def main():
parser = argparse.ArgumentParser()
parser.add_argument('--model',
help='Path of the detection model.',
required=True)
parser.add_argument('--draw', help='If to draw the results.', default=True)
parser.add_argument('--label', help='Path of the labels file.')
args = parser.parse_args()
renderer = None
# Initialize engine.
engine = DetectionEngine(args.model)
labels = read_label_file(args.label) if args.label else None
shown = False
frames = 0
start_seconds = time.time()
print('opening socket.')
# s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
receiveSocket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
# senderSocket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.bind((TCP_IP, TCP_PORT))
s.listen(1)
# senderSocket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
receiveSocket.bind((UDP_IP, UDP_RECEIVE_PORT))
# senderSocket.bind((UDP_IP, UDP_SEND_PORT))
print('listening...')
_, width, height, channels = engine.get_input_tensor_shape()
imageSize = width * height * 3
print('waiting for client')
conn, addr = s.accept()
print('Connection address:', addr)
# Open image.
while 1:
print('waiting for packet')
data, addr = receiveSocket.recvfrom(66507)
# print('got packet of length', len(data))
if (len(data) > 0):
start_s = time.time()
# print('processing image')
try:
image = Image.open(io.BytesIO(data)).convert('RGB')
except OSError:
print('Could not read image')
continue
input = np.frombuffer(image.tobytes(), dtype=np.uint8)
results = engine.DetectWithInputTensor(input,
threshold=0.25,
top_k=10)
print('time to process image', (time.time() - start_s) * 1000)
output = to_output(results, image.size, labels)
message = json.dumps({'results': output}) + '|'
# print('sending', message)
try:
conn.send(message.encode('utf-8'))
except ConnectionResetError:
print('Socket disconnected...waiting for client')
conn, addr = s.accept()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--model',
help='Path of the detection model.',
required=True)
parser.add_argument('--draw', help='If to draw the results.', default=True)
parser.add_argument('--label', help='Path of the labels file.')
args = parser.parse_args()
renderer = None
# Initialize engine.
engine = DetectionEngine(args.model)
labels = read_label_file(args.label) if args.label else None
shown = False
frames = 0
start_seconds = time.time()
FULL_SIZE_W = 640
FULL_SIZE_H = 480
img = Image.new('RGBA', (FULL_SIZE_W, FULL_SIZE_H))
draw = ImageDraw.Draw(img)
# Open image.
with picamera.PiCamera() as camera:
camera.resolution = (FULL_SIZE_W, FULL_SIZE_H)
camera.framerate = 30
_, width, height, channels = engine.get_input_tensor_shape()
print('input dims', width, height)
camera.start_preview(fullscreen=False,
window=(700, 200, FULL_SIZE_W, FULL_SIZE_H))
# camera.start_preview()
# rasberry pi requires images to be resizes to multiples of 32x16
camera_multiple = (16, 32)
valid_resize_w = width - width % camera_multiple[1]
valid_resize_h = height - height % camera_multiple[0]
padding_w = (width - valid_resize_w) // 2
padding_h = (height - valid_resize_h) // 2
scale_w = FULL_SIZE_W / width
scale_h = FULL_SIZE_H / height
try:
stream = io.BytesIO()
for foo in camera.capture_continuous(
stream,
format='rgb',
# format='jpeg',
use_video_port=True,
resize=(valid_resize_w, valid_resize_h)):
stream.truncate()
stream.seek(0)
start_frame = time.time()
input = np.frombuffer(stream.getvalue(), dtype=np.uint8)
if padding_w > 0 or padding_h > 0:
flattened = pad_and_flatten(
input, (valid_resize_h, valid_resize_w), padding_h,
padding_w)
else:
flattened = input
# flatten padded element
reshape_time = time.time() - start_frame
start_s = time.time()
# Run inference.
results = engine.DetectWithInputTensor(flattened,
threshold=0.2,
top_k=10)
elapsed_s = time.time() - start_frame
if padding_w > 0 or padding_h > 0:
boxes = translate_and_scale_boxes(\
results, \
(valid_resize_w, valid_resize_h),\
(padding_w, padding_h), \
(FULL_SIZE_W, FULL_SIZE_H))
else:
boxes = scale_boxes(results, (FULL_SIZE_W, FULL_SIZE_H))
if args.draw:
img.putalpha(0)
draw_boxes(draw, boxes)
if labels:
draw_labels(draw, results, boxes, labels)
# display_results(ans, labels, img)
imbytes = img.tobytes()
if renderer == None:
renderer = camera.add_overlay(imbytes,
size=img.size,
layer=4,
format='rgba',
fullscreen=False,
window=(700, 200, 640,
FULL_SIZE_H))
else:
# print('updating')
renderer.update(imbytes)
frame_seconds = time.time()
# print(frame_seconds - start_seconds, frames)
fps = frames * 1.0 / (frame_seconds - start_seconds)
frames = frames + 1
# time.sleep(1)
camera.annotate_text = "%.2fms, %d fps" % (elapsed_s * 1000.0,
fps)
finally:
camera.stop_preview()