sensor.set_framesize(sensor.B64X64)
# Disable automatic exposure and gain
sensor.set_auto_exposure(False)
sensor.set_auto_gain(False)
sensor.set_contrast(0)
sensor.set_brightness(0)
# Wait two seconds for settings take effect.
sensor.skip_frames(time=2000)
# Create a clock object to track the FPS.
clock = time.clock()
# Load the Tensorflow Lite model
model = tf.load('whattolabel_resnet9.tflite')
def setLED(color='none'):
"""Function to set LED to a color (red, green, blue, none)
"""
red_LED = pyb.LED(1)
green_LED = pyb.LED(2)
blue_LED = pyb.LED(3)
if color == 'red':
red_LED.on()
green_LED.off()
blue_LED.off()
elif color == 'green':
# Face Recognition
#
# Use this script to run a TensorFlow lite image classifier on faces detected within an image.
# The classifier is free to do facial recognition, expression detection, or whatever.
import sensor, image, time, tf
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
sensor.skip_frames(time=2000)
clock = time.clock()
net = tf.load("trained.tflite", load_to_fb=True)
labels = [l.rstrip('\n') for l in open("labels.txt")]
while (True):
clock.tick()
# Take a picture and brighten things up for the frontal face detector.
img = sensor.snapshot().gamma_corr(contrast=1.5)
# Returns a list of rects (x, y, w, h) where faces are.
faces = img.find_features(image.HaarCascade("frontalface"))
for f in faces:
# Classify a face and get the class scores list
scores = net.classify(img, roi=f)[0].output()
# TensorFlow Lite Object Detection Example
#
# This example shows off object detection. Object detect is much more powerful than
# object classification. It can locate multiple objects in the image.
import sensor, image, time, os, tf
sensor.reset() # Reset and initialize the sensor.
sensor.set_pixformat(
sensor.RGB565) # Set pixel format to RGB565 (or GRAYSCALE)
sensor.set_framesize(sensor.QVGA) # Set frame size to QVGA (320x240)
sensor.set_windowing((240, 240)) # Set 240x240 window.
sensor.skip_frames(time=2000) # Let the camera adjust.
net = tf.load('<object_detection_network>', load_to_fb=True)
labels = []
try: # Load labels if they exist
labels = [line.rstrip('\n') for line in open("labels.txt")]
except:
pass
colors = [ # Add more colors if you are detecting more than 7 types of classes at once.
(255, 0, 0),
(0, 255, 0),
(255, 255, 0),
(0, 0, 255),
(255, 0, 255),
(0, 255, 255),
(255, 255, 255),
]
sensor.set_framesize(sensor.B64X64)
# Disable automatic exposure and gain
sensor.set_auto_exposure(False)
sensor.set_auto_gain(False)
sensor.set_contrast(0)
sensor.set_brightness(0)
# Wait two seconds for settings take effect.
sensor.skip_frames(time=2000)
# Create a clock object to track the FPS.
clock = time.clock()
# Load the Tensorflow Lite model
model = tf.load('lightly_resnet9.tflite')
def setLED(color='none'):
"""Function to set LED to a color (red, green, blue, none)
"""
red_LED = pyb.LED(1)
green_LED = pyb.LED(2)
blue_LED = pyb.LED(3)
if color == 'red':
red_LED.on()
green_LED.off()
blue_LED.off()
elif color == 'green':
import audio, time, tf, micro_speech, pyb
labels = ['Silence', 'Unknown', 'Yes', 'No']
led_red = pyb.LED(1)
led_green = pyb.LED(2)
model = tf.load('/model.tflite')
speech = micro_speech.MicroSpeech()
audio.init(channels=1, frequency=16000, gain=24, highpass=0.9883)
# Start audio streaming
audio.start_streaming(speech.audio_callback)
while (True):
# Run micro-speech without a timeout and filter detections by label index.
idx = speech.listen(model, timeout=0, threshold=0.78, filter=[2, 3])
led = led_green if idx == 2 else led_red
print(labels[idx])
for i in range(0, 4):
led.on()
time.sleep_ms(25)
led.off()
time.sleep_ms(25)
# Stop streaming
audio.stop_streaming()
# TensorFlow Lite Cats vs Dogs Classification Example
#
# Classify in view dogs and cats.
import sensor, image, time, os, tf
sensor.reset() # Reset and initialize the sensor.
sensor.set_pixformat(
sensor.RGB565) # Set pixel format to RGB565 (or GRAYSCALE)
sensor.set_framesize(sensor.QVGA) # Set frame size to QVGA (320x240)
sensor.set_windowing((240, 240)) # Set 240x240 window.
sensor.skip_frames(time=2000) # Let the camera adjust.
# Load the built-in cats vs dogs classification network (the network is in the OpenMV Cam's firmware contained in this repo).
net = tf.load('catsvsdogs_classification')
labels = ['cat', 'dog']
clock = time.clock()
while (True):
clock.tick()
img = sensor.snapshot()
for obj in net.classify(img,
min_scale=1.0,
scale_mul=0.99,
x_overlap=0.0,
y_overlap=0.0):
print("**********\nClassification at [x=%d,y=%d,w=%d,h=%d]" %
obj.rect())
for i in range(len(obj.output())):
