def run(callback, use_appsrc=False):
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--mirror', help='flip video horizontally', action='store_true')
parser.add_argument('--model', help='.tflite model path.', required=False)
parser.add_argument('--res', help='Resolution', default='640x480',
choices=['480x360', '640x480', '1280x720'])
parser.add_argument('--videosrc', help='Which video source to use', default='/dev/video0')
parser.add_argument('--h264', help='Use video/x-h264 input', action='store_true')
args = parser.parse_args()
default_model = 'models/posenet_mobilenet_v1_075_%d_%d_quant_decoder_edgetpu.tflite'
if args.res == '480x360':
src_size = (640, 480)
appsink_size = (480, 360)
model = args.model or default_model % (353, 481)
elif args.res == '640x480':
src_size = (640, 480)
appsink_size = (640, 480)
model = args.model or default_model % (481, 641)
elif args.res == '1280x720':
src_size = (1280, 720)
appsink_size = (1280, 720)
model = args.model or default_model % (721, 1281)
# print('Loading model: ', model)
# engine = PoseEngine(model, mirror=args.mirror)
gstreamer.run_pipeline(callback,
src_size, appsink_size,
use_appsrc=use_appsrc, mirror=args.mirror,
videosrc=args.videosrc, h264input=args.h264)
def run(inf_callback, render_callback):
global t
if t >= 3:
return
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--mirror',
help='flip video horizontally',
action='store_true')
parser.add_argument('--model', help='.tflite model path.', required=False)
parser.add_argument('--res',
help='Resolution',
default='640x480',
choices=['480x360', '640x480', '1280x720'])
parser.add_argument('--videosrc',
help='Which video source to use',
default='/dev/video0')
parser.add_argument('--h264',
help='Use video/x-h264 input',
action='store_true')
parser.add_argument('--jpeg',
help='Use image/jpeg input',
action='store_true')
args = parser.parse_args()
default_model = 'models/mobilenet/posenet_mobilenet_v1_075_%d_%d_quant_decoder_edgetpu.tflite'
if args.res == '480x360':
src_size = (480, 360)
appsink_size = (480, 360)
model = args.model or default_model % (353, 481)
elif args.res == '640x480':
src_size = (640, 480)
appsink_size = (640, 480)
model = args.model or default_model % (481, 641)
elif args.res == '1280x720':
src_size = (1280, 720)
appsink_size = (1280, 720)
model = args.model or default_model % (721, 1281)
print('Loading model: ', model)
engine = PoseEngine(model)
input_shape = engine.get_input_tensor_shape()
inference_size = (input_shape[2], input_shape[1])
gstreamer.run_pipeline(
partial(inf_callback, engine),
partial(render_callback, engine),
src_size,
inference_size,
# mirror=args.mirror,
mirror=True,
videosrc=args.videosrc,
h264=args.h264,
jpeg=args.jpeg)
def main():
found = set()
decode = BarCodeDecoder()
def user_callback(image, svg_canvas):
for decoded in decode.decode(image):
text = "{} - {} ({})".format(datetime.datetime.now(),
decoded['data'], decoded['type'])
print(text)
if decoded['data'] not in found:
found.add(decoded['data'])
gstreamer.run_pipeline(user_callback)
def run(inf_callback, render_callback):
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--mirror',
help='flip video horizontally',
action='store_true')
parser.add_argument('--model', help='.tflite model path.', required=False)
parser.add_argument('--res',
help='Resolution',
default='640x480',
choices=['480x360', '640x480', '1280x720'])
parser.add_argument('--videosrc',
help='Which video source to use',
default='/dev/video0')
parser.add_argument('--h264',
help='Use video/x-h264 input',
action='store_true')
parser.add_argument('--jpeg',
help='Use image/jpeg input',
action='store_true')
args = parser.parse_args()
default_model = 'models/posenet_mobilenet_v1_075_%d_%d_quant.tflite'
if args.res == '480x360':
src_size = (640, 480)
model = args.model or default_model % (353, 481)
elif args.res == '640x480':
src_size = (640, 480)
model = args.model or default_model % (481, 641)
elif args.res == '1280x720':
src_size = (1280, 720)
model = args.model or default_model % (721, 1281)
print('Loading model: ', model)
engine = PoseEngine(model,
offsetRefineStep=10,
scoreThreshold=0.8,
maxPoseDetections=5,
nmsRadius=30,
minPoseConfidence=0.15)
input_shape = engine.get_input_tensor_shape()
inference_size = (input_shape[2], input_shape[1])
gstreamer.run_pipeline(partial(inf_callback, engine),
partial(render_callback, engine),
src_size,
inference_size,
mirror=args.mirror,
videosrc=args.videosrc,
h264=args.h264,
jpeg=args.jpeg)
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--mirror',
help='flip video horizontally',
action='store_true')
parser.add_argument('--model', help='.tflite model path.', required=False)
parser.add_argument('--res',
help='Resolution',
default='640x480',
choices=['480x360', '640x480', '1280x720'])
parser.add_argument('--videosrc',
help='Which video source to use',
default='/dev/video0')
parser.add_argument('--videotgt',
help='Where to write the video to',
default='autovideosink')
parser.add_argument('--anonymize',
help='Use anonymizer mode',
action='store_true')
parser.add_argument('--jpg',
help='Use image/jpeg input',
action='store_true')
args = parser.parse_args()
default_model = 'models/posenet_mobilenet_v1_075_%d_%d_quant_decoder_edgetpu.tflite'
if args.res == '480x360':
src_size = (640, 480)
appsink_size = (480, 360)
model = args.model or default_model % (353, 481)
elif args.res == '640x480':
src_size = (640, 480)
appsink_size = (640, 480)
model = args.model or default_model % (481, 641)
elif args.res == '1280x720':
src_size = (1280, 720)
appsink_size = (1280, 720)
model = args.model or default_model % (721, 1281)
print('Loading model: ', model)
engine = PoseEngine(model, mirror=args.mirror)
gstreamer.run_pipeline(Callback(engine, anonymize=args.anonymize),
src_size,
appsink_size,
use_appsrc=True,
mirror=args.mirror,
videosrc=args.videosrc,
jpginput=args.jpg,
videotgt=args.videotgt)
def main():
model = '../traffic_sign_detection/road_signs_quantized_v2_edgetpu.tflite'
labels = '../traffic_sign_detection/road_sign_labels.txt'
print("Loading %s with %s labels."%(model, labels))
engine = DetectionEngine(model)
labels = load_labels(labels)
last_time = time.monotonic()
def user_callback(image, svg_canvas):
#nonlocal last_time
start_time = time.monotonic()
objs = engine.DetectWithImage(image, threshold=0.1, keep_aspect_ratio=True, relative_coord=True, top_k=5)
end_time = time.monotonic()
text_lines = [
'Inference: %.2f ms' %((end_time - start_time) * 1000),
'FPS: %.2f fps' %(1.0/(end_time - last_time)),
]
#print(' '.join(text_lines))
last_time = end_time
generate_svg(svg_canvas, objs, labels, text_lines)
#status = cv.imwrite('output_image.png', np.array(image))
output_uart()
angles = input_output(image)
if (angles == False):
print ("no angles found")
else:
#continue
print (angles, traffic_sign)
result = gstreamer.run_pipeline(user_callback)
def run(callback, use_appsrc=False):
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("--mirror",
help="flip video horizontally",
action="store_true")
parser.add_argument("--model", help=".tflite model path.", required=False)
parser.add_argument(
"--res",
help="Resolution",
default="1280x720",
choices=["480x360", "640x480", "1280x720"],
)
parser.add_argument("--videosrc",
