def test_get_stop_true(self):
"""Test that the stop value can be returned if the tracker has been stopped
--positive test case"""
tt = TemperatureTracker()
tt.stop()
self.assertIsNotNone(tt.get_stop())
def main():
# if you would like to test an additional model, add one to the list below:
models = ["alwaysai/mobilenet_ssd",
"alwaysai/ssd_inception_v2_coco_2018_01_28"]
# if you've added a model, add a new color in as a list of tuples in BGR format
# to make visualization easier (e.g. [(B, G, R)]).
colors = [[(66, 68, 179)], [(50, 227, 62)]]
detectors = []
# initialize a list to hold temperature data
TEMP_DATA = []
temperature_tracker = TemperatureTracker(TEMP_DATA)
# load all the models (creates a new object detector for each model)
for model in models:
# start up a first object detection model
obj_detect = edgeiq.ObjectDetection(model)
obj_detect.load(engine=edgeiq.Engine.DNN)
# track the generated object detection items by storing them in detectors
detectors.append(obj_detect)
# print the details of each model to the console
print("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))
fps = edgeiq.FPS()
try:
with edgeiq.WebcamVideoStream(cam=0) as video_stream, \
edgeiq.Streamer() as streamer:
# Allow Webcam to warm up
time.sleep(2.0)
fps.start()
# start the temperature tracker
temperature_tracker.start()
# loop detection
while True:
frame = video_stream.read()
text = [""]
# gather data from the all the detectors
for i in range(0, len(detectors)):
results = detectors[i].detect_objects(
frame, confidence_level=.5)
object_frame = edgeiq.markup_image(
frame, results.predictions, show_labels=False, colors=colors[i])
# for the first frame, overwrite the input feed
if i == 0:
display_frame = object_frame
else:
# otherwise, append the new marked-up frame to the previous one
display_frame = numpy.concatenate(
(object_frame, display_frame))
# append each prediction
for prediction in results.predictions:
text.append("Model {} detects {}: {:2.2f}% (inference time: {:1.2f})".format(detectors[i].model_id,
prediction.label, prediction.confidence * 100, results.duration))
# get an instance of the cpu temperature
temperature_tracker.update()
# send the image frame and the predictions for both
# prediction models to the output stream
streamer.send_data(display_frame, text)
fps.update()
if streamer.check_exit():
break
finally:
fps.stop()
temperature_tracker.stop()
print("elapsed time: {:.2f}".format(fps.get_elapsed_seconds()))
print("approx. FPS: {:.2f}".format(fps.compute_fps()))
summary = temperature_tracker.summary()
print(summary)
print(*TEMP_DATA)
print("Program Ending")
def main():
obj_detect = edgeiq.ObjectDetection("alwaysai/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))
temperature_tracker = TemperatureTracker()
fps = edgeiq.FPS()
try:
with edgeiq.WebcamVideoStream(cam=0) as video_stream, \
edgeiq.Streamer() as streamer:
# Allow Webcam to warm up
time.sleep(2.0)
fps.start()
# start the temperature tracker
temperature_tracker.start()
# loop detection
while True:
frame = video_stream.read()
results = obj_detect.detect_objects(frame, confidence_level=.5)
frame = edgeiq.markup_image(frame,
results.predictions,
colors=obj_detect.colors)
# 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 prediction in results.predictions:
text.append("{}: {:2.2f}%".format(
prediction.label, prediction.confidence * 100))
# get an instance of the cpu temperature
temperature_tracker.update()
# gather the current temperature and timestamp and print it
now = temperature_tracker.now()
# log block showing current temperature
print(
str(now[0]) + " " +
time.strftime('%Y-%m-%d %H:%M:%S', now[1]))
# details whether the temperature is safe for a Raspberry Pi 4
if now[0] < temperature_tracker.MAX_TEMP_RASP4:
print("Temperature is safe")
else:
print("You should shut down")
streamer.send_data(frame, text)
fps.update()
# exit program if maximum safe temp has been reached
if now[0] >= temperature_tracker.MAX_TEMP_RASP4:
print("Maximum safe temperature reached, stopping program")
break
if streamer.check_exit():
break
finally:
fps.stop()
# stop the temperature tracker
temperature_tracker.stop()
# print summary details for inference time
print("elapsed time: {:.2f}".format(fps.get_elapsed_seconds()))
print("approx. FPS: {:.2f}".format(fps.compute_fps()))
# print summary details for the temperature tracker
summary = temperature_tracker.summary()
print(summary)
print("Program Ending")