def main():
semantic_segmentation = edgeiq.SemanticSegmentation(
"alwaysai/enet")
semantic_segmentation.load(engine=edgeiq.Engine.DNN)
print('Outside of try')
try:
with edgeiq.FileVideoStream("driving_downtown.mp4", play_realtime=False) as video_stream, \
edgeiq.VideoWriter(output_path="output.avi") as video_writer:
print('Inside of try')
if video_stream is None:
print('no video stream!')
else:
print('video stream available of type {}'.format(type(video_stream)))
if video_writer is None:
print('no video writer!')
else:
print('video writer available of type {}'.format(type(video_writer)))
more = getattr(video_stream, "more", None)
if callable(more):
print('video_stream has an attribute called more')
else:
print('video_stream has no MORE function!?')
if video_stream.more():
print('At least one video frame available before we bgin')
while video_stream.more():
print('Inside of while')
# image = video_stream.read()
# # video_writer.write_frame(image)
# if image is None:
# print('no image')
# else:
# print('image available of type {}'.format(type(image)))
# results = semantic_segmentation.segment_image(image)
# if results is None:
# print('no results')
# else:
# print('results available of type {}'.format(type(results)))
# mask = semantic_segmentation.build_image_mask(results.class_map)
# if mask is None:
# print('no mask')
# else:
# print('mask available of type {}'.format(type(mask)))
# blended = edgeiq.blend_images(image, mask, alpha=0.5)
# if blended is None:
# print('no blended')
# else:
# print('blended available of type {}'.format(type(blended)))
# video_writer.write_frame(image)
finally:
print("Program Ending")
def start_detection(config,
did_start_callback,
enter_callback,
exit_callback,
did_end_object_callback,
should_log=True):
"""
Enable features dependent on config and pass callback down to actual detection functions
"""
global ENTER_CALLBACK
global EXIT_CALLBACK
ENTER_CALLBACK = enter_callback
EXIT_CALLBACK = exit_callback
print('alwaysai.py: start_detection: enter_callback: {}'.format(
ENTER_CALLBACK))
# Configs
od_config = alwaysai_configs.ObjectDetector(config)
ct_config = alwaysai_configs.CentroidTracker(config)
vs_config = alwaysai_configs.VideoStream(config)
od = object_detector(od_config.model_id)
t = edgeiq.CentroidTracker(deregister_frames=ct_config.deregister_frames,
max_distance=ct_config.max_distance)
en_zones_config = config.get('entry_zones', [])
ex_zones_config = config.get('exit_zones', [])
entry_zones = zones_from_config(en_zones_config)
exit_zones = zones_from_config(ex_zones_config)
vs = None
# print('alwaysai.py: start_detection: en_zones_config: {}'.format(en_zones_config))
# print('alwaysai.py: start_detection: entry_zones: {}'.format(entry_zones))
# Inits
if vs_config.mode == 'camera':
if should_log:
print('alwaysai.py: start_detection: enabling camera w/ id: {}'.
format(vs_config.camera_id))
vs = edgeiq.WebcamVideoStream(cam=vs_config.camera_id)
if vs_config.mode == 'file':
if should_log:
print('alwaysai.py: start_detection: reading from file')
vs = edgeiq.FileVideoStream(vs_config.filename, play_realtime=True)
enable_streamer = config.get('enable_streamer', False)
streamer = alwaysai_configs.DummyStreamer()
if enable_streamer:
print('alwaysai.py: start_detection: ENABLING streamer')
streamer = edgeiq.Streamer()
# Start
start_video_detection_with_streamer(vs, od_config, od, streamer, t,
entry_zones, exit_zones,
did_start_callback, did_detect,
did_end_object_callback)
def main():
semantic_segmentation = edgeiq.SemanticSegmentation("alwaysai/enet")
semantic_segmentation.load(engine=edgeiq.Engine.DNN_CUDA)
print("Loaded model:\n{}\n".format(semantic_segmentation.model_id))
print("Engine: {}".format(semantic_segmentation.engine))
print("Accelerator: {}\n".format(semantic_segmentation.accelerator))
print("Labels:\n{}\n".format(semantic_segmentation.labels))
fps = edgeiq.FPS()
try:
with edgeiq.FileVideoStream('toronto.mp4', play_realtime=True) as video_stream, \
edgeiq.Streamer() as streamer: # play_realtime simulates video feed from a camera
# Allow Webcam to warm up
time.sleep(2.0)
fps.start()
# loop detection
while True:
frame = video_stream.read()
results = semantic_segmentation.segment_image(frame)
# Generate text to display on streamer
text = ["Model: {}".format(semantic_segmentation.model_id)]
text.append("Inference time: {:1.3f} s".format(
results.duration))
text.append("Legend:")
text.append(semantic_segmentation.build_legend())
mask = semantic_segmentation.build_image_mask(
results.class_map)
blended = edgeiq.blend_images(frame, mask, alpha=0.5)
streamer.send_data(blended, text)
fps.update()
if streamer.check_exit():
break
finally:
fps.stop()
print("elapsed time: {:.2f}".format(fps.get_elapsed_seconds()))
print("approx. FPS: {:.2f}".format(fps.compute_fps()))
print("Program Ending")
def main():
obj_detect = edgeiq.ObjectDetection("alwaysai/yolo_v3")
obj_detect.load(engine=edgeiq.Engine.DNN)
video_path = f"data/inputs/{difficulty}.mp4"
stream_context = edgeiq.FileVideoStream(f"{video_path}",
play_realtime=True)