help="Which video source to use",
default="/dev/video0")
parser.add_argument("--h264",
help="Use video/x-h264 input",
action="store_true")
args = parser.parse_args()
default_model = "models/posenet_mobilenet_v1_075_%d_%d_quant_decoder_edgetpu.tflite"
if args.res == "480x360":
src_size = (640, 480)
appsink_size = (480, 360)
model = args.model or default_model % (353, 481)
elif args.res == "640x480":
src_size = (640, 480)
appsink_size = (640, 480)
model = args.model or default_model % (481, 641)
elif args.res == "1280x720":
src_size = (1280, 720)
appsink_size = (1280, 720)
model = args.model or default_model % (721, 1281)
print("Loading model: ", model)
engine = PoseEngine(model, mirror=args.mirror)
gstreamer.run_pipeline(
partial(callback, engine),
src_size,
appsink_size,
use_appsrc=use_appsrc,
mirror=args.mirror,
videosrc=args.videosrc,
h264input=args.h264,
)
def main():
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--mirror', help='flip video horizontally', action='store_true')
parser.add_argument('--model', help='.tflite model path.', required=False)
parser.add_argument('--width', help='Source width', default='640')
parser.add_argument('--height', help='Source height', default='480')
parser.add_argument('--videosrc', help='Which video source to use', default='/dev/video0')
parser.add_argument('--anonymize', dest='anonymize', action='store_true', help='Use anonymizer mode [--noanonymize]')
parser.add_argument('--noanonymize', dest='anonymize', action='store_false', help=argparse.SUPPRESS)
parser.set_defaults(anonymize=False)
parser.add_argument('--bodyparts', dest='bodyparts', action='store_true', help='Color by bodyparts [--nobodyparts]')
parser.add_argument('--nobodyparts', dest='bodyparts', action='store_false', help=argparse.SUPPRESS)
parser.set_defaults(bodyparts=True)
parser.add_argument('--h264', help='Use video/x-h264 input', action='store_true')
parser.add_argument('--jpeg', help='Use video/jpeg input', action='store_true')
args = parser.parse_args()
if args.h264 and args.jpeg:
print('Error: both mutually exclusive options h264 and jpeg set')
sys.exit(1)
default_model = 'models/bodypix_mobilenet_v1_075_640_480_16_quant_edgetpu_decoder.tflite'
model = args.model if args.model else default_model
print('Model: {}'.format(model))
engine = PoseEngine(model)
inference_size = (engine.image_width, engine.image_height)
print('Inference size: {}'.format(inference_size))
src_size = (int(args.width), int(args.height))
if args.videosrc.startswith('/dev/video'):
print('Source size: {}'.format(src_size))
gstreamer.run_pipeline(Callback(engine,
anonymize=args.anonymize,
bodyparts=args.bodyparts),
src_size, inference_size,
mirror=args.mirror,
videosrc=args.videosrc,
h264=args.h264,
jpeg=args.jpeg)
def main():
default_model_dir = 'model'
default_model = 'ssdlite_mobiledet_quant_postprocess_edgetpu.tflite'
parser = argparse.ArgumentParser()
parser.add_argument('--model',
help='.tflite model path',
default=os.path.join(default_model_dir, default_model))
parser.add_argument(
'--top_k',
type=int,
default=3,
help='number of categories with highest score to display')
parser.add_argument('--threshold',
type=float,
default=0.5,
help='classifier score threshold')
parser.add_argument('--videosrc',
help='Which video source to use. ',
default='/dev/video0')
parser.add_argument('--videofmt',
help='Input video format.',
default='raw',
choices=['raw', 'h264', 'jpeg'])
args = parser.parse_args()
interpreter = common.make_interpreter(args.model)
interpreter.allocate_tensors()
w, h, _ = common.input_image_size(interpreter)
inference_size = (w, h)
# Average fps over last 30 frames.
fps_counter = common.avg_fps_counter(100)
def user_callback(input_tensor, src_size, inference_box):
nonlocal fps_counter
start_time = time.monotonic()
common.set_input(interpreter, input_tensor)
interpreter.invoke()
# For larger input image sizes, use the edgetpu.classification.engine for better performance
objs = get_output(interpreter, args.threshold, args.top_k)
end_time = time.monotonic()
text_lines = [
'Inference: {:.2f} ms'.format((end_time - start_time) * 1000),
'FPS: {} fps'.format(round(next(fps_counter))),
]
print(' '.join(text_lines))
return generate_svg(src_size, inference_size, inference_box, objs,
text_lines)
result = gstreamer.run_pipeline(user_callback,
src_size=(640, 480),
appsink_size=inference_size,
videosrc=args.videosrc,
videofmt=args.videofmt)
def main():
default_model_dir = '../all_models'
default_model = 'mobilenet_v2_1.0_224_quant_edgetpu.tflite'
default_labels = 'imagenet_labels.txt'
parser = argparse.ArgumentParser()
parser.add_argument('--model', help='.tflite model path',
default=os.path.join(default_model_dir,default_model))
parser.add_argument('--labels', help='label file path',
default=os.path.join(default_model_dir, default_labels))
parser.add_argument('--top_k', type=int, default=3,
help='number of categories with highest score to display')
parser.add_argument('--threshold', type=float, default=0.1,
help='classifier score threshold')
parser.add_argument('--videosrc', help='Which video source to use. ',
default='/dev/video0')
parser.add_argument('--videofmt', help='Input video format.',
default='raw',
choices=['raw', 'h264', 'jpeg'])
args = parser.parse_args()
print('Loading {} with {} labels.'.format(args.model, args.labels))
interpreter = common.make_interpreter(args.model)
interpreter.allocate_tensors()
labels = load_labels(args.labels)
w, h, _ = common.input_image_size(interpreter)
inference_size = (w, h)
# Average fps over last 30 frames.
fps_counter = common.avg_fps_counter(30)
def user_callback(input_tensor, src_size, inference_box):
nonlocal fps_counter
start_time = time.monotonic()
common.set_input(interpreter, input_tensor)
interpreter.invoke()
# For larger input image sizes, use the edgetpu.classification.engine for better performance
results = get_output(interpreter, args.top_k, args.threshold)
end_time = time.monotonic()
text_lines = [
' ',
'Inference: {:.2f} ms'.format((end_time - start_time) * 1000),
'FPS: {} fps'.format(round(next(fps_counter))),
]
for result in results:
text_lines.append('score={:.2f}: {}'.format(result.score, labels.get(result.id, result.id)))
print(' '.join(text_lines))
return generate_svg(src_size, text_lines)
result = gstreamer.run_pipeline(user_callback,
src_size=(640, 480),
appsink_size=inference_size,
videosrc=args.videosrc,
videofmt=args.videofmt)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--bucket', help='GCS bucket name')
parser.add_argument('--path', help='GCS path prefix for uploading images')
args = parser.parse_args()
print('Press enter or button when you want to take a picture')
input_monitor = InputMonitor(gpio_pin=8)
def user_callback(image, svg_canvas):
nonlocal input_monitor
if input_monitor.is_key_pressed():
upload.upload(args.bucket, args.path, image)
input_monitor.daemon = True
input_monitor.start()
print('monitoring keyboard input...')
gstreamer.run_pipeline(user_callback)
def main():
"""Creates camera pipeline, and pushes pipeline through ClassificationEngine
model. Logs results to user-defined storage. Runs either in training mode to
gather images for custom model creation or in deterrent mode that sounds an
'alarm' if a defined label is detected."""
