def validateInstaUser(username, num_jitters):
images = getInstaLinks(username)
if len(images) >= cfg.instaLimit():
images = images[:cfg.instaLimit()]
r = FaceRecog(username, images, num_jitters=num_jitters)
r.loadKnown(username)
profile_links, _ = r.getValidLinksAndImg(username)
return len(profile_links) > 0
def main(skipFB=False, skipY=False, FBUrls=[], jsonRep=None):
if not skipFB:
# collect user input
console.prompt('Enter the persons name to find on FB: ')
name = input('')
while not name:
console.prompt('Enter the persons name to find on FB: ')
name = input('')
else:
console.task('Skipping FB Search')
name = "Unknown"
console.prompt(
'How many jitters, higher is better [max 100] (default=70): ')
num_jitters = input('')
if not num_jitters:
console.task('Settings jitters to 70')
num_jitters = 70
num_jitters = int(num_jitters)
if num_jitters > 100:
console.subfailure('Dude wtf?!')
num_jitters = 100
console.subfailure('Using 100 jitters...')
if not skipFB:
# grab profile urls
f = FBGrabber(name)
f.grabData()
# do face recognition on those profile images
r = FaceRecog(f.getProfileLinks(),
f.getProfileImages(),
num_jitters=num_jitters)
r.loadKnown(name)
profile_links, profile_imgs = r.getValidLinksAndImg(name)
console.section('Result')
console.task('Found the following Profiles:')
for i in range(len(profile_links)):
console.subtask(profile_links[i])
else:
if len(FBUrls) > 0:
f = FBProfileGrabber(FBUrls)
img_urls = f.grabLinks()
#FBURLS are our profile links synchron with img_urls
# so FBURLS[0] <=> img_urls[0]
r = FaceRecog(FBUrls, img_urls, num_jitters=num_jitters)
r.loadKnown(name)
profile_links, profile_imgs = r.getValidLinksAndImg(name)
console.section('Result')
console.task('Found the following Profiles:')
for i in range(len(profile_links)):
console.subtask(profile_links[i])
else:
profile_links = []
profile_imgs = []
# google reverse image search on profile pics
g = GoogleGrabber()
for img in profile_imgs:
g.collectLinks(img)
# google reverse image search on reference pic
g.collectLinksLocal()
rev_links, predictions = g.finish()
#TODO: Fix yandex search
#if not skipY:
if False:
yandex = YandexGrabber()
for img in profile_imgs:
yandex.collectLinks(img)
yandex.collectLinksLocal()
#add to rev_links
for e in yandex.finish():
rev_links.append(e)
else:
console.task('Skipping Yandex Search')
rev_links = list(set(rev_links))
instaNames = parseInstaUsername(filterInstaLinks(rev_links))
validatedInstaNames = []
console.section("Validating Instagram Profiles")
for un in instaNames:
console.task("Validating Profile: '{0}'".format(un))
if validateInstaUser(un, num_jitters):
validatedInstaNames.append(un)
raider_img_list = profile_imgs
for v in validatedInstaNames:
l = getInstaLinks(v)
for li in l:
raider_img_list.append(li)
if len(raider_img_list) <= 0:
console.failure('No Links found...')
else:
console.task('RIP Imageraider')
rev_links = list(set(rev_links))
predictions = list(set(predictions))
console.section('Links')
print(rev_links)
console.section('Predictions')
try:
predictions = [x.lower() for x in predictions]
except:
predictions = []
print(predictions)
presentResult(predictions)
for pl in profile_links:
rev_links.append(pl)
rev_links = list(set(rev_links))
#estimate age
ageEstimator = PictrievGrabber()
if len(validatedInstaNames) > 0:
for v in validatedInstaNames:
l = getInstaLinks(v)
if len(l) >= cfg.instaLimit():
l = l[:cfg.instaLimit()]
for li in l:
ageEstimator.collectAges(li)
age = ageEstimator.finish()
else:
console.failure('No Instagram Images to upload...')
#ageEstimator.finish()
age = "Unknown"
if jsonRep:
console.section("Dumping JSON Report")
makeJSONReport(name, rev_links, predictions, validatedInstaNames, age,
jsonRep)
else:
console.section("Creating PDF Report")
makeReport(name, rev_links, predictions, validatedInstaNames, age)
p = os.path.join(tempfile.gettempdir(), 'imageraider')
if os.path.isdir(p):
pathlist = Path(p).glob('**/*')
for path in pathlist:
s_p = str(path)
os.remove(s_p)
console.task("KTHXBYE")
from control_psi import ControlPsi
from face_recog import FaceRecog
from face_track import FaceTrack
from sound_track import SoundTrack
from room_brightness import RoomBrightness
from saliency_track import SaliencyTrack
from tts_feedback import TTSFeedback
rospy.init_node("OpenCog_ROS_bridge")
logging.info("Starting the OpenCog ROS Bridge")
print "Starting the OpenCog ROS Bridge"
co = Control()
cp = ControlPsi()
af = Affect()
ap = AudioPower()
ct = ChatTrack()
fc = FaceRecog()
ft = FaceTrack()
st = SoundTrack()
br = RoomBrightness()
sl = SaliencyTrack()
tf = TTSFeedback
try:
rospy.spin()
except rospy.ROSInterruptException as e:
print(e)
print "Exit OpenCog ROS bridge"
def __init__(self):
# initialize the video stream and allow the camera sensor to warm up
print("[INFO] starting video stream...")
