def main():
obj_detect = edgeiq.ObjectDetection(
"alwaysai/ssd_mobilenet_v1_coco_2018_01_28")
obj_detect.load(engine=edgeiq.Engine.DNN)
image_paths = sorted(list(edgeiq.list_images("images/")))
print("Images:\n{}\n".format(image_paths))
with edgeiq.Streamer(queue_depth=len(image_paths),
inter_msg_time=4) as streamer:
for image_path in image_paths:
# Load image from disk
image = cv2.imread(image_path)
results = obj_detect.detect_objects(image, confidence_level=.5)
image = edgeiq.markup_image(image,
results.predictions,
colors=[(255, 255, 255)])
# Generate text to display on streamer
text = ["<b>Model:</b> {}".format(obj_detect.model_id)]
text.append("<b>Inference time:</b> {:1.3f} s".format(
results.duration))
text.append("<b>Objects:</b>")
for prediction in results.predictions:
text.append("{}: {:2.2f}%".format(prediction.label,
prediction.confidence * 100))
if image_path == 'images/example_08.jpg':
text.append("<br><br><b><em>Hello, World!</em></b>")
streamer.send_data(image, text)
streamer.wait()
print("Program Ending")
def perform_object_detection(frame):
"""Perform object detction on an image, update
the table data, and returns a string.
Args:
frame (numpy array): The frame from the camera stream.
Returns:
string: The string representation of the image
"""
frame = edgeiq.resize(frame, width=800, height=300)
results = obj_detect.detect_objects(frame, confidence_level=.5)
frame = edgeiq.markup_image(frame,
results.predictions,
colors=obj_detect.colors)
frame = cv2.imencode('.jpg', frame)[1].tobytes()
# update data for table
objects = {
'timestamp': str(round((time.time() - START_TIME), 0)),
'labels': ", ".join([p.label for p in results.predictions])
}
global data
if data is None:
data = pd.DataFrame({k: [v] for k, v in objects.items()})
else:
data = data.append(pd.DataFrame({k: [v] for k, v in objects.items()}))
data = data.drop_duplicates()
return frame
def _run_detection(self):
obj_detect = edgeiq.ObjectDetection(self._model_id)
obj_detect.load(engine=self._engine)
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))
with edgeiq.WebcamVideoStream(cam=self.idx) as video_stream:
# Allow Webcam to warm up
time.sleep(2.0)
self._fps.start()
while True:
frame = video_stream.read()
if self._stop_event.is_set():
break
results = obj_detect.detect_objects(frame, confidence_level=.5)
frame = edgeiq.markup_image(frame,
results.predictions,
colors=obj_detect.colors)
output_results = {
"idx": self.idx,
"frame": frame,
"results": results,
"model_id": obj_detect.model_id
}
self._results_q.put(output_results)
self._fps.update()
def main():
#ardunio = serial.Serial('COM1', 115200, timeout = 1)
GPIO.setmode(GPIO.BCM)
buzzer = 23
GPIO.setup(buzzer, GPIO.OUT)
obj_detect = edgeiq.ObjectDetection("alwaysai/mobilenet_ssd")
obj_detect.load(engine=edgeiq.Engine.DNN_OPENVINO)
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))
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()
# loop detection
while True:
frame = video_stream.read()
results = obj_detect.detect_objects(frame, confidence_level=.5)
predictions = edgeiq.filter_predictions_by_label(
results.predictions, ["bottle"])
frame = edgeiq.markup_image(frame,
results.predictions,
show_confidences=False,
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 predictions:
text.append("{}: {:2.2f}%".format(
prediction.label, prediction.confidence * 100))
GPIO.output(buzzer, GPIO.HIGH)
GPIO.input(buzzer)
sleep(0.5)
GPIO.output(buzzer, GPIO.LOW)
GPIO.input(buzzer)
sleep(0.5)
streamer.send_data(frame, 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():
label_defs = {}
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))
fps = edgeiq.FPS()
try:
with edgeiq.WebcamVideoStream(cam=1) as video_stream, \
edgeiq.Streamer() as streamer:
# Allow Webcam to warm up
time.sleep(2.0)
fps.start()
# loop detection
while True:
frame = video_stream.read()
results = obj_detect.detect_objects(frame, confidence_level=.5)
frame = edgeiq.markup_image(
frame, labelToString(label_defs, results.predictions), show_labels = False,
show_confidences = False, colors=obj_detect.colors,
line_thickness = 0)
frame = addNotes(frame, results.predictions)
# 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:")
text.append("fps:{:2.2f}".format(fps.compute_fps()))