args = user_selections()
print("Loading %s with %s labels." % (args.model, args.labels))
engine = ClassificationEngine(args.model)
labels = load_labels(args.labels)
storage_dir = args.storage
#Initialize logging file
logging.basicConfig(filename='%s/results.log' % storage_dir,
format='%(asctime)s-%(message)s',
level=logging.DEBUG)
last_time = time.monotonic()
last_results = [('label', 0)]
def user_callback(image, svg_canvas):
nonlocal last_time
nonlocal last_results
start_time = time.monotonic()
results = engine.classify_with_image(image,
threshold=args.threshold,
top_k=args.top_k)
end_time = time.monotonic()
results = [(labels[i], score) for i, score in results]
if args.print:
print_results(start_time, last_time, end_time, results)
if args.training:
if do_training(results, last_results, args.top_k):
save_data(image, results, storage_dir)
else:
#Custom model mode:
#The labels can be modified to detect/deter user-selected items
if results[0][0] != 'background':
save_data(image, storage_dir, results)
if 'fox squirrel, eastern fox squirrel, Sciurus niger' in results:
if args.sound is not None:
playsound(args.sound)
logging.info('Deterrent sounded')
last_results = results
last_time = end_time
# Note: we don't use the framerate paramter because our USB cam doesn't like it
result = gstreamer.run_pipeline(user_callback,
device='/dev/video1',
src_caps='video/x-raw,format=YUY2')
def main():
default_model_dir = '../all_models'
default_model = 'mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite'
default_labels = 'coco_labels.txt'
parser = argparse.ArgumentParser()
parser.add_argument('--model', help='.tflite model path',
default=os.path.join(default_model_dir,default_model))
parser.add_argument('--labels', help='label file path',
default=os.path.join(default_model_dir, default_labels))
parser.add_argument('--top_k', type=int, default=10,
help='number of classes with highest score to display')
parser.add_argument('--threshold', type=float, default=0.3,
help='class score threshold')
args = parser.parse_args()
print("Loading %s with %s labels."%(args.model, args.labels))
engine = DetectionEngine(args.model)
labels = load_labels(args.labels)
last_time = time.monotonic()
def user_callback(image, svg_canvas):
nonlocal last_time
start_time = time.monotonic()
objs = engine.DetectWithImage(image, threshold=args.threshold,
keep_aspect_ratio=True, relative_coord=True,
top_k=args.top_k)
end_time = time.monotonic()
text_lines = [
'Inference: %.2f ms' %((end_time - start_time) * 1000),
'FPS: %.2f fps' %(1.0/(end_time - last_time)),
]
print(' '.join(text_lines))
last_time = end_time
# keep only relevant classes, e.g. person, car, truck, train, stop sign, traffic lights, etc.
objs = [obj for obj in objs if obj.label_id in [0,1,2,3,5,6,7,9,12]]
# non max suppression
objs.sort(key=lambda x: x.score)
keep = []
while objs:
max_score_obj = objs.pop()
keep.append(max_score_obj)
tmp = []
for obj in objs:
if iou(obj.bounding_box.flatten().tolist(),max_score_obj.bounding_box.flatten().tolist()) < 0.1:
tmp.append(obj)
objs = tmp
generate_svg(svg_canvas, keep, labels, text_lines)
result = gstreamer.run_pipeline(user_callback)
def main():
default_model_dir = '../models'
default_model = 'mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite'
default_labels = 'coco_labels.txt'
parser = argparse.ArgumentParser()
parser.add_argument('--model',
help='.tflite model path',
default=os.path.join(default_model_dir, default_model))
parser.add_argument('--labels',
help='label file path',
default=os.path.join(default_model_dir,
default_labels))
parser.add_argument('--top_k',
type=int,
default=3,
help='number of classes with highest score to display')
parser.add_argument('--threshold',
type=float,
default=0.4,
help='class score threshold')
args = parser.parse_args()
# load the label file
print("Loading %s with %s labels." % (args.model, args.labels))
engine = DetectionEngine(args.model)
labels = load_labels(args.labels)
last_time = time.monotonic()
def user_callback(image, svg_canvas):
# get the inference time
nonlocal last_time
start_time = time.monotonic()
# perform the image detection
objs = engine.detect_with_image(image,
threshold=args.threshold,
keep_aspect_ratio=True,
relative_coord=True,
top_k=args.top_k)
end_time = time.monotonic()
text_lines = [
'Inference: %.2f ms' % ((end_time - start_time) * 1000),
'FPS: %.2f fps' % (1.0 / (end_time - last_time)),
]
print(' '.join(text_lines))
last_time = end_time
# generates the image for viewing
generate_svg(svg_canvas, objs, labels, text_lines)
result = gstreamer.run_pipeline(user_callback)
def main():
default_model_dir = "../all_models"
default_model = 'mobilenet_v2_1.0_224_quant_edgetpu.tflite'
default_labels = 'imagenet_labels.txt'
parser = argparse.ArgumentParser()
parser.add_argument('--model',
help='.tflite model path',
default=os.path.join(default_model_dir, default_model))
parser.add_argument('--labels',
help='label file path',
default=os.path.join(default_model_dir,
default_labels))
parser.add_argument('--top_k',
type=int,
default=3,
help='number of classes with highest score to display')
parser.add_argument('--threshold',
type=float,
default=0.1,
help='class score threshold')
args = parser.parse_args()
print("Loading %s with %s labels." % (args.model, args.labels))
engine = ClassificationEngine(args.model)
labels = load_labels(args.labels)
input_shape = engine.get_input_tensor_shape()
inference_size = (input_shape[1], input_shape[2])
# Average fps over last 30 frames.
fps_counter = common.avg_fps_counter(30)
def user_callback(input_tensor, src_size, inference_box):
nonlocal fps_counter
start_time = time.monotonic()
results = engine.classify_with_input_tensor(input_tensor,
threshold=args.threshold,
top_k=args.top_k)
end_time = time.monotonic()
text_lines = [
'Inference: %.2f ms' % ((end_time - start_time) * 1000),
'FPS: %d fps' % (round(next(fps_counter))),
]
for index, score in results:
text_lines.append('score=%.2f: %s' % (score, labels[index]))
print(' '.join(text_lines))
return generate_svg(src_size, text_lines)
result = gstreamer.run_pipeline(user_callback, appsink_size=inference_size)
def main():
default_model_dir = "../all_models"
default_model = 'mobilenet_v2_1.0_224_quant_edgetpu.tflite'
default_labels = 'imagenet_labels.txt'
parser = argparse.ArgumentParser()
parser.add_argument('--model',
help='.tflite model path',
default=os.path.join(default_model_dir, default_model))
parser.add_argument('--labels',
help='label file path',
default=os.path.join(default_model_dir,
default_labels))
parser.add_argument('--top_k',
type=int,
default=3,
help='number of classes with highest score to display')
parser.add_argument('--threshold',
type=float,
default=0.1,
help='class score threshold')
args = parser.parse_args()
print("Loading %s with %s labels." % (args.model, args.labels))
engine = ClassificationEngine(args.model)
labels = load_labels(args.labels)
last_time = time.monotonic()
def user_callback(image, svg_canvas):
nonlocal last_time
start_time = time.monotonic()
results = engine.ClassifyWithImage(image,
threshold=args.threshold,
top_k=args.top_k)
end_time = time.monotonic()
text_lines = [
'Inference: %.2f ms' % ((end_time - start_time) * 1000),
'FPS: %.2f fps' % (1.0 / (end_time - last_time)),
]
for index, score in results:
text_lines.append('score=%.2f: %s' % (score, labels[index]))
print(' '.join(text_lines))
last_time = end_time
generate_svg(svg_canvas, text_lines)
result = gstreamer.run_pipeline(user_callback)
def main(args):
parser = argparse.ArgumentParser()
parser.add_argument('--model', help='File path of Tflite model.',
default='models/mobilenet_quant_v1_224_headless_edgetpu.tflite')
parser.add_argument('--testui', dest='testui', action='store_true',
help='Run test of UI. Ctrl-C to abort.')
parser.add_argument('--keyboard', dest='keyboard', action='store_true',
help='Run test of UI. Ctrl-C to abort.')
