def __init__(self, vs, args):
setup_definitions(self, vs, args)
init_ui(self)
self.open_main_view()
print("self.cpu_type : ",self.cpu_type)
if self.cpu_type == 0:
self.top_label_cpu_text.set("CPU")
if self.cpu_type == 1:
self.top_label_cpu_text.set("VPU")
if self.cpu_type == 2:
self.top_label_cpu_text.set("GPU")
self.root.wm_title("ThinkerFarm")
self.root.wm_protocol("WM_DELETE_WINDOW", self.on_close)
print("cuda list :",cuda.get_num_devices())
def __init__(self):
print(dlib.DLIB_USE_CUDA)
print(cuda.get_num_devices())
import face_recognition
from PIL import Image
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from time import time
import dlib
import dlib.cuda as cuda
print(cuda.get_num_devices())
dlib.DLIB_USE_CUDA = 1
dlib.USE_AVX_INSTRUCTIONS = 1
image = face_recognition.load_image_file("baseDatos.jpeg")
start = time()
face_locations = face_recognition.face_locations(image, model="cnn")
print("tiempo: ", time() - start)
print(format(len(face_locations)))
i = 0
for face_location in face_locations:
top, right, bottom, left = face_location
#print("bla {}bla{} lba{} right{}", format(top, left, bottom, right))
face_image = image[top:bottom, left:right]
pil_image = Image.fromarray(face_image)
pil_image.save("out/face-{}.png".format(i))
i = i + 1
"""
my_list = [] #almacena las imagenes leidas por face:recognition
for j in range (0, i):
my_list.append(face_recognition.load_image_file("out/face-{}.png".format(j)))
import logging
import os
import random
from argparse import ArgumentParser
import coloredlogs
import cv2
import numpy as np
import matplotlib.pyplot as plt
import dlib
LOCATION_MODEL = 'hog'
try:
import dlib.cuda as cuda
if cuda.get_num_devices() > 0 and dlib.DLIB_USE_CUDA:
LOCATION_MODEL = 'cnn'
except ImportError:
pass
import face_recognition as fr
logger = logging.getLogger()
def load_types(basedir, human_prob):
type_names = []
type_imgs = {}
for fname in os.listdir(basedir):
full_path = os.path.join(basedir, fname)
type_name = os.path.splitext(fname)[0]
# python recognize_faces_video.py --encodings encodings.pickle
# python recognize_faces_video.py --encodings encodings.pickle --output output/jurassic_park_trailer_output.avi --display 0
# import the necessary packages
from imutils.video import VideoStream
import face_recognition
import argparse
import imutils
import pickle
import time
import cv2
import dlib.cuda as cuda
import dlib
print("CUDA devices number : " + str(cuda.get_num_devices()))
dlib.DLIB_USE_CUDA = True
print("dlib.DLIB_USE_CUDA = " + str(dlib.DLIB_USE_CUDA))
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-e",
"--encodings",
required=True,
help="path to serialized db of facial encodings")
ap.add_argument("-o", "--output", type=str, help="path to output video")
ap.add_argument("-y",
"--display",
type=int,
import os
import numpy as np
import bz2
import shutil
import urllib.request
from PIL import Image
import cv2
import math
from . import utils
DLIB_USE_CNN = False
try:
import dlib.cuda as cuda
if cuda.get_num_devices() >= 1:
if dlib.DLIB_USE_CUDA:
DLIB_USE_CNN = True
except Exception as e:
print(e)
class MyDlib:
def __init__(self):
print("Dlib is loading")
if DLIB_USE_CNN:
dlib_cnn_face_detector_path = os.path.join(
utils.ROOT_DIR, "mmod_human_face_detector.dat")
if not os.path.exists(dlib_cnn_face_detector_path):
self.download_cnn_face_detector(dlib_cnn_face_detector_path)
self.face_detector = dlib.cnn_face_detection_model_v1(dlib_cnn_face_detector_path)