for prediction in results.predictions:
text.append("{}: {:2.2f}%".format(
prediction.label, prediction.confidence * 100))
streamer.send_data(frame, 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(camera, use_streamer, server_addr):
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))
fps = edgeiq.FPS()
try:
streamer = None
if use_streamer:
streamer = edgeiq.Streamer().setup()
else:
streamer = CVClient(server_addr).setup()
with edgeiq.WebcamVideoStream(cam=camera) as video_stream:
# Allow Webcam to warm up
time.sleep(2.0)
fps.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))
streamer.send_data(frame, text)
fps.update()
if streamer.check_exit():
break
finally:
if streamer is not None:
streamer.close()
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/res10_300x300_ssd_iter_140000")
obj_detect.load(engine=edgeiq.Engine.DNN)
print("Engine: {}".format(obj_detect.engine))
print("Accelerator: {}\n".format(obj_detect.accelerator))
print("Model:\n{}\n".format(obj_detect.model_id))
centroid_tracker = edgeiq.CentroidTracker(deregister_frames=20,
max_distance=50)
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()
# loop detection and centroid tracker
while True:
frame = video_stream.read()
frame = edgeiq.resize(frame, width=400)
results = obj_detect.detect_objects(frame, confidence_level=.5)
text = ["Model: {}".format(obj_detect.model_id)]
text.append("Inference time: {:1.3f} s".format(
results.duration))
text.append("Objects:")
objects = centroid_tracker.update(results.predictions)
# Update the label to reflect the object ID
predictions = []
for (object_id, prediction) in objects.items():
new_label = 'face {}'.format(object_id)
prediction.label = new_label
text.append(new_label)
predictions.append(prediction)
frame = edgeiq.markup_image(frame, predictions)
streamer.send_data(frame, 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():
# Set up object detection API
obj_detect = edgeiq.ObjectDetection(
"alwaysai/mobilenet_ssd")
obj_detect.load(engine=edgeiq.Engine.DNN)
# Set up rpi ports
GPIO.setmode(GPIO.BOARD)
GPIO.setup(LEFT_PORT, GPIO.OUT)
GPIO.setup(RIGHT_PORT, GPIO.OUT)
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()
# loop detection
while True:
frame = video_stream.read()
results = obj_detect.detect_objects(frame, confidence_level=.8) # Maybe filter the result to bottles or bags for demo?
image_Centering(results.predictions)
# Debug information
if(debug_On):
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))
streamer.send_data(frame, text)
fps.update()
time.sleep(FRAME_A_RATE)
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()))
def main():
facial_detector = edgeiq.ObjectDetection(
"alwaysai/res10_300x300_ssd_iter_140000")
facial_detector.load(engine=edgeiq.Engine.DNN)
print("Engine: {}".format(facial_detector.engine))
print("Accelerator: {}\n".format(facial_detector.accelerator))
print("Model:\n{}\n".format(facial_detector.model_id))
fps = edgeiq.FPS()
try:
with edgeiq.WebcamVideoStream(cam=0) as webcam, \
edgeiq.Streamer() as streamer:
# Allow webcam to warm up
time.sleep(2.0)
fps.start()
# loop detection
while True:
frame = webcam.read()
# detect human faces
results = facial_detector.detect_objects(frame,
confidence_level=.5)
frame = edgeiq.markup_image(frame,
results.predictions,
colors=facial_detector.colors)
# Generate text to display on streamer
text = ["Model: {}".format(facial_detector.model_id)]
text.append("Inference time: {:1.3f} s".format(
results.duration))
text.append("Faces:")
for prediction in results.predictions:
text.append("{}: {:2.2f}%".format(
prediction.label, prediction.confidence * 100))
streamer.send_data(frame, text)
fps.update()
if streamer.check_exit():
break
finally:
# stop fps counter and display information
fps.stop()
print("[INFO] elapsed time: {:.2f}".format(fps.get_elapsed_seconds()))
print("[INFO] approx. FPS: {:.2f}".format(fps.compute_fps()))
print("Program Ending")
def main():
"""Run csi video stream and object detector."""
obj_detect = edgeiq.ObjectDetection(
"alwaysai/ssd_mobilenet_v1_coco_2018_01_28")
obj_detect.load(engine=edgeiq.Engine.DNN_CUDA)
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 enhanced_csi.JetsonVideoStream(cam=0,
rotation=enhanced_csi.