parser.add_argument('--method', dest='method',
help='method for transfer learning, support knn or imprinting',
default='knn',
choices=['knn', 'imprinting'])
parser.add_argument('--outputmodel', help='File path of output Tflite model, only for imprinting method.',
default='output.tflite')
parser.add_argument('--keepclasses', dest='keepclasses', action='store_true',
help='Whether to keep base model classes, only for imprinting method.')
args = parser.parse_args()
# The UI differs a little depending on the system because the GPIOs
# are a little bit different.
print('Initialize UI.')
platform = detectPlatform()
if args.keyboard:
ui = UI_Keyboard()
else:
if platform == 'raspberry': ui = UI_Raspberry()
elif platform == 'devboard': ui = UI_EdgeTpuDevBoard()
else:
print('No GPIOs detected - falling back to Keyboard input')
ui = UI_Keyboard()
ui.wiggleLEDs()
if args.testui:
ui.testButtons()
return
print('Initialize Model...')
if args.method == 'knn':
teachable = TeachableMachineKNN(args.model, ui)
else:
teachable = TeachableMachineImprinting(args.model, ui, args.outputmodel, args.keepclasses)
print('Start Pipeline.')
result = gstreamer.run_pipeline(teachable.classify)
ui.wiggleLEDs(4)
def main(args):
parser = argparse.ArgumentParser()
parser.add_argument(
'--model',
help='File path of Tflite model.',
default='mobilenet_quant_v1_224_headless_edgetpu.tflite')
parser.add_argument('--testui',
dest='testui',
action='store_true',
help='Run test of UI. Ctrl-C to abort.')
parser.add_argument('--keyboard',
dest='keyboard',
action='store_true',
help='Run test of UI. Ctrl-C to abort.')
args = parser.parse_args()
# The UI differs a little depending on the system because the GPIOs
# are a little bit different.
print('Initialize UI.')
platform = detectPlatform()
if args.keyboard:
ui = UI_Keyboard()
else:
if platform == 'raspberry': ui = UI_Raspberry()
elif platform == 'devboard': ui = UI_EdgeTpuDevBoard()
else:
print('No GPIOs detected - falling back to Keyboard input')
ui = UI_Keyboard()
ui.wiggleLEDs()
if args.testui:
ui.testButtons()
return
print('Initialize Model...')
teachable = TeachableMachine(args.model, ui)
print('Start Pipeline.')
result = gstreamer.run_pipeline(teachable.classify)
ui.wiggleLEDs(4)
teachable._servoFlag.set()
time.sleep(1)
teachable._servoFlag.clear()
def main():
default_model_dir = '../all_models'
default_model = 'mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite'
default_labels = 'coco_labels.txt'
parser = argparse.ArgumentParser()
parser.add_argument('--model', help='.tflite model path',
default=os.path.join(default_model_dir,default_model))
parser.add_argument('--labels', help='label file path',
default=os.path.join(default_model_dir, default_labels))
parser.add_argument('--top_k', type=int, default=3,
help='number of categories with highest score to display')
parser.add_argument('--threshold', type=float, default=0.1,
help='classifier score threshold')
args = parser.parse_args()
print('Loading {} with {} labels.'.format(args.model, args.labels))
interpreter = common.make_interpreter(args.model)
interpreter.allocate_tensors()
labels = load_labels(args.labels)
w, h, _ = common.input_image_size(interpreter)
inference_size = (w, h)
# Average fps over last 30 frames.
fps_counter = common.avg_fps_counter(30)
def user_callback(input_tensor, src_size, inference_box):
nonlocal fps_counter
start_time = time.monotonic()
common.set_input(interpreter, input_tensor)
interpreter.invoke()
# For larger input image sizes, use the edgetpu.classification.engine for better performance
objs = get_output(interpreter, args.threshold, args.top_k)
end_time = time.monotonic()
text_lines = [
'Inference: {:.2f} ms'.format((end_time - start_time) * 1000),
'FPS: {} fps'.format(round(next(fps_counter))),
]
print(' '.join(text_lines))
return generate_svg(src_size, inference_size, inference_box, objs, labels, text_lines)
result = gstreamer.run_pipeline(user_callback, appsink_size=inference_size)
def main(args):
parser = argparse.ArgumentParser()
parser.add_argument('--model', help='File path of Tflite model.')
parser.add_argument('--testui',
dest='testui',
action='store_true',
help='Run test of UI. Ctrl-C to abort.')
args = parser.parse_args()
# The UI differs a little depending on the system because the GPIOs
# are a little bit different.
print('Initialize UI.')
platform = detectPlatform()
if platform == 'raspberry': ui = UI_Raspberry()
else:
raise ValueError('Unsupported platform: %s ' % platform +
'This Demo is for Raspberry Pi.')
ui.wiggleLEDs()
if args.testui:
ui.testButtons()
return
print('Initialize Model...')
teachable = TeachableMachine(args.model, ui)
assert os.path.isfile(args.model)
print('Start Pipeline.')
def user_callback(img, overlay):
return teachable.classify(img, overlay)
# TODO(mtyka) Refactor this once offial gstreamer.py is
# programmable and supports rpi. Then get rid of our custom
# gstreamer.py
result = gstreamer.run_pipeline(user_callback, platform)
ui.wiggleLEDs(4)
return 0 if teachable.clean_shutdown else 1
def main():
# default_model_dir = '../all_models'
# default_model = 'mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite'
# default_labels = 'coco_labels.txt'
default_model_dir = '../cmpe297_model'
# default_model = 'ssdlite_6C_SB_10K_mobiledet_screws.tflite' #5 classes small BB
# default_model = 'ssdlite_6C_SB_10K_mobiledet_screws_edgetpu.tflite' #5 classes small BB
# default_model = 'ssdlite_6C_SB_25K_mobiledet_screws.tflite' #5 classes small BB
default_model = 'ssdlite_6C_SB_25K_mobiledet_screws_edgetpu.tflite' #5 classes small BB
# default_model = 'ssdlite_6C_BB_10K_mobiledet_screws.tflite' #5 classes big BB 1K
# default_model = 'ssdlite_6C_BB_10K_mobiledet_screws_edgetpu.tflite' #5 classes big BB 1K
default_labels = 'ssdlite_mobiledet_screws_6c_labels.txt'
# default_model = 'ssdlite_2C_BB_10K_mobiledet_screws.tflite' #5 classes big BB 1K
# default_model = 'ssdlite_2C_BB_10K_mobiledet_screws_edgetpu.tflite' #5 classes big BB 1K
# default_labels = 'ssdlite_mobiledet_screws_2c_labels.txt'
# default_model_dir = '../cmpe297_model'
# default_model = 'Sergio_v3_ssdlite_mobiledet_dog_vs_cat.tflite'
# # default_model = 'Sergio_v3_sdlite_mobiledet_dog_vs_cat_edgetpu.tflite'
# default_labels = 'cat_vs_doc_All.txt'
# default_model = 'mobilenet_v2_1.0_224_quant_edgetpu_cmpe297.tflite'
# # default_model = 'mobilenet_v2_1.0_224_quant_cmpe297.tflite'
# default_labels = 'flower_labels_cmpe297.txt'
# default_model = 'eager_mobilenet_v2_1.0_224_quant.tflite' #no edgeTPU
# default_model = 'eager_mobilenet_v2_1.0_224_quant_edgetpu.tflite' #eager
#
# default_model = 'eager2_mobilenet_v2_1.0_224_quant.tflite' #eager
# default_model = 'eager2_mobilenet_v2_1.0_224_quant_edgetpu.tflite' #eager
# default_labels = 'duckylabels.txt'
# default_model = 'quant_coco-tiny-v3-relu.tflite'
# default_model = 'quant_coco-tiny-v3-relu_edgetpu.tflite'
# default_model = 'ssdlite_mobiledet_dog_vs_cat_edgetpu.tflite'
# default_labels = 'cat_vs_doc.txt'
# default_model = 'cmpe297_ssdlite_mobiledet_dog.tflite'
# default_model = 'cmpe297_ssdlite_mobiledet_dog_edgetpu.tflite'
# default_model = 'cmpe297v2_ssdlite_mobiledet_dog_edgetpu.tflite'
# default_labels = 'dogs_labels.txt'
# default_model = 'ssdlite_mobiledet_dog_vs_cat_edgetpuAcha.tflite'
# default_labels = 'cat_vs_doc_All.txt'
parser = argparse.ArgumentParser()
parser.add_argument('--model',
help='.tflite model path',
default=os.path.join(default_model_dir, default_model))
parser.add_argument('--labels',
help='label file path',
default=os.path.join(default_model_dir,
default_labels))
parser.add_argument(
'--top_k',
type=int,
default=3,
help='number of categories with highest score to display')
parser.add_argument('--threshold',
type=float,
default=0.1,
help='classifier score threshold')
parser.add_argument('--videosrc',
help='Which video source to use. ',
default='/dev/video0')
parser.add_argument('--videofmt',
help='Input video format.',
default='raw',
choices=['raw', 'h264', 'jpeg'])
args = parser.parse_args()
print('Loading {} with {} labels.'.format(args.model, args.labels))
interpreter = common.make_interpreter(args.model)
interpreter.allocate_tensors()
labels = load_labels(args.labels)
w, h, _ = common.input_image_size(interpreter)
inference_size = (w, h)
