if __name__ == "__main__":
    # coordinate: (y,x), left_top, right_rop, left_bottom, right_bottom
    src = np.float32([[20.0, 0.0], [20.0 ,315.0], [186.0, 17.2], [181.0, 299.0]])
    dst = np.float32([[0.0, 0.0],  [0.0,  315.0], [202.0, 7.0], [200.0, 306.0]])

    # load image
    warp_img = cv2.imread("./my_wide_angle_orig.jpg")
    warp_img = cv2.cvtColor(warp_img, cv2.COLOR_BGR2RGB)
    print("warp_img: ",warp_img.shape) # (638, 958, 3)
    width = int(warp_img.shape[1]/3)
    height = int(warp_img.shape[0]/3)
    warp_img  = cv2.resize(warp_img, (width,height), interpolation=cv2.INTER_LINEAR)
    print("warp_img.shape:",warp_img.shape) # (212, 319, 3)

    ## orig image
    plt.subplot(121),
    plt.title("warp_img")
    plt.imshow(warp_img)

    # cv2 warp perspective
    cv2_matrix = cv2.getPerspectiveTransform(src, dst)
    print("cv2_matrix:\n",cv2_matrix)
    cv2_fix_img = cv2.warpPerspective(warp_img, cv2_matrix, (width,height))
    plt.subplot(122),
    plt.title('cv2_fix_img')
    plt.imshow(cv2_fix_img) 
    plt.show()

my_warp_matrix reshape:
 [[ 1.13729359e+00 -8.24289989e-18 -2.27458717e+01]
 [-6.10786436e-02  9.69843448e-01  1.22157287e+00]
 [-3.44743207e-04 -7.38466280e-05  1.00000000e+00]]

image_path = "fasterRCNN/faster-rcnn-keras-master/img/street.jpg";

from PIL import Image
import numpy as np
image = Image.open(image_path) # RGB
tmp_image = np.array(image)
print("PIL open shape:",tmp_image.shape, tmp_image)


# load with cv2
import cv2
image = cv2.imread(image_path)  # mode: BGR
print("cv2 imread shape:", image.shape) # (width,height,channel)


# load with matplotlib
import matplotlib.image as mpimg
image = mpimg.imread(image_path) #  mode: RGB
print("matplotlib imread shape:",image.shape, image) # ( height, width,channel(RGB) )

import numpy as np

tmp_list = list([-3.3, -2.22, -1.56, -0.56, 0.56, 1.56, 2.22, 3.33])

tmp_list = np.array(tmp_list)
print("type(tmp_list[0]):", type(tmp_list[0]) )
print("type(np.floor(tmp_list)[0]):",  type(np.floor(tmp_list)[0]) )
print("np.floor(tmp_list):", np.floor(tmp_list))

# Output:
# type(tmp_list[0]): <class 'numpy.float64'>
# type(np.floor(tmp_list)[0]): <class 'numpy.float64'>
# np.floor(tmp_list): [-4. -3. -2. -1.  0.  1.  2.  3.]


for x in tmp_list:
	print("x:",x,",int:",int(x)) 

# output:
# x: -3.3 ,int: -3
# x: -2.22 ,int: -2
# x: -1.56 ,int: -1
# x: -0.56 ,int: 0
# x: 0.56 ,int: 0
# x: 1.56 ,int: 1
# x: 2.22 ,int: 2
# x: 3.33 ,int: 3

import numpy as np

list1 = np.array([1, 2, 3]);
list2= np.array([10, 20, 30]);

print("list1:",list1,"list2:",list2);
# Print: list1: [1 2 3] list2: [10 20 30]

added_list = list1 + list2;
print("added_list.shape:",added_list.shape,"\nadded_list:",added_list);
# Print:
# added_list.shape: (3,) 
# added_list: [11 22 33]

added_list = np.add(list1, list2);
print("added_list.shape:",added_list.shape,"\nadded_list:",added_list);
# Print:
# added_list.shape: (3,) 
# added_list: [11 22 33]

x = np.arange(8).reshape((4,2))
print(x)
print(x.T)
# output:
# [[0 1] # x
#  [2 3]
#  [4 5]
#  [6 7]]
# [[0 2 4 6] # x.T
#  [1 3 5 7]]

x = np.arange(9).reshape((3,3))
print(x)
print(x.T)
print(x.shape, x.T.shape)
# output:
# [[0 1 2]
#  [3 4 5]
#  [6 7 8]]
# [[0 3 6]
#  [1 4 7]
#  [2 5 8]]

x = np.arange(8).reshape((2,4))
print(x)
print(x.T)
# output:
# [[0 1 2 3]
#  [4 5 6 7]]
# [[0 4]
#  [1 5]
#  [2 6]
#  [3 7]]

def my_sum(*args):
    result = 0
    # Iterating over the Python args tuple
    for x in args:
        result += x
    return result

print(my_sum(1, 2, 3))
# output: 6

def print_three_things(a, b, c):
    print( 'a = {0}, b = {1}, c = {2}'.format(a,b,c))

mylist = ['aardvark', 'baboon', 'cat']
print_three_things(*mylist)
# output: a = aardvark, b = baboon, c = cat

input_shape = (1,2,3)
import tensorflow as tf
tf.enable_eager_execution()

print("----------x1 tensor-----------")
x1 = tf.random.uniform(input_shape, maxval=10, dtype=tf.dtypes.int32)
tf.print(x1);
print("----------x2 tensor-----------")
x2 = tf.random.uniform(input_shape, maxval=10, dtype=tf.dtypes.int32)
tf.print(x2);
print("----------add 2 tensors-----------")
y = tf.keras.layers.add([x1,x2])
tf.print(y);

import numpy as np
arr = np.array([1,2,3,4,5]);
print("arr shape: ",arr.shape)
print("arr shape: ",arr)
arr2 = np.expand_dims(arr, 0);
print("expand_dims axis=0, shape:",arr2.shape)
print("expand_dims axis=0, arr2:",arr2)
arr2 = np.expand_dims(arr, 1);
print("expand_dims axis=1, shape:",arr2.shape)
print("expand_dims axis=1, arr2:",arr2)