FrameRotation.ROTATE_180,
camera_mode=enhanced_csi.
JetsonCameraMode.
IMX477_4032x3040_30_0,
display_width=640,
display_height=480) as video_stream,\
edgeiq.Streamer() as streamer:
time.sleep(2.0)
video_stream.start_counting_fps()
# loop detection
while True:
frame = enhanced_csi.read_camera(video_stream, True)
results = obj_detect.detect_objects(frame, confidence_level=.4)
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))
video_stream.frames_displayed += 1
streamer.send_data(frame, text)
if streamer.check_exit():
break
video_stream.release_fps_stats()
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:
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_det = edgeiq.ObjectDetection("tester2204/CE-Recog")
if edgeiq.is_jetson():
obj_det.load(engine=edgeiq.Engine.DNN_CUDA)
print("Nvidia Jetson Detected\n")
else:
obj_det.load(engine=edgeiq.Engine.DNN)
print("Device is not a Nvidia Jetson Board\n")
print("Initializing Application...\n")
print("Model:\n{}\n".format(obj_det.model_id))
print("Engine:\n{}\n".format(obj_det.engine))
print("Labels:\n{}\n".format(obj_det.labels))
#imgURL = "https://specials-images.forbesimg.com/imageserve/5e88b867e2bb040006427704/0x0.jpg"
#urllib.request.urlretrieve(imgURL, "this.jpg") #Change based on OS and User
#image = "Images/this.jpg"
image_lists = sorted(list(edgeiq.list_images("Images/")))
with edgeiq.Streamer(queue_depth=len(image_lists),
inter_msg_time=7) as streamer:
i = 0
while i < 3:
for image_list in image_lists:
show_image = cv2.imread(image_list)
image = show_image.copy()
results = obj_det.detect_objects(image, confidence_level=.5)
image = edgeiq.markup_image(image,
results.predictions,
colors=obj_det.colors)
shown = ["Model: {}".format(obj_det.model_id)]
shown.append("Inference time: {:1.3f} s".format(
results.duration))
shown.append("Objects:")
for prediction in results.predictions:
shown.append("{}: {:2.2f}%".format(
prediction.label, prediction.confidence * 100))
streamer.send_data(image, shown)
streamer.wait()
i = i + 1
#if streamer.check_exit():
print("That's it folks!")
print("Thanks for using Ben's Object Recognition Model & Software")
print("Sponsored by: Darien's Face")
def _runDetectionModel(model, frame):
results = model.detect_objects(frame, confidence_level=.5)
frame = edgeiq.markup_image(frame,
results.predictions,
colors=model.colors)
# Generate text to display on streamer
text = ["Model: {}".format(model.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))
return frame, text
def end_tracking_loop(components, predictions, text):
# print("alwaysai_helper.py: end_tracking_loop")
fps = components[FPS]
streamer = components[STREAMER]
video_stream = components[VIDEO_STREAM]
frame = components[CURRENT_FRAME]
if fps is None:
raise Exception(
"alwaysai_helper.py: end_tracking_loop: fps missing from components"
)
if video_stream is None:
raise Exception(
"alwaysai_helper.py: end_tracking_loop: video_stream missing from components"
)
frame = edgeiq.markup_image(frame, predictions)
if streamer is not None:
streamer.send_data(frame, text)
fps.update()
def main():
"""Run object detector and centroid tracker."""
tracker = edgeiq.CorrelationTracker(
max_objects=5, enter_cb=face_enters, exit_cb=face_exits)
fps = edgeiq.FPS()
try:
with edgeiq.oak.Oak('alwaysai/face_detection_0200_oak',
sensor=edgeiq.Sensor.res_1080,
video_mode=edgeiq.VideoMode.preview) as oak_camera, \
edgeiq.Streamer() as streamer:
# Allow Webcam to warm up
time.sleep(2.0)
fps.start()
# loop detection and tracking
while True:
frame = oak_camera.get_frame()
results = oak_camera.get_model_result(confidence_level=.6)
if results:
fps.update()
text = ["Faces Detected:"]
objects = tracker.update(results.predictions, frame)
# Update the label to reflect the object ID
predictions = []
for (object_id, prediction) in objects.items():
prediction.label = "face {}".format(object_id)
text.append("{}".format(prediction.label))
predictions.append(prediction)
text.append(("approx. FPS: {:.2f}".