# Average fps over last 30 frames.
fps_counter = common.avg_fps_counter(30)
def user_callback(input_tensor, src_size, inference_box):
nonlocal fps_counter
start_time = time.monotonic()
common.set_input(interpreter, input_tensor)
interpreter.invoke()
# For larger input image sizes, use the edgetpu.classification.engine for better performance
objs = get_output(interpreter, args.threshold, args.top_k)
# print(objs[0].bbox)
end_time = time.monotonic()
text_lines = [
'Inference: {:.2f} ms'.format((end_time - start_time) * 1000),
'FPS: {} fps'.format(round(next(fps_counter))),
]
print(' '.join(text_lines))
return generate_svg(src_size, inference_size, inference_box, objs,
labels, text_lines)
result = gstreamer.run_pipeline(user_callback,
src_size=(640, 480),
appsink_size=inference_size,
videosrc=args.videosrc,
videofmt=args.videofmt)
def main():
#///-----------------------------------------------------------\\\
#// Scanning Image \\
def user_callback(input_tensor, src_size, inference_box):
global access
global house
global parcel
nonlocal fps_counter
start_time = time.monotonic()
common.set_input(interpreter, input_tensor)
interpreter.invoke()
# For larger input image sizes, use the edgetpu.classification.engine for better performance
results = get_output(interpreter, args.top_k, args.threshold)
end_time = time.monotonic()
text_lines = [
' ',
'Inference: {:.2f} ms'.format((end_time - start_time) * 1000),
'FPS: {} fps'.format(round(next(fps_counter))),
]
for result in results:
text_lines.append('score={:.2f}: {}'.format(
result.score, labels.get(result.id, result.id)))
if gpio6.read() == True:
access = 2
Gtk.main_quit()
elif house:
if labels.get(
result.id, result.id
) == "tree frog, tree-frog" and result.score > 0.3:
access = 1
Gtk.main_quit()
elif (labels.get(result.id, result.id) == "acoustic guitar"
or labels.get(result.id, result.id) == "jigsaw puzzle"
or labels.get(result.id, result.id) == "jellyfish"
or labels.get(result.id, result.id) == "basketball"
or labels.get(result.id, result.id)
== "soccer ball") and result.score > 0.3:
access = 0
Gtk.main_quit()
elif parcel:
if labels.get(
result.id,
result.id) == "acoustic guitar" and result.score > 0.3:
access = 1
Gtk.main_quit()
elif (labels.get(result.id,
result.id) == "tree frog, tree-frog"
or labels.get(result.id, result.id) == "jigsaw puzzle"
or labels.get(result.id, result.id) == "jellyfish"
or labels.get(result.id, result.id) == "basketball"
or labels.get(result.id, result.id)
== "soccer ball") and result.score > 0.3:
access = 0
Gtk.main_quit()
print(' '.join(text_lines))
return generate_svg(src_size, text_lines)
#\\ //
#\\\-----------------------------------------------------------///
while (1):
global access
global answer
global house
global parcel
gpio7.write(True)
gpio8.write(True)
while (gpio6.read() == False): # Waiting for signal
time.sleep(0.05)
time.sleep(2)
# Setting up voice recogniton
parser = argparse.ArgumentParser()
model.add_model_flags(parser)
args = parser.parse_args()
interpreter = model.make_interpreter(args.model_file)
interpreter.allocate_tensors()
mic = args.mic if args.mic is None else int(args.mic)
model.classify_audio(
mic,
interpreter,
1, # Calling Listening Function
labels_file="config/labels_gc2.raw.txt",
result_callback=print_results,
sample_rate_hz=int(args.sample_rate_hz),
num_frames_hop=int(args.num_frames_hop))
if answer == 3: # Timed out
answer = 0
house = False
parcel = False
elif answer == 1: # Yes
gpio8.write(True)
gpio7.write(False)
while (gpio6.read() == False):
time.sleep(0.05)
gpio7.write(True)
answer = 0
house = True
parcel = False
elif answer == 2: # No
gpio8.write(False)
gpio7.write(False)
while (gpio6.read() == False):
time.sleep(0.05)
gpio7.write(True)
answer = 0
house = False
time.sleep(1)
model.classify_audio(
mic,
interpreter,
2, # Calling Listening Function
labels_file="config/labels_gc2.raw.txt",
result_callback=print_results,
sample_rate_hz=int(args.sample_rate_hz),
num_frames_hop=int(args.num_frames_hop))
if answer == 3: # Timed out
answer = 0
parcel = False
elif answer == 1: # Yes
gpio8.write(True)
gpio7.write(False)
while (gpio6.read() == False):
time.sleep(0.05)
gpio7.write(True)
answer = 0
parcel = True
elif answer == 2: # No
gpio8.write(False)
gpio7.write(False)
while (gpio6.read() == False):
time.sleep(0.05)
gpio7.write(True)
answer = 0
parcel = False
if house or parcel:
# Setting up image recogniton
default_model_dir = '../all_models'
default_model = 'mobilenet_v2_1.0_224_quant_edgetpu.tflite'
default_labels = 'imagenet_labels.txt'
parser = argparse.ArgumentParser()
parser.add_argument('--model',
help='.tflite model path',
default=os.path.join(default_model_dir,
default_model))
parser.add_argument('--labels',
help='label file path',
default=os.path.join(default_model_dir,
default_labels))
parser.add_argument(
'--top_k',
type=int,
default=3,
help='number of categories with highest score to display')
parser.add_argument('--threshold',
type=float,
default=0.1,
help='classifier score threshold')
parser.add_argument('--videosrc',
help='Which video source to use. ',
default='/dev/video0')
parser.add_argument('--videofmt',
help='Input video format.',
default='raw',
choices=['raw', 'h264', 'jpeg'])
args = parser.parse_args()
print('Loading {} with {} labels.'.format(args.model, args.labels))
interpreter = common.make_interpreter(args.model)
interpreter.allocate_tensors()
labels = load_labels(args.labels)
w, h, _ = common.input_image_size(interpreter)
inference_size = (w, h)
