img = img[int(shape[0] / 3):shape[0] - 150, 0:shape[1]]
# Resize the image
img = cv2.resize(img, (params.img_w, params.img_h),
interpolation=cv2.INTER_AREA)
# Return the image sized as a 4D array
return np.resize(img, (params.img_c, params.img_w, params.img_h))
# Process video
for epoch_id in epoch_ids:
print(
'---------- processing video for epoch {} ----------'.format(epoch_id))
vid_path = utils.join_dir(params.data_dir,
'epoch{:0>2}_front.mkv'.format(epoch_id))
assert os.path.isfile(vid_path)
frame_count = utils.frame_count(vid_path)
cap = cv2.VideoCapture(vid_path)
machine_steering = []
print('performing inference...')
time_start = time.time()
for frame_id in range(frame_count):
ret, img = cap.read()
assert ret
# you can modify here based on your model
img = img_pre_process(img)
img = img[None, :, :, :]
deg = float(model.predict(img, batch_size=1))
machine_steering.append(deg)
"""
## Chop off 1/3 from the top and cut bottom 150px(which contains the head of car)
shape = img.shape
img = img[int(shape[0]/3):shape[0]-150, 0:shape[1]]
## Resize the image
img = cv2.resize(img, (params.FLAGS.img_w, params.FLAGS.img_h), interpolation=cv2.INTER_AREA)
## Return the image sized as a 4D array
return np.resize(img, (params.FLAGS.img_w, params.FLAGS.img_h, params.FLAGS.img_c))
## Process video
for epoch_id in epoch_ids:
print('---------- processing video for epoch {} ----------'.format(epoch_id))
vid_path = utils.join_dir(params.data_dir, 'epoch{:0>2}_front.mkv'.format(epoch_id))
assert os.path.isfile(vid_path)
frame_count = utils.frame_count(vid_path)
cap = cv2.VideoCapture(vid_path)
machine_steering = []
print('performing inference...')
time_start = time.time()
for frame_id in range(frame_count):
ret, img = cap.read()
assert ret
## you can modify here based on your model
img = img_pre_process(img)
img = img[None,:,:,:]
deg = float(model.predict(img, batch_size=1))
machine_steering.append(deg)