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face_spoofing.py
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face_spoofing.py
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import numpy as np
import cv2
from sklearn.externals import joblib
from face_detector import get_face_detector, find_faces
def calc_hist(img):
"""
To calculate histogram of an RGB image
Parameters
----------
img : Array of uint8
Image whose histogram is to be calculated
Returns
-------
histogram : np.array
The required histogram
"""
histogram = [0] * 3
for j in range(3):
histr = cv2.calcHist([img], [j], None, [256], [0, 256])
histr *= 255.0 / histr.max()
histogram[j] = histr
return np.array(histogram)
face_model = get_face_detector()
clf = joblib.load('models/face_spoofing.pkl')
cap = cv2.VideoCapture(0)
sample_number = 1
count = 0
measures = np.zeros(sample_number, dtype=np.float)
while True:
ret, img = cap.read()
faces = find_faces(img, face_model)
measures[count%sample_number]=0
height, width = img.shape[:2]
for x, y, x1, y1 in faces:
roi = img[y:y1, x:x1]
point = (0,0)
img_ycrcb = cv2.cvtColor(roi, cv2.COLOR_BGR2YCR_CB)
img_luv = cv2.cvtColor(roi, cv2.COLOR_BGR2LUV)
ycrcb_hist = calc_hist(img_ycrcb)
luv_hist = calc_hist(img_luv)
feature_vector = np.append(ycrcb_hist.ravel(), luv_hist.ravel())
feature_vector = feature_vector.reshape(1, len(feature_vector))
prediction = clf.predict_proba(feature_vector)
prob = prediction[0][1]
measures[count % sample_number] = prob
cv2.rectangle(img, (x, y), (x1, y1), (255, 0, 0), 2)
point = (x, y-5)
# print (measures, np.mean(measures))
if 0 not in measures:
text = "True"
if np.mean(measures) >= 0.7:
text = "False"
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(img=img, text=text, org=point, fontFace=font, fontScale=0.9, color=(0, 0, 255),
thickness=2, lineType=cv2.LINE_AA)
else:
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(img=img, text=text, org=point, fontFace=font, fontScale=0.9,
color=(0, 255, 0), thickness=2, lineType=cv2.LINE_AA)
count+=1
cv2.imshow('img_rgb', img)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()