def load_imgs():
global imgs
global wheels
for p in purposes:
for epoch_id in epochs[p]:
print 'processing and loading "{}" datasets {} into memory, current num of imgs is {}...'.format(
p, epoch_id, len(imgs[p]))
vid_path = data_dir + "/dataset{0}/out-mencoder.avi".format(
epoch_id)
assert os.path.isfile(vid_path)
frame_count = cm.frame_count(vid_path)
cap = cv2.VideoCapture(vid_path)
csv_path = data_dir + "/dataset%i/data.csv" % epoch_id
assert os.path.isfile(csv_path)
rows = cm.fetch_csv_data(csv_path)
assert frame_count == len(rows)
yy = [[float(row['angle'])] for row in rows]
while True:
ret, img = cap.read()
if not ret:
break
if img.any():
img = preprocess.preprocess(img)
imgs[p].append(img)
wheels[p].extend(yy)
assert len(imgs[p]) == len(wheels[p])
cap.release()
def load_imgs():
global imgs
global wheels
for p in purposes:
for epoch_id in epochs[p]:
print 'processing and loading "{}" epoch {} into memory, current num of imgs is {}...'.format(
p, epoch_id, len(imgs[p]))
vid_path = cm.jn(data_dir, 'epoch{:0>2}_front.mkv'.format(epoch_id))
assert os.path.isfile(vid_path)
frame_count = cm.frame_count(vid_path)
cap = cv2.VideoCapture(vid_path)
csv_path = cm.jn(data_dir, 'epoch{:0>2}_steering.csv'.format(epoch_id))
assert os.path.isfile(csv_path)
rows = cm.fetch_csv_data(csv_path)
assert frame_count == len(rows)
yy = [[float(row['wheel'])] for row in rows]
while True:
ret, img = cap.read()
if not ret:
break
img = preprocess.preprocess(img)
imgs[p].append(img)
wheels[p].extend(yy)
assert len(imgs[p]) == len(wheels[p])
cap.release()
def load_imgs():
global imgs
global wheels
for p in purposes:
for epoch_id in epochs[p]:
print 'processing and loading "{}" datasets {} into memory, current num of imgs is {}...'.format(p, epoch_id, len(imgs[p]))
vid_path = data_dir +"/dataset{0}/out-mencoder.avi".format(epoch_id)
assert os.path.isfile(vid_path)
frame_count = cm.frame_count(vid_path)
cap = cv2.VideoCapture(vid_path)
csv_path = data_dir + "/dataset%i/data.csv" % epoch_id
assert os.path.isfile(csv_path)
rows = cm.fetch_csv_data(csv_path)
assert frame_count == len(rows)
yy = [[float(row['angle'])] for row in rows]
while True:
ret, img = cap.read()
if not ret:
break
if img.any():
img = preprocess.preprocess(img)
imgs[p].append(img)
wheels[p].extend(yy)
assert len(imgs[p]) == len(wheels[p])
cap.release()
def get_human_steering(epoch_id):
epoch_dir = params.data_dir
assert os.path.isdir(epoch_dir)
steering_path = epoch_dir + "/dataset%i/data.csv" % epoch_id
assert os.path.isfile(steering_path)
rows = cm.fetch_csv_data(steering_path)
human_steering = [row['angle'] for row in rows]
return human_steering
def get_human_steering(epoch_id):
epoch_dir = params.data_dir
assert os.path.isdir(epoch_dir)
steering_path = epoch_dir + "/dataset%i/data.csv" % epoch_id
assert os.path.isfile(steering_path)
rows = cm.fetch_csv_data(steering_path)
human_steering = [row['angle'] for row in rows]
return human_steering
def get_human_steering(epoch_id):
epoch_dir = params.data_dir
assert os.path.isdir(epoch_dir)
steering_path = cm.jn(epoch_dir, 'epoch{:0>2}_steering.csv'.format(epoch_id))
assert os.path.isfile(steering_path)
rows = cm.fetch_csv_data(steering_path)
human_steering = [row['wheel'] for row in rows]
return human_steering
def load_imgs_v2():
global imgs
global wheels
for epoch_id in epochs['all']:
print('processing and loading epoch {} into memorys. train:{}, val:{}'.