self.vs = VideoStream(usePiCamera=False).start()
self.face_recog = FaceRecog()
class Camera(object):
def __init__(self):
# initialize the video stream and allow the camera sensor to warm up
print("[INFO] starting video stream...")
self.vs = VideoStream(usePiCamera=False).start()
self.face_recog = FaceRecog()
def adjust_gamma(self, image, gamma=0.5):
# build a lookup table mapping the pixel values [0, 255] to
# their adjusted gamma values
invGamma = 1.0 / gamma
table = np.array([((i / 255.0) ** invGamma) * 255 for i in np.arange(0, 256)]).astype("uint8")
# apply gamma correction using the lookup table
return cv2.LUT(image, table)
def get_frame(self):
current_barcode = []
frame = self.vs.read()
if frame is None:
return frame, []
rgb_frame = imutils.resize(frame, width=500)
frame = cv2.cvtColor(rgb_frame, cv2.COLOR_BGR2GRAY)
# reduce brightness
frame = self.adjust_gamma(frame)
# find the barcodes in the frame and decode each of the barcodes
barcodes = pyzbar.decode(frame)
# loop over the detected barcodes
for barcode in barcodes:
# extract the bounding box location of the barcode and draw
# the bounding box surrounding the barcode on the image
(x, y, w, h) = barcode.rect
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 255), 2)
# the barcode data is a bytes object so if we want to draw it
# on our output image we need to convert it to a string first
barcodeData = barcode.data.decode("utf-8")
barcodeType = barcode.type
# draw the barcode data and barcode type on the image
text = "{} ({})".format(barcodeData, barcodeType)
cv2.putText(frame, text, (x, y - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
current_barcode.append(barcodeData)
print('barcodedata: {}'.format(barcodeData))
faces = self.face_recog.process(rgb_frame, draw_on_frame=False)
for face in faces:
top, right, bottom, left = face.get('location')
name = face.get('name')
# Draw a box around the face
cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
# Draw a label with a name below the face
cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)
ret, jpeg = cv2.imencode('.jpg', frame)
return jpeg.tobytes(), current_barcode
def __del__(self):
# close the output CSV file do a bit of cleanup
print("[INFO] cleaning up...")
cv2.destroyAllWindows()
def main():
# collect user input
console.prompt('Enter the persons name to find on FB: ')
name = input('')
console.prompt('How many jitters, higher is better [max 100]: ')
num_jitters = input('')
num_jitters = int(num_jitters)
if num_jitters > 100:
console.subfailure('Dude wtf?!')
num_jitters = 100
console.subfailure('Using 100 jitters...')
# grab profile urls
f = FBGrabber(name)
f.grabData()
# do face recognition on those profile images
r = FaceRecog(f.getProfileLinks(), f.getProfileImages(), num_jitters=num_jitters)
r.loadKnown(name)
profile_links, profile_imgs = r.getValidLinksAndImg(name)
console.section('Result')
console.task('Found the following Profiles:')
for i in range(len(profile_links)):
console.subtask(profile_links[i])
# google reverse image search on profile pics
g = GoogleGrabber()
for img in profile_imgs:
g.collectLinks(img)
# google reverse image search on reference pic
g.collectLinksLocal()
rev_links, predictions = g.finish()
yandex = YandexGrabber()
for img in profile_imgs:
yandex.collectLinks(img)
#add to rev_links
for e in yandex.finish():
rev_links.append(e)
rev_links = list(set(rev_links))
instaNames = parseInstaUsername(filterInstaLinks(rev_links))
validatedInstaNames = []
console.section("Validating Instagram Profiles")
for un in instaNames:
console.task("Validating Profile: '{0}'".format(un))
if validateInstaUser(un, num_jitters):
validatedInstaNames.append(un)
raider_img_list = profile_imgs
for v in validatedInstaNames:
l = getInstaLinks(v)
for li in l:
raider_img_list.append(li)
if len(raider_img_list) <= 0:
console.failure('No Links founds...')
else:
raider = ImageRaiderGrabber()
raider.insertImageLinks(raider_img_list)
raider.downloadCSV()
raider_links = raider.processCSV()
for raider_link in raider_links:
rev_links.append(raider_link)
rev_links = list(set(rev_links))
predictions = list(set(predictions))
console.section('Links')
print(rev_links)
console.section('Predictions')
try:
predictions = [x.lower() for x in predictions]
except:
predictions = []
print(predictions)
presentResult(predictions)
console.section("Creating PDF Report")
makeReport(name, rev_links, predictions, validatedInstaNames)
p = os.path.join(tempfile.gettempdir(), 'imageraider')
if os.path.isdir(p):
pathlist = Path(p).glob('**/*')
for path in pathlist:
s_p = str(path)
os.remove(s_p)
console.task("KTHXBYE")
from face_recog import FaceRecog
#from PAB_Vision.Machine_Learning.bot.botAPI import *
import cv2
import face_recognition
#botResponseReciever("Friend Shyam")
face_recog = FaceRecog()
#frame = face_recognition.load_image_file("G:\\shyam\\Pictures\\Camera Roll\\test\\shyam\\pic.jpg")
#frame = cv2.imread("G:\\shyam\\Pictures\\Camera Roll\\test\\shyam\\pic.jpg")
#frame = frame[:, :, ::-1]
#frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
#print(face_recog.render_frame(frame))
face_recog.start_video(detect_faces=True)
#face_recog.add_new_face("Shyam", frame)
