class LivenessDetection():
def __init__(self, face_detection_model_path, liveness_model_path,
threshold, image_size):
self.model = None
self.liveness_model_path = liveness_model_path
self.face_detection_model_path = face_detection_model_path
self.threshold = threshold
self.image_size = image_size
def initial(self):
self.model = load_model(self.liveness_model_path)
self.model._make_predict_function()
self.face_detect = FaceDetection(self.face_detection_model_path)
def process(self, image, mode):
data = []
x, y, w, h = self.face_detect.process(
cv2.cvtColor(image, cv2.COLOR_RGB2BGR))
if w > 0 and h > 0:
face_image = image[y:y + h, x:x + w]
face_image = cv2.cvtColor(face_image, cv2.COLOR_BGR2GRAY)
face_image = cv2.resize(face_image,
(self.image_size, self.image_size))
else:
face_image = np.zeros((self.image_size, self.image_size),
dtype=int)
image = cv2.resize(image, (self.image_size, self.image_size))
image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
image[:, :, 0] = face_image
data.append(image)
data = np.array(data, dtype="float") / 255.0
preds = self.model.predict(data)
score = preds[0][0]
predict = True if score > self.threshold else False
return score, predict
class LivenessDetection():
def __init__(self, face_detection_model_path, liveness_model_path,
threshold, image_size):
self.model = None
self.liveness_model_path = liveness_model_path
self.face_detection_model_path = face_detection_model_path
self.threshold = threshold
self.image_size = image_size
def initial(self):
self.model = keras.models.load_model(self.liveness_model_path)
self.model._make_predict_function()
self.face_detect = FaceDetection(self.face_detection_model_path)
def process(self, image, mode):
data = []
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
# image = imutils.resize(image, width=800)
x, y, w, h = self.face_detect.process(image)
if w > 0 and h > 0:
# img_face = image[y:y+h, x:x+w]
new_pad = np.min(
[x, image.shape[1] - (x + w), y, image.shape[0] - (y + h)])
image = image[y - new_pad:y + h + new_pad,
x - new_pad:x + w + new_pad, :]
image = cv2.resize(image, (self.image_size, self.image_size))
image = image.astype(np.float32) / 127.5 - 1
score = self.model.predict_on_batch(np.expand_dims(image,
axis=0))[0]
predict = True if score > self.threshold else False
else:
score = -999
predict = False
return score, predict
from face_detection import FaceDetection
from util.db import DynamoDBUtils
from util import misc
import random
import shutil
import pprint
import os
OUTPUT_DIR = '../videos'
if __name__ == '__main__':
BUCKET_NAME = 'smart-cam'
'''
if os.path.exists(OUTPUT_DIR):
shutil.rmtree(OUTPUT_DIR)
os.makedirs(OUTPUT_DIR)
ret, local_file = misc.download_from_s3(BUCKET_NAME, 'videos/video_1.avi', OUTPUT_DIR)
print ret, local_file
'''
#local_file = '../videos/video_100_frames_2.mp4'
local_file = '../videos/video_100_frames_1.mp4'
#fd = FaceDetectionV1()
fd = FaceDetection()
report = fd.process(local_file, show_frame=False)
pprint.pprint(report)
from face_detection_v1 import FaceDetectionV1
from face_detection import FaceDetection
from util.db import DynamoDBUtils
from util import misc
import random
import shutil
import pprint
import os
OUTPUT_DIR = '../videos'
if __name__ == '__main__':
BUCKET_NAME = 'smart-cam'
'''
if os.path.exists(OUTPUT_DIR):
shutil.rmtree(OUTPUT_DIR)
os.makedirs(OUTPUT_DIR)
ret, local_file = misc.download_from_s3(BUCKET_NAME, 'videos/video_1.avi', OUTPUT_DIR)
print ret, local_file
'''
#local_file = '../videos/video_100_frames_2.mp4'
local_file = '../videos/video_100_frames_1.mp4'
#fd = FaceDetectionV1()
fd = FaceDetection()
report = fd.process(local_file, show_frame=False)
pprint.pprint(report)