def telemetry(sid, data):
if data:
# The current steering angle of the car
steering_angle = str_to_point_decimal_mark_float(
data["steering_angle"])
# The current throttle of the car
throttle = str_to_point_decimal_mark_float(data["throttle"])
# The current speed of the car
speed = str_to_point_decimal_mark_float(data["speed"])
# The current image from the center camera of the car
imgString = data["image"]
image = Image.open(BytesIO(base64.b64decode(imgString)))
image_array = image_utils.preprocess(np.asarray(image),
image_is_rgb=True)
steering_angle = float(
model.predict(image_array[None, :, :, :], batch_size=1))
throttle = controller.update(float(speed))
print(steering_angle, throttle)
send_control(float_to_comma_decimal_mark_str(steering_angle),
float_to_comma_decimal_mark_str(throttle))
# save frame
if args.image_folder != '':
timestamp = datetime.utcnow().strftime('%Y_%m_%d_%H_%M_%S_%f')[:-3]
image_filename = os.path.join(args.image_folder, timestamp)
image.save('{}.jpg'.format(image_filename))
else:
# NOTE: DON'T EDIT THIS.
sio.emit('manual', data={}, skip_sid=True)
def telemetry(sid, data):
if data:
# The current steering angle of the car
steering_angle = data["steering_angle"]
# The current throttle of the car
throttle = data["throttle"]
# The current speed of the car
speed = data["speed"]
# The current image from the center camera of the car
imgString = data["image"]
image = Image.open(BytesIO(base64.b64decode(imgString)))
image_array = np.asarray(image)
image_array = preprocess(image_array)
image_array = np.asarray(image_array, dtype=np.float32)
steering_angle = float(model.predict(image_array[None, :, :, :])[0])
throttle = controller.update(float(speed))
print(steering_angle, throttle)
send_control(steering_angle, throttle)
# save frame
if args.image_folder != '':
timestamp = datetime.utcnow().strftime('%Y_%m_%d_%H_%M_%S_%f')[:-3]
image_filename = os.path.join(args.image_folder, timestamp)
image.save('{}.jpg'.format(image_filename))
else:
# NOTE: DON'T EDIT THIS.
sio.emit('manual', data={}, skip_sid=True)
def tv_loss_test(correct, tv_loss):
content_image = 'styles_images/tubingen.jpg'
image_size = 192
tv_weight = 2e-2
content_img = preprocess(PIL.Image.open(content_image), size=image_size)
content_img_var = Variable(content_img.type(dtype))
student_output = tv_loss(content_img_var, tv_weight).data.numpy()
error = rel_error(correct, student_output)
print('TV Loss Error is {:.3f}'.format(error))
def show_saliency_maps(self, X, y, class_names, model):
# Convert X and y from numpy arrays to Torch Tensors
X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X],
dim=0)
y_tensor = torch.LongTensor(y)
# Compute saliency maps for images in X
saliency = self.compute_saliency_maps(X_tensor, y_tensor, model)
# Convert the saliency map from Torch Tensor to numpy array and show images
# and saliency maps together.
saliency = saliency.numpy()
N = X.shape[0]
for i in range(N):
plt.subplot(2, N, i + 1)
plt.imshow(X[i])
plt.axis('off')
plt.title(class_names[y[i]])
plt.subplot(2, N, N + i + 1)
plt.imshow(saliency[i], cmap=plt.cm.gray)
plt.axis('off')
plt.gcf().set_size_inches(12, 5)
plt.savefig('visualization/saliency_map.png')
plt.show()
import cv2
import numpy as np
from tensorflow.keras.models import load_model
from image_utils import preprocess
model = load_model('./model.h5')
print(model.summary())
img = cv2.imread('/home/sajith/Documents/Acedamic/self-driving-car/data/data/IMG/center_2021_01_12_13_20_30_027.jpg')
cv2.imshow('tem', img)
img = preprocess(img)
img = np.asarray(img, dtype=np.float32)
print(img.shape)
print(float(model.predict(img[None,:,:,:])[0]))
def load_and_preprocess_image(path):
"""Load an image from a given path and preprocess it."""
image_center = image_utils.load_image(path)
return image_utils.preprocess(image_center)
from PIL import Image
import matplotlib.pyplot as plt
import torchvision
import numpy as np
from image_utils import preprocess, deprocess
from data_utils import load_imagenet_val
model = torchvision.models.squeezenet1_1(pretrained=True)
X, y, class_names = load_imagenet_val(num=5)
idx = 0
target_y = 6 # target label. Change to a different label to see the difference.
X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0)
fi = FoolingImage()
X_fooling = fi.make_fooling_image(X_tensor[idx:idx + 1], target_y, model)
scores = model(Variable(X_fooling))
if target_y == scores.data.max(1)[1][0]:
print('Fooled the model!')
else:
print('The model is not fooled!')
X_fooling_np = deprocess(X_fooling.clone())
X_fooling_np = np.asarray(X_fooling_np).astype(np.uint8)
plt.subplot(1, 4, 1)
plt.imshow(X[idx])
