You don't need to convert NumPy array to Mat because OpenCV cv2 module can accept NumPyarray. The only thing you need to care for is that {0,1} is mapped to {0,255} and any value bigger than 1 in NumPy array is equal to 255. So you should divide by 255 in your code, as shown below.

img = numpy.zeros([5,5,3])

img[:,:,0] = numpy.ones([5,5])*64/255.0
img[:,:,1] = numpy.ones([5,5])*128/255.0
img[:,:,2] = numpy.ones([5,5])*192/255.0

cv2.imwrite('color_img.jpg', img)
cv2.imshow("image", img)
cv2.waitKey()

I see a couple of issues that need to be resolved in the code:

• In Python the comments start with # as opposed to // in the attached snippet.
• Using nested for loops to iterate over the image is the slowest approach we can take. Try looking for numpy methods which do the same job instead of using nested for loops. The numpy routines are much much faster in most of the cases.
• OpenCV follows BGR convention instead of RGB convention. So to access the red channel you need to get the 3rd element in the color array not the 1st.

After fixing the issues your code may look like:

im = cv2.imread("original.jpg",1)

im[:,:,2] = 0

cv2.imwrite("changed.jpg", im)

You can use newer OpenCV python interface (if I'm not mistaken it is available since OpenCV 2.2). It natively uses numpy arrays:

import cv2
im = cv2.imread("abc.tiff")
print(type(im))

result:

<type 'numpy.ndarray'>

I think this is what you are looking for. you need to add one more dimension to your list of points using np.expand_dims

import numpy as np
import cv2

my_list = np.array([[1, 0, 1], [2, 0, 1], [2, 0, 2]], dtype=np.uint8)
img = np.expand_dims(my_list, axis=0)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray, 70, 110, apertureSize=3)
# and so on
lines = cv2.HoughLines(edges, 1, np.pi / 180, 150)

EDIT:

As you have clarified that your input list's values are from the gray colour space. you can simply change array datatype to np.uint8 and it will work.

import numpy as np
import cv2

my_list = np.array([[1, 0, 1], [2, 0, 1], [2, 0, 2]], dtype=np.uint8)
edges = cv2.Canny(my_list, 70, 110, apertureSize=3)
# and so on
lines = cv2.HoughLines(edges, 1, np.pi / 180, 150)