Mehdi-Amine

# 1- Generating a dataset.
from sklearn.datasets import make_moons
 
# X are the generated instances, an array of shape (500,2).
# y are the labels of X, with values of either 0 or 1.
X, y = make_moons(n_samples=500, noise=0.3, random_state=42)

# 2- Visualizing the dataset.
from matplotlib import pyplot as plt

Mehdi-Amine

class Perceptron:
    def __init__(self, input_size):
        np.random.seed(42)
        self.sizes = [input_size, 1]
        self.bias = np.random.randn(1, 1)
        self.weights = np.random.randn(1, input_size)
        # used for plotting convergence
        self.parameters_as_they_change = [np.concatenate((self.bias[0], self.weights.squeeze()), axis=0)] 
        
        print("Generated Perceptron:")

Mehdi-Amine

!python visualize_lane.py --image-path=IMAGE_PATH
                          --save-path=SAVE_PATH
                          --method=baseline 
                          --backbone=erfnet 
                          --dataset=tusimple 
                          --continue-from=erfnet_baseline_tusimple_20210424.pt 
                          --height=360 
                          --width=640

Mehdi-Amine

from visualize_lane import *
!python visualize_lane.py --image-path=IMAGE_PATH 
                          --save-path=SAVE_PATH 
                          --method=baseline 
                          --backbone=erfnet 
                          --dataset=culane 
                          --continue-from=erfnet_baseline_culane_20210204.pt 
                          --height=288 
                          --width=800

Mehdi-Amine

!pip install torch==1.8.1+cu111 torchvision==0.9.1+cu111 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html
!pip install mmcv
!pip install ujson
!pip install filetype

Mehdi-Amine

from google.colab import drive
drive.mount('/content/gdrive')
%cd gdrive/MyDrive/auto-drive/pytorch-auto-drive/
!git clone https://github.com/voldemortX/pytorch-auto-drive.git

Mehdi-Amine

lines = cv2.HoughLinesP(cropped_image, 2, np.pi/180, 100, np.array([]), minLineLength=40, maxLineGap=5)

Mehdi-Amine

def region_of_interest(image):
    height = image.shape[0]
    width = image.shape[1]
    polygons = np.array([[(10,height), (width,height), (width,1100), (630, 670), (10, 1070)]])
    mask = np.zeros_like(image)
    cv2.fillPoly(mask, polygons, 255)
    masked_image = cv2.bitwise_and(image, mask)
    return masked_image

Mehdi-Amine

def canny(image):
  gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
  blur = cv2.GaussianBlur(gray, (5, 5), 0)
  canny = cv2.Canny(blur, 10, 30)
  return canny

Mehdi-Amine

def inverse_scale_for(fp_cases, fn_cases):
  # Converting to Numpy to be compatible with Sklearn
  fp_cases = x_test[fp_indx].numpy() 
  fn_cases = x_test[fn_indx].numpy()

  # Inverse transformation for the columns that were scaled: tea temp + internet speed
  fp_cases_inv = std_scaler.inverse_transform(fp_cases[:,:2])
  fn_cases_inv = std_scaler.inverse_transform(fn_cases[:,:2])

  # Concatenating the now normally scaled columns with the book column