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model.py
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model.py
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import os
import csv
import pandas as pd
import numpy as np
import cv2
import matplotlib.pylab as plt
np.random.seed(802)
df = pd.read_csv("./data/driving_log.csv")
print("Number of frames from each camera:", len(df))
# Removing items with very less steering angles
straight_items = df[(df.steering >= -0.045) & (df.steering <= 0.045)].index
downsample_items = np.random.choice(straight_items,int(len(straight_items)*.05), )
new_items = np.concatenate([df[~((df.steering >= -0.045) & (df.steering <= 0.045))].index, downsample_items])
new_df = df.iloc[np.sort(new_items)].reset_index(drop=True)
correction=0.2
samples = []
samples = np.vstack([np.hstack([new_df.left.values,
new_df.center.values,
new_df.right.values]),
np.hstack([new_df.steering+correction,
new_df.steering,
new_df.steering-correction])]).T.tolist()
from skimage import transform
# modifying luminescence
def modify_sat_lum(image, uni_low=0.4, uni_high=1.6):
sat_f, lum_f =np.random.uniform(uni_low, uni_high, 2).tolist()
hls = cv2.cvtColor(image, cv2.COLOR_BGR2HLS).astype(float)
hls[:,:,1] = hls[:,:,1] * lum_f
hls[:,:,1][hls[:,:,1] > 255] = 255
hls[:,:,2] = hls[:,:,2] * sat_f
hls[:,:,2][hls[:,:,2] > 255] = 255
return cv2.cvtColor(hls.astype('uint8'),cv2.COLOR_HLS2BGR)
# adding a shadow
def cast_shadow(image):
shadow_side = np.random.choice(['left','right','top', 'bottom'])
if (shadow_side == 'top') | (shadow_side == 'bottom'):
divider_left, divider_right = np.random.randint(0, image.shape[0], 2)
if (shadow_side == 'left') | (shadow_side == 'right'):
divider_top, divider_bot = np.random.randint(0, image.shape[1], 2)
if shadow_side == 'top':
ul = [0, 0]
ur = [image.shape[1], 0]
ll = [0, divider_left]
lr = [image.shape[1], divider_right]
elif shadow_side == 'bottom':
ul = [0, divider_left]
ur = [image.shape[1], divider_right]
ll = [0, image.shape[0]]
lr = [image.shape[1], image.shape[0]]
elif shadow_side == 'left':
ul = [0, 0]
ur = [divider_top, 0]
ll = [0, image.shape[0]]
lr = [divider_bot, image.shape[0]]
elif shadow_side == 'right':
ul = [divider_top, 0]
ur = [image.shape[1], 0]
ll = [divider_bot, image.shape[0]]
lr = [image.shape[1], image.shape[0]]
vertices = [np.array([ll,ul,ur,lr])]
mask = np.zeros_like(image[:,:,0])
mask = cv2.fillPoly(mask, vertices, 255).astype('bool')
hls = cv2.cvtColor(image, cv2.COLOR_BGR2HLS)
hls[:,:,1][mask]=hls[:,:,1][mask] * np.random.uniform(0.1, 0.3, 1)
return cv2.cvtColor(hls.astype('uint8'),cv2.COLOR_HLS2BGR)
# shifting the image
def horizontal_vertical_shift(image, steering, hlim=[-60,60], vlim=[-20,20]):
hlim=sorted(hlim); vlim=sorted(vlim)
hv_trans= [np.random.randint(hlim[0], hlim[1], 1)[0],
np.random.randint(vlim[0], vlim[1], 1)[0]]
trans_img = transform.warp(image, transform.AffineTransform(translation=hv_trans))
steering = steering + hv_trans[0]*-0.007
return (trans_img*255).astype('uint8'), steering
# rotating the image
def rotate(image, steering, deglim=[-15,15], center=[160,70]):
degree = np.random.randint(deglim[0], deglim[1], 1)[0]
shift_x,shift_y = center
tf_shift = transform.SimilarityTransform(translation=[-shift_x, -shift_y])
tf_rotate = transform.SimilarityTransform(rotation=np.deg2rad(degree))
tf_shift_inv = transform.SimilarityTransform(translation=[shift_x, shift_y])
rot_img = transform.warp(image, (tf_shift + (tf_rotate + tf_shift_inv)).inverse)
_steering = steering + degree*0.02
return (rot_img*255).astype('uint8'), _steering
import sklearn
from sklearn.model_selection import train_test_split
train_samples, validation_samples = train_test_split(samples, test_size = 0.2)
# Generator
def generator(samples, batch_size=32):
num_samples = len(samples)
while 1:
sklearn.utils.shuffle(samples)
for offset in range(0, num_samples, batch_size):
batch_samples = samples[offset:offset+batch_size]
images = []
measurements = []
for batch_sample in batch_samples:
imgpth = './data/IMG/' + batch_sample[0].split('/')[-1]
img = cv2.imread(imgpth)
steering = float(batch_sample[1])
if np.random.binomial(1, 0.7) > 0:
if np.random.choice([True, False]):
if np.random.choice([True, False]):
img, steering = rotate(img, steering)
else:
img, steering = horizontal_vertical_shift(img, steering)
if np.random.choice([True, False]):
img = modify_sat_lum(img)
if np.random.choice([True, False]):
img = cast_shadow(img)
images.append(img)
measurements.append(steering)
X_train = np.array(images)
y_train = np.array(measurements)
yield sklearn.utils.shuffle(X_train, y_train)
train_generator = generator(train_samples, batch_size=32)
validation_generator = generator(validation_samples, batch_size=32)
from keras.models import Sequential
from keras.layers import Flatten, Dense, Lambda, Cropping2D, Dropout
from keras.layers.convolutional import Conv2D
from keras.layers.pooling import MaxPool2D
from keras.callbacks import ModelCheckpoint
# Model
model = Sequential()
x = model.add(Cropping2D(cropping=((70,20), (0,0)), input_shape=(160,320,3)))
model.add(Lambda(lambda x: (x/255.0) - 0.5))
model.add(Conv2D(24, (5, 5), strides=(2,2), padding='same', activation='relu'))
model.add(Dropout(0.1))
model.add(Conv2D(36, (5, 5), strides=(2,2), padding='same', activation='relu'))
model.add(Dropout(0.1))
model.add(Conv2D(48, (5, 5), strides=(2,2), padding='same', activation='relu'))
model.add(Dropout(0.1))
model.add(Conv2D(64, (3, 3), strides=(3,3), padding='same', activation='relu'))
model.add(Dropout(0.1))
model.add(Conv2D(64, (3, 3), strides=(3,3), padding='same', activation='relu'))
model.add(Dropout(0.1))
model.add(Flatten())
model.add(Dense(100))
model.add(Dense(50))
model.add(Dense(10))
model.add(Dense(1))
model.compile(loss='mse', optimizer = 'adam')
filepath="model.h5"
checkpoint = ModelCheckpoint(filepath)
callbacks_list = [checkpoint]
history_object = model.fit_generator(train_generator,
steps_per_epoch=len(train_samples)*2/32,
validation_data=validation_generator,
validation_steps=len(validation_samples)*2/32,
epochs=10,
callbacks=callbacks_list)
print(model.summary())