sumlab2

1:
from gpiozero import LED, Button
from signal import pause
from time import sleep

button = Button(6, False)
led1 = LED(13)

button.when_pressed = led1.on
button.when_released = led1.off

pause()

2 GPIO:
from gpiozero import LED, Button
from signal import pause
from time import sleep

button = Button(6, False)
led1 = LED(13)

button.when_pressed = led1.on
button.when_released = led1.off

pause()

3 I2C:
import smbus2
import bme280

port = 1
address = 0x77
bus = smbus2.SMBus(port)

calibration_params = bme280.load_calibration_params(bus, address)

# the sample method will take a single reading and return a
# compensated_reading object
data = bme280.sample(bus, address, calibration_params)

# the compensated_reading class has the following attributes
print(data.id)
print(data.timestamp)
print(data.temperature)
print(data.pressure)
print(data.humidity)

# there is a handy string representation too
print(data)

4 i2c-2:
import time
import board
import adafruit_tsl2591

# Create sensor object, communicating over the board's default I2C bus
i2c = board.I2C()  # uses board.SCL and board.SDA
# i2c = board.STEMMA_I2C()  # For using the built-in STEMMA QT connector on a microcontroller

# Initialize the sensor.
sensor = adafruit_tsl2591.TSL2591(i2c)

# You can optionally change the gain and integration time:
# sensor.gain = adafruit_tsl2591.GAIN_LOW (1x gain)
# sensor.gain = adafruit_tsl2591.GAIN_MED (25x gain, the default)
# sensor.gain = adafruit_tsl2591.GAIN_HIGH (428x gain)
# sensor.gain = adafruit_tsl2591.GAIN_MAX (9876x gain)
# sensor.integration_time = adafruit_tsl2591.INTEGRATIONTIME_100MS (100ms, default)
# sensor.integration_time = adafruit_tsl2591.INTEGRATIONTIME_200MS (200ms)
# sensor.integration_time = adafruit_tsl2591.INTEGRATIONTIME_300MS (300ms)
# sensor.integration_time = adafruit_tsl2591.INTEGRATIONTIME_400MS (400ms)
# sensor.integration_time = adafruit_tsl2591.INTEGRATIONTIME_500MS (500ms)
# sensor.integration_time = adafruit_tsl2591.INTEGRATIONTIME_600MS (600ms)

# Read the total lux, IR, and visible light levels and print it every second.
while True:
    # Read and calculate the light level in lux.
    lux = sensor.lux
    print("Total light: {0}lux".format(lux))
    # You can also read the raw infrared and visible light levels.
    # These are unsigned, the higher the number the more light of that type.
    # There are no units like lux.
    # Infrared levels range from 0-65535 (16-bit)
    infrared = sensor.infrared
    print("Infrared light: {0}".format(infrared))
    # Visible-only levels range from 0-2147483647 (32-bit)
    visible = sensor.visible
    print("Visible light: {0}".format(visible))
    # Full spectrum (visible + IR) also range from 0-2147483647 (32-bit)
    full_spectrum = sensor.full_spectrum
    print("Full spectrum (IR + visible) light: {0}".format(full_spectrum))
    time.sleep(1.0)


5 camera:
import arducam_mipicamera as arducam
import v4l2
import time
import cv2
import copy

def align_down(size, align):
    return (size & ~((align)-1))

def align_up(size, align):
    return align_down(size + align - 1, align)

def set_controls(camera):
    try:
        camera.software_auto_exposure(enable = False)
        camera.software_auto_white_balance(enable = True)
        camera.set_control(v4l2.V4L2_CID_EXPOSURE, 15000)
        camera.set_control(v4l2.V4L2_CID_FOCUS_ABSOLUTE, 150)
        camera.manual_set_awb_compensation(20,150)
    except Exception as e:
        print(e)

# TODO: haarcascades file path
xml_file = "/home/rpi/Desktop/labs rpi/haarcascades/haarcascade_frontalface_alt2.xml"

bodzenta = cv2.imread("/home/rpi/Desktop/labs rpi/bodzenta.png")




classifier = cv2.CascadeClassifier(xml_file)


camera = arducam.mipi_camera()
camera.init_camera()
fmt = camera.set_resolution(1920, 1080)
print("Current resolution is {}".format(fmt))
set_controls(camera)

started = True
while started:
    frame = camera.capture(encoding = 'i420')
    height = int(align_up(fmt[1], 16))
    width = int(align_up(fmt[0], 32))
    image = frame.as_array.reshape(int(height * 1.5), width)
    image = cv2.cvtColor(image, cv2.COLOR_YUV2BGR_I420)

    image = cv2.resize(image, (1280, 720))
    faces = classifier.detectMultiScale(image)

    image2 = copy.copy(image)
    # TODO: Add rectangle / image drawing
    for (x, y, w, h) in faces:
        cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
        bodzenta2 = cv2.resize(bodzenta, (w,h))
        image2[y: y+h, x: x+w] = bodzenta2


    cv2.imshow("Faces", image2)

    key = cv2.waitKey(1) & 0xFF
    if key == ord("q"):
        started = False

camera.close_camera()