def main():
serial_numbers = []
text = "RealSense Camera"
serial_numbers = edgeiq.enumerate_realsense_cameras()
if not serial_numbers:
sys.exit("Program Ending No RealSense Camera Found")
try:
with edgeiq.RealSense(serial_numbers[0]) as video_stream, \
edgeiq.Streamer() as streamer:
print("starting RealSense camera")
# let camera warm up
time.sleep(2.0)
# loop over to get frames
while True:
# Get color and depth images
depth_image, color_image = video_stream.read()
depth_colormap = video_stream.render_depth_image(depth_image)
combined = np.vstack((color_image, depth_colormap))
streamer.send_data(combined, text)
if streamer.check_exit():
break
finally:
print("Closing RealSense Camera")
video_stream.stop()
streamer.close()
print("Program Ending")
def main():
obj_detect = edgeiq.ObjectDetection(
"alwaysai/ssd_mobilenet_v2_coco_2018_03_29")
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))
try:
with edgeiq.RealSense() as video_stream, \
edgeiq.Streamer() as streamer:
print("starting RealSense camera")
time.sleep(2.0)
# loop detection
while True:
distances = []
depth_image, color_image = video_stream.read()
roi = video_stream.roi(depth_image, color_image, min=None, max=0.9)
# frame = edgeiq.resize(color_image, width=416)
results = obj_detect.detect_objects(roi, confidence_level=.6)
roi = edgeiq.markup_image(
roi, results.predictions, colors=obj_detect.colors)
for prediction in results.predictions:
distances.append(video_stream.compute_object_distance(prediction.box,depth_image))
# 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:")
for i, prediction in enumerate(results.predictions):
text.append("{}: {:2.1f}% Distance = {:2.2f}m".format(
prediction.label, prediction.confidence * 100, distances[i]))
streamer.send_data(roi, text)
if streamer.check_exit():
break
finally:
print("Program Ending")
def main():
obj_detect = edgeiq.ObjectDetection(
"alwaysai/ssd_mobilenet_v2_coco_2018_03_29")
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))
try:
with edgeiq.RealSense() as video_stream, \
edgeiq.Streamer() as streamer:
print("Starting RealSense camera")
time.sleep(2.0)
# loop detection
while True:
depth_image, color_image = video_stream.read()
segments = []
mid_point = []
center_object_one = []
center_object_two = []
results = obj_detect.detect_objects(color_image, confidence_level=.6)
if len(results.predictions) > 1:
for i in range(len(results.predictions)):
for j in range((i+1), len(results.predictions)):
segments.append(video_stream.compute_distance_between_objects(
results.predictions[i].box, results.predictions[j].box))
center_object_one.append(results.predictions[i].box.center)
center_object_two.append(results.predictions[j].box.center)
mid_point.append(midpoint(results.predictions[i].box.center,
results.predictions[j].box.center))
# 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("Segment Distnces in Meters:")
for i, segment in enumerate(segments, 0):
text.append("Segment {}: = {:1.2} Meters".format(i, segment))
cv2.putText(color_image, '{:1.2} Meters'.format(segment),
(int(mid_point[i][0]), int(mid_point[i][1]-50)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
cv2.arrowedLine(color_image, (int(center_object_one[i][0]), int(center_object_one[i][1])),
(int(center_object_two[i][0]), int(center_object_two[i][1])), color=(0, 0, 255),
thickness=3)
streamer.send_data(color_image, text)
if streamer.check_exit():
break
finally:
video_stream.stop()
print("Program Ending")