in_img1[:, 0] = frames[:, 0:H:2, 0::2]
in_img1[:, 1] = frames[:, 1:H:2, 0::2]
in_img1[:, 2] = frames[:, 0:H:2, 1::2]
in_img1[:, 3] = frames[:, 1:H:2, 1::2]
in_img1[:, 4] = frames[:, H:H + H // 4].reshape((-1, H // 2, W // 2))
in_img1[:, 5] = frames[:, H + H // 4:H + H // 2].reshape(
(-1, H // 2, W // 2))
return in_img1
imgs_med_model = np.zeros((len(imgs), 384, 512), dtype=np.uint8)
for i, img in tqdm(enumerate(imgs)):
imgs_med_model[i] = transform_img(img,
from_intr=eon_intrinsics,
to_intr=medmodel_intrinsics,
yuv=True,
output_size=(512, 256))
frame_tensors = frames_to_tensor(np.array(imgs_med_model)).astype(
np.float32) / 128.0 - 1.0
from tensorflow.keras.models import load_model
supercombo = load_model('supercombo.keras')
print(supercombo.summary())
# Just passing zeros for desire and state
poses = []
state = np.zeros((1, 512))
## Saving lane data
left_lane = []
right_lane = []
# In this case, a Logitech C920 is used (default for undistortion helper).
# Just perform chessboard calibration to get the matrices!
frame = undist.frame(frame)
# Crop the edges out and try to get to (512,256), since that's what
# the SuperCombo model uses. Note that this is skewed a bit more
# to the sky, since my camera can "see" the hood and that probably won't
# help us in the task of lane detection, so we crop that out
frame = frame[14:270, 24:536]
# Then we want to convert this to YUV
frameYUV = cv2.cvtColor(frame, cv2.COLOR_BGR2YUV_I420)
# Use Comma's transformation to get our frame into a format that SuperCombo likes
frameYUV = transform_img(frameYUV, from_intr=eon_intrinsics,
to_intr=medmodel_intrinsics, yuv=True,
output_size=(512, 256)).astype(np.float32) \
/ 128.0 - 1.0
# We want to push our image in fr1 to fr0, and replace fr1 with
# the current frame (to feed into the network)
fr0 = fr1
fr1 = frameYUV
# SuperCombo input shape is (12, 128, 256): two consecutive images
# in YUV space. We concatenate fr0 and fr1 together to get to that
networkInput = np.concatenate((fr0, fr1))
# We then want to reshape this into the shape the network requires
networkInput = networkInput.reshape((1, 12, 128, 256))