format(fps.compute_fps())))
frame = edgeiq.markup_image(frame, predictions)
streamer.send_data(frame, text)
if streamer.check_exit():
break
finally:
fps.stop()
print("elapsed time: {:.2f}".format(fps.get_elapsed_seconds()))
print("Program Ending")
def main():
fps = edgeiq.FPS()
try:
streamer = edgeiq.Streamer()
streamer.setup()
video_stream = edgeiq.WebcamVideoStream(
cam=0) # replace with FileVideoStream if need be
# Allow application to warm up
video_stream.start()
time.sleep(2.0)
fps.start()
text = [""]
# initialize Vaccine Trakcer
vaccine_tracker = VaccineTracker()
# loop detection
while True:
frame = video_stream.read()
vaccine_tracker.update(frame)
# draw the vaccination box in the frame
frame = edgeiq.markup_image(frame, [
edgeiq.ObjectDetectionPrediction(label="vaccination",
index=0,
box=vaccine_tracker.box,
confidence=100.00)
])
streamer.send_data(frame, text)
fps.update()
if streamer.check_exit():
break
finally:
fps.stop()
streamer.close()
video_stream.stop()
print("elapsed time: {:.2f}".format(fps.get_elapsed_seconds()))
print("approx. FPS: {:.2f}".format(fps.compute_fps()))
print("Program Ending")
def main():
"""Run object detector."""
fps = edgeiq.FPS()
try:
with edgeiq.Oak('alwaysai/ssd_v2_coco_oak') as oak_camera,\
edgeiq.Streamer() as streamer:
# Allow Oak camera to warm up
time.sleep(2.0)
fps.start()
# loop detection
while True:
frame = oak_camera.get_frame()
results = oak_camera.get_model_result(confidence_level=.8)
if results:
fps.update()
text = ["Oak Camera Detections:"]
text.append("approx. FPS: {:.2f}".format(
fps.compute_fps()))
text.append("Objects:")
for prediction in results.predictions:
center = tuple(
int(round(val)) for val in prediction.box.center)
b, g, r = frame[center[1], center[0]]
cname = getColorName(r, g, b)
text.append("{}: {:2.2f}% color = {}".format(
prediction.label, prediction.confidence * 100,
cname))
# Mark up image for display
frame = edgeiq.markup_image(frame, results.predictions)
streamer.send_data(frame, text)
if streamer.check_exit():
break
finally:
fps.stop()
print("elapsed time: {:.2f}".format(fps.get_elapsed_seconds()))
print("Program Ending")
def detection_base(model, confidence, image_array):
detector = edgeiq.ObjectDetection(
model) # model example: "alwaysai/res10_300x300_ssd_iter_140000"
detector.load(engine=edgeiq.Engine.DNN)
centroid_tracker = edgeiq.CentroidTracker(deregister_frames=100,
max_distance=50)
results = detector.detect_objects(image_array,
confidence_level=confidence)
objects = centroid_tracker.update(results.predictions)
predictions = []
for (object_id, prediction) in objects.items():
prediction.label = "{}: {}".format(prediction.label, object_id)
predictions.append(prediction)
image = edgeiq.markup_image(image_array, predictions)
return image, results, None
def handle(self):
'''
Method to be called by the socketserver.BaseServer.
'''
data = self.read()
image = self.unpack_img(data)
results = self.server.inferencer.detect_objects(image,
confidence_level=.5)
final = edgeiq.markup_image(image,
results.predictions,
colors=self.server.inferencer.colors)
image = self.pack_img(final)
if image is not None:
self.send(image)
def object_detection():
"""
Oak.get_model_result can return different results based on the purpose of the model running on the camera.
This function shows how to work with object detection models.
"""
fps = edgeiq.FPS()
with edgeiq.Oak('alwaysai/mobilenet_ssd_oak') as camera, edgeiq.Streamer(
) as streamer:
fps.start()
while True:
text = ['FPS: {:2.2f}'.format(fps.compute_fps())]
frame = camera.get_frame()
result = camera.get_model_result(confidence_level=.75)
# Check for inferencing results. Oak.get_model_result is a non-blocking call and will return None when new data is not available.
