I would like to run Project 3 - Behavioral Cloning but with my own images and control. How can I do it? It was not very clear which is the training steep, and which is the inference step. Also, what is the best way to replace the data with my own images and control? My control data also have throttle data, but I can start with just steering.

Hello sir, Here shouldn't be X_train instead of X_train_norm inside train_test_split()? X_train_norm, X_val_norm, y_train, y_val = train_test_split(X_train_norm, y_train, test_size=VAL_RATIO, random_state=0) Anyway, here img_rgb = X_train[0], you used X_train[0] which will always give me the first picture of the training dataset! will never be random. And here: plt.title('Example of RGB image (class = {})'.format(y_train[0])), you used the new training labels (after splitation).

Not sure if you got my point, but it is not correct, I am new to python so I couldn't figure out how to fix the issue here, so any help?

I got this error when I try to run the project 12

Traceback (most recent call last): File "main_27.py", line 284, in run() File "main_27.py", line 257, in run image_input, keep_prob, vgg_layer3_out, vgg_layer4_out, vgg_layer7_out = load_vgg(sess, vgg_path) File "main_27.py", line 132, in load_vgg tf.saved_model.loader.load(sess, ['vgg16'], vgg_path) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/saved_model/loader_impl.py", line 200, in load saved_model = _parse_saved_model(export_dir) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/saved_model/loader_impl.py", line 78, in _parse_saved_model constants.SAVED_MODEL_FILENAME_PB)) IOError: SavedModel file does not exist at: /home/jack/code/self-driving-car/project_12_road_segmentation/data/vgg/{saved_model.pbtxt|saved_model.pb} `

def draw_back_onto_the_road(img_undistorted, Minv, line_lt, line_rt, keep_state):
    height, width, _ = img_undistorted.shape

    left_fit = line_lt.average_fit if keep_state else line_lt.last_fit_pixel
    right_fit = line_rt.average_fit if keep_state else line_rt.last_fit_pixel
    left_fit.sort()
    right_fit.sort()
    # Generate x and y values for plotting
    ploty = np.linspace(0, height - 1, height)
    left_fitx = left_fit[0] * ploty ** 2 + left_fit[1] * ploty + left_fit[2]
    right_fitx = right_fit[0] * ploty ** 2 + right_fit[1] * ploty + right_fit[2]

Error

left_fitx = left_fit_pixel[0] * ploty ** 2 + left_fit_pixel[1] * ploty + left_fit_pixel[2]
TypeError: 'NoneType' object is not subscriptable

Someone please help me with this

VPGNet dataset is available. It contains around 21K images with human-annotated lane, road markings, and vanishing point labels. For more details, please check our project page.

When I run main_ssd.py I got this:

Traceback (most recent call last):
  File "/home/binli/self-driving-car/project_5_vehicle_detection/main_ssd.py", line 10, in <module>
    ssd_model, bbox_helper, color_palette = get_SSD_model()
  File "/home/binli/self-driving-car/project_5_vehicle_detection/SSD.py", line 805, in get_SSD_model
    model_ssd = SSD300(input_shape=(300, 300, 3), num_classes=NUM_CLASSES, pretrained=True)
  File "/home/binli/self-driving-car/project_5_vehicle_detection/SSD.py", line 679, in SSD300
    mode='concat', concat_axis=1, name='mbox_loc')
TypeError: 'module' object is not callable

I tried a lot but I cannot fix this? Any suggestions about this? @ndrplz thanks a lot

i m unable to access video. error: from moviepy.editor import VideoFileClip ModuleNotFoundError: No module named 'moviepy'

i have installed moviepy package but its still not working

Hello,

I'm stuck in this issue when I run project 4:

$ python ./main.py Loading cached camera calibration... Traceback (most recent call last): File "./main.py", line 135, in ret, mtx, dist, rvecs, tvecs = calibrate_camera(calib_images_dir='camera_cal') File "/home/nxnam/DigitalRace/hanson/self-driving-car/project_4_advanced_lane_finding/calibration_utils.py", line 20, in wrapper calibration = pickle.load(dump_file) File "/usr/lib/python2.7/pickle.py", line 1384, in load return Unpickler(file).load() File "/usr/lib/python2.7/pickle.py", line 864, in load dispatchkey File "/usr/lib/python2.7/pickle.py", line 892, in load_proto raise ValueError, "unsupported pickle protocol: %d" % proto ValueError: unsupported pickle protocol: 3

How can I fix this?

Thank you.

In project 4, VideoClip generates RGB image instead of BGR, which would take serious effect on extracting yellow color for thresh_frame_in_HSV() in binarization_utils.py.

You have used train.p and test.p. But I did not find your repository. Please, give these datasets.

# Load pickled data
train, test = load_traffic_sign_data('../traffic_signs_data/train.p', '../traffic_signs_data/test.p')

Our ENet-Label-Torch has been released. More details can be found in my repo.

Key features:

(1) ENet-label is a light-weight lane detection model based on ENet and adopts self attention distillation (more details can be found in our paper which will be published soon).

(2) It has 20 × fewer parameters and runs 10 × faster compared to the state-of-the-art SCNN, and achieves 72.0 (F1-measure) on CULane testing set (better than SCNN which achieves 71.6).

(Do not hesitate to try our model!!!)

Performance on CULane testing set (F1-measure):

|Category|SCNN-Torch|SCNN-Tensorflow|ENet-Label-Torch| |:---:|:---:|:---:|:---:| |Normal|90.6|90.2|90.7| |Crowded|69.7|71.9|70.8| |Night|66.1|64.6|65.9| |No line|43.4|45.8|44.7| |Shadow|66.9|73.8|70.6| |Arrow|84.1|83.8|85.8| |Dazzle light|58.5|59.5|64.4| |Curve|64.4|63.4|65.4| |Crossroad|1990|4137|2729| |Total|71.6|71.3|72.0| |Runtime(ms)|133.5|--|13.4| |Parameter(M)|20.72|--|0.98|

Try to run project 3 on ubuntu18: python3 drive.py got error: SystemError: Unknown opcode

seems it is python version mismatch, ubuntu 18 is python3.6, what version of python do you use to train the model?

Thanks

Hello, I cannot find the exact detail to make sure we have a fix-time loop when computing future road points in Project 11 (Path Planning). Could you please tell me where the detail is. Thank you!

I have worked for hours and hours with your code. It is wonderful! Thanks for sharing it. However, one thing that makes it hard to implement in an actual bot is the fact that the road curvature and offset values are always positive. Could you please help me make them negative if the road curves, for example, left, and positive if it curves right? Same for the offset within the lane. My self-driving motercycle needs to know which way it is drifting. Thanks so much!