async def main():
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-c",
"--conf",
required=True,
help="Path to the input configuration file")
args = vars(ap.parse_args())
# load the configuration file and label encoder
conf = Conf(args["conf"])
await asyncio.gather(read_frame(), process_frame(conf))
cv2.destroyAllWindows()
import cv2
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-c",
"--conf",
required=True,
help="path to the configuration file")
ap.add_argument("-i",
"--image",
required=True,
help="path to the image to be classified")
args = vars(ap.parse_args())
# load the configuration file
conf = Conf(args["conf"])
# load the classifier, then initialize the Histogram of Oriented Gradients descriptor
# and the object detector
model = pickle.loads(open(conf["classifier_path"], "rb").read())
hog = HOG(orientations=conf["orientations"],
pixelsPerCell=tuple(conf["pixels_per_cell"]),
cellsPerBlock=tuple(conf["cells_per_block"]),
normalize=conf["normalize"],
block_norm="L1")
od = ObjectDetector(model, hog)
# load the image and convert it to grayscale
image = cv2.imread(args["image"])
image = imutils.resize(image, width=min(260, image.shape[1]))
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
from utils import Conf
from imutils import paths
import face_recognition
import argparse
import pickle
import cv2
import os
config_file = "/home/pi/Desktop/Attendance Management System/config/config.json"
conf = Conf(config_file)
print("[INFO] quantifying faces...")
imagePaths = list(
paths.list_images(os.path.join(conf["dataset_path"], conf["class"])))
knownEncodings = []
knownNames = []
for (i, imagePath) in enumerate(imagePaths):
print("[INFO] processing image {}/{}".format(i + 1, len(imagePaths)))
print(imagePath)
name = imagePath.split(os.path.sep)[-2]
print(name)
image = cv2.imread(imagePath)
rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
encodings = face_recognition.face_encodings(rgb)
for encoding in encodings:
knownEncodings.append(encoding)
knownNames.append(name)
print("[INFO] serializing encodings...")
data = {"encodings": knownEncodings, "names": knownNames}
f = open(conf["encodings_path"], "wb")
f.write(pickle.dumps(data))
time.sleep(5)
# Processes results
if status >= 400:
light.info(
"[ERROR] Could not send data after 5 attempts, please check \
your token credentials and internet connection")
return False
# light.info("[INFO] request made properly, your device is updated")
return True
def send_action(self, trackingId, actionName):
light.info("send trackingId {} {} ".format(trackingId, actionName))
thread = threading.Thread(target=self._send_action,
args=(
trackingId,
actionName,
))
thread.daemon = True
thread.start()
if __name__ == '__main__':
config_server = '../jetson/configs.json'
conf = Conf(config_server)
light_api = ApiLight(conf)
light_api.send_action(123, "out")
help="Path to input video")
ap.add_argument("-c","--config", default="./config.json",
help="Path to the input configuration file")
ap.add_argument("-s","--save",default=False,
help="Save processed video (True/False)")
args = vars(ap.parse_args())
# initialize the lit of class labels MobileNet SSD
CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
"bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
"dog", "horse", "motorbike", "person", "pottedplant", "sheep",
"sofa", "train", "tvmonitor"]
# load the configuration file
conf = Conf(args["config"])
# load our serialized model from disk
def load_model():
print("[INFO] Loading model...")
if os.path.isfile(conf["prototxt_path"]) and os.path.isfile(conf["model_path"]):
# net = cv2.dnn.readNetFromCaffe("./MobileNetSSD/MobileNetSSD_deploy.prototxt",
# "./MobileNetSSD/MobileNetSSD_deploy.caffemodel")
net = cv2.dnn.readNetFromCaffe(conf["prototxt_path"],conf["model_path"])
print("[INFO] Loaded model successfully...")
return net
else:
print("Model is not found...")
# main function
def run_core(conf):
# bait
global lock, frame_out
detector_lock = threading.Lock()
logruncv.info('Get camera List to run')
# cam_cfgs = get_config_file()
# camera_infor = Conf(conf['camera_info'])
camera_infor = Conf(conf['camera_info'])
zone_infor = Conf(conf['zone_info'])
if camera_infor['camera_link'] is None or camera_infor[
'camera_link'] is None or camera_infor['camera_id'] is None:
raise "camera_infor slack information"
if zone_infor['1'] is None or zone_infor['2'] is None or zone_infor[
'3'] is None:
raise "zone_infor slack information"
cam_cfgs = {}
cam_cfgs['cam_Link'] = camera_infor['camera_link']
cam_cfgs['step'] = 1
cam_cfgs['cam_Name'] = camera_infor['camera_name']
cam_cfgs['cam_ID'] = camera_infor['camera_id']
cam_cfgs['funcs'] = []
zone_fillter = {}
counter_infor = {}
zone_fillter["func_ID"] = 5
zone_fillter["func_Name"] = "zone filtter"
zone_fillter["func_note"] = decode_note_polygon(zone_infor['1'])
counter_infor["func_ID"] = 1
counter_infor["func_Name"] = "counter"
counter_infor["func_line"] = decode_note_counter(zone_infor['2'])
counter_infor["func_pont"] = decode_note_counter(zone_infor['3'])
cam_cfgs['funcs'].append(zone_fillter)
cam_cfgs['funcs'].append(counter_infor)
logruncv.info(cam_cfgs)
# start 1 thread for 1 camera
thread = threading.Thread(target=core_thread,
args=(conf, cam_cfgs, detector_lock, frame_out))
thread.daemon = True
thread.start()
# start the flask app
app.run(host='0.0.0.0',
port=8888,
debug=False,
threaded=True,
use_reloader=False)
def preloop(self):
cfg = Conf('../config.ini', 'LOCAL')
self.game = GoldQuest(cfg)