# Average fps over last 30 frames.
fps_counter = common.avg_fps_counter(30)
result = gstreamer.run_pipeline(
user_callback, # Calling Scanning Image Function
src_size=(640, 480),
appsink_size=inference_size,
videosrc=args.videosrc,
videofmt=args.videofmt)
# Communication with ESP32 Board
if access == 1:
gpio8.write(True)
gpio7.write(False)
while (gpio6.read() == False):
time.sleep(0.05)
gpio7.write(True)
elif access == 0:
gpio8.write(False)
gpio7.write(False)
while (gpio6.read() == False):
time.sleep(0.05)
gpio7.write(True)
time.sleep(2)
def main():
default_model_dir = 'models'
default_model = 'mobilenet_ssd_v2_face_quant_postprocess_edgetpu.tflite'
default_labels = 'face_labels.txt'
parser = argparse.ArgumentParser()
parser.add_argument('--model',
help='.tflite model path',
default=os.path.join(default_model_dir, default_model))
parser.add_argument('--labels',
help='label file path',
default=os.path.join(default_model_dir,
default_labels))
parser.add_argument('--top_k',
type=int,
default=5,
help='number of classes with highest score to display')
parser.add_argument('--threshold',
type=float,
default=0.1,
help='class score threshold')
args = parser.parse_args()
print("Loading %s with %s labels." % (args.model, args.labels))
engine = DetectionEngine(args.model)
labels = load_labels(args.labels)
last_time = time.monotonic()
def user_callback(image, svg_canvas):
nonlocal last_time
start_time = time.monotonic()
objs = engine.DetectWithImage(image,
threshold=args.threshold,
keep_aspect_ratio=True,
relative_coord=True,
top_k=args.top_k)
end_time = time.monotonic()
if objs:
print()
for obj in objs:
if labels:
print(labels[obj.label_id], 'score = ', obj.score)
else:
print('score = ', obj.score)
[x1, y1, x2, y2] = obj.bounding_box.flatten().tolist()
print(x1, y1, x2, y2)
# calculate pixel coords
pix_x = (x1 + x2) * 320 # 640/2 = 320
pix_y = (y1 + y2) * 240 # 480/2 = 240
# calculate angles with respect to center
# TODO: an accurate parameter replacing 480 needs to be calculated
yaw = math.atan((pix_x - 640./2) / 480) * \
1800 / math.pi + YAW_MID
pitch = math.atan((pix_y - 480./2) / 480) * \
1800 / math.pi + PITCH_MID
serial.yaw = yaw
serial.pitch = pitch
else:
print('No object detected!')
text_lines = [
'Inference: %.2f ms' % ((end_time - start_time) * 1000),
'FPS: %.2f fps' % (1.0 / (end_time - last_time)),
]
print(' '.join(text_lines))
last_time = end_time
generate_svg(svg_canvas, objs, labels, text_lines)
result = gstreamer.run_pipeline(user_callback)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--model', help='.tflite model file')
parser.add_argument('--labels', help='.txt label file')
parser.add_argument('--threshold',
help='Class Score Threshold',
type=float,
default=0.7)
parser.add_argument('--top_k', help='Class Top K', type=int, default=2)
parser.add_argument('--manual',
help='Take a picture when key pressed',
action='store_true')
args = parser.parse_args()
engine = ClassificationEngine(args.model)
labels = load_labels(args.labels)
input_monitor = InputMonitor(gpio_pin=8)
led = LED(gpio_r=6, gpio_g=7, gpio_b=None, invert=True)
led.switch_off_all()
light_duration = 3 if args.manual else 0.1
last_time = time.monotonic()
if args.manual:
input_monitor.daemon = True
input_monitor.start()
def user_callback(image, svg_canvas):
nonlocal last_time
if args.manual:
if not input_monitor.is_key_pressed():
return
start_time = time.monotonic()
results = engine.ClassifyWithImage(image,
threshold=0.1,
top_k=args.top_k)
end_time = time.monotonic()
text_lines = [
'Inference: %.2f ms' % ((end_time - start_time) * 1000),
'FPS: %.2f fps' % (1.0 / (end_time - last_time)),
]
if len(results) == 0:
led.switch_off_all()
else:
results.sort(key=lambda result: result[1], reverse=True)
for index, score in results:
text_lines.append('score=%.2f: %s' % (score, labels[index]))
top_label = labels[results[0][0]]
top_score = results[0][1]
if top_score >= args.threshold:
if top_label == 'roadway_green':
led.switch_green(duration=light_duration)
elif top_label == 'roadway_red':
led.switch_red(duration=light_duration)
elif top_label == 'roadway_yellow':
led.switch_yellow(duration=light_duration)
else:
led.switch_off_all()
last_time = end_time
print(' '.join(text_lines))
generate_svg(svg_canvas, text_lines)
gstreamer.run_pipeline(user_callback)
def main():
def user_callback(input_tensor, src_size, inference_box):
global access
global house
global parcel
nonlocal fps_counter
start_time = time.monotonic()
common.set_input(interpreter, input_tensor)
interpreter.invoke()
# For larger input image sizes, use the edgetpu.classification.engine for better performance
results = get_output(interpreter, args.top_k, args.threshold)
end_time = time.monotonic()
text_lines = [
' ',
'Inference: {:.2f} ms'.format((end_time - start_time) * 1000),
'FPS: {} fps'.format(round(next(fps_counter))),
]
for result in results:
text_lines.append('score={:.2f}: {}'.format(result.score, labels.get(result.id, result.id)))
if house:
if labels.get(result.id, result.id) == "tree frog, tree-frog":
access = 1
gpio6.write(True)
Gtk.main_quit()
elif labels.get(result.id, result.id) == "acoustic guitar" or labels.get(result.id, result.id) == "jigsaw puzzle" or labels.get(result.id, result.id) == "jellyfish" or labels.get(result.id, result.id) == "basketball" or labels.get(result.id, result.id) == "soccer ball":
access = 0
gpio73.write(True)
Gtk.main_quit()
elif parcel:
if labels.get(result.id, result.id) == "acoustic guitar":
access = 1
gpio7.write(True)
Gtk.main_quit()
elif labels.get(result.id, result.id) == "tree frog, tree-frog" or labels.get(result.id, result.id) == "jigsaw puzzle" or labels.get(result.id, result.id) == "jellyfish" or labels.get(result.id, result.id) == "basketball" or labels.get(result.id, result.id) == "soccer ball":
access = 0
gpio8.write(True)
Gtk.main_quit()
print(' '.join(text_lines))
return generate_svg(src_size, text_lines)
while(1):
global access
global answer
global house
global parcel
gpio6.write(False)
gpio73.write(False)
gpio7.write(False)
gpio8.write(False)
motion.start_ranging(VL53L0X.VL53L0X_BETTER_ACCURACY_MODE)
timing = motion.get_timing()
if (timing < 20000):
timing = 20000
distance = motion.get_distance()
while(distance > 500):
distance = motion.get_distance()
time.sleep(timing/1000000.00)
motion.stop_ranging()
wave_obj = sa.WaveObject.from_wave_file("welcome.wav")
play_obj = wave_obj.play()
play_obj.wait_done()
wave_obj = sa.WaveObject.from_wave_file("entry.wav")
play_obj = wave_obj.play()
play_obj.wait_done()
# Voice Recognition
parser = argparse.ArgumentParser()
model.add_model_flags(parser)
args = parser.parse_args()
interpreter = model.make_interpreter(args.model_file)
interpreter.allocate_tensors()
mic = args.mic if args.mic is None else int(args.mic)
model.classify_audio(mic, interpreter, 1,
labels_file="config/labels_gc2.raw.txt",
result_callback=print_results,
sample_rate_hz=int(args.sample_rate_hz),
num_frames_hop=int(args.num_frames_hop))
if answer == 1:
wave_obj = sa.WaveObject.from_wave_file("key.wav")
play_obj = wave_obj.play()
play_obj.wait_done()
answer = 0
house = True
parcel = False
elif answer == 2:
wave_obj = sa.WaveObject.from_wave_file("package.wav")
play_obj = wave_obj.play()
play_obj.wait_done()
answer = 0
house = False
# Voice Recognition
model.classify_audio(mic, interpreter, 2,
labels_file="config/labels_gc2.raw.txt",
result_callback=print_results,
sample_rate_hz=int(args.sample_rate_hz),
num_frames_hop=int(args.num_frames_hop))
if answer == 1:
wave_obj = sa.WaveObject.from_wave_file("key.wav")
play_obj = wave_obj.play()
play_obj.wait_done()
answer = 0
parcel = True
elif answer == 2:
wave_obj = sa.WaveObject.from_wave_file("goodday.wav")
play_obj = wave_obj.play()
play_obj.wait_done()
answer = 0
parcel = False
if house or parcel:
default_model_dir = '../all_models'
default_model = 'mobilenet_v2_1.0_224_quant_edgetpu.tflite'
default_labels = 'imagenet_labels.txt'
parser = argparse.ArgumentParser()
parser.add_argument('--model', help='.tflite model path',
default=os.path.join(default_model_dir,default_model))
parser.add_argument('--labels', help='label file path',
default=os.path.join(default_model_dir, default_labels))
parser.add_argument('--top_k', type=int, default=3,
help='number of categories with highest score to display')
parser.add_argument('--threshold', type=float, default=0.1,
help='classifier score threshold')
parser.add_argument('--videosrc', help='Which video source to use. ',
default='/dev/video0')
parser.add_argument('--videofmt', help='Input video format.',
default='raw',
choices=['raw', 'h264', 'jpeg'])
args = parser.parse_args()
print('Loading {} with {} labels.'.format(args.model, args.labels))
interpreter = common.make_interpreter(args.model)
interpreter.allocate_tensors()
labels = load_labels(args.labels)
w, h, _ = common.input_image_size(interpreter)
inference_size = (w, h)
