Example: 12-Protocol/sensors_channel.py
import csi
import tof
import struct
import random
from protocol import CBORChannel
import protocol
import ml
from ml.postprocessing.mediapipe import BlazeFace
csi0 = csi.CSI()
csi0.reset()
csi0.pixformat(csi.RGB565)
csi0.framesize(csi.QVGA)
model = ml.Model(
"/rom/blazeface_front_128.tflite",
postprocess=BlazeFace(threshold=0.5),
)
print(model)
tof.init()
tof_w = tof.width()
tof_h = tof.height()
sensors = CBORChannel()
sensors.add("temp", type="label", unit="Cel")
sensors.add("humidity", type="label", unit="%RH")
sensors_ch = protocol.register(name="sensors", backend=sensors)
depth = CBORChannel()
depth.add("depth", type="depth", width=tof_w, height=tof_h)
depth_ch = protocol.register(name="ToF depth", backend=depth)
temp = 25.0
humidity = 50.0
while True:
img = csi0.snapshot()
for r, score, keypoints in model.predict([img]):
ml.utils.draw_predictions(img, [r], ("face",), ((0, 0, 255),), format=None)
ml.utils.draw_keypoints(img, keypoints, color=(255, 0, 0))
temp += random.uniform(-0.3, 0.3)
temp = max(15.0, min(35.0, temp))
humidity += random.uniform(-0.5, 0.5)
humidity = max(20.0, min(80.0, humidity))
try:
depth_data, dmin, dmax = tof.read_depth(vflip=True, hmirror=True)
except RuntimeError:
continue
depth_bytes = struct.pack("<%df" % len(depth_data), *depth_data)
sensors["temp"] = round(temp, 1)
sensors["humidity"] = round(humidity, 1)
depth["depth"] = depth_bytes
depth_ch.send_event(0xFFFF)
sensors_ch.send_event(0xFFFF)
print(
"temp=%.1f | humidity=%.1f | depth=%d bytes"
% (temp, humidity, len(depth_bytes))
)