if result:
frame = edgeiq.markup_image(frame, result.predictions)
text.append("Objects:")
for prediction in result.predictions:
text.append("{}: {:2.2f}%".format(
prediction.label, prediction.confidence * 100))
streamer.send_data(frame, text)
if streamer.check_exit():
break
fps.update()
print('fps = {}'.format(fps.compute_fps()))
def main():
obj_detect = edgeiq.ObjectDetection(
"alwaysai/ssd_mobilenet_v1_coco_2018_01_28")
obj_detect.load(engine=edgeiq.Engine.DNN)
print("Engine: {}".format(obj_detect.engine))
print("Accelerator: {}\n".format(obj_detect.accelerator))
print("Model:\n{}\n".format(obj_detect.model_id))
print("Labels:\n{}\n".format(obj_detect.labels))
image_paths = sorted(list(edgeiq.list_images("images/")))
print("Images:\n{}\n".format(image_paths))
with edgeiq.Streamer(queue_depth=len(image_paths),
inter_msg_time=3) as streamer:
for image_path in image_paths:
# Load image from disk
image = cv2.imread(image_path)
results = obj_detect.detect_objects(image, confidence_level=.5)
image = edgeiq.markup_image(image,
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))
streamer.send_data(image, text)
streamer.wait()
print("Program Ending")
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 = []
# 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()
# 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}%".format(detectors[i].model_id,
prediction.label, prediction.confidence * 100))
# 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()
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/ssd_mobilenet_v1_coco_2018_01_28")
obj_detect.load(engine=edgeiq.Engine.DNN)
print("Engine: {}".format(obj_detect.engine))
print("Accelerator: {}\n".format(obj_detect.accelerator))
print("Model:\n{}\n".format(obj_detect.model_id))
print("Labels:\n{}\n".format(obj_detect.labels))
image_paths = sorted(list(edgeiq.list_images("images/")))
print("Images:\n{}\n".format(image_paths))
with edgeiq.Streamer(queue_depth=len(image_paths),
inter_msg_time=3) as streamer:
for image_path in image_paths:
# Load image from disk
image = cv2.imread(image_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
r, g, b = cv2.split(image)
fig = plt.figure()
axis = fig.add_subplot(1, 1, 1, projection="3d")
pixel_colors = image.reshape(
(np.shape(image)[0] * np.shape(image)[1], 3))
norm = colors.Normalize(vmin=-1., vmax=1.)
norm.autoscale(pixel_colors)
pixel_colors = norm(pixel_colors).tolist()
axis.scatter(r.flatten(),
g.flatten(),
b.flatten(),
facecolors=pixel_colors,
marker=".")
axis.set_xlabel("Red")
axis.set_ylabel("Green")
axis.set_zlabel("Blue")
plt.show()
#convert from rgb to hsv and pick out 2 shades
hsv_image = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
hsv_drot = (18, 24, 61)
hsv_lrot = (13, 203, 55)
#build the color mask
mask = cv2.inRange(hsv_image, hsv_lrot, hsv_drot)
res = cv2.bitwise_and(image, image, mask=mask)
plt.subplot(1, 2, 1)
plt.imshow(mask, cmap="gray")
plt.subplot(1, 2, 2)
plt.imshow(res)
plt.show()
#2nd layer mask, did not display
hsv_olive = (34, 32, 120)
hsv_dolive = (37, 240, 27)
mask_ol = cv2.inRange(hsv_image, hsv_olive, hsv_dolive)
res_w = cv2.bitwise_and(image, image, mask=mask_ol)
plt.subplot(1, 2, 1)
plt.imshow(mask_ol, cmap="gray")
plt.subplot(1, 2, 2)
plt.imshow(res_w)
plt.show()
#final mask
final_mask = mask + mask_ol
final_result = cv2.bitwise_and(image, image, mask=final_mask)
plt.subplot(1, 2, 1)
plt.imshow(final_mask, cmap="gray")
plt.subplot(1, 2, 2)
plt.imshow(final_result)
plt.show()
#testing .shape and typecast image
print("The type of this input is {}".format(type(image)))
print("Shape: {}".format(image.shape))
#piee
##text.append(get_colors(get_image(image_path), 4, True))
# Generate text to display on streamer
text = ["Model: {}".format(obj_detect.model_id)]
text.append("Inference time: {:1.3f} s".format(results.duration))
#need to convert from bgr to rgb
swapped_colors = swap(obj_detect.colors)
text.append("Colors printed!")
