ServoとAIの連携(回帰)

サンプルのコピー

Terminalで下記コマンドで、regressionのサンプルをpwmフォルダ以下にコピーします。

cp -r /nvdli-nano/regression/* /nvdli-nano/aicar/

カテゴリ名の修正

import torchvision.transforms as transforms
from dataset import XYDataset

TASK = 'car'
# TASK = 'diy'

CATEGORIES = ['xy', 'speed']
# CATEGORIES = [ 'diy_1', 'diy_2', 'diy_3']

DATASETS = ['A', 'B']
# DATASETS = ['A', 'B', 'C']

TRANSFORMS = transforms.Compose([
    transforms.ColorJitter(0.2, 0.2, 0.2, 0.2),
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

datasets = {}
for name in DATASETS:
    datasets[name] = XYDataset('../data/regression/' + TASK + '_' + name, CATEGORIES, TRANSFORMS)

print("{} task with {} categories defined".format(TASK, CATEGORIES))

推論結果でサーボを制御

以下の黄色い領域のコードを追加します。

import threading
import time
from utils import preprocess
import torch.nn.functional as F
import Fabo_PCA9685
import time
import pkg_resources
import smbus

# Param
CENTER = 200
RIGHT=150
LEFT = 250

# init
BUSNUM=1
SERVO_HZ=60
INITIAL_VALUE=300
bus = smbus.SMBus(BUSNUM)
PCA9685 = Fabo_PCA9685.PCA9685(bus,INITIAL_VALUE,address=0x40)

def map(x, in_min, in_max, out_min, out_max):
    return (x - in_min) * (out_max - out_min) // (in_max - in_min) + out_min

state_widget = ipywidgets.ToggleButtons(options=['stop', 'live'], description='state', value='stop')
with open("../images/ready_img.jpg", "rb") as file:
    default_image = file.read()
prediction_widget = ipywidgets.Image(format='jpeg', width=camera.width, height=camera.height, value=default_image)

def live(state_widget, model, camera, prediction_widget):
    global dataset
    while state_widget.value == 'live':
        image = camera.value
        preprocessed = preprocess(image)
        output = model(preprocessed).detach().cpu().numpy().flatten()
        category_index = dataset.categories.index(category_widget.value)
        x = output[2 * category_index]
        y = output[2 * category_index + 1]

        x = int(camera.width * (x / 2.0 + 0.5))
        y = int(camera.height * (y / 2.0 + 0.5))

        if category_index == 0:
            x = map(x, 0, 224, LEFT, RIGHT):
            pwm = x
            PCA9685.set_channel_value(0,pwm)

        prediction = image.copy()
        prediction = cv2.circle(prediction, (x, y), 8, (255, 0, 0), 3)
        prediction_widget.value = bgr8_to_jpeg(prediction)

def start_live(change):
    if change['new'] == 'live':
        execute_thread = threading.Thread(target=live, args=(state_widget, model, camera, prediction_widget))
        execute_thread.start()

state_widget.observe(start_live, names='value')

live_execution_widget = ipywidgets.VBox([
    prediction_widget,
    state_widget
])

# display(live_execution_widget)
print("live_execution_widget created")