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()

Your code can be fixed as follows:

import numpy as np, cv
vis = np.zeros((384, 836), np.float32)
h,w = vis.shape
vis2 = cv.CreateMat(h, w, cv.CV_32FC3)
vis0 = cv.fromarray(vis)
cv.CvtColor(vis0, vis2, cv.CV_GRAY2BGR)

Short explanation:

1. np.uint32 data type is not supported by OpenCV (it supports uint8, int8, uint16, int16, int32, float32, float64)
2. cv.CvtColor can't handle numpy arrays so both arguments has to be converted to OpenCV type. cv.fromarray do this conversion.
3. Both arguments of cv.CvtColor must have the same depth. So I've changed source type to 32bit float to match the ddestination.

Also I recommend you use newer version of OpenCV python API because it uses numpy arrays as primary data type:

import numpy as np, cv2
vis = np.zeros((384, 836), np.float32)
vis2 = cv2.cvtColor(vis, cv2.COLOR_GRAY2BGR)

On the last line of that script, image is a numpy array with shape (rows, cols, color-plane) with the color planes in BGR order - which is precisely how OpenCV represents image data. In other words, you can just pass that array straight to OpenCV functions:

import time
import picamera
import picamera.array
import cv2

with picamera.PiCamera() as camera:
    time.sleep(2)
    with picamera.array.PiRGBArray(camera) as stream:
        camera.capture(stream, format='bgr')
        # At this point the image is available as stream.array
        image = stream.array
        image = cv2.dilate(image, None)
        image = cv2.erode(image, None)
        image = cv2.cvtColor(image, cv2.cv.CV_BGR2GRAY)
        r, image = cv2.threshold(image, 127, 255, 1)
        cv2.namedWindow('win')
        cv2.imshow('win', image)
        cv2.waitKey(0)