# text.append(swapped_colors)
print(swapped_colors)
# print(obj_detect.colors)
# converted = np.array([np.array(rgb) for rgb in swapped_colors]) // numpy arrays with lists (like numpy contained within itself, list of lists)
# print(converted.shape)
results = obj_detect.detect_objects(image, confidence_level=.5)
image = edgeiq.markup_image(image,
results.predictions,
colors=obj_detect.colors)
# print(rgb2hex(swapped_colors))
# print(converted)
# iterate through tuple list and convert
# for x in obj_detect.colors:
# text.append(rgb2hex(swapped_colors))
# text.append(format(x))
text.append("Objects:")
for prediction in results.predictions:
text.append("{}: {:2.2f}%".format(prediction.label,
prediction.confidence * 100))
streamer.send_data(image, text)
streamer.wait()
print("Program Ending")
def main():
obj_detect = edgeiq.ObjectDetection(
"alwaysai/mobilenet_ssd")
obj_detect.load(engine=edgeiq.Engine.DNN)
tracker = edgeiq.CentroidTracker(deregister_frames=30)
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))
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()
prev_tracked_people = {}
logs = []
currentPeople = 0
# loop detection
while True:
frame = video_stream.read()
results = obj_detect.detect_objects(frame, confidence_level=.5)
people = edgeiq.filter_predictions_by_label(results.predictions, ['person'])
tracked_people = tracker.update(people)
people = []
for (object_id, prediction) in tracked_people.items():
new_label = 'Person {}'.format(object_id)
prediction.label = new_label
people.append(prediction)
frame = edgeiq.markup_image(
frame, people, colors=obj_detect.colors)
new_entries = set(tracked_people) - set(prev_tracked_people)
for entry in new_entries:
save_snapshot(frame, entry)
logs.append('Person {} entered'.format(entry))
currentPeople += 1
new_exits = set(prev_tracked_people) - set(tracked_people)
for exit in new_exits:
logs.append('Person {} exited'.format(exit))
currentPeople -= 1
prev_tracked_people = dict(tracked_people)
# 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 people:
text.append("{}: {:2.2f}%".format(
prediction.label, prediction.confidence * 100))
text.append('Current Occupancy:')
text += str(currentPeople)
streamer.send_data(frame, 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():
# The current frame index
frame_idx = 0
# The number of frames to skip before running detector
detect_period = 50
# if you would like to test an additional model, add one to the list below:
models = [
"alwaysai/ssd_mobilenet_v2_oidv4",
"alwaysai/ssd_inception_v2_coco_2018_01_28"
]
# include any labels that you wish to detect from any models (listed above in 'models') here in this list
detected_contraband = [
"Pen", "cell phone", "backpack", "book", "Book", "Ring binder",
"Headphones", "Calculator", "Mobile phone", "Telephone", "Microphone",
"Ipod", "Remote control"
]
# load all the models (creates a new object detector for each model)
detectors = []
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))
tracker = edgeiq.CorrelationTracker(max_objects=5)
fps = edgeiq.FPS()
contraband_summary = ContrabandSummary()
try:
with edgeiq.WebcamVideoStream(cam=0) as video_stream, \
edgeiq.Streamer() as streamer:
# Allow Webcam to warm up
time.sleep(2.0)
fps.start()
# loop detection
while True:
frame = video_stream.read()
predictions_to_markup = []
text = [""]
# only analyze every 'detect_period' frame (i.e. every 50th in original code)
if frame_idx % detect_period == 0:
# gather data from the all the detectors
for i in range(0, len(detectors)):
results = detectors[i].detect_objects(
frame, confidence_level=.2)
# Stop tracking old objects
if tracker.count:
tracker.stop_all()
# append each prediction
predictions = results.predictions
for prediction in predictions:
if (prediction.label.strip()
in detected_contraband):
contraband_summary.contraband_alert(
prediction.label, frame)
predictions_to_markup.append(prediction)
tracker.start(frame, prediction)
else:
# if there are objects being tracked, update the tracker with the new frame
if tracker.count:
# get the new predictions for the objects being tracked, used to markup the frame
predictions_to_markup = tracker.update(frame)
# mark up the frame with the predictions for the contraband objects
frame = edgeiq.markup_image(frame,
predictions_to_markup,