with stream_context as video_stream, edgeiq.Streamer() as streamer:
while video_stream.more():
image = video_stream.read()
results = obj_detect.detect_objects(image, confidence_level=.5)
specific_predictions = [
r for r in results.predictions if r.label == 'person'
]
res = tracker.update(specific_predictions)
image = draw_tracked_boxes(image, res)
# image = edgeiq.markup_image(image, people_predictions)
streamer.send_data(image)
def _video_file_stream(filepath):
if filepath is None:
raise Exception(
"alwaysai_helper.py: video_file_stream: filepath not provided")
return edgeiq.FileVideoStream(filepath)
def main():
# The current frame index
frame_idx = 0
# The number of frames to skip before running detector
detect_period = 30
obj_detect = edgeiq.ObjectDetection(
"alwaysai/vehicle_license_mobilenet_ssd")
obj_detect.load(engine=edgeiq.Engine.DNN)
print("Loaded model:\n{}\n".format(obj_detect.model_id))
print("Engine: {}".format(obj_detect.engine))
print("Accelerator: {}\n".format(obj_detect.accelerator))
print("Labels:\n{}\n".format(obj_detect.labels))
tracker = edgeiq.CorrelationTracker(max_objects=5)
fps = edgeiq.FPS()
try:
video_paths = edgeiq.list_files(base_path="./video/",
valid_exts=".mp4")
streamer = edgeiq.Streamer().setup()
for video_path in video_paths:
with edgeiq.FileVideoStream(video_path) as video_stream:
# Allow Webcam to warm up
time.sleep(2.0)
fps.start()
# loop detection
while video_stream.more():
frame = video_stream.read()
predictions = []
# if using new detections, update 'predictions'
if frame_idx % detect_period == 0:
results = obj_detect.detect_objects(
frame, confidence_level=.5)
# Generate text to display on streamer
text = ["Model: {}".format(obj_detect.model_id)]
text.append("Inference time: {:1.3f} s".format(
results.duration))
text.append("Objects:")
# Stop tracking old objects
if tracker.count:
tracker.stop_all()
# Set predictions to the new predictions
predictions = results.predictions
if not predictions:
text.append("no predictions")
# use 'number' to identify unique objects
number = 0
for prediction in predictions:
number = number + 1
text.append("{}_{}: {:2.2f}%".format(
prediction.label, number,
prediction.confidence * 100))
tracker.start(frame, prediction)
else:
# otherwise, set 'predictions' to the tracked predictions
if tracker.count:
predictions = tracker.update(frame)
# either way, use 'predictions' to mark up the image and update text
frame = edgeiq.markup_image(frame,
predictions,
show_labels=True,
show_confidences=False,
colors=obj_detect.colors,
line_thickness=6,
font_size=2,
font_thickness=6)
streamer.send_data(frame, text)
frame_idx += 1
fps.update()
if streamer.check_exit():
break
finally:
fps.stop()
streamer.close()
print("elapsed time: {:.2f}".format(fps.get_elapsed_seconds()))
print("approx. FPS: {:.2f}".format(fps.compute_fps()))
print("Program Ending")
def start_file_detection_and_tracking(delivery_object,
filter_for,
model_name,
filename,
detection_confidence=.5,
enable_streamer=True,
streamer_show_labels=True,
tracker_deregister_frames=20,
tracker_max_distance=50,
should_log=False):
"""Starts a detection loop"""
obj_detect = object_detector(model_name)
tracker = edgeiq.CentroidTracker(
deregister_frames=tracker_deregister_frames,
max_distance=tracker_max_distance)
fps = edgeiq.FPS()
try:
# Enables video camera and streamer
# TODO: add streamer disable feature here
with edgeiq.FileVideoStream(
filename) as video_stream, edgeiq.Streamer() as streamer:
# Start tracking of frames per second
fps.start()
# loop detection
while True:
frame = video_stream.read()
text = []
# Run detection
# detect human faces
results = obj_detect.detect_objects(
frame, confidence_level=detection_confidence)
# TODO: Add filter option here
frame = edgeiq.markup_image(frame,
results.predictions,
show_labels=streamer_show_labels)
# Generate text to display on streamer
text.append("Model: {}".format(obj_detect.model_id))
text.append("Inference time: {:1.3f} s".format(
results.duration))
predictions = []
objects = tracker.update(results.predictions)
# predictions = results.predictions
for (object_id, prediction) in objects.items():
# print(vars(prediction))
text.append("{}: {}: {:2.2f}%".format(
object_id, prediction.label,
prediction.confidence * 100))
predictions.append(prediction)
if delivery.should_send_image(object_id):
# Extract image
face_image = edgeiq.cutout_image(frame, prediction.box)
# Send data to server
delivery.send_image(object_id, prediction.label,
face_image)
elif delivery.should_send_data(object_id):
delivery.send_data(object_id, prediction.label)
# if delivery.should_send_data(object_id):
# delivery.send_data(object_id, prediction.label)
frame = edgeiq.markup_image(frame, predictions)
streamer.send_data(frame, text)
fps.update()
if streamer.check_exit():
break
finally:
# stop fps counter and display information
fps.stop()
if should_log == True:
print("[INFO] elapsed time: {:.2f}".format(
fps.get_elapsed_seconds()))
print("[INFO] approx. FPS: {:.2f}".format(fps.compute_fps()))
print("Program Ending")
