Example scripts for the detection of lanes using the ultra fast lane detection model in Tensorflow Lite.

Related tags

Deep Learning raspberry-pi tensorflow inference lane-detection tensorflow-lite tflite lane-segmentation pi4b
Overview

TFlite Ultra Fast Lane Detection Inference

Example scripts for the detection of lanes using the ultra fast lane detection model in Tensorflow Lite.

!Ultra fast lane detection Source: https://www.flickr.com/photos/32413914@N00/1475776461/

Requirements

  • OpenCV, scipy and tensorflow/tflite_runtime. pafy and youtube-dl are required for youtube video inference.

Installation

pip install -r requirements.txt
pip install pafy youtube-dl

For the tflite runtime, you can either use tensorflow pip install tensorflow or the TensorFlow Runtime

tflite model

The original model was converted to different formats (including .tflite) by PINTO0309, download the models from his repository and save it into the models folder.

Original Pytorch model

The Pytorch pretrained model from the original repository.

Ultra fast lane detection - TuSimple(link)

  • Input: RGB image of size 800 x 200 pixels.
  • Output: Keypoints for a maximum of 4 lanes (left-most lane, left lane, right lane, and right-most lane).

Examples

  • Image inference:
python imageLaneDetection.py
  • Webcam inference:
python webcamLaneDetection.py
  • Video inference:
python videoLaneDetection.py

Pytorch inference

For performing the inference in Pytorch, check my other repository Ultrafast Lane Detection Inference Pytorch.

ONNX inference

For performing the inference in ONNX, check my other repository ONNX Ultra Fast Lane Detection Inference.

Inference video Example

!Ultrafast lane detection on video

Original video: https://youtu.be/2CIxM7x-Clc (by Yunfei Guo)

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Comments
  • Bad inference Result!

    Bad inference Result!

    I used the pretrained mode "model_float16_quant.tflite" from this repo https://github.com/PINTO0309/PINTO_model_zoo/tree/main/140_Ultra-Fast-Lane-Detection but I got the following result: out27

    and the orginal image is: SC600_Forward_20220206130511_26

    any hints I can do to enhance the output!? @ibaiGorordo

    opened by AbdulFMS 2
  • Name 'lanes_points' is not defined

    Name 'lanes_points' is not defined

    Hello, thanks for the code. I'm running "imageLaneDetection.py" with culan tflite model and change model_type value in imageLaneDetection.py like:

    model_type = ModelType.CULANE

    then, i got these on terminal:

    163: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  return np.array(lane_points_mat), np.array(lanes_detected)
Traceback (most recent call last):
  File "imageLaneDetection.py", line 18, in <module>
    output_img = lane_detector.detect_lanes(img)
  File "/home/ubuntu/Codes/TfLite-Ultra-Fast-Lane-Detection-Inference/ultrafastLaneDetector/ultrafastLaneDetector.py", line 82, in detect_lanes
    visualization_img = self.draw_lanes(image, self.lanes_points, self.lanes_detected, self.cfg, draw_points)
  File "/home/ubuntu/Codes/TfLite-Ultra-Fast-Lane-Detection-Inference/ultrafastLaneDetector/ultrafastLaneDetector.py", line 175, in draw_lanes
    cv2.fillPoly(lane_segment_img, pts = [np.vstack((lanes_points[1],np.flipud(lanes_points[2])))], color =(255,191,0))
NameError: name 'lanes_points' is not defined

    image

    is the error message means the "lanes_points" variable in ultrafastLaneDetector.py is not defined? Where should I define the "lanes_points" in code?

    opened by Yan-Long-Huang 2
  • cannot find the model_float32.tflite in the ModelZoo

    cannot find the model_float32.tflite in the ModelZoo

    i keep getting this error:

    _interpreter_wrapper.CreateWrapperFromFile( ValueError: Could not open 'models/model_float32.tflite'.

    Can anyone help please ?

    opened by gm24med 0
Owner
Ibai Gorordo
Passionate about sensors, technology and their potential to help people.
Ibai Gorordo
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