# Average fps over last 30 frames.
fps_counter = common.avg_fps_counter(30)
result = gstreamer.run_pipeline(user_callback,
src_size=(640, 480),
appsink_size=inference_size,
videosrc=args.videosrc,
videofmt=args.videofmt)
if access:
if house:
wave_obj = sa.WaveObject.from_wave_file("stay.wav")
elif parcel:
wave_obj = sa.WaveObject.from_wave_file("parcel.wav")
play_obj = wave_obj.play()
play_obj.wait_done()
else:
wave_obj = sa.WaveObject.from_wave_file("denied.wav")
play_obj = wave_obj.play()
play_obj.wait_done()
time.sleep(3)
def main():
default_model_dir = '../all_models'
default_model = 'mobilenet_ssd_v2_face_quant_postprocess_edgetpu.tflite'
default_labels = 'fer_labels.txt'
parser = argparse.ArgumentParser()
parser.add_argument('--model',
help='.tflite model path',
default=os.path.join(default_model_dir, default_model))
parser.add_argument('--labels',
help='label file path',
default=os.path.join(default_model_dir,
default_labels))
parser.add_argument(
'--top_k',
type=int,
default=1,
help='number of categories with highest score to display')
parser.add_argument('--threshold',
type=float,
default=0.1,
help='classifier score threshold')
parser.add_argument('--videosrc',
help='Which video source to use. ',
default='/dev/video0')
parser.add_argument('--videofmt',
help='Input video format.',
default='raw',
choices=['raw', 'h264', 'jpeg'])
args = parser.parse_args()
print('Loading {} with {} labels.'.format(args.model, args.labels))
face_interpreter = common.make_interpreter(
os.path.join(default_model_dir, default_model))
face_interpreter.allocate_tensors()
# fer interpreter
fer_interpreter = common.make_interpreter(args.model)
fer_interpreter.allocate_tensors()
labels = load_labels(args.labels)
w, h, _ = common.input_image_size(face_interpreter)
inference_size = (w, h)
# Average fps over last 30 frames.
fps_counter = common.avg_fps_counter(30)
def user_callback(input_tensor, src_size, inference_box):
nonlocal fps_counter
start_time = time.monotonic()
common.set_input(face_interpreter, input_tensor)
face_interpreter.invoke()
# For larger input image sizes, use the edgetpu.classification.engine for better performance
objs = get_output(face_interpreter, args.threshold, args.top_k)
# Get face detected part
from PIL import Image
im = Image.fromarray(common.input_tensor(face_interpreter))
src_w, src_h = src_size
inf_w, inf_h = inference_size
results = []
emo_objs = []
for obj in objs:
x0, y0, x1, y1 = list(obj.bbox)
# Relative coordinates.
x, y, w, h = x0, y0, x1 - x0, y1 - y0
# Absolute coordinates, input tensor space.
x, y, w, h = int(x * inf_w), int(y * inf_h), int(w * inf_w), int(
h * inf_h)
crop_rectangle = (x, y, x + w, y + h)
# get face
face = im.crop(crop_rectangle)
face = np.array(face)
# convert to grayscale
#face = cv2.cvtColor(face, cv2.COLOR_RGB2GRAY)
print(face.shape)
face = cv2.resize(face, (224, 224))
face = face.astype(np.uint8)
#face /= float(face.max())
face = np.reshape(face.flatten(), (224, 224, 3))
# invoke fer interpreter
common.set_input2(fer_interpreter, face)
fer_interpreter.invoke()
# process results
results = get_emotion(fer_interpreter)
if len(results) > 0:
setattr(obj, "id", results[0].id)
setattr(obj, "score", results[0].score)
emo_objs.append(obj)
objs = emo_objs
end_time = time.monotonic()
text_lines = []
if len(objs) > 0:
text_lines = [
'Inference: {:.2f} ms'.format((end_time - start_time) * 1000),
'FPS: {} fps'.format(round(next(fps_counter))),
]
for result in results:
text_lines.append('score={:.2f}: {}'.format(
result.score, labels.get(result.id, result.id)))
#print(' '.join(text_lines))
return generate_svg(src_size, inference_size, inference_box, objs,
labels, text_lines)
result = gstreamer.run_pipeline(user_callback,
src_size=(640, 480),
appsink_size=inference_size,
videosrc=args.videosrc,
videofmt=args.videofmt)
def main():
"""Creates camera pipeline, and pushes pipeline through ClassificationEngine
model. Logs results to user-defined storage. Runs either in training mode to
gather images for custom model creation or in deterrent mode that sounds an
'alarm' if a defined label is detected."""
args = user_selections()
print("Loading %s with %s labels." % (args.model, args.labels))
engine = ClassificationEngine(args.model)
labels = load_labels(args.labels)
storage_dir = args.storage
#Initialize logging files
logging.basicConfig(filename='%s/results.log' % storage_dir,
format='%(asctime)s-%(message)s',
level=logging.DEBUG)
last_time = time.monotonic()
last_results = [('label', 0)]
last_tweet = None
def user_callback(image, svg_canvas):
nonlocal last_time
nonlocal last_results
nonlocal last_tweet
start_time = time.monotonic()
results = engine.classify_with_image(image,
threshold=args.threshold,
top_k=args.top_k)
end_time = time.monotonic()
results = [(labels[i], score) for i, score in results]
if args.print:
print_results(start_time, last_time, end_time, results)
if args.training:
print("training mode")
if do_training(results, last_results, args.top_k):
save_data(image, results, storage_dir)
else:
print("looking for birds")
# Custom model mode:
# Save the images if the label is one of the targets and its probability is relatively high
if results[0][1] >= 0.8:
filename = save_data(image, results, storage_dir)
if (last_tweet is None) or ((time.time() - last_tweet > 300)
and results[0][1] >= 0.9):
try:
#imageFile = take_a_picture(storage_dir)
status = "I'm %d percent sure this is a %s. #ai" % (
results[0][1] * 100, results[0][0])
logging.info('Trying to tweet : %s', status)
logging.info('Reading file %s', filename)
tweet(status, filename)
last_tweet = time.time()
except:
logging.exception('Failed to send tweet')
last_tweet = None
last_results = results
last_time = end_time
result = gstreamer.run_pipeline(user_callback)
def main():
default_model_dir = 'models'
# default_model = '2019_05_13_whole(1)/output_tflite_graph_1557776948_edgetpu.tflite'
# default_model = '2/output_tflite_graph_edgetpu.tflite'
# default_model = '3/output_tflite_graph_edgetpu.tflite'
default_model = '4/output_tflite_graph_edgetpu.tflite'
