human
I am working on the file "CarND-Traffic-Sign-Classifier-Project/Traffic_Sign_Classifier.ipynb".
The first task is:
Question 1
Describe how you preprocessed the data. Why did you choose that technique?
Answer:
1) Convert RGB to Gray and in order to make model insensitive to color variations
2) Apply histogram equalization to mitigate the influence caused by illumination changes.
3) Data normalization to make the problem well conditioned, which is helpful to initialization and learning convergence.
Uncorrect augmented data results in worse performance.
The uncorrect augmentation generates images with black border which cut the performance.
The correct data augmentation should like this https://github.com/navoshta/traffic-signs/blob/master/Traffic_Signs_Recognition.ipynb .
Can you write Python code for it?
 Write me a the Python code you requested?
assistant
#Here is the Python code you requested.

### Generate additional data (OPTIONAL!).
### and split the data into training/validation/testing sets here.
### Feel free to use as many code cells as needed.

# data split with shuffle
# from sklearn.model_selection import train_test_split
# import os
# X_tr_val,  X_ts,  y_tr_val,  y_ts = train_test_split(
#     X_gray,
#     y,
#     test_size=0.1,
#     random_state=832289)
# X_tr,  X_val,  y_tr,  y_val = train_test_split(
#     X_tr_val,
#     y_tr_val,
#     test_size=0.1,
#     random_state=832289)

# print('Training features and labels randomized and split.')

# ### Generate additional data (OPTIONAL!)
# def augment_brightness_camera_images(image):
#     image1 = cv2.cvtColor(image,cv2.COLOR_RGB2HSV)
#     random_bright = .25+np.random.uniform()
#     #print(random_bright)
#     image1[:,:,2] = image1[:,:,2]*random_bright
#     image1 = cv2.cvtColor(image1,cv2.COLOR_HSV2RGB)
#     return image1

# def transform_image(img,ang_range,shear_range,trans_range,brightness=0):
#     '''
#     This function transforms images to generate new images.
#     The function takes in following arguments,
#     1- Image
#     2- ang_range: Range of angles for rotation
#     3- shear_range: Range of values to apply affine transform to
#     4- trans_range: Range of values to apply translations over.

#     A Random uniform distribution is used to generate different parameters for transformation
#     '''
#     # Rotation
#     ang_rot = np.random.uniform(ang_range)-ang_range/2
#     rows,cols,ch = img.shape    
#     Rot_M = cv2.getRotationMatrix2D((cols/2,rows/2),ang_rot,1)
#     # Translation
#     tr_x = trans_range*np.random.uniform()-trans_range/2
#     tr_y = trans_range*np.random.uniform()-trans_range/2
#     Trans_M = np.float32([[1,0,tr_x],[0,1,tr_y]])
#     # Shear
#     pts1 = np.float32([[5,5],[20,5],[5,20]])
#     pt1 = 5+shear_range*np.random.uniform()-shear_range/2
#     pt2 = 20+shear_range*np.random.uniform()-shear_range/2
#     # Brightness
#     pts2 = np.float32([[pt1,5],[pt2,pt1],[5,pt2]])
#    ...