def addCamera(self, name, port=0, exposure=0.2, wait = 0 ):
time.sleep(wait)
save_camera_config(port, exposure)
img_array = take_picture()
face = self.img_to_array(img_array)[0]
print('There are this many faces: ', len(face))
if len(face) == 0:
print(name, ' was not detected. Please try again!')
return None
print(img_array.shape)
if len(face) > 1:
print('Too many faces were detected. Please try again!')
return None
print(name, ' was successfully added to the database.')
border = test.detectFromImg(img_array)[1][0]
fig,ax = plt.subplots()
ax.imshow(img_array)
ax.add_patch(patches.Rectangle((border[1], border[3]),border[0]-border[1],border[2]-border[3],edgecolor = 'pink', fill=False))
ax.set_xticks([])
ax.set_yticks([])
self.add(name,face)
import os
import dlib
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import cv2
from collections import Counter
import tensorflow as tf
import logging
logging.getLogger("tensorflow").setLevel(logging.FATAL)
print("Imports complete", flush = True)
import time
app = Flask(__name__)
ask = Ask(app, '/')
#camera set up (change port for different camera)
save_camera_config(port=2, exposure=0.5)
#for template matching
methods = ['cv2.TM_CCOEFF', 'cv2.TM_CCOEFF_NORMED','cv2.TM_CCORR_NORMED', 'cv2.TM_SQDIFF', 'cv2.TM_SQDIFF_NORMED']
ntemplates = [file for file in os.scandir("./masks/nums")]
#for matching output of classifier --> actual suit
suits = ["dclubs", "ddiamonds", "dhearts", "dspades","uclubs","udiamonds", "uhearts", "uspades"]
suits_true = ["clubs", "diamonds", "hearts", "spades"]
tmp = {}
for i, suit in enumerate(suits):
tmp[suit] = i
tmp[i] = suit
suits= tmp
def camera(self, port=0, exposure=0.2):
save_camera_config(port, exposure)
img_array = take_picture()
if img_array == []:
raise ValueError("No face detected")
return img_array
from camera import take_picture
import cv2
from camera import save_camera_config
import matplotlib.pyplot as plt
import numpy as np
from model_tester import model
save_camera_config(port=0, exposure=1)
def get_signs(n, weights1, bias1, weights2, bias2, weights3, bias3, weights4,
bias4):
"""
Will take in a bunch of pictures every second for a specified amount of time
creating np.ndarrays of our signs
Parameter: n : number of pictures desired to be taken
Returns: np.ndarray of our image arrays
"""
uppercase = "ABCDEFGHIJKLMNOPQRSTUVWXYZ!? "
str = ""
img_session = []
fig, ax = plt.subplots()
for cnt in range(n):
img_array = take_picture()
print("Picture taken")
img_array = img_array[:, 280:1000, :]
resized = cv2.resize(img_array, (200, 200),
interpolation=cv2.INTER_AREA)
gray = cv2.cvtColor(resized, cv2.COLOR_RGB2GRAY)
ax.imshow(gray, cmap=plt.cm.gray)
import os
import dlib
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import cv2
from collections import Counter
import tensorflow as tf
import logging
logging.getLogger("tensorflow").setLevel(logging.FATAL)
print("Imports complete", flush=True)
import time
app = Flask(__name__)
ask = Ask(app, '/')
#camera set up (change port for different camera)
save_camera_config(port=2, exposure=0.5)
#for template matching
methods = [
'cv2.TM_CCOEFF', 'cv2.TM_CCOEFF_NORMED', 'cv2.TM_CCORR_NORMED',
'cv2.TM_SQDIFF', 'cv2.TM_SQDIFF_NORMED'
]
ntemplates = [file for file in os.scandir("./masks/nums")]
#for matching output of classifier --> actual suit
suits = [
"dclubs", "ddiamonds", "dhearts", "dspades", "uclubs", "udiamonds",
"uhearts", "uspades"
]
suits_true = ["clubs", "diamonds", "hearts", "spades"]
tmp = {}
# coding: utf-8
# In[2]:
import ImageLoader
import ImageCompare
import database
import matplotlib.pyplot as plt
import matplotlib.patches as patches
get_ipython().magic('matplotlib notebook')
from camera import save_camera_config
save_camera_config(port=1, exposure=0.7)
# In[3]:
def identify(save=True, force_input=False, from_file=False):
"""
Takes a picture with configured camera and identifies all of the faces in the picture
Parameters:
-----------
save (boolean):
whether or not to add the captured image to the database
from_file(boolean):
whether or not expect a filename instead of taking a picture
Returns:
--------
names (list)
the list of the name of each person in the picture
def __init__(self):
self.db = Database()
self.db.load('celebrities.pkl')
self.logged_in = False
save_camera_config(0, exposure=0.2)
def camera(self, port=0, exposure=0.2):
save_camera_config(port, exposure)
img_array = take_picture()
self.database.append(img_array)
def camera(self, port=0, exposure=0.2):
save_camera_config(port, exposure)
img_array = take_picture()
return img_array