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data_loader.py
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data_loader.py
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#########################################################################
##
## Data loader source code for TuSimple dataset
##
#########################################################################
import math
import numpy as np
import cv2
import json
import random
from copy import deepcopy
from parameters import Parameters
#########################################################################
## some iamge transform utils
#########################################################################
def Translate_Points(point,translation):
point = point + translation
return point
def Rotate_Points(origin, point, angle):
ox, oy = origin
px, py = point
qx = ox + math.cos(angle) * (px - ox) - math.sin(angle) * (py - oy)
qy = oy + math.sin(angle) * (px - ox) + math.cos(angle) * (py - oy)
return qx, qy
#########################################################################
## Data loader class
#########################################################################
class Generator(object):
def __init__(self, test_mode=False):
self.p = Parameters()
self.test_mode = test_mode
if not self.test_mode:
# load training set
self.train_data = []
with open("/home/vsw1/data/TuSimple/train_set/label_data_0313.json") as f:
while True:
line = f.readline()
if not line:
break
jsonString = json.loads(line)
self.train_data.append(jsonString)
self.size_train = len(self.train_data)
print(self.size_train)
# load test set
self.test_data = []
with open(self.p.test_root_url+'test_tasks_0627.json') as f:
while True:
line = f.readline()
if not line:
break
jsonString = json.loads(line)
self.test_data.append(jsonString)
self.size_test = len(self.test_data)
#################################################################################################################
## Generate data as much as batchsize and augment data (filp, translation, rotation, gaussian noise, scaling)
#################################################################################################################
def Generate(self):
cuts = [(b, min(b + self.p.batch_size, self.size_train)) for b in range(0, self.size_train, self.p.batch_size)]
for start, end in cuts:
# resize original image to 512*256
inputs = []
target_lanes = []
target_h = []
# choose data from each number of lanes
for i in range(start, end):
data = random.sample(self.train_data, 1)[0]
# train set image
temp_image = cv2.imread(self.p.train_root_url+data['raw_file'])
ratio_w = self.p.x_size*1.0/temp_image.shape[1]
ratio_h = self.p.y_size*1.0/temp_image.shape[0]
temp_image = cv2.resize(temp_image, (self.p.x_size,self.p.y_size))
inputs.append( np.rollaxis(temp_image, axis=2, start=0) )
temp_lanes = []
temp_h = []
for j in data['lanes']:
l = np.array(j)
h = np.array(data['h_samples'])
l, h = self.make_dense_x(l, h)
temp_h.append( h*ratio_h )
temp_lanes.append( l*ratio_w )
target_lanes.append(np.array(temp_lanes))
target_h.append(np.array(temp_h))
#test set image
test_index = random.randrange(0, self.size_test-1)
test_image = cv2.imread(self.p.test_root_url+self.test_data[test_index]['raw_file'])
test_image = cv2.resize(test_image, (self.p.x_size,self.p.y_size))
self.inputs, self.target_lanes, self.target_h, self.test_image = np.array(inputs), target_lanes, target_h, np.rollaxis(test_image, axis=2, start=0)
self.actual_batchsize = self.inputs.shape[0]
self.Flip()
self.Translation()
self.Rotate()
self.Gaussian()
self.Change_intensity()
self.Shadow()
yield self.inputs/255.0, self.target_lanes, self.target_h, self.test_image/255.0 # generate normalized image
#################################################################################################################