# more to come...
default_labels = 'armor_plate_labels.txt'
# default_model = 'mobilenet_ssd_v2_face_quant_postprocess_edgetpu.tflite'
# default_labels = 'face_labels.txt'
parser = argparse.ArgumentParser()
parser.add_argument('-d', action="store_true", help='enable debug mode')
parser.add_argument('--model',
help='.tflite model path',
default=os.path.join(default_model_dir, default_model))
parser.add_argument('--labels',
help='label file path',
default=os.path.join(default_model_dir,
default_labels))
parser.add_argument('--top_k',
type=int,
default=5,
help='number of classes with highest score to display')
parser.add_argument('--threshold',
type=float,
default=0.1,
help='class score threshold')
args = parser.parse_args()
if args.d:
global DEBUG
DEBUG = True
print("Debug mode is on!")
print("Loading model:", default_model)
print("Loading labels:", default_labels)
engine = DetectionEngine(args.model)
labels = load_labels(args.labels)
last_time = time.monotonic()
try:
dev = slcan.slcanBus(PORT[0], bitrate=1000000)
dev.open()
print('Connection found at port', PORT[0])
except:
dev = slcan.slcanBus(PORT[1], bitrate=1000000)
dev.open()
print('Connection found at port', PORT[1])
# finally:
# dev = None
# print('No connection found but still running')
# pass
yaw = YAW_MID
pitch = PITCH_MID
def user_callback(image, svg_canvas):
nonlocal last_time
start_time = time.monotonic()
objs = engine.DetectWithImage(image,
threshold=args.threshold,
keep_aspect_ratio=True,
relative_coord=True,
top_k=args.top_k)
end_time = time.monotonic()
obj = choose_obj(objs, start_time)
if obj:
# if labels:
# print(labels[obj.label_id], 'score = ', obj.score)
# else:
# print('score = ', obj.score)
[x1, y1, x2, y2] = obj.bounding_box.flatten().tolist()
# calculate pixel coords
pix_x = (x1 + x2) * X_PIXEL / 2 # 640/2 = 320
pix_y = (y1 + y2) * Y_PIXEL / 2 # 480/2 = 240
# calculate angles with respect to center
yaw = math.atan((pix_x - X_PIXEL/2) / CAMERA_PARAM) * \
1800 / math.pi + YAW_MID
pitch = math.atan((pix_y - Y_PIXEL/2) / CAMERA_PARAM) * \
1800 / math.pi + PITCH_MID
sendMessage(dev, yaw, pitch)
else:
print('No object detected!')
sendMessage(dev, DEFAULT_YAW, DEFAULT_PITCH)
text_lines = [
'Inference: %.2f ms' % ((end_time - start_time) * 1000),
'SPF: %.2f ms' % ((end_time - last_time) * 1000),
'FPS: %.2f fps' % (1.0 / (end_time - last_time)),
]
print(' '.join(text_lines))
last_time = end_time
if DEBUG:
generate_svg(svg_canvas, objs, labels, text_lines)
result = gstreamer.run_pipeline(DEBUG, user_callback)
def main():
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--mirror', help='flip video horizontally', action='store_true')
parser.add_argument('--model', help='.tflite model path.', required=False)
parser.add_argument('--res', help='Resolution', default='640x480',
choices=['480x360', '640x480', '1280x720'])
parser.add_argument('--videosrc', help='Which video source to use', default='/dev/video0')
parser.add_argument('--h264', help='Use video/x-h264 input', action='store_true')
parser.add_argument('--jpeg', help='Use image/jpeg input', action='store_true')
args = parser.parse_args()
#cambiar el modelo
default_model = 'pose_detection.tflite'
if args.res == '480x360':
src_size = (640, 480)
appsink_size = (480, 360)
#model = args.model or default_model % (353, 481)
elif args.res == '640x480':
src_size = (640, 480)
appsink_size = (640, 480)
#model = args.model or default_model % (481, 641)
elif args.res == '1280x720':
src_size = (1280, 720)
appsink_size = (1280, 720)
#model = args.model or default_model % (721, 1281)
model=default_model
n = 0
sum_process_time = 0
sum_inference_time = 0
ctr = 0
fps_counter = avg_fps_counter(30)
#Engine contiene el modelo de CNN
input_tensor=engine.run_inference(input_tensor)
print('Loading model: ', model)
engine = PoseEngine(model)
input_shape = engine.get_input_tensor_shape()
inference_size = (input_shape[2], input_shape[1])
def render_overlay(engine, output, src_size, inference_box):
nonlocal n, sum_process_time, sum_inference_time, fps_counter
svg_canvas = svgwrite.Drawing('', size=src_size)
start_time = time.monotonic()
outputs, inference_time = engine.ParseOutput(output)
end_time = time.monotonic()
n += 1
sum_process_time += 1000 * (end_time - start_time)
sum_inference_time += inference_time
avg_inference_time = sum_inference_time / n
text_line = 'PoseNet: %.1fms (%.2f fps) TrueFPS: %.2f Nposes %d' % (
avg_inference_time, 1000 / avg_inference_time, next(fps_counter), len(outputs)
)
shadow_text(svg_canvas, 10, 20, text_line)
for pose in outputs:
draw_pose(svg_canvas, pose, src_size, inference_box)
return (svg_canvas.tostring(), False)
pose= gstreamer.run_pipeline(input_tensor, render_overlay,
src_size, inference_size,
mirror=args.mirror,
videosrc=args.videosrc,
h264=args.h264,
jpeg=args.jpeg)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--project', help='GCP Project')
parser.add_argument('--bucket', help='GCS bucket name')
parser.add_argument('--path', help='GCS path prefix for uploading images')
parser.add_argument('--region', help='GCP Region')
parser.add_argument('--registry_id', help='IoT Core Registry ID')
parser.add_argument('--device_id', help='IoT Core Device ID')
parser.add_argument('--private_key', help='IoT Core Private Key File')
parser.add_argument('--algorithm', help='IoT Core JWT Algorithm',
default='RS256')
parser.add_argument('--ca_certs', help='IoT Core roots.pem',
default='roots.pem')
parser.add_argument('--mqtt_host', help='IoT Core hostname',
default='mqtt.googleapis.com')
parser.add_argument('--mqtt_port', help='IoT Core port',
type=int,
default=443)
args = parser.parse_args()
input_monitor = InputMonitor(gpio_pin=8)
led = LED(gpio_r=6, gpio_g=7, gpio_b=None, invert=True)
led.switch_off_all()
def user_callback(image, svg_canvas):
nonlocal input_monitor
if input_monitor.is_key_pressed():
upload.upload(args.bucket, args.path, image)
input_monitor.daemon = True
input_monitor.start()
print('monitoring keyboard input...')
mqtt.setup_mqtt_client(
args.project,
args.registry_id,
args.private_key,
args.device_id,
args.region,
args.algorithm,
args.ca_certs,
args.mqtt_host,
args.mqtt_port)
def message_callback(payload):
try:
preds = json.loads(payload)
preds.sort(key=lambda pred: pred['class_score'], reverse=True)
top = preds[0]['class_name']
if top == 'roadway_green':
led.switch_green(duration=3)
elif top == 'roadway_red':
led.switch_red(duration=3)
elif top == 'roadway_yellow':
led.switch_yellow(duration=3)
else:
led.switch_off_all()
except Exception as ex:
print(ex)
mqtt.add_message_callback(message_callback)
gstreamer.run_pipeline(user_callback)
