Hi there,
is there a option to run:
import setproctitle
import cv2
import time
import hailo
from hailo_rpi_common import (
get_default_parser,
QUEUE,
get_caps_from_pad,
get_numpy_from_buffer,
GStreamerApp,
app_callback_class,
)
# -----------------------------------------------------------------------------------------------
# User-defined class to be used in the callback function
# -----------------------------------------------------------------------------------------------
# Inheritance from the app_callback_class
class user_app_callback_class(app_callback_class):
def __init__(self):
super().__init__()
self.new_variable = 42 # New variable example
def new_function(self): # New function example
return "The meaning of life is: "
# -----------------------------------------------------------------------------------------------
# User-defined callback function
# -----------------------------------------------------------------------------------------------
# This is the callback function that will be called when data is available from the pipeline
@@ -40,7 +42,7 @@ def app_callback(pad, info, user_data):
if buffer is None:
return Gst.PadProbeReturn.OK
# Using the user_data to count the number of frames
user_data.increment()
string_to_print = f"Frame count: {user_data.get_count()}\n"
@@ -50,25 +52,25 @@ def app_callback(pad, info, user_data):
# If the user_data.use_frame is set to True, we can get the video frame from the buffer
frame = None
if user_data.use_frame and format is not None and width is not None and height is not None:
# Get video frame
frame = get_numpy_from_buffer(buffer, format, width, height)
# Get the detections from the buffer
roi = hailo.get_roi_from_buffer(buffer)
detections = roi.get_objects_typed(hailo.HAILO_DETECTION)
# Parse the detections
detection_count = 0
for detection in detections:
label = detection.get_label()
bbox = detection.get_bbox()
confidence = detection.get_confidence()
if label == "person":
string_to_print += f"Detection: {label} {confidence:.2f}\n"
detection_count += 1
if user_data.use_frame:
# Note: using imshow will not work here, as the callback function is not running in the main thread
# Let's print the detection count to the frame
cv2.putText(frame, f"Detections: {detection_count}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
# Example of how to use the new_variable and new_function from the user_data
# Let's print the new_variable and the result of the new_function to the frame
@@ -81,26 +83,36 @@ def app_callback(pad, info, user_data):
return Gst.PadProbeReturn.OK
# -----------------------------------------------------------------------------------------------
# User Gstreamer Application
# -----------------------------------------------------------------------------------------------
# This class inherits from the hailo_rpi_common.GStreamerApp class
class GStreamerDetectionApp(GStreamerApp):
def __init__(self, args, user_data):
# Call the parent class constructor
super().__init__(args, user_data)
# Additional initialization code can be added here
# Set Hailo parameters these parameters should be set based on the model used
self.batch_size = 2
self.network_width = 640
self.network_height = 640
self.network_format = "RGB"
nms_score_threshold = 0.3
nms_iou_threshold = 0.45
# Temporary code: new postprocess will be merged to TAPPAS.
# Check if new postprocess so file exists
new_postprocess_path = os.path.join(self.current_path, '../resources/libyolo_hailortpp_post.so')
if os.path.exists(new_postprocess_path):
self.default_postprocess_so = new_postprocess_path
else:
self.default_postprocess_so = os.path.join(self.postprocess_dir, 'libyolo_hailortpp_post.so')
if args.hef_path is not None:
self.hef_path = args.hef_path
# Set the HEF file path based on the network
elif args.network == "yolov6n":
self.hef_path = os.path.join(self.current_path, '../resources/yolov6n.hef')
elif args.network == "yolov8s":
self.hef_path = os.path.join(self.current_path, '../resources/yolov8s_h8l.hef')
@@ -109,67 +121,105 @@ def __init__(self, args, user_data):
else:
assert False, "Invalid network type"
# User-defined label JSON file
if args.labels_json is not None:
self.labels_config = f' config-path={args.labels_json} '
# Temporary code
if not os.path.exists(new_postprocess_path):
print("New postprocess so file is missing. It is required to support custom labels. Check documentation for more information.")
exit(1)
else:
self.labels_config = ''
self.app_callback = app_callback
self.thresholds_str = (
f"nms-score-threshold={nms_score_threshold} "
f"nms-iou-threshold={nms_iou_threshold} "
f"output-format-type=HAILO_FORMAT_TYPE_FLOAT32"
)
# Set the process title
setproctitle.setproctitle("Hailo Detection App")
self.create_pipeline()
def get_pipeline_string(self):
if self.source_type == "rpi":
source_element = (
"libcamerasrc name=src_0 auto-focus-mode=2 ! "
f"video/x-raw, format={self.network_format}, width=1536, height=864 ! "
+ QUEUE("queue_src_scale")
+ "videoscale ! "
f"video/x-raw, format={self.network_format}, width={self.network_width}, height={self.network_height}, framerate=30/1 ! "
)
elif self.source_type == "usb":
source_element = (
f"v4l2src device={self.video_source} name=src_0 ! "
"video/x-raw, width=640, height=480, framerate=30/1 ! "
)
else:
source_element = (
f"filesrc location={self.video_source} name=src_0 ! "
+ QUEUE("queue_dec264")
+ " qtdemux ! h264parse ! avdec_h264 max-threads=2 ! "
" video/x-raw, format=I420 ! "
)
source_element += QUEUE("queue_scale")
source_element += "videoscale n-threads=2 ! "
source_element += QUEUE("queue_src_convert")
source_element += "videoconvert n-threads=3 name=src_convert qos=false ! "
source_element += f"video/x-raw, format={self.network_format}, width={self.network_width}, height={self.network_height}, pixel-aspect-ratio=1/1 ! "
pipeline_string = (
"hailomuxer name=hmux "
+ source_element
+ "tee name=t ! "
+ QUEUE("bypass_queue", max_size_buffers=20)
+ "hmux.sink_0 "
+ "t. ! "
+ QUEUE("queue_hailonet")
+ "videoconvert n-threads=3 ! "
f"hailonet hef-path={self.hef_path} batch-size={self.batch_size} {self.thresholds_str} force-writable=true ! "
+ QUEUE("queue_hailofilter")
+ f"hailofilter so-path={self.default_postprocess_so} {self.labels_config} qos=false ! "
+ QUEUE("queue_hmuc")
+ "hmux.sink_1 "
+ "hmux. ! "
+ QUEUE("queue_hailo_python")
+ QUEUE("queue_user_callback")
+ "identity name=identity_callback ! "
+ QUEUE("queue_hailooverlay")
+ "hailooverlay ! "
+ QUEUE("queue_videoconvert")
+ "videoconvert n-threads=3 qos=false ! "
+ QUEUE("queue_hailo_display")
+ f"fpsdisplaysink video-sink={self.video_sink} name=hailo_display sync={self.sync} text-overlay={self.options_menu.show_fps} signal-fps-measurements=true "
)
print(pipeline_string)
return pipeline_string
if __name__ == "__main__":
# Create an instance of the user app callback class
user_data = user_app_callback_class()
parser = get_default_parser()
# Add additional arguments here
parser.add_argument(
"--network",
default="yolov6n",
choices=['yolov6n', 'yolov8s', 'yolox_s_leaky'],
help="Which Network to use, default is yolov6n",
)
parser.add_argument(
"--hef-path",
default=None,
help="Path to HEF file",
)
parser.add_argument(
"--labels-json",
default=None,
help="Path to costume labels JSON file",
)
args = parser.parse_args()
app = GStreamerDetectionApp(args, user_data)
app.run()
with a custom callback → print to terminal when person is seen by hailo.
without a gui so no terminal window is opend to show the camera feed.
Has anyone managed to get that done guiless?
Greetings
Robert
