOM: your bot's own state variables! #
        ###########################################

        self.register_custom_handler(openshift_wake_up, 60 * 60 * 12)

        self.face_regions = partial(
            core.face_regions,
            core.load_face_detector())

        self.face_recognizer = FaceRecognizer()

        self.store = Store()
Esempio n. 2
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import cv2
import torch
from facerec import FaceRecognizer, image_to_tensor, PersonType, PersonInfo
from utils.images import hconcat_resize_min, get_int_rect

import imutils
import matplotlib.pyplot as plt

VIDEO_PATH = "/media/popikeyshen/30c5a789-895a-4cc2-910a-3c678cc563d7/deepfake/train_sample_videos/alvgwypubw.mp4"

### init torch device
device = torch.device('cuda:0')

### init face detector
distance_threshold = 1.0
recognizer = FaceRecognizer(device=device,
                            distance_threshold=distance_threshold)

### init video capture
cap = cv2.VideoCapture(VIDEO_PATH)
while (1):

    ### read and resize frame
    ret, im = cap.read()
    im = imutils.resize(im, height=800)

    ### detect faces
    embeddings, boxes = recognizer.get_faces_from_frame(image_to_tensor(im))
    for box in boxes:

        ### draw box around face
        min_x, min_y, max_x, max_y = get_int_rect(box)
Esempio n. 3
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class Autofriend(TwitterBot):

    def bot_init(self):

        ############################
        # REQUIRED: LOGIN DETAILS! #
        ############################

        self.config['api_key'] = os.environ['TWITTER_API_KEY']
        self.config['api_secret'] = os.environ['TWITTER_API_SECRET']
        self.config['access_key'] = os.environ['TWITTER_ACCESS_KEY']
        self.config['access_secret'] = os.environ['TWITTER_ACCESS_SECRET']

        ######################################
        # SEMI-OPTIONAL: OTHER CONFIG STUFF! #
        ######################################

        # how often to tweet, in seconds
        self.config['tweet_interval'] = 30 * 60     # default: 30 minutes

        # only include bot followers (and original tweeter) in @-replies
        self.config['reply_followers_only'] = True

        # fav any tweets that mention this bot?
        self.config['autofav_mentions'] = False

        # fav any tweets containing these keywords?
        self.config['autofav_keywords'] = []

        # follow back all followers?
        self.config['autofollow'] = True

        ###########################################
        # CUSTOM: your bot's own state variables! #
        ###########################################

        self.register_custom_handler(openshift_wake_up, 60 * 60 * 12)

        self.face_regions = partial(
            core.face_regions,
            core.load_face_detector())

        self.face_recognizer = FaceRecognizer()

        self.store = Store()

    def get_confidence(self):
        return float(os.environ.get('AUTOFRIEND_CONFIDENCE') or 50)

    def on_scheduled_tweet(self):
        pass

    def on_follow(self, follower_id):

        TwitterBot.on_follow(self, follower_id)

        try:
            self.store.save_friend((follower_id,))
        except pg.IntegrityError:
            # aborting is harmless, most likely an unfollow/refollow
            self.log("tried to add duplicate twitter friend %s" % follower_id)

    def _process_photo(self, friend_id, url):
        with DownloadedFile(url) as downloaded:
            if self.store.photo_seen(downloaded):
                logging.info(
                    '{} tried to add duplicate photo'.format(friend_id))
            else:
                face_regions = self.face_regions(
                    core.prepare_image(downloaded))
                self.face_recognizer.update(
                    [(face_region, friend_id)
                     for face_region in face_regions])
                self.store.remember_photo(downloaded)

    def on_direct_message(self, dm):

        media = dm.entities.get('media', [])
        photo = media[0] if len(media) > 0 else None

        if photo:
            friend_id = self.store.get_or_create_twitter_friend(
                dm.sender.id)['id']
            self._process_photo(friend_id, get_photo_url(photo))

        self.send_direct_message(dm.sender, compliments.get_compliment())

    def on_mention(self, tweet, prefix):

        if 'PLEASE FORGET ME' in tweet.text.upper():
            self.store.forget_friend(
                self.store.get_twitter_friend(tweet.author.id))
            self.api.destroy_friendship(tweet.author.id)
        else:

            friend_id = self.store.get_or_create_twitter_friend(
                tweet.author.id)['id']

            photo_urls = [get_photo_url(photo) for photo in get_photos(tweet)]

            for url in photo_urls:
                self._process_photo(friend_id, url)

        self.favorite_tweet(tweet)

    def on_timeline(self, tweet, prefix):
        """
        Defines actions to take on a timeline tweet.

        tweet - a tweepy.Status object. You can access the text with
        tweet.text

        prefix - the @-mentions for this reply. No need to include this in the
        reply string; it's provided so you can use it to make sure the value
        you return is within the 140 character limit with this.

        It's up to you to ensure that the prefix and tweet are less than 140
        characters.

        When calling post_tweet, you MUST include reply_to=tweet, or
        Twitter won't count it as a reply.
        """

        photos = get_photos_from_tweet(tweet)

        face_regions = core.flatten(
            [self.face_regions(photo) for photo in photos])

        recognitions = []
        for region in face_regions:
            try:
                recognitions.append(
                    self.face_recognizer.recognize_face(region))
            except cv2.error as e:
                logging.error("Error recognizing face region: " + e.message)

        likely_recognitions = filter(
            lambda (_, margin): margin < self.get_confidence(),
            recognitions)

        recognized_labels = set([label for (label, _) in likely_recognitions])

        for label in recognized_labels:
            recognized = self.store.get_friend(label)
            # it's possible someone is in the model but not in the database,
            # e.g. people that asked to be forgotten
            if recognized and recognized.get('twitter_id', None):
                twitter_friend = self.api.get_user(recognized['twitter_id'])
                self.favorite_tweet(tweet)
                self.post_tweet(
                    prefix +
                    ' ' + compliments.get_compliment() + ' ' +
                    '@' + twitter_friend.screen_name,
                    reply_to=tweet)
Esempio n. 4
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from facerec import FaceRecognizer, ImageSet, LabelSet, concatenate

recognizer = FaceRecognizer()
img1 = ImageSet("Female_faces")
label1 = LabelSet("Female", img1)
img2 = ImageSet("Male_faces")
label2 = LabelSet("Male", img2)

imgs = concatenate(img1, img2)
labels = concatenate(label1, label2)
recognizer.train(imgs, labels)

img = ImageSet("Test_images")
print recognizer.predict(img)
recognizer.save("gender.xml")