format(epoch_id, len(imgs['train']), len(imgs['val'])))
# vid_path = cm.jn(data_dir, 'epoch{:0>2}_front.mkv'.format(epoch_id))
vid_path = cm.jn(data_dir, 'out-video-{}.avi'.format(epoch_id))
print("ppppppppppppppppp : data_dir : ", data_dir)
print("ppppppppppppppppp : vid_path : ", vid_path)
if not os.path.isfile(vid_path):
continue
frame_count = cm.frame_count(vid_path)
cap = cv2.VideoCapture(vid_path)
# csv_path = cm.jn(data_dir, 'epoch{:0>2}_steering.csv'.format(epoch_id))
csv_path = cm.jn(data_dir, 'out-key-{}.csv'.format(epoch_id))
assert os.path.isfile(csv_path)
rows = cm.fetch_csv_data(csv_path)
print("{}, {}".format(len(rows), frame_count))
assert frame_count == len(rows)
for row in rows:
ret, img = cap.read()
if not ret:
break
img = preprocess.preprocess(img)
angle = float(row['wheel'])
if random.random() < params.train_pct:
imgs['train'].append(img)
wheels['train'].append([angle])
else:
imgs['val'].append(img)
wheels['val'].append([angle])
cap.release()
print('Total data: train:{}, val:{}'.format(len(imgs['train']),
len(imgs['val'])))
def load_batch(purpose):
global current_batch_id
xx = []
yy = []
# fetch the batch definition
batch_id = current_batch_id[purpose]
assert batch_id < len(batches[purpose])
batch = batches[purpose][batch_id]
epoch_id, frame_start, frame_end = batch['epoch_id'], batch[
'frame_start'], batch['frame_end']
assert epoch_id is not None and frame_start is not None and frame_end is not None
# update the current batch
current_batch_id[purpose] = (current_batch_id[purpose] + 1) % len(
batches[purpose])
# fetch image and steering data
vid_path = cm.jn(data_dir, 'epoch{:0>2}_front.mkv'.format(epoch_id))
assert os.path.isfile(vid_path)
frame_count = cm.frame_count(vid_path)
cap = cv2.VideoCapture(vid_path)
cm.cv2_goto_frame(cap, frame_start)
csv_path = cm.jn(data_dir, 'epoch{:0>2}_steering.csv'.format(epoch_id))
assert os.path.isfile(csv_path)
rows = cm.fetch_csv_data(csv_path)
assert frame_count == len(rows)
yy = [[float(row['wheel'])] for row in rows[frame_start:frame_end + 1]]
for frame_id in xrange(frame_start, frame_end + 1):
ret, img = cap.read()
assert ret
img = preprocess.preprocess(img)
#cv2.imwrite(os.path.abspath('output/sample_frame.jpg'), img)
xx.append(img)
assert len(xx) == len(yy)
cap.release()
return xx, yy
def load_batch(purpose):
global current_batch_id
xx = []
yy = []
# fetch the batch definition
batch_id = current_batch_id[purpose]
assert batch_id < len(batches[purpose])
batch = batches[purpose][batch_id]
epoch_id, frame_start, frame_end = batch['epoch_id'], batch['frame_start'], batch['frame_end']
assert epoch_id is not None and frame_start is not None and frame_end is not None
# update the current batch
current_batch_id[purpose] = (current_batch_id[purpose] + 1) % len(batches[purpose])
# fetch image and steering data
vid_path = cm.jn(data_dir, 'epoch{:0>2}_front.mkv'.format(epoch_id))
assert os.path.isfile(vid_path)
frame_count = cm.frame_count(vid_path)
cap = cv2.VideoCapture(vid_path)
cm.cv2_goto_frame(cap, frame_start)
csv_path = cm.jn(data_dir, 'epoch{:0>2}_steering.csv'.format(epoch_id))
assert os.path.isfile(csv_path)
rows = cm.fetch_csv_data(csv_path)
assert frame_count == len(rows)
yy = [[float(row['wheel'])] for row in rows[frame_start:frame_end+1]]
for frame_id in xrange(frame_start, frame_end+1):
ret, img = cap.read()
assert ret
img = preprocess.preprocess(img)
#cv2.imwrite(os.path.abspath('output/sample_frame.jpg'), img)
xx.append(img)
assert len(xx) == len(yy)
cap.release()
return xx, yy
def load_imgs():
global imgs
global wheels
for p in purposes:
for epoch_id in epochs[p]:
print(
'processing and loading "{}" epoch {} into memory, current num of imgs is {}...'