## Generate test data : resize original image to 512*256 and matching correspond points
#################################################################################################################
def Generate_Test(self):
cuts = [(b, min(b + self.p.batch_size, self.size_test)) for b in range(0, self.size_test, self.p.batch_size)]
for start, end in cuts:
inputs = []
path = []
target_h = []
gt = []
for i in range(start, end):
data = self.test_data[i]
temp_image = cv2.imread(self.p.test_root_url+data['raw_file'])
ratio_w = self.p.x_size*1.0/temp_image.shape[1]
ratio_h = self.p.y_size*1.0/temp_image.shape[0]
temp_image = cv2.resize(temp_image, (self.p.x_size,self.p.y_size))
inputs.append( np.rollaxis(temp_image, axis=2, start=0) )
path.append(i)
gt.append( np.array(data['lanes']) )
target_h.append( np.array(data['h_samples']) )
test_image = np.array(inputs)
yield test_image/255.0, target_h, ratio_w, ratio_h, path, gt
def make_dense_x(self, l, h):
out_x = []
out_y = []
p_x = -1
p_y = -1
for x, y in zip(l, h):
if x > 0:
if p_x < 0:
p_x = x
p_y = y
else:
out_x.append(x)
out_y.append(y)
for dense_x in range(min(p_x, x), max(p_x, x), 10):
out_x.append(dense_x)
if p_x<x:
out_y.append( p_y + abs(p_x - dense_x) * abs(p_y-y)/float(abs(p_x - x)) )
else:
out_y.append( p_y + abs(p_x - dense_x) * abs(p_y-y)/float(abs(p_x - x)) )
p_x = x
p_y = y
return np.array(out_x), np.array(out_y)
#################################################################################################################
## Generate random unique indices according to ratio
#################################################################################################################
def Random_indices(self, ratio):
size = int(self.actual_batchsize * ratio)
return np.random.choice(self.actual_batchsize, size, replace=False)
#################################################################################################################
## Add Gaussian noise
#################################################################################################################
def Gaussian(self):
indices = self.Random_indices(self.p.noise_ratio)
img = np.zeros((256,512,3), np.uint8)
m = (0,0,0)
s = (20,20,20)
for i in indices:
test_image = deepcopy(self.inputs[i])
test_image = np.rollaxis(test_image, axis=2, start=0)
test_image = np.rollaxis(test_image, axis=2, start=0)
cv2.randn(img,m,s)
test_image = test_image + img
test_image = np.rollaxis(test_image, axis=2, start=0)
self.inputs[i] = test_image
#################################################################################################################
## Change intensity
#################################################################################################################
def Change_intensity(self):
indices = self.Random_indices(self.p.intensity_ratio)
for i in indices:
test_image = deepcopy(self.inputs[i])
test_image = np.rollaxis(test_image, axis=2, start=0)
test_image = np.rollaxis(test_image, axis=2, start=0)
hsv = cv2.cvtColor(test_image, cv2.COLOR_BGR2HSV)
h, s, v = cv2.split(hsv)
value = int(random.uniform(-60.0, 60.0))
if value > 0:
lim = 255 - value
v[v > lim] = 255
v[v <= lim] += value
else:
lim = -1*value
v[v < lim] = 0
v[v >= lim] -= lim
final_hsv = cv2.merge((h, s, v))
test_image = cv2.cvtColor(final_hsv, cv2.COLOR_HSV2BGR)
test_image = np.rollaxis(test_image, axis=2, start=0)
self.inputs[i] = test_image
#################################################################################################################
## Generate random shadow in random region
#################################################################################################################
def Shadow(self, min_alpha=0.5, max_alpha = 0.75):
indices = self.Random_indices(self.p.shadow_ratio)
for i in indices:
test_image = deepcopy(self.inputs[i])
test_image = np.rollaxis(test_image, axis=2, start=0)
