CODEWITHSUNDEEP
pythonopencvnumpy
Zaw Lin
Zaw Lin

Reputation: 5708

opencv show black image after numpy vectorize

i am trying to use np's vectorize but imshow is showing a black image where it should be white if i understand vectorize correctly. i think the problem is the outputtype but i cant get it to work. 

import numpy as np
import cv2
class Test():
    def run(self):        
        arr = np.zeros((25,25))
        arr[:]=255
        cv2.imshow('white',arr)
        flatarr = np.reshape(arr,25*25)
        vfunc = np.vectorize(self.func)
        #vfunc = np.vectorize(self.func,otypes=[np.int])#same effect
        flatres = vfunc(flatarr)
        shouldbewhite = np.reshape(flatres,(25,25))
        cv2.imshow('shouldbewhite',shouldbewhite)        
    def func(self,a):
        return 255
cv2.namedWindow('white',0)
cv2.namedWindow('shouldbewhite',0)
a = Test()
a.run()
cv2.waitKey(0)

Upvotes: 2

Views: 2117

Answers (1)

Jaime
Jaime

Reputation: 67427

From the docs :

The function imshow displays an image in the specified window. If the window was created with the CV_WINDOW_AUTOSIZE flag, the image is shown with its original size. Otherwise, the image is scaled to fit the window. The function may scale the image, depending on its depth:

  • If the image is 8-bit unsigned, it is displayed as is.
  • If the image is 16-bit unsigned or 32-bit integer, the pixels are divided by 256. That is, the value range [0,255*256] is mapped to [0,255].
  • If the image is 32-bit floating-point, the pixel values are multiplied by 255. That is, the value range [0,1] is mapped to [0,255].

If you run the following code:

class Test():
    def run(self):        
        arr = np.zeros((25,25))
        arr[:]=255
        print arr.dtype
        flatarr = np.reshape(arr,25*25)
        vfunc = np.vectorize(self.func)
        flatres = vfunc(flatarr)
        print flatres.dtype
        shouldbewhite = np.reshape(flatres,(25,25))
        print shouldbewhite.dtype
    def func(self,a):
        return 255

You'll get something like:

float64
int32
int32

So your second case is divided by 256, and it being integer division, it rounds off to 0. Try with

vfunc = np.vectorize(self.func,otypes=[np.uint8])

and you may also want to consider replacing the first array with 

arr = np.zeros((25,25), dtype='uint8')

Upvotes: 5

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