1. রাস্পবেরি পাই বোর্ড,
2. পাই ক্যামেরা,
3. জাম্পারের তারগুলি
4. চ্যাসি, মোটর, টায়ার
5. মোটর নিয়ন্ত্রণ আইসি (L293d)

1. সিএনএন-এর জন্য চিত্রগুলি ক্যাপচার করুন।
2. সিএনএন প্রশিক্ষণ
3. রাস্পবেরি পিআই এ সিএনএন স্থাপন করুন

সিএনএন-এর জন্য চিত্রগুলি ক্যাপচার করুন

# import the necessary packages
from picamera.array import PiRGBArray
from picamera import PiCamera
import time
import cv2
 
# initialize the camera and grab a reference to the raw camera capture
camera = PiCamera()
camera.resolution = (640, 480) # set the resolution
camera.framerate = 32 # set the frame rate
rawCapture = PiRGBArray(camera, size=(640, 480))
 
# allow the camera to warm up
time.sleep(0.1)
 
# capture frames from the camera
start = 1for frame in camera.capture_continuous(rawCapture, format="bgr", use_video_port=True):
  # grab the raw NumPy array representing the image, then initialize the timestamp and occupied/unoccupied text
  image = frame.array
  # show the frame
  cv2.imshow("Frame", image)
  key = cv2.waitKey(1) & 0xFF
  cv2.imwrite(str(start) + ".jpg", image)
  start = start + 1
 
  # clear the stream in preparation for the next frame
  rawCapture.truncate(0)
 
  # if the `q` key was pressed, break from the loop
  if key == ord("q"):
    break
  time.sleep(.5)

সিএনএন প্রশিক্ষণ

class LeNet:
  @staticmethod
  def build(width, height, depth, classes):
    # initialize the model
    model = Sequential()
    inputShape = (height, width, depth)# first set of CONV => RELU => POOL layers
    model.add(Conv2D(20, (5, 5), padding="same",
      input_shape=inputShape))
    model.add(Activation("relu"))
    model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))# second set of CONV => RELU => POOL layers
    model.add(Conv2D(50, (5, 5), padding="same"))
    model.add(Activation("relu"))
    model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))# first (and only) set of FC => RELU layers
    model.add(Flatten())
    model.add(Dense(500))
    model.add(Activation("relu"))# softmax classifier
    model.add(Dense(classes))
    model.add(Activation("softmax"))# return the constructed network architecture
    return model

dataset = '/home/pi/Desktop/tutorials/raspberry/trainImages/' # please change this path
# initialize the data and labels
print("[INFO] loading images...")
data = []
labels = []
 
# grab the image paths and randomly shuffle them
imagePaths = sorted(list(paths.list_images(dataset)))
random.seed(42)
random.shuffle(imagePaths)# loop over the input images
for imagePath in imagePaths:
    # load the image, pre-process it, and store it in the data list
    image = cv2.imread(imagePath)
    image = cv2.resize(image, (28, 28))
    image = img_to_array(image)
    data.append(image)# extract the class label from the image path and update the
    # labels list
    label = imagePath.split(os.path.sep)[-2]
    print(label)
    if label == 'forward':
        label = 0
    elif label == 'right':
        label = 1
    else:
        label =2
    labels.append(label)

# scale the raw pixel intensities to the range [0, 1]
data = np.array(data, dtype="float") / 255.0
labels = np.array(labels)
 
# partition the data into training and testing splits using 75% of
# the data for training and the remaining 25% for testing
(trainX, testX, trainY, testY) = train_test_split(data,
    labels, test_size=0.25, random_state=42)# convert the labels from integers to vectors
trainY = to_categorical(trainY, num_classes=3)
testY = to_categorical(testY, num_classes=3)

# initialize the number of epochs to train for, initial learning rate,
# and batch size
EPOCHS = 15
INIT_LR = 1e-3
BS = 32# initialize the model
print("[INFO] compiling model...")
model = LeNet.build(width=28, height=28, depth=3, classes=3)
opt = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)
model.compile(loss="binary_crossentropy", optimizer=opt,
    metrics=["accuracy"])
 
# train the network
print("[INFO] training network...")
H = model.fit(trainX, trainY, batch_size=BS,
    validation_data=(testX, testY),# steps_per_epoch=len(trainX) // BS,
    epochs=EPOCHS, verbose=1)
 
# save the model to disk
print("[INFO] serializing network...")
model.save("model")

সিএনএন মোতায়েন করুন

GPIO.setmode(GPIO.BCM)#choose the GPIO pins for mptor control
fwd1 = 23  # pin 16
bwd1 = 24 #pin 18
fwd2 = 16 # pin 36
bwd2 = 20 # pin 38# declare selected pin as output pin
GPIO.setup(fwd1, GPIO.OUT)
GPIO.setup(bwd1, GPIO.OUT)
GPIO.setup(fwd2, GPIO.OUT)
GPIO.setup(bwd2, GPIO.OUT)

model = load_model("model")if __name__ == "__main__":
  try:
    mc.stop()
    # initialize the camera and grab a reference to the raw camera capture
    camera = PiCamera()
    camera.resolution = (640, 480)
    camera.framerate = 32
    rawCapture = PiRGBArray(camera, size=(640, 480))
    # allow the camera to warmup
    time.sleep(0.1)
    # capture frames from the camerafor frame in camera.capture_continuous(rawCapture, format="bgr", use_video_port=True):
     # grab the raw NumPy array representing the image, then initialize the timestamp
     # and occupied/unoccupied text
     image = frame.array
     # show the frame
     key = cv2.waitKey(1) & 0xFF     image = cv2.resize(image, (28, 28))
     image = img_to_array(image)
     image = np.array(image, dtype="float") / 255.0
     image = image.reshape(-1, 28, 28, 3)
     #cv2.imshow("Frame", image[0])     control_robot(image)     # clear the stream in preparation for the next frame
     rawCapture.truncate(0)  except KeyboardInterrupt:
    mc.stop()
    GPIO.cleanup()
    sys.exit()