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import cv2, os
import numpy as np
import matplotlib.image as mpimg
IMAGE_HEIGHT, IMAGE_WIDTH, IMAGE_CHANNELS = 66, 200, 3
INPUT_SHAPE = (IMAGE_HEIGHT, IMAGE_WIDTH, IMAGE_CHANNELS)
def load_image(data_dir, image_file):
"""
Load RGB images from a file
"""
return mpimg.imread(os.path.join(data_dir, image_file.strip()))
def crop(image):
"""
Crop the image (removing the sky at the top and the car front at the bottom)
"""
return image[60:-25, :, :] # remove the sky and the car front
def resize(image):
"""
Resize the image to the input shape used by the network model
"""
return cv2.resize(image, (IMAGE_WIDTH, IMAGE_HEIGHT), cv2.INTER_AREA)
def rgb2yuv(image):
"""
Convert the image from RGB to YUV (This is what the NVIDIA model does)
"""
return cv2.cvtColor(image, cv2.COLOR_RGB2YUV)
def preprocess(image):
"""
Combine all preprocess functions into one
"""
image = crop(image)
image = resize(image)
image = rgb2yuv(image)
return image
def choose_image(data_dir, center, left, right, steering_angle):
"""
Randomly choose an image from the center, left or right, and adjust
the steering angle.
"""
choice = np.random.choice(3)
if choice == 0:
return load_image(data_dir, left), steering_angle + 0.2
elif choice == 1:
return load_image(data_dir, right), steering_angle - 0.2
return load_image(data_dir, center), steering_angle
def random_flip(image, steering_angle):
"""
Randomly flipt the image left <-> right, and adjust the steering angle.
"""
if np.random.rand() < 0.5:
image = cv2.flip(image, 1)
steering_angle = -steering_angle
return image, steering_angle
def random_translate(image, steering_angle, range_x, range_y):
"""
Randomly shift the image virtially and horizontally (translation).
"""
trans_x = range_x * (np.random.rand() - 0.5)
trans_y = range_y * (np.random.rand() - 0.5)
steering_angle += trans_x * 0.002
trans_m = np.float32([[1, 0, trans_x], [0, 1, trans_y]])
height, width = image.shape[:2]
image = cv2.warpAffine(image, trans_m, (width, height))
return image, steering_angle
def random_shadow(image):
"""
Generates and adds random shadow
"""
# (x1, y1) and (x2, y2) forms a line
# xm, ym gives all the locations of the image
x1, y1 = IMAGE_WIDTH * np.random.rand(), 0
x2, y2 = IMAGE_WIDTH * np.random.rand(), IMAGE_HEIGHT
xm, ym = np.mgrid[0:IMAGE_HEIGHT, 0:IMAGE_WIDTH]
# mathematically speaking, we want to set 1 below the line and zero otherwise
# Our coordinate is up side down. So, the above the line:
# (ym-y1)/(xm-x1) > (y2-y1)/(x2-x1)
# as x2 == x1 causes zero-division problem, we'll write it in the below form:
# (ym-y1)*(x2-x1) - (y2-y1)*(xm-x1) > 0
mask = np.zeros_like(image[:, :, 1])
mask[(ym - y1) * (x2 - x1) - (y2 - y1) * (xm - x1) > 0] = 1
# choose which side should have shadow and adjust saturation
cond = mask == np.random.randint(2)
s_ratio = np.random.uniform(low=0.2, high=0.5)
# adjust Saturation in HLS(Hue, Light, Saturation)
hls = cv2.cvtColor(image, cv2.COLOR_RGB2HLS)
hls[:, :, 1][cond] = hls[:, :, 1][cond] * s_ratio
return cv2.cvtColor(hls, cv2.COLOR_HLS2RGB)
def random_brightness(image):
"""
Randomly adjust brightness of the image.
"""
# HSV (Hue, Saturation, Value) is also called HSB ('B' for Brightness).
hsv = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
ratio = 1.0 + 0.4 * (np.random.rand() - 0.5)
hsv[:,:,2] = hsv[:,:,2] * ratio
return cv2.cvtColor(hsv, cv2.COLOR_HSV2RGB)
def augument(data_dir, center, left, right, steering_angle, range_x=100, range_y=10):
"""
Generate an augumented image and adjust steering angle.
(The steering angle is associated with the center image)
"""
image, steering_angle = choose_image(data_dir, center, left, right, steering_angle)
image, steering_angle = random_flip(image, steering_angle)
image, steering_angle = random_translate(image, steering_angle, range_x, range_y)
image = random_shadow(image)
image = random_brightness(image)
return image, steering_angle
def batch_generator(data_dir, image_paths, steering_angles, batch_size, is_training):
"""
Generate training image give image paths and associated steering angles
"""
images = np.empty([batch_size, IMAGE_HEIGHT, IMAGE_WIDTH, IMAGE_CHANNELS])
steers = np.empty(batch_size)
while True:
i = 0
for index in np.random.permutation(image_paths.shape[0]):
center, left, right = image_paths[index]
steering_angle = steering_angles[index]
# argumentation
if is_training and np.random.rand() < 0.6:
image, steering_angle = augument(data_dir, center, left, right, steering_angle)
else:
image = load_image(data_dir, center)
# add the image and steering angle to the batch
images[i] = preprocess(image)
steers[i] = steering_angle
i += 1
if i == batch_size:
break
yield images, steers
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