show_labels=True,
show_confidences=False,
colors=obj_detect.colors)
# send the collection of contraband detection points (string and video frame) to the streamer
text = contraband_summary.get_contraband_string()
streamer.send_data(frame, text)
frame_idx += 1
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():
# Spin up the object detector
obj_detect = edgeiq.ObjectDetection("alwaysai/" + OBJECT_DETECTION_MODEL)
obj_detect.load(engine=edgeiq.Engine.DNN_CUDA,
accelerator=edgeiq.Accelerator.NVIDIA)
print("Engine: {}".format(obj_detect.engine))
print("Accelerator: {}\n".format(obj_detect.accelerator))
print("Model:\n{}\n".format(obj_detect.model_id))
print("Labels:\n{}\n".format(obj_detect.labels))
# Prepare to track frames per second calculations
fps = edgeiq.FPS()
# Load any prior instance of the tracker, otherwise spin up a new one
centroid_tracker = file_manager.load(
CENTROID_TRACKER,
edgeiq.CentroidTracker(deregister_frames=TRACKER_DEREGISTER_FRAMES,
max_distance=TRACKER_MAX_DISTANCE))
# Load any prior instance of the metrics data, otherwise start a new one
metrics = file_manager.load(METRICS_MANAGER,
metrics_manager.MetricsManager())
try:
if IP_CAMERA_FEED is not None:
stream_details = edgeiq.IPVideoStream(IP_CAMERA_FEED)
else:
stream_details = edgeiq.WebcamVideoStream(cam=0)
with stream_details as video_stream, \
edgeiq.Streamer() as streamer:
# Allow Webcam to warm up
time.sleep(2.0)
fps.start()
# Loop detection and centroid tracker
while True:
metrics.newLoop()
frame = video_stream.read()
results = obj_detect.detect_objects(
frame, confidence_level=DETECT_CONFIDENCE_THRESHOLD)
# Ignore detections of anything other than people
filter = edgeiq.filter_predictions_by_label(
results.predictions, ['person'])
# Adding info for streamer display
text = ["Model: {}".format(obj_detect.model_id)]
text.append("Inference time: {:1.3f} s".format(
results.duration))
text.append("People currently detected:")
objects = centroid_tracker.update(filter)
# Store active predictions for just this loop
predictions = []
# Store the active object ids for just this loop
if len(objects.items()) == 0:
# No people detected
text.append("-- NONE")
for (object_id, prediction) in objects.items():
metrics.addTimeFor(object_id)
timeForId = metrics.timeForId(object_id)
# Correcting for fact that index 0 is first object in an array
idAdjusted = object_id + 1
# Display text with bounding box in video
new_label = "Person {i} | {t} sec".format(i=idAdjusted,
t=timeForId)
prediction.label = new_label
text.append(new_label)
predictions.append(prediction)
# Add metrics to text going to streamer
m = metrics.currentMetrics()
text.append("") # Spacing
text.append("Total people seen: {}".format(m["count"]))
text.append("Total time: {} sec".format(m["total"]))
text.append("Average time: {0:.1f} sec".format(m["avg"]))
text.append("Longest individual time: {} sec".format(m["max"]))
# Update output streamer
frame = edgeiq.markup_image(frame, predictions)
streamer.send_data(frame, text)
fps.update()
if streamer.check_exit():
break
finally:
fps.stop()
# TODO: Update to save every few seconds in case a crash occurs
file_manager.save(metrics, METRICS_MANAGER)
file_manager.save(centroid_tracker, CENTROID_TRACKER)
print("elapsed time: {:.2f}".format(fps.get_elapsed_seconds()))
print("approx. FPS: {:.2f}".format(fps.compute_fps()))
print("Program Ending")
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/ssd_mobilenet_v1_coco_2018_01_28")
obj_detect.load(engine=edgeiq.Engine.DNN)
print("Engine: {}".format(obj_detect.engine))
print("Accelerator: {}\n".format(obj_detect.accelerator))
print("Model:\n{}\n".format(obj_detect.model_id))
print("Labels:\n{}\n".format(obj_detect.labels))
tracker = edgeiq.CorrelationTracker(max_objects=5)
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()
while True:
frame = video_stream.read()
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()
predictions = results.predictions
for prediction in predictions:
text.append("{}: {:2.2f}%".format(
prediction.label, prediction.confidence * 100))
tracker.start(frame, prediction)
else:
if tracker.count:
predictions = tracker.update(frame)
frame = edgeiq.markup_image(frame,
predictions,
show_labels=True,
show_confidences=False,
colors=obj_detect.colors)
streamer.send_data(frame, text)