test_image = np.rollaxis(test_image, axis=2, start=0)
top_x, bottom_x = np.random.randint(0, 512, 2)
coin = 0
rows, cols, _ = test_image.shape
shadow_img = test_image.copy()
if coin == 0:
rand = np.random.randint(2)
vertices = np.array([[(50, 65), (45, 0), (145, 0), (150, 65)]], dtype=np.int32)
if rand == 0:
vertices = np.array([[top_x, 0], [0, 0], [0, rows], [bottom_x, rows]], dtype=np.int32)
elif rand == 1:
vertices = np.array([[top_x, 0], [cols, 0], [cols, rows], [bottom_x, rows]], dtype=np.int32)
mask = test_image.copy()
channel_count = test_image.shape[2] # i.e. 3 or 4 depending on your image
ignore_mask_color = (0,) * channel_count
cv2.fillPoly(mask, [vertices], ignore_mask_color)
rand_alpha = np.random.uniform(min_alpha, max_alpha)
cv2.addWeighted(mask, rand_alpha, test_image, 1 - rand_alpha, 0., shadow_img)
shadow_img = np.rollaxis(shadow_img, axis=2, start=0)
self.inputs[i] = shadow_img
#################################################################################################################
## Flip
#################################################################################################################
def Flip(self):
indices = self.Random_indices(self.p.flip_ratio)
for i in indices:
temp_image = deepcopy(self.inputs[i])
temp_image = np.rollaxis(temp_image, axis=2, start=0)
temp_image = np.rollaxis(temp_image, axis=2, start=0)
temp_image = cv2.flip(temp_image, 1)
temp_image = np.rollaxis(temp_image, axis=2, start=0)
self.inputs[i] = temp_image
x = self.target_lanes[i]
for j in range(len(x)):
x[j][x[j]>0] = self.p.x_size - x[j][x[j]>0]
x[j][x[j]<0] = -2
x[j][x[j]>=self.p.x_size] = -2
self.target_lanes[i] = x
#################################################################################################################
## Translation
#################################################################################################################
def Translation(self):
indices = self.Random_indices(self.p.translation_ratio)
for i in indices:
temp_image = deepcopy(self.inputs[i])
temp_image = np.rollaxis(temp_image, axis=2, start=0)
temp_image = np.rollaxis(temp_image, axis=2, start=0)
tx = np.random.randint(-50, 50)
ty = np.random.randint(-30, 30)
temp_image = cv2.warpAffine(temp_image, np.float32([[1,0,tx],[0,1,ty]]), (self.p.x_size, self.p.y_size))
temp_image = np.rollaxis(temp_image, axis=2, start=0)
self.inputs[i] = temp_image
x = self.target_lanes[i]
for j in range(len(x)):
x[j][x[j]>0] = x[j][x[j]>0] + tx
x[j][x[j]<0] = -2
x[j][x[j]>=self.p.x_size] = -2
y = self.target_h[i]
for j in range(len(y)):
y[j][y[j]>0] = y[j][y[j]>0] + ty
x[j][y[j]<0] = -2
x[j][y[j]>=self.p.y_size] = -2
self.target_lanes[i] = x
self.target_h[i] = y
#################################################################################################################
## Rotate
#################################################################################################################
def Rotate(self):
indices = self.Random_indices(self.p.rotate_ratio)
for i in indices:
temp_image = deepcopy(self.inputs[i])
temp_image = np.rollaxis(temp_image, axis=2, start=0)
temp_image = np.rollaxis(temp_image, axis=2, start=0)
angle = np.random.randint(-10, 10)
M = cv2.getRotationMatrix2D((self.p.x_size//2,self.p.y_size//2),angle,1)
temp_image = cv2.warpAffine(temp_image, M, (self.p.x_size, self.p.y_size))
temp_image = np.rollaxis(temp_image, axis=2, start=0)
self.inputs[i] = temp_image
x = self.target_lanes[i]
y = self.target_h[i]
for j in range(len(x)):
index_mask = deepcopy(x[j]>0)
x[j][index_mask], y[j][index_mask] = Rotate_Points((self.p.x_size//2,self.p.y_size//2),(x[j][index_mask], y[j][index_mask]),(-angle * 2 * np.pi)/360)
x[j][x[j]<0] = -2
x[j][x[j]>=self.p.x_size] = -2
x[j][y[j]<0] = -2
x[j][y[j]>=self.p.y_size] = -2
self.target_lanes[i] = x
self.target_h[i] = y