.format(p, epoch_id, len(imgs[p])))
# vid_path = cm.jn(data_dir, 'epoch{:0>2}_front.mkv'.format(epoch_id))
data_dir = os.path.abspath('training_images{}'.format(epoch_id))
assert os.path.isdir(data_dir)
print("upload images from:", data_dir)
# csv_path = cm.jn(data_dir, 'epoch{:0>2}_steering.csv'.format(epoch_id))
csv_path = os.path.abspath('out-key{}.csv'.format(epoch_id))
assert os.path.isfile(csv_path)
print("upload ground truth from:", csv_path)
rows = cm.fetch_csv_data(csv_path)
print(len(rows), frame_count)
assert frame_count == len(rows)
yy = [[float(row['wheel'])] for row in rows]
for image in data_dir:
img = cv2.read(image)
img = preprocess.preprocess(img)
imgs[p].append(img)
wheels[p].extend(yy)
assert len(imgs[p]) == len(wheels[p])
cap.release()
sys.exit(1)
print("epoch_id: {}, skip_count: {}".format(epoch_id, skip_count))
assert os.path.isfile(vid_path)
assert os.path.isfile(csv_path)
frame_count = cm.frame_count(vid_path)
cap = cv2.VideoCapture(vid_path)
cam_width = int(cap.get(3))
cam_height = int(cap.get(4))
cam_fps = int(cap.get(5))
print("w: {}, h:{}, fps: {}".format(cam_width, cam_height, cam_fps))
rows = cm.fetch_csv_data(csv_path)
assert frame_count == len(rows)
# data files
# fourcc = cv2.cv.CV_FOURCC(*'XVID')
fourcc = cv2.VideoWriter_fourcc(*'XVID')
vidfile = cv2.VideoWriter(conv_vid_path, fourcc, cam_fps,
(cam_width, cam_height))
keyfile = open(conv_csv_path, 'w+')
keyfile.write("ts_micro,frame,wheel\n")
# skip video frames
for i in range(skip_count):
ret, img = cap.read()
# record adjusted data
def load_imgs():
global imgs
global wheels
for p in purposes:
print p
for epoch_id in epochs[p]:
print 'processing and loading "{}" epoch {} into memory, current num of imgs is {}...'.format(
p, epoch_id, len(imgs[p]))
# csv_path = cm.jn(data_dir, 'epoch{:0>2}_steering.csv'.format(epoch_id))
csv_path = cm.jn(data_dir, 'data{}/output.txt'.format(epoch_id))
assert os.path.isfile(csv_path)
print "DBG:", csv_path
rows = cm.fetch_csv_data(csv_path)
# print len(rows)
#print len(rows), frame_count
#assert frame_count == len(rows)
for row in rows:
# print row
yy = float(row['wheel'])
# yy = yy / 22500.
print yy, "before"
yy = 1.0 * scale_down(yy, params.input_max,
params.scale) / params.scale
print yy, "after ==="
img_path = row['img']
# print "img"
# print img_path
# print yy
############ using opencv to read img here
img_path = cm.jn(data_dir,
'data{}/{}'.format(epoch_id, img_path))
if not os.path.isfile(img_path):
continue
img = cv2.imread(img_path)
########### doing data augmentation
# move along height of photo
# for i in range(-2,3,1):
# for i in range(-1,2,1):
for i in range(1):
i *= 2
# move along width of photo
# for j in range(-1,2,1):
for j in range(1):
j *= 2
crop = img[40 + i:200 + i, 50 + j:270 + j]
# resize to 200*66
rsz = cv2.resize(crop, (200, 66))
rsz = rsz / 255.
imgs[p].append(rsz)
wheels[p].append(yy)
# flip image and add again
flip = np.flip(rsz, 1)
imgs[p].append(flip)
wheels[p].append(-yy)
# img = preprocess.preprocess(img)
# imgs[p].append(img)
# wheels[p].append(yy)
# print "wheels"
# print wheels
# print imgs[p][0]
assert len(imgs[p]) == len(wheels[p])