test.py

# # # import numpy as np 
# # # # a = np.zeros((5,5))
# # # # print a

# # # # print a[0:3, 1:3]


# # # component_transformation_matrix = np.array([[0.2999, 0.587, 0.114],
# # #             [-0.16875, -0.33126, 0.5],[0.5, -0.41869, -0.08131]])

# # # a = np.array([[1], [2], [3]])
# # # print a.shape
# # # print component_transformation_matrix.shape

# # # print np.matmul(component_transformation_matrix, a)
# # # # print component_transformation_matrix

# # # print np.zeros((3,3))


# # import numpy as np
# # # import pywt
# # # data = np.ones((4,4), dtype=np.float64)
# # # coeffs = pywt.dwt2(data, 'haar')
# # # cA, (cH, cV, cD) = coeffs

# # # print cA

# # a = np.ones((4, 4, 3))
# # print np.zeros_like(a)

# # # img = decoder.decode(openjpeg.IMAGE_EIT)

# # import math

# # print math.floor(0.5)

# import numpy as np
# import math


# def quantization(img):
#     step = 1.0/2
#     (h, w, _) = img.shape
#     quantization_img = np.empty_like(img)

#     for i in range(0, w):    # for every pixel:
#         for j in range(0, h):
#             if img[j][i] >= 0:
#                 sign = 1
#             else:
#                 sign = -1
#             quantization_img[j][i] = sign * math.floor(abs(img[j][i])/step)
#     return quantization_img

# # a = np.zeros((4,4,1))
# # a = np.array([[4, 4, 4], [4, 4, 4], [4, 4, 4], [4, 4, 4]])
# b = np.random.randint(10, size=(4, 4, 1))

# print b[0]
# print quantization(b)[0]



from PIL import Image
import cv2

path = "data/image.jpg"
img = cv2.imread(path)
img = Image.fromarray(img, 'RGB')
# img.save('my.png')
img.show()