frame_idx += 1
fps.update()
if streamer.check_exit():
break
finally:
tracker.stop_all()
fps.stop()
print("elapsed time: {:.2f}".format(fps.get_elapsed_seconds()))
print("approx. FPS: {:.2f}".format(fps.compute_fps()))
print("Program Ending")
def updateStream(frame, streamer, fps, predictions, text):
frame = edgeiq.markup_image(frame, predictions)
streamer.send_data(frame, text)
fps.update()
def main():
obj_detect = edgeiq.ObjectDetection("alwaysai/mobilenet_ssd")
obj_detect.load(engine=edgeiq.Engine.DNN)
print("Engine: {}".format(obj_detect.engine))
print("Accelerator: {}\n".format(obj_detect.accelerator))
print("Model:\n{}\n".format(obj_detect.model_id))
print("Labels:\n{}\n".format(obj_detect.labels))
print("Detecting:\n{}\n".format(OBJECTS))
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()
# loop detection
while True:
frame = video_stream.read()
results = obj_detect.detect_objects(frame, confidence_level=.5)
predictions = edgeiq.filter_predictions_by_label(
results.predictions, OBJECTS)
frame = edgeiq.markup_image(frame,
predictions,
show_confidences=False,
colors=obj_detect.colors)
# Print date and time on frame
current_time_date = str(datetime.datetime.now())
(h, w) = frame.shape[:2]
cv2.putText(frame, current_time_date, (10, h - 5),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
# Count objects
counter = {obj: 0 for obj in OBJECTS}
for prediction in predictions:
# increment the counter of the detected object
counter[prediction.label] += 1
# 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("Object counts:")
for label, count in counter.items():
text.append("{}: {}".format(label, count))
streamer.send_data(frame, 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():
fps = edgeiq.FPS()
# Change parameter to alwaysai/human_pose_eyecloud to run the human pose model.
with edgeiq.EyeCloud('alwaysai/mobilenet_ssd_eyecloud'
) as camera, edgeiq.Streamer() as streamer:
fps.start()
centroid_tracker = edgeiq.CentroidTracker(deregister_frames=20, max_distance=100)
number=0
while True:
text = ["FPS:{}".format(fps.compute_fps())]
frame = camera.get_frame()
#print('image sequence = {}'.format(frame.sequence_index))
result = camera.get_model_result(confidence_level=0.9)
# Check for inferencing results.
if result:
#print('model sequence = {}'.format(result.sequence_index))
text.append("Model: {}".format(camera.model_id))
if camera.model_purpose == 'PoseEstimation':
frame = result.draw_poses(frame)
text.append("Inference time: {:1.3f} s".format(result.duration))
for ind, pose in enumerate(result.poses):
text.append("Person {}".format(ind))
text.append('-' * 10)
text.append("Key Points:")
for key_point in pose.key_points:
text.append(str(key_point))
elif camera.model_purpose == 'ObjectDetection':
#移除交并比过大的检测框
if(len(result.predictions)>1):
for i,prediction in enumerate(result.predictions):
for j in range((len(result.predictions)-1),i,-1):
#print("i：%s j：%s"%(i,j))
IOU=prediction.box.compute_overlap(result.predictions[j].box)
print(IOU)
if(IOU>0.3):
#print("距离过近")
result.predictions.pop()
#划定FOV有效检测区
for i,prediction in enumerate(result.predictions):
if(prediction.box.center[0]<500 or prediction.box.center[0]>1300):
result.predictions.pop(i)
#print("x：%s y：%s"%(prediction.box.center[0],prediction.box.center[1]))
objects = centroid_tracker.update(result.predictions)
text.append("Inference time: {:1.3f} s".format(result.duration))
text.append("Objects:")
for (object_id, prediction) in objects.items():
new_label = 'person {}'.format(object_id)
if(object_id+1>number):
number=object_id+1
prediction.label = new_label
text.append("{}: {:2.2f}%".format(prediction.label, prediction.confidence * 100))
result.predictions.append(prediction)
frame = edgeiq.markup_image(frame, result.predictions)
text.append("people pass: {}".format(number))
elif camera.model_purpose == 'Classification':
if len(result.predictions) > 0:
top_prediction = result.predictions[0]
text = "Classification: {}, {:.2f}%".format(
top_prediction.label,
top_prediction.confidence * 100)
else:
text = None
cv2.putText(frame, text, (5, 25), cv2.FONT_HERSHEY_SIMPLEX,
0.4, (0, 0, 255), 2)
cv2.rectangle(frame,(500,0),(1300,1080),(255,0,0), 2)
streamer.send_data(frame, text)
if streamer.check_exit():
break
fps.update()
print('fps = {}'.format(fps.compute_fps()))
