def stream_capture(q, fr_q, fr_q_results):
#start camera capture
vcap = VideoStream(src=url).start()
#main loop
fr = FaceRecognizer(ctx='gpu',
fd_model_path='./fd_model',
gpu=0,
license=license)
fr_results = None
while (True):
try:
frame = vcap.read()
#initialize FR class
if not fr_q_results.empty():
fr_results = fr_q_results.get_nowait()
if fr_results:
for i, box in enumerate(fr_results['boxes']):
fr.draw_box(frame, box)
if fr_results['labels'][i]['predicted_label'] is not None:
fr.draw_label(
frame, (int(box[0]), int(box[1])),
fr_results['labels'][i]['predicted_label'], 2, 2)
#pass frame to streaming service and fr queue
if q.full():
q.get()
if fr_q.full():
fr_q.get()
q.put(frame)
fr_q.put(frame)
# time.sleep(0.01)
except Exception as e:
print traceback.format_exc()
def live_search(request):
if 'username' not in request.session:
request.session["username"] = None
print("in function live ==============")
contact = None
# fr = FaceRecognizer(ctx='cpu',
# fd_model_path='./fd_model',
# fr_model_path='./model-tfv2/model.trueface',
# params_path='./model-tfv2/model.params',
# license='eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJlbW90aW9uIjpudWxsLCJmciI6dHJ1ZSwicGFja2FnZV9pZCI6bnVsbCwiZXhwaXJ5X2RhdGUiOiIyMDE5LTA5LTI3IiwidGhyZWF0X2RldGVjdGlvbiI6bnVsbCwibWFjaGluZXMiOiI1IiwiYWxwciI6bnVsbCwibmFtZSI6IkpvaG4gQnJpZGdld2F0ZXIiLCJ0a2V5IjoibmV3IiwiZXhwaXJ5X3RpbWVfc3RhbXAiOjE1Njk1NDI0MDAuMCwiYXR0cmlidXRlcyI6dHJ1ZSwidHlwZSI6Im9mZmxpbmUiLCJlbWFpbCI6ImpvaG5iQGJsdWVzdG9uZS5uZXR3b3JrIn0._B9h-H4sZ5tQBslIVZtM1b2Y4_-TSN1e4dAo6KAp0nU'
# )
# fr.create_collection('collection', 'collection.npz', return_features=False)
vcap = VideoStream(src=0).start()
t_end = time.time() + 60 * 0.5
while time.time() < t_end:
#while(True):
frame = vcap.read()
frame = cv2.resize(frame, (640, 480))
bounding_boxes, points, chips = fr.find_faces(frame,
return_chips=True,
return_binary=True)
if bounding_boxes is None:
continue
for i, chip in enumerate(chips):
identity = fr.identify(chip,
threshold=0.3,
collection='./collection.npz')
print("========================")
print(identity)
if identity['predicted_label'] != None:
contact = Contact.objects.filter(lost_one__folder_name__contains=identity['predicted_label'])
print (contact)
break
if contact:
print('==============================')
print('in if contact')
# vcap.stopped = True
# vcap.stream.release()
break
else:
print('==============================')
print('in else contact')
continue
vcap.stopped = True
vcap.stream.release()
return render(request, 'index.html', {"contacts":contact, "user":request.session["username"]})
from multiprocessing import Pool, Process
import time
import signal
from trueface.utils import RedisQueue
import base64
import json
import os
import traceback
face_detector = FaceRecognizer(ctx='gpu',
fd_model_path='./fd_model',
license=os.environ['TF_TOKEN'])
q = RedisQueue('office_camera')
vcap = VideoStream(src="rtsp://192.168.1.177:554/stream2").start()
def init_worker():
signal.signal(signal.SIGINT, signal.SIG_IGN)
def detect(frame):
bounding_boxes, points, chips = face_detector.find_faces(
frame, return_chips=True, return_binary=True)
return bounding_boxes, points, chips
p = Pool(1, init_worker)
frames = []
url = int(url) if len(url) == 1 else url
threshold = config['config']['threshold']
webhook_url = config['config']['webhook_url']
webhook_holdout = config['config']['webhook_holdout']
license = config['config']['license']
#initialize FR class
fr = FaceRecognizer(ctx='gpu',
fd_model_path='./fd_model',
fr_model_path='./model-tfv2/model.trueface',
params_path='./model-tfv2/model.params',
gpu=0,
license=license)
#start camera capture
vcap = VideoStream(src=url).start()
#webhook function
def webhook(identity):
"""performs webhook"""
identity['probability'] = fr.cosine_sim_to_prob(identity['confidence'])
data = {
"identity": identity,
"timestamp": strftime("%a, %d %b %Y %H:%M:%S +0000", gmtime())
}
r = requests.post(webhook_url, json=data)
print r.text
#simple streaming server
from trueface.threat_detection import ThreatDetection
from trueface.video import VideoStream
import tensorflow as tf
import cv2
import os
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.5)
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
threat_detection = ThreatDetection(
"./threat-model/model.trueface",
"./threat-model/model.params",
"./threat-model/trueface-classes.csv",
os.environ['TF_TOKEN'])
cap = VideoStream(src=0).start()
while True:
frame = cap.read()
if frame is None:
print('image none')
break
prediction = threat_detection.predict(frame)
print(prediction)
if prediction:
for pred in prediction:
if pred['score'] < 0.5:
continue
from trueface.motion import MotionDetector
from trueface.video import VideoStream
import cv2
#please use 0.6.2 if you desire
cap = VideoStream(src=0).start()
# apply a binary threshold only keeping pixels above thresh and setting the result to maxValue. If you want
# motion to be picked up more, increase the value of maxValue. To pick up the least amount of motion over time, set maxValue = 1
# frames count allows you to pick the count of frames to track heatmaps
motion = MotionDetector(cap.read(), threshold=1, max_value=3, frames=100)
count = 0
while True:
frame = cap.read()
frame = motion.detect(frame)
count += 1
#fades motion, use if not use frames= param
#motion.fade(ratio=0.75)
cv2.imshow("image", frame)
if cv2.waitKey(33) == ord('q'):
break