def main(_argv):
flags.mark_flag_as_required("model")
flags.mark_flag_as_required("video")
flags.mark_flag_as_required("labels")
labels = read_label_pbtxt(FLAGS.labels)
start_time = time.time()
model = tf.saved_model.load(FLAGS.model)
end_time = time.time()
logging.info("model loaded")
logging.info(f"Elapsed time: {str(end_time - start_time)}sec")
start_time = time.time()
cap = cv2.VideoCapture(FLAGS.video)
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
out = None
if FLAGS.output:
fps = int(cap.get(cv2.CAP_PROP_FPS))
codec = cv2.VideoWriter_fourcc(*"XVID")
out = cv2.VideoWriter(FLAGS.output, codec, fps, (width, height))
while True:
_, img = cap.read()
if img is None:
logging.warning("Empty Frame")
time.sleep(0.1)
break
image_tensor = np.expand_dims(img, axis=0)
detections = model(image_tensor)
boxes = detections["detection_boxes"][0].numpy()
classes = detections["detection_classes"][0].numpy().astype(np.int32)
scores = detections["detection_scores"][0].numpy()
output_image = draw_boxes(
img.copy(),
boxes,
classes,
scores,
labels,
height,
width,
min_threshold=FLAGS.threshold,
)
output_image = cv2.resize(output_image, (width, height))
cv2.imshow("Object Detection", output_image)
if out:
out.write(output_image)
if cv2.waitKey(1) & 0xFF == ord("q"):
break
cap.release()
end_time = time.time()
logging.info(f"Elapsed time: {str(end_time - start_time)}sec")
def main(_argv):
flags.mark_flag_as_required("model")
flags.mark_flag_as_required("ip")
flags.mark_flag_as_required("labels")
labels = read_label_pbtxt(FLAGS.labels)
stream_url = f"rtsp://{FLAGS.ip}:{FLAGS.port}/h264_ulaw.sdp"
if FLAGS.username and FLAGS.password:
stream_url = f"rtsp://{FLAGS.username}:{FLAGS.password}@{FLAGS.ip}:{FLAGS.port}/h264_ulaw.sdp"
start_time = time.time()
model = tf.saved_model.load(FLAGS.model)
end_time = time.time()
logging.info("model loaded")
logging.info(f"Elapsed time: {str(end_time - start_time)}sec")
start_time = time.time()
cap = VideoStream(cam=stream_url).start()
width = int(cap.stream.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.stream.get(cv2.CAP_PROP_FRAME_HEIGHT))
out = None
if FLAGS.output:
fps = int(cap.get(cv2.CAP_PROP_FPS))
codec = cv2.VideoWriter_fourcc(*"XVID")
out = cv2.VideoWriter(FLAGS.output, codec, fps, (width, height))
while True:
img = cap.read()
image_tensor = np.expand_dims(img, axis=0)
detections = model(image_tensor)
boxes = detections["detection_boxes"][0].numpy()
classes = detections["detection_classes"][0].numpy().astype(np.int32)
scores = detections["detection_scores"][0].numpy()
output_image = draw_boxes(
img.copy(),
boxes,
classes,
scores,
labels,
height,
width,
min_threshold=FLAGS.threshold,
)
cv2.imshow("Object Detection", cv2.resize(output_image, (800, 600)))
if out:
out.write(output_image)
if cv2.waitKey(1) & 0xFF == ord("q"):
break
cap.stop()
end_time = time.time()
logging.info(f"Elapsed time: {str(end_time - start_time)}sec")
def get_security_cam_arguments(flag: FLAGS) -> tuple:
"""parse arguments from config file
Args:
flag (FLAGS): command line arguments
Raises:
Exception: Error when config file cannot be loaded
Returns:
tuple: (config file args in form of dictionary, labels as dictionary)
"""
try:
with open(flag.config, "r") as f:
cfg = json.load(f)
args = {}
cam_args = cfg["camera"]
if cam_args["network"].get("ip") is not None:
stream_url = cam_args["network"]["url_format"]
stream_url = stream_url.replace("<ip>", cam_args["network"]["ip"])
stream_url = stream_url.replace("<port>",
cam_args["network"]["port"])
username = cam_args["network"]["username"]
password = cam_args["network"]["password"]
if (username and password) or (username != "" and password != ""):
stream_url = stream_url.replace("<username>", username)
stream_url = stream_url.replace("<password>", password)
else:
stream_url = stream_url.replace("<username>:<password>@", "")
args["cam"] = stream_url
else:
args["cam"] = cam_args["id"]
args["model"] = cfg["detector"]["detector_model_path"]
args["fps"] = cfg["detector"]["fps"]
args["res"] = cfg["detector"]["resolution"]
args["recording_save_path"] = cfg["detector"]["recording_save_path"]
args["temp_dir"] = cfg["detector"]["temp_dir"]
args["neglect_categories"] = cfg["detector"]["neglect_categories"]
args["min_threshold"] = cfg["detector"]["min_threshold"]
args["min_recording_time"] = cfg["detector"]["min_recording_time"]
args["wait_after_message_send"] = cfg["notifier"][
"wait_after_message_send"]
args["send_mail_to"] = cfg["notifier"]["send_mail_to"]
labels = read_label_pbtxt(cfg["detector"]["labels_path"])
return args, labels
except Exception as e:
raise Exception("An error occurred while loading config: ", e)
def main(_argv):
flags.mark_flag_as_required("model")
flags.mark_flag_as_required("image")
flags.mark_flag_as_required("labels")
labels = read_label_pbtxt(FLAGS.labels)
start_time = time.time()
tf.keras.backend.clear_session()
model = tf.saved_model.load(FLAGS.model)
end_time = time.time()
logging.info("model loaded")
logging.info(f"Elapsed time: {str(end_time - start_time)}sec")
image_np = load_image(FLAGS.image)
image_tensor = np.expand_dims(image_np, axis=0)
image_tensor = preprocess_input(image_tensor)
height, width, _ = image_np.shape
start_time = time.time()
detections = model(image_tensor)
end_time = time.time()
boxes = detections["detection_boxes"][0].numpy()
classes = detections["detection_classes"][0].numpy().astype(np.int32)
scores = detections["detection_scores"][0].numpy()
output_image = draw_boxes(
image_np.copy(),
boxes,
classes,
scores,
labels,
height,
width,
min_threshold=FLAGS.threshold,
)
output_image = cv2.cvtColor(output_image, cv2.COLOR_BGR2RGB)
cv2.imwrite(FLAGS.output, output_image)
cv2.imshow("Object Detection", output_image)
cv2.waitKey(0)
logging.info(f"Elapsed time: {str(end_time